Increasing the Potential of Biogas in Sub-Saharan Africa · 2018. 9. 18. · assisting biogas installers, program implementers, and other stakeholders in the biogas industry to carry

Increasing the Potential of Biogas in Sub-Saharan

Africa Development of the optimal biogas

system design model

By Gloria Vivienne Rupf

BEng(Dist.) & BSc Murd.

School of Engineering & Information Technology Murdoch University

Perth, Western Australia

This thesis is presented for the degree of Doctor of Philosophy of Murdoch University

September 2018

I declare that this thesis is my own account of my research and contains, as its main content, work which has not previously been submitted for a degree at any tertiary education institution.

Gloria Vivienne Rupf BEng(Dist.) & BSc

Abstract This research presents the development of an optimal biogas system design

model (OBSDM), which determines the most suitable biogas system design

based on the context and priorities of the intended user(s) in the region of

Sub-Saharan Africa (SSA). The model can be used as a decision-making tool,

assisting biogas installers, program implementers, and other stakeholders

in the biogas industry to carry out initial assessments on the type of biogas

systems that are optimal for specific applications, particularly at the

household-scale. To determine the optimal biogas system design, the model

assesses the feasibility of different types of biogas system designs and sizes

based on user-defined inputs, including energy and fertiliser requirements,

feedstock (type, amount, and rate of supply), water supply, land use (area,

soil type, groundwater level), and climate (ambient temperatures). The

feasible biogas system designs and sizes are then compared and ranked

based on the priorities of the intended user(s). These priorities are defined

in the input through rating eight sustainability criteria related to biogas

technology – reliability, robustness, simple operation & construction, low-

cost, technical efficiency, environmentally benign, local materials and

labour, and save time – according to their importance to the intended user.

The output of the model provides a recommended biogas system design and

size with estimates of expected costs, energy and fertiliser production, and

links to contact the supplier.

To develop the OBSDM, literature reviews were carried out on the types of

biogas system designs applicable to SSA, specifically at the household-scale;

the types of feedstocks available in the region; and, the tools and models

that currently exist for biogas technology. Part of this literature review was

used to help assess the energy production potential of different types of

feedstocks that could be used in biogas systems in SSA. Databases on

available digester types, sizes, and feedstocks were also developed for the

model. The OBSDM was created in Microsoft Excel using Visual Basic for

Applications (VBA) programming, and is recommended to be made freely

accessible. It has been tested by applying household survey data from Kenya

and Cameroon, as well as a detailed study of household biogas systems in

the central and eastern districts of Rwanda. The outcomes from this analysis

indicated that the model is able to recommend biogas system designs that

are appropriate to the context and priorities of the intended user. However,

the accuracy of the model outputs is highly dependent on the accuracy of the

inputs. Through the Kenyan, Cameroonian, and Rwandan case studies, it is

apparent that future development of biogas technologies in the region

should focus on systems that require minimal water, and can be constructed

from less expensive and energy intensive, local materials. Overall, this

research aims to help increase biogas dissemination in the region through

raising awareness about its potential, as well as encouraging industry

stakeholders to make appropriate design choices that will ensure long-term

sustainability of the biogas system and maximum benefits to the intended

user(s).

vii

Table of Contents Abstract ......................................................................................................... xi

List of figures ................................................................................................ xi

List of tables ................................................................................................. xv

List of tables in Appendices ........................................................................ xvi

List of equations .......................................................................................... xx

Publications .............................................................................................. xxiii

Nomenclature ............................................................................................ xxv

List of Acronyms .................................................................................... xxv

List of Symbols .................................................................................... xxviii

Glossary .................................................................................................. xxxiv

Acknowledgements ................................................................................ xxxvii

Chapter 1 Introduction .............................................................................. 1

1.1 Thesis overview ................................................................................ 3

1.2 Research aim and objectives ............................................................ 4

1.3 Research Method ............................................................................. 8

1.3.1 Literature review ....................................................................... 8

1.3.2 Feedstock assessment ............................................................... 8

1.3.3 Develop the optimal biogas system design model .................. 10

1.3.4 Test the optimal biogas system design model by applying it to

case studies ........................................................................................... 12

1.3.5 Data and sensitivity analysis ................................................... 13

1.3.6 Final model and recommendations ........................................ 14

Chapter 2 Energy situation and biogas dissemination in SSA ................ 15

2.1 Energy situation in SSA ................................................................. 15

2.2 Biogas dissemination in SSA ......................................................... 23

2.2.1 Overview .................................................................................. 23

2.2.2 Main barriers ........................................................................... 25

2.2.3 Main opportunities .................................................................30

2.2.4 Country specific examples of biogas dissemination ............... 33

2.3 Biogas dissemination in developing regions outside of SSA ......... 43

2.3.1 China ....................................................................................... 43

2.3.2 India ........................................................................................ 47

viii

2.3.3 Nepal ....................................................................................... 50

2.4 Biogas dissemination in Europe .................................................... 53

2.5 Conclusions and Recommendations ............................................. 57

2.5.1 Key recommendations for improving biogas dissemination in

SSA 57

2.5.2 Conclusions on biogas dissemination in SSA ......................... 61

Chapter 3 Biogas technology: influential factors and available design types 63

3.1 Anaerobic digestion and biogas production .................................. 63

3.1.1 Factors that influence biogas production and digester design

66

3.2 Biogas system design options ........................................................ 84

3.2.1 Batch systems .......................................................................... 86

3.2.2 Continuously stirred tank reactors (CSTRs) .......................... 87

3.2.3 Covered anaerobic lagoons (CALs) ......................................... 89

3.2.4 Fixed film digesters and other anaerobic wastewater treatment

systems 90

3.2.5 Fixed dome digester ................................................................ 93

3.2.6 Floating cover digester ............................................................ 97

3.2.7 Plug flow digester .................................................................. 100

3.2.8 Comparison of different digester designs ............................. 104

3.2.9 Key priorities for biogas systems .......................................... 106

3.3 Conclusions on biogas system design selection .......................... 108

Chapter 4 Biogas feedstock assessment for SSA: unlocking the energy production potential from organic waste .................................................. 111

4.1 Biogas feedstock assessments in SSA ........................................... 111

4.2 Agro-processing and food production feedstocks ........................ 113

4.2.1 Biogas and energy production potential from the livestock

industry ................................................................................................ 113

4.2.2 Biogas and energy production potential from the crop farming

industry ............................................................................................... 122

4.3 Municipal feedstocks .................................................................... 131

4.3.1 Methane and energy production potential from domestic

wastewater ........................................................................................... 131

ix

4.3.2 Methane and energy production potential from municipal solid

waste 136

4.4 Summary of feedstock assessment for SSA .................................. 141

Chapter 5 Development of the Biogas System Design Model ............... 145

5.1 Interacting factors in the design of biogas systems .................... 146

5.2 Review of existing biogas models and design tools ..................... 147

5.3 OBSDM Inputs ............................................................................. 152

5.3.1 Energy demand ..................................................................... 152

5.3.2 Feedstock ............................................................................... 157

5.3.3 Location .................................................................................. 161

5.3.4 Economics ............................................................................. 170

5.3.5 User priorities ........................................................................ 171

5.4 Digester sizing and design in the OBSDM................................... 173

5.4.1 Determining the ideal digester size ...................................... 173

5.4.2 Identifying the optimal available digester size ..................... 176

5.4.3 Determining the required gasholder volume ....................... 180

5.4.4 Identifying feasible biogas system designs based on the

proposed installation site and intended system application ............. 183

5.5 Determining the optimal biogas system design using MCDA .... 185

5.5.1 Calculating cost savings, GHG emissions avoided, EROI and

other sustainability criteria parameters ............................................. 185

5.5.2 Applying the TOPSIS method to identify the optimal biogas

system design ...................................................................................... 190

5.6 Applying data from rural households in Cameroon and Kenya to the

OBSDM ................................................................................................... 196

5.6.1 Model inputs based on Kenyan and Cameroonian household

survey data .......................................................................................... 196

5.6.2 Model outputs – optimal biogas system designs for Kenyan and

Cameroonian households ................................................................... 201

5.7 Summary and conclusions on the development and preliminary

testing of the OBSDM ............................................................................ 205

5.7.1 Comparison of top four biogas system designs for rural

households in Kenya and Cameroon .................................................. 205

5.7.2 Limitations of the OBSDM ................................................... 209

x

Chapter 6 Validation and sensitivity analysis of the OBSDM using household data from Rwanda .................................................................... 211

6.1 Rwandan Comparative Biodigester Study Background .............. 212

6.2 Applying Rwandan household biodigester study data to the

OBSDM .................................................................................................. 215

6.2.1 Inputs -Energy use, feedstock, climate data, water availability,

and financial situation ........................................................................ 215

6.2.2 Results and analysis .............................................................. 221

6.2.3 Sensitivity analysis ................................................................ 234

6.3 Conclusions on model validation and sensitivity analysis .......... 259

Chapter 7 Conclusions and recommendations for future work ............ 263

7.1 Conclusions .................................................................................. 263

7.2 Recommendations for future work ............................................. 269

7.2.1 Recommendations for biogas research and system designs 269

7.2.2 Recommendations for OBSDM development ...................... 270

7.2.3 Recommendations for the application of biogas technology:

271

References ................................................................................................. 273

Appendices ................................................................................................ 322

Appendix A – Databases and details from the OBSDM .................... 322

References........................................................................................... 342

Appendix B – Detailed results for the Kenyan and Cameroonian case

studies 351

Appendix C – Details from the validation and sensitivity analysis of the

OBSDM 379

xi

List of figures Figure 1-1: Approach to PhD Project ............................................................. 9

Figure 1-2: Outline of biogas system model for optimal design .................. 11

Figure 2-1: Urban SSA population cooking fuel use in 2007 [42] .............. 19

Figure 2-2: Rural SSA population cooking fuel use in 2007 [42] ..............20

Figure 3-1: Biogas composition based on figures from [55] ....................... 64

Figure 3-2: The four phases of anaerobic digestion [18, 43] ...................... 66

Figure 3-3: Schematic of a UASB reactor [207] .......................................... 93

Figure 3-4: Diagram of a fixed dome digester [208] .................................. 94

Figure 3-5: Community-scale fixed dome digester at a Rwandan school .. 95

Figure 3-6: Household-scale floating cover digester in South Africa [221]

.................................................................................................................... 100

Figure 3-7: Low-cost plug flow digester in Rwanda with small inlet [Photo

by G.V. Rupf] ............................................................................................. 103

Figure 3-8: Plug flow digester in Rwanda with an inlet mixer [Photo by

G.V. Rupf] .................................................................................................. 103

Figure 3-9: ‘Biogas backpack’ used to test different types of cookstoves at a

German university [Photo by G.V. Rupf] .................................................. 104

Figure 4-1: Energy production potential from using livestock manure as

feedstock in anaerobic digestion for each SSA region (calculated using

2012 data from FAOSTAT [245]) ............................................................... 115

Figure 4-2: Per capita energy production potential from livestock manure

for SSA countries (excluding South Sudan) (calculated using 2012 data

from FAOSTAT [245] and 2012 World Bank population data [252]) ....... 117

Figure 4-3: Energy production potential from using livestock product

waste as feedstock in AD for each SSA region (calculated using 2009 data

from FAOSTAT [253]) ............................................................................... 120

Figure 4-4: Per capita energy production potential from livestock product

waste for SSA countries (excluding South Sudan) (calculated using 2009

data from FAOSTAT [253] and 2012 World Bank population data [252])

..................................................................................................................... 121

Figure 4-5: Energy production potential from crop residues normally

burned used as feedstock in AD for each SSA region (calculated using 2012

data from FAOSTAT [256]) ....................................................................... 123

Figure 4-6: Per capita energy production potential from crop waste that is

normally burned for SSA countries (excluding South Sudan) (calculated

using 2012 data from FAOSTAT [256] and 2012 World Bank population

data [252]) ................................................................................................. 124

Figure 4-7: Energy production potential from crop equivalent waste used

as feedstock in AD for each SSA region (calculated using 2013 data from

FAOSTAT [262]) ........................................................................................ 129

xii

Figure 4-8: Per capita energy production potential from crop primary

equivalent waste for SSA countries (excluding the Democratic Republic of

the Congo, Equatorial Guinea, Somalia, and South Sudan) (calculated

using 2013 data from FAOSTAT [262] and 2012 World Bank population

data [252]) ................................................................................................. 130

Figure 4-9: Estimated energy production potential from domestic

wastewater use as feedstock in AD for each SSA region (calculated using

2012 World Bank population data on access to improved sanitation

facilities [251]) ........................................................................................... 134

Figure 4-10: Per capita energy production potential from domestic sewage

for SSA countries (excluding South Sudan) (calculated using 2012 World

Bank population data on access to improved sanitation facilities [251]) .135

Figure 4-11: Estimated energy potential of the organic fraction of MSW as

feedstock in AD from the urban population of each SSA region (calculated

using 2012 World Bank population and GDP data [276] and waste

generation rates from [272, 274, 275]) ..................................................... 139

Figure 4-12: Per capita energy production potential from MSW for SSA

countries (excluding South Sudan) (calculated using 2012 World Bank

population and GDP data [276] and waste generation rates from [272, 274,

275]) ........................................................................................................... 140

Figure 4-13: Total energy production potential based on the assessment of

feedstocks suitable for AD in each SSA region ......................................... 143

Figure 4-14: Per capita total energy production potential based on the

assessment of feedstocks suitable for AD in each SSA region .................. 143

Figure 4-15: Per capita net energy production potential based on feedstock

assessment for AD for each SSA country (excluding South Sudan)

(calculated using 2012 World Bank population data [252]) .................... 144

Figure 5-1: Factors influencing the design of biogas systems .................. 148

Figure 5-2: Energy demand input section of the OBSDM ......................... 155

Figure 5-3: Cooking requirements input options in the OBSDM ............ 156

Figure 5-4: Feedstock input section of the OBSDM ................................. 159

Figure 5-5: Feedstock inputs with warnings based on feedstock amount

and combination in the OBSDM ............................................................... 159

Figure 5-6: Location input section of the OBSDM (excluding construction

materials) ................................................................................................... 166

Figure 5-7: Warnings for water supply in the location input section of the

OBSDM ...................................................................................................... 166

Figure 5-8: Construction materials section in location input of the OBSDM

................................................................................................................... 169

Figure 5-9: Economics input section of the OBSDM ................................ 170

Figure 5-10: Subsidy type options in economics input of the OBSDM .... 170

Figure 5-11: Priorities input section of the OBSDM .................................. 171

xiii

Figure 5-12: OBSDM output section – summary of recommended biogas

system design ............................................................................................. 193

Figure 5-13: OBSDM output section, graphical summary of the scores for

sustainability parameters for the top four ranked biogas system designs 194

Figure 5-14: OBSDM output section – tabular summary of the top four

ranked biogas system designs ................................................................... 195

Figure 5-15: Summary of the MCDA analysis of the top four biogas system

designs identified by the OBSDM for an average rural Kenyan household

.................................................................................................................... 207

Figure 5-16: Summary of the MCDA analysis of the top four biogas system

designs identified by the OBSDM for an average rural Cameroonian

household in the Adamawa region ........................................................... 208

Figure 6-1: Map of provinces and districts in Rwanda [Source:

Government of Rwanda 2009] .................................................................. 214

Figure 6-2: Recommended biodigester types using equal priority criteria

rating, categorised according to the installed system (horizontal axis) ... 222

Figure 6-3: Recommended biodigester types using priority criteria

favourable to installed biodigester types, categorised according to the

installed system (horizontal axis) ............................................................. 223

Figure 6-4: Revised recommended biodigester types using equal priority

criteria rating with updated EROI figures, categorised according to the

installed system (horizontal axis) .............................................................228

Figure 6-5: Revised recommended biodigester types using priority criteria

rating favourable to installed biodigester types with updated EROI figures,

categorised according to the installed system (horizontal axis) ...............228

Figure 6-6: Comparison of installed and recommended biogas systems

according to district with mean daily temperature .................................. 230

Figure 6-7: Recommended biodigester types when no subsidies are

available using equal priority criteria rating, categorised according to the

installed system (horizontal axis) ............................................................. 232

Figure 6-8: Recommended biodigester types per district and amount

available for capital expenditure (excluding subsidies) ........................... 232

Figure 6-9: Recommended biodigester type per district and subsidy

amount available ....................................................................................... 233

Figure 6-10: Recommended biodigester type according to household water

supply ......................................................................................................... 234

Figure 6-11: Comparison of recommended biogas systems using local and

default climate data ................................................................................... 236

Figure 6-12: Change in biodigester size recommended by the OBSDM

when using default and local (measured) climate data ............................ 236

xiv

Figure 6-13: Comparison of recommended biodigester types using

standard lifespan values and maximum lifespan values, categorised

according to the installed system (horizontal axis) .................................. 239


standard lifespan values and the lowest lifespan values, categorised



standard production efficiency values and minimum production efficiency

values (maximum gas loss), categorised according to the installed system

(horizontal axis) ........................................................................................ 243


standard production efficiency values and maximum production efficiency

values (no gas loss), categorised according to the installed system

(horizontal axis) ........................................................................................ 244

Figure 6-17: Comparison of recommended biodigester types using default

and measured VS and TS values for cattle dung in the OBSDM, categorised


Figure 6-18: Difference in feedstock amounts (kg cattle dung/d) estimated

based on the number of cattle and amounts measured on site, and

resulting difference in biodigester size recommended by the OBSDM ... 250

Figure 6-19: Comparison of recommended biodigester types using location

specific cattle dung supply and estimated cattle dung supply based on

number of cattle in the OBSDM, categorised according to the installed

system (horizontal axis) ............................................................................ 250

Figure 6-20: Recommended biodigester types according to the number of

cattle (where the amount of cattle dung was estimated according to the

number of cattle) in the OBSDM .............................................................. 251

Figure 6-21: Comparison of recommended biodigester types using equal

priority criteria rating with and without consideration of import costs in

the OBSDM, categorised according to the installed system (horizontal axis)

................................................................................................................... 254

Figure 6-22: Percentage change in economic parameters for biogas system

designs recommended by the OBSDM when considering import costs for

construction materials .............................................................................. 254

Figure 6-23: Comparison of OBSDM output using equal priority rating for

all criteria and the highest rating (5) for each criterion at a time while all

others are given the lowest rating (1) ........................................................ 256

Figure 6-24: Comparison of OBSDM output using equal rating for all

priority criteria and the highest rating (5) for each criterion at a time while

all others are given a moderate rating (3) ................................................. 257

xv

List of tables Table 2-1: Main barriers to biogas dissemination in SSA .......................... 29

Table 2-2: The benefits that biogas technology can provide in Sub-Saharan

Africa ............................................................................................................ 33

Table 2-3: Recommended strategies for improving biogas dissemination in

SSA .............................................................................................................. 60

Table 3-1: Average percentage of volatile solids and the biochemical

methane potential of selected feedstocks .................................................... 71

Table 3-2: Comparison of parameters for six main types of biogas digesters

used to treat organic slurries and solid waste ............................................. 85

Table 3-3: Comparison of performance of six main types of biogas systems

used to treat organic slurries and solid waste ........................................... 106

Table 4-1: Previous studies on biogas feedstocks in SSA ........................... 112

Table 4-2: Average dry matter and organic dry matter content, biogas and

methane yields by mass for livestock product waste ................................ 120

Table 4-3: Dry matter and organic dry matter content, biogas yield by

mass, and methane content by volume for crop residues that are normally

burned ........................................................................................................ 123

Table 4-4: Dry matter, organic dry matter, biogas and methane yields for

crop wastes ................................................................................................ 127

Table 4-5: Per capita GDP, GDP ranges, waste generation, and the organic

fraction of MSW for selected SSA countries used to estimate the methane

potential from the organic fraction of MSW ............................................. 138

Table 5-1: Examples of existing models and tools applicable to the design

and assessment of biogas systems ............................................................ 149

Table 5-2: Estimated power consumption of household energy applications

.................................................................................................................... 154

Table 5-3: Calorific values and CO₂ equivalent GHG emissions per kWh of

delivered energy for conventional fuel types used in SSA ........................ 157

Table 5-4: Feedstock unit conversions used in the OBSDM .................... 158

Table 5-5: Soil types database in OBSDM based on [320, 321] ............... 167

Table 5-6: Priority criteria and associated parameters and source in the

OBSDM ...................................................................................................... 172

Table 5-7: Equations to determine best and worst normalised scores for

sizing parameters....................................................................................... 179

Table 5-8: Gas pressure requirements for different biogas technology

applications [118]....................................................................................... 184

Table 5-9: Equations to determine the best and worst scores for

sustainability criteria in the OBSDM ......................................................... 191

Table 5-10: Energy demand and feedstock inputs to the OBSDM based on

averaged survey data from rural households in Kenya and Cameroon [338,

339] ............................................................................................................ 197

xvi

Table 5-11: Location and economic inputs to the OBSDM based on

averaged survey data from rural households in Kenya and Cameroon [338,

339] ............................................................................................................ 198

Table 5-12: Optimal biogas system design details from the OBSDM for

rural Kenyan and Cameroonian households ............................................ 204

Table 6-1: Estimated consumption in 2015 for households in the Kigali City

and Eastern Province of Rwanda based on the 4th Population and Housing

Census and the Integrated Housing and Living Conditions Survey 2010-

2011 [362, 363] .......................................................................................... 220

Table 6-2: Estimated disposable incomes in 2015 for households in the

Kigali City and Eastern Province of Rwanda based on the 4th Population

and Housing Census and the Integrated Housing and Living Conditions

Survey 2010-2011 [362, 363] .................................................................... 220

Table 6-3: Priority criteria rating* according to biodigester type for

Rwandan households based on the results from the Comparative

Biodigester Study [353] ............................................................................. 220

Table 6-4: Revised GHG emissions and embodied energy for biodigester

materials imported to Rwanda ................................................................. 225

Table 6-5: Comparison of greenhouse gas emissions and embodied energy

of biodigesters in OBSDM with and without consideration of transport for

imported construction materials .............................................................. 226

Table 6-6: Lifespan ranges for biodigester types used for sensitivity

analysis in OBSDM .................................................................................... 239

Table 6-7: Gas leakage ranges for biodigester types used to determine

biogas production efficiency for sensitivity analysis in OBSDM .............. 241

Table 6-8: Measured TS and VS values for cattle dung from surveyed

Rwandan households and comparison to values in OBSDM ................... 245

Table 6-9: Estimated costs of import to Rwanda as a percentage of cost,

insurance, freight, based on the average CIF percentages of export for

selected commodities [377] ....................................................................... 252

List of tables in Appendices Table A-1: Feedstock database in the OBSDM ......................................... 322

Table A-2: Biodigester database in the OBSDM ....................................... 327

Table A-3: Country database in OBSDM with climate data from

Weatherbase and currency conversion rates as at 06.05.2017 from Google

currency converter [56, 57] ....................................................................... 330

Table A-4: Construction material database in OBSDM with prices and local

availability for Kenya [37] ......................................................................... 333

xvii

Table A-5: Construction material cost database in the OBSDM with prices

given in local currency and the regional average prices in USD based on

currency conversion rates as at 06.05.2017 [57] ...................................... 336

Table A-6: Biodigester size database in the OBSDM with costs and

recommended sizes based on an average rural Kenyan household with 77

kg of cattle manure available as feedstock per day ................................... 339

Table B-1: MCDA parameters for biodigester size selection in the OBSDM

for a rural Kenyan household based on average survey data ................... 351

Table B-2: MCDA normalised and overall scores for biodigester size

selection in the OBSDM for a rural Kenyan household based on average

survey data ................................................................................................. 354

Table B-3: MCDA parameters for biodigester size selection in the OBSDM

for a rural Cameroonian household based on average survey data .......... 356

Table B-4: MCDA normalised and overall scores for biodigester size

selection in the OBSDM for a rural Cameroonian household based on

average survey data ................................................................................... 359

Table B-5: Identifying feasible biogas system types and digester sizing in

the OBSDM in a rural Kenyan household based on average survey data 361

Table B-6: Identifying feasible biogas system types and digester sizing in

the OBSDM in a rural Cameroonian household based on average survey

data ............................................................................................................ 365

Table B-7: MCDA parameter values and standardised scores in the

OBSDM for feasible biogas systems for a rural Kenyan household based on

average survey data ................................................................................... 370

Table B-8: MCDA parameter values and standardised scores in the

OBSDM for feasible biogas systems for a rural Cameroonian household

based on average survey data .................................................................... 374

Table B-9: MCDA with weighted scores in OBSDM for rural Kenyan

households based on average survey data (best scores in green, worst sores

in red, overall best scores in bold) ............................................................ 377

Table B-10: MCDA with weighted scores in OBSDM for rural Cameroonian

households based on average survey data (best scores in green, worst sores

in red, overall best scores in bold) ............................................................ 378

Table C-1: Inputs to the OBSDM for households with fiberglass biogas

systems installed from the Comparative Biodigester Study ..................... 379

Table C-2: Inputs to the OBSDM for households with fixed dome biogas

systems installed from the Comparative Biodigester Study .....................382

Table C-3: Inputs to the OBSDM for households with flexbag biogas

systems installed from the Comparative Biodigester Study ..................... 385

Table C-4: Comparison of recommended biogas systems from OBSDM,

where all priority criteria ratings are equal, with installed systems from the

Rwandan Comparative Biodigester Study ............................................... 388

xviii

Table C-5: Detailed output from the OBSDM for Households 1 to 10 when

equal priority criteria rating and updated EROI figures are used ........... 391


equal priority criteria rating and updated EROI figures are used ........... 394


priority criteria rating favourable to the installed biodigester types and

updated EROI figures are used ................................................................. 396


priority criteria rating favourable to the installed biodigester types and

updated EROI figures are used ................................................................. 399

Table C-9: Comparison of months of savings required to meet installation

costs of biogas systems recommended by the OBSDM when no subsidies

are available and with subsidies ............................................................... 401

Table C-10: Comparison of digester size and estimated biogas production

for biogas systems recommended by the OBSDM when using default and

local climate data ....................................................................................... 404

Table C-11: Selected details from the OBSDM output for Households 1 to

10 when equal priority criteria rating and maximum biodigester lifespan

values are used ..........................................................................................408


19 when equal priority criteria rating and maximum biodigester lifespan

values are used .......................................................................................... 410


10 when equal priority criteria rating and the second highest biodigester

lifespan values are used .............................................................................. 411


19 when equal priority criteria rating and the second highest biodigester

lifespan values are used ............................................................................. 413


10 when equal priority criteria rating and the third highest biodigester



19 when equal priority criteria rating and the third highest biodigester


Table C-17: Comparison of recommended biogas systems from OBSDM

using default feedstock TS and VS values and location specific measured

TS and VS values for cattle dung from the Rwandan Comparative

Biodigester Study ....................................................................................... 417

Table C-18: Comparison of recommended biogas systems from OBSDM

using number of cattle to estimate the amount of feedstock and location

specific (measured) daily supply of cattle dung from the Rwandan

Comparative Biodigester Study ................................................................ 420

xix

Table C-19: Details from the OBSDM output for Households 1 to 10 when

equal priority criteria rating and the estimated cattle dung supply based on

the number of cattle are used .................................................................... 423

Table C-20: Details from the OBSDM output for Households 11 to 19 when

equal priority criteria rating and the estimated cattle dung supply based on

the number of cattle are used .................................................................... 426

Table C-21: Comparison of economic parameters of recommended

biodigester types using equal priority criteria rating with and without

consideration of import costs in the OBSDM .......................................... 428

Table C-22: Comparison of highest scoring biogas system designs for

reliability and the systems recommended by the OBSDM when reliability is

the top priority ........................................................................................... 432


robustness and the systems recommended by the OBSDM when

robustness is the top priority .................................................................... 433


simple operation and construction and the systems recommended by the

OBSDM when simple operation and construction is the top priority ...... 434

Table C-25: Comparison of highest scoring biogas system designs for low-

cost and the systems recommended by the OBSDM when low-cost is the

top priority ................................................................................................. 435


technical efficiency and the systems recommended by the OBSDM when

technical efficiency is the top priority ....................................................... 436


environmentally benign and the systems recommended by the OBSDM

when environmentally benign is the top priority ..................................... 437

Table C-28: Comparison of highest scoring biogas system designs for local

material and labour and the systems recommended by the OBSDM when

local material and labour is the top priority .............................................438

Table C-29: Comparison of highest scoring biogas system designs for save

time and the systems recommended by the OBSDM when save time is the

top priority ................................................................................................. 439

xx

List of equations Equation 3-1 [55] ......................................................................................... 64

Equation 3-2 [18] ........................................................................................ 75

Equation 4-1 ............................................................................................... 118

Equation 4-2 ............................................................................................... 119

Equation 4-3 .............................................................................................. 133

Equation 5-1 .............................................................................................. 154

Equation 5-2 .............................................................................................. 154

Equation 5-3 .............................................................................................. 160

Equation 5-4 .............................................................................................. 160

Equation 5-5 ............................................................................................... 161

Equation 5-6 ............................................................................................... 161

Equation 5-7 .............................................................................................. 162

Equation 5-8 .............................................................................................. 164

Equation 5-9 .............................................................................................. 164

Equation 5-10 ............................................................................................. 173

Equation 5-11 .............................................................................................. 173

Equation 5-12 ............................................................................................. 174

Equation 5-13 ............................................................................................. 174

Equation 5-14 ............................................................................................. 174

Equation 5-15 ............................................................................................. 175

Equation 5-16 ............................................................................................. 175

Equation 5-17.............................................................................................. 176

Equation 5-18 ............................................................................................. 176

Equation 5-19 ............................................................................................. 177

Equation 5-20 ............................................................................................. 177

Equation 5-21 ............................................................................................ 178

Equation 5-22 ............................................................................................ 178

Equation 5-23 ............................................................................................ 178

Equation 5-24 ............................................................................................. 179

Equation 5-25 ............................................................................................. 179

Equation 5-26 ............................................................................................ 180

Equation 5-27 ............................................................................................ 180

Equation 5-28 ............................................................................................. 181

Equation 5-29 ............................................................................................. 181

Equation 5-30 ............................................................................................. 181

Equation 5-31 ............................................................................................. 181

Equation 5-32 ............................................................................................ 182

Equation 5-33 ............................................................................................ 182

Equation 5-34 ............................................................................................ 186

Equation 5-35 ............................................................................................ 186

Equation 5-36 ............................................................................................ 186

xxi

Equation 5-37 ............................................................................................ 187

Equation 5-38 ............................................................................................ 187

Equation 5-39 ............................................................................................ 188

Equation 5-40 ............................................................................................ 189

Equation 5-41 ............................................................................................. 189

Equation 5-42 ............................................................................................ 189

Equation 5-43 ............................................................................................ 190

Equation 5-44 ............................................................................................. 191

Equation 5-45 ............................................................................................. 191

Equation 5-46 ............................................................................................ 192

Equation 5-47 ............................................................................................ 192

Equation 5-48 ............................................................................................ 192

Equation 6-1............................................................................................... 218

xxii

xxiii

Publications Several parts of the work and ideas presented in this thesis have been

published in journals and conference proceedings during the course of this

research. These publications are listed below.

G.V. Rupf, P.A. Bahri, K. de Boer, M.P. McHenry, Development of an

optimal biogas system design model for Sub-Saharan Africa with case

studies from Kenya and Cameroon, Renewable Energy 109 (2017) 586-601.

DOI: https://doi.org/10.1016/j.renene.2017.03.048.

G.V. Rupf, P.A. Bahri, K. de Boer, M.P. McHenry, Broadening the potential

of biogas in Sub-Saharan Africa: An assessment of feasible technologies and

feedstocks, Renewable and Sustainable Energy Reviews 61 (2016) 556-571.

DOI: https://doi.org/10.1016/j.rser.2016.04.023.

G.V. Rupf, P.A. Bahri, K. de Boer, M.P. McHenry, Development of a model

for identifying the optimal biogas system design in Sub-Saharan Africa,

Computer Aided Chemical Engineering, Vol. 38, 2016, pp. 1533-1538.

G.V. Rupf, P.A. Bahri, K. de Boer, M.P. McHenry, Barriers and opportunities

of biogas dissemination in Sub-Saharan Africa and lessons learned from

Rwanda, Tanzania, China, India, and Nepal, Renewable and Sustainable

Energy Reviews 52 (2015) 468-476. DOI:

https://doi.org/10.1016/j.rser.2015.07.107.

G.V. Rupf, P. Arabzadeh Bahri, K. de Boer, M.P. McHenry, The Energy

Production Potential from Organic Solid Waste in Sub-Saharan Africa. In:

International Conference on Solid Waste 2015: Knowledge Transfer for

Sustainable Resource Management (ICSW2015), 19 - 23 May 2015. Hong

Kong.

G.V. Rupf, P. Arabzadeh Bahri, K. de Boer, M.P. McHenry, Green gas for

Sub-Saharan Africa: Current situation and opportunities for improving

biogas dissemination (Abstract). In: Royal Society of Western Australia

16th Annual Postgraduate Symposium, 14 September 2014.University of

Western Australia, Perth.

https://doi.org/10.1016/j.renene.2017.03.048

https://doi.org/10.1016/j.rser.2016.04.023

https://doi.org/10.1016/j.rser.2015.07.107

xxiv

xxv

Nomenclature List of Acronyms

ABPP African Biogas Partnership Programme

ABR Anaerobic baffled reactor

AD Anaerobic digestion

AU African Union

BEA Bioeconomy Africa

BES Biogas extension service (Tanzania)

BiogasST Biogas support for Tanzania

BMP Biochemical methane potential

BY Biogas yield

CAL Covered anaerobic lagoon

CAMARTEC Centre for Agricultural Mechanization and Rural

Technology (Tanzania)

CDM Clean development mechanism

CHP Combined heat and power plant

COD Chemical oxygen demand

CSTR Continuous stirred tank reactor

CITT Centre for Innovations and Technology Transfer (Rwanda)

DALYs Disability-adjusted life years

DEWATS Decentralised wastewater treatment systems

DM Dry matter

DRC Democratic Republic of the Congo

EROI Energy return on energy invested

EU European Union

FAO Food and Agricultural Organization

FAOSTAT Food and Agriculture Organization Corporate Statistical

Database

FBR Fluidized bed reactor

GHG Greenhouse gas

GIZ Gesellschaft für Internationale Zusammenarbeit

(Organisation for International Cooperation) (Germany)

xxvi

GTZ Gesellschaft für Technische Zusammenarbeit

(Organisation for Technical Cooperation) (Germany)

HDPE High density polyethylene

Hivos Humanistisch Instituut voor Ontwikkelingssamenwerking

(Humanistic Institute for Development Cooperation) (the

Netherlands)

HRT Hydraulic retention time

IBS Integrated bioeconomy system

IEA International Energy Agency

ISSB Interlocking stabilised soil blocks

JI Joint implementation

KENBIM Kenya Biogas Model

KIST Kigali Institute of Science, Technology and Management

(Rwanda)

KVIC Khadi Village Industries Commission

LCSA Life cycle sustainability assessment

LLDPE Low-density polyethylene

MCD Modified CAMARTEC design

MCDA Multi-criteria decision analysis

MCSA Multi-criteria sustainability assessment

MIGESADO Dodoma Biogas and Alternative Energies Organisation

(Tanzania)

MSW Municipal solid waste

NBMMP National biogas and manure management programme

(India)

NDBP National domestic biogas programme

NGO Non-governmental organisations

NPBD National programme on biogas development (India)

oDM Organic dry matter

OFMSW Organic fraction of municipal solid waste

OLR Organic loading rate

PE Polyethylene

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PP Polypropylene

PVC Polyvinyl chloride

RPE Reinforced polyethylene

SDGs Sustainable Development Goals

SIDO Small Industries Development Organisation (Tanzania)

SNV Stichting Nederlandse Vrijwilligers (Netherlands

Development Organisation)

SRB Sulphate reducing bacteria

SRT Solids retention time

SSA Sub-Saharan Africa

SSD Solid state digester

TDBP Tanzania Domestic Biogas Programme

TS Total solids

UASB Upflow anaerobic sludge blanket (reactor)

UN United Nations

USA United States of America

USR Up-flow solids reactor

UV Ultraviolet

VAT Value-added tax

VFAs Volatile fatty acids

VS Volatile solids

WHO World Health Organization

xxviii

List of Symbols

ηBP biogas production efficiency

Bc Biogas consumption rate

Bd Daily biogas demand

BMPi Methane yield (biomethane production potential) for a

chosen feedstock per kg of oDM

BOD Country-specific per capita biological oxygen demand

BO Maximum methane producing capacity in kg of

methane per kg BOD

BP Daily biogas production

BPP Biogas production potential

BYFM,i Biogas yield per t of fresh matter

BYi Biogas yields per kg of oDM

% change Difference between the old and new value relative to the

old value i.e.(new-old)/old

C/Ni C:N ratio of a chosen feedstock type

C/Nmix C:N ratio of the mixture of chosen feedstocks

costmat-

Vdig_avail_feas

Installation cost of a feasible digester size based on the

total costs required construction materials

costRRP-

Vdig_avail_feas

Installation cost of a feasible digester size based on the

recommended retail price (RRP)

costsE-d Annual energy costs associated with the use of current

conventional energy resources

costVdig_avail_feas Installation cost of a feasible digester size

CVi calorific value of a given fuel

% difference Difference between two values, calculated as the

difference between two values relative to the mean of

the values i.e. (𝑣𝑎𝑙𝑢𝑒 1 − 𝑣𝑎𝑙𝑢𝑒 2) (𝑣𝑎𝑙𝑢𝑒 1, 𝑣𝑎𝑙𝑢𝑒 2)̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅⁄

d- Distance from the worst score

d+ Distance from the best score

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DM Dry matter

DMi Dry matter of a chosen feedstock type

Ed Daily energy demand

EEi,mat Embodied energy per kg of a given construction

material

EEmat Embodied energy of a given digester type based on the

construction materials

EP Estimated daily energy production

EPP Energy production potential

EROI Energy returned on energy investment

ECS Energy cost savings from fuel replacement in USD per

year

EYi Energy yield in kWh per m3 of biogas produced for a

chosen feedstock type

fCH4 Fraction of methane in biogas

FM Fresh matter

GHGe_eng CO₂-e emissions from the consumption of current fuels

GHGe_mat Embodied CO₂-e emissions from construction

materials

GHGeavoidedeng CO₂-e emission savings from fuel replacement

GHGeavoidedWM CO₂-e emissions avoided from the management of

organic waste through anaerobic digestion

GHGi,e/eng CO₂-e emission rate per kWh of delivered energy for a

given fuel source

GHGi,e/mat Embodied CO₂-e emissions per kg of a given

construction material

GWhth Thermal gigawatt-hours of energy (before

consumption/application) for feedstock assessment

calculations

hgroundwater shallowest groundwater depth at the installation site at

any point throughout the year

xxx

hinst maximum depth below the ground level of the proposed

biogas system

HRT Hydraulic retention time in days

HRTdig_max Maximum HRT for a given digester type

HRTdig_min Minimum HRT for a given digester type

HRTFS_max Maximum HRT based on the feedstock

HRTFS_min Minimum HRT based on the feedstock

HRTmax Maximum HRT for a feasible digester volume

HRTmin Minimum HRT for a feasible digester volume

HRTmix HRT range for a mixture of feedstocks

HRTth_max Maximum feasible HRT based on the digester and

feedstock type

HRTth_min Minimum feasible HRT based on the digester and

feedstock type

K Relative substrate micro-organism constant

kWhth Thermal kilowatt-hours of energy (before


calculations

lifespandig Lifespan of a given digester type

µm Maximum specific growth rate

M Mesophilic operating temperature range

MCF Methane correction factor

mCN Annual mass of dry fertiliser required

mi Daily mass input of for a chosen feedstock type

mi,fuel daily consumption of a given fuel

mi,mat Mass of a given construction material

MPP Daily methane production potential

MPww Methane potential from wastewater

mw Daily mass of water available

mw_max Maximum mass of water required

xxxi

mw_min Minimum mass of water required

ncookstoves Number of cookstoves

ndig Number of digesters

oDM Organic dry matter

oDMi Organic dry matter for a chosen feedstock type

OLR Organic loading rate

OLRmax Maximum organic loading rate

OLRmax,adj Adjusted maximum OLR

OLRmin Minimum organic loading rate

OLRmin,adj Adjusted minimum OLR

% change percentage change

P Psychrophilic operating temperature range

Pc Power consumption rate

Pop Total population

rs rating given to each sustainability criterion

s- Worst score

s+ Best score

ss- Worst sizing score

ss+ Best sizing score

T Thermophilic operating temperature range

Ta Ambient temperature

Ta_max Mean ambient high temperature

Ta_min Mean ambient low temperature

tc Number of cooking hours per day

Tdig Digester temperature

Tdig_op Digester operating temperature range

THRT The digester temperature for which the HRT range

was assigned

Ti Fraction of urban/rural population with improved

sanitation facilities

xxxii

Top_min Minimum outside temperature in which the

biodigester can operate

TS Total solids

TSdig_max maximum TS content required for a given digester

type

TSdig_min minimum TS content required for a given digester type

Tset Set temperature of a heating system

TSin_max maximum TS based on the input feedstock mix

TSin_min minimum TS based o the input feedstock mix

TWhth Thermal terawatt-hours of energy (before


calculations

Ui Fraction of urban/rural population

Vdig Chosen digester volume and size

Vdig_avail Available digester size (volume) for a given digester

type

Vdig_avail_feas Feasible digester volume based on the availabe

digester size for a given digester type

Vdig_ideal Ideal digester volume for a given digester type

Vdig_max Maximum feasible digester volume for a given digester

type

Vdig_max,adj Adjusted maximum feasible digester volume for a

given digester type

Vdig_min Minimum feasible digester volume for a given digester

type

Vdig_min,adj Adjusted minimum feasible digester volume for a

given digester type

Vgh Gasholder volume

Vsp Volume of the slurry pit

wij Weight assigned to a priority criteria score for a given

biogas system design option

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x Parameter value

Xij Summed value for a biogas system design option (i)

and sustainability criterion (j)

xs,i Value for a given sizing parameter (s) and a feasible

digester size (i)

z Overall weighted score

z(ssi) Overall sizing score for a feasible digester size and type

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Glossary

Acetogenesis The third phase in the anaerobic digestion process where alcohol and fatty acids are transformed into acetic acid

Acidogenesis The second phase in the anaerobic digestion process where sugars, amino-acids and long-chain fatty acids are turned into alcohols and volatiles fatty acids

Anaerobic digestion

The process by which organic materials are broken down by several groups of bacteria in the absence of free oxygen and are converted into biogas and a nutrient rich slurry

Biodigester An air tight vessel or tank, also known as biogas system, biogas plant, or anaerobic digester, used to produce biogas through the anaerobic digestion process

Biogas A gas consisting of methane, carbon dioxide, and trace gases, which is created through the anaerobic digestion process

Biological oxygen demand (BOD)

Also known as biochemical oxygen demand. A measure of wastewater strength, specifically the amount of dissolved oxygen required for the aerobic biodegradation of organic material in a given water sample under specific temperature and time conditions

Bioslurry A nutrient rich slurry, also known as digestate, created through the anaerobic digestion process

Cell lysate The destruction of bacteria through lysin, which disrupts the cell membrane

Chemical oxygen demand (COD)

A measure of wastewater strength, specifically the amount of oxygen required to complete the chemical oxidation of organic and inorganic material in a given water sample under specific temperature and time conditions

xxxv

DALYs ‘Disability-adjusted life years’, denote “the years of life lost as a result of premature death caused by a disease as well as the years lived with the disease”1

Digestate A nutrient rich slurry, commonly referred to as ‘bioslurry’, created through the anaerobic digestion process

Fuelwood Unprocessed woody biomass used to fuel a small fire2

Hydrolysis The first phase of the anaerobic digestion process where proteins, fats, and carbohydrates are converted into sugars, amino-acids, and long-chain fatty acids

Inoculation The act of introducing microorganisms to a biogas system to stimulate the growth of the bacteria required for the anaerobic digestion process

Methane correction factor (MCF)

A factor indicating the extent to which the methane producing capacity (BO) can be realised in a particular wastewater treatment and discharge pathway3

Mesophilic An operating temperature range for the anaerobic digestion process, normally between 35°C and 42°C

Methanogenesis The final phase of the anaerobic digestion process where acetic acid, hydrogen, and some carbon dioxide is metabolised to produce biogas (methane and carbon dioxide)

Psychrophilic An operating temperature range of below 20°C for the anaerobic digestion process

Substrate Also known as feedstock, which is the organic material that is fed into a biogas system

1 Legros G, Havet I, Bruce N, Bonjour S. The energy access situation in developing countries: a review focussing on the least developed countries and Sub-Saharan Africa. New York, USA: United Nations Development Programme (UNDP) and World Health Organisation (WHO); 2009. p. 142.

2 May-Tobin C. Chapter 8: Wood for fuel. In: Boucher D, May-Tobin C, Lininger K, Roquemore S, Elias P, Saxon E, editors. The root of the problem: What's driving tropical deforestation today?: Union of Concerned Scientists; 2011. 3 Doorn MRJ, Towprayoon S, Manso Vieira SM, Irving W, Palmer C, Pipatti R, et al. Wastewater treatment and discharge. In: Eggleston S, Buendia L, Miwa K, Ngara T, Tanabe K, editors. 2006 IPCC guidelines for national greenhouse gas inventories. Institute for Global Environmental Strategies (IGES), Hayama, Japan: National Greenhouse Gas Inventories Programme; 2006. p. 6.1-6.28.

xxxvi

Thermophilic An operating temperature range for the anaerobic digestion process, normally defined as between 50°C and 60°C

Volatile solids The portion of an organic material, which is readily digested

Wood fuels Any energy source coming from woody biomass, including: fuelwood, charcoal, industrial fuelwood, wood pellets, biogas, cellulosic ethanol, and other advanced forms of bioenergy4

4 May-Tobin C. Chapter 8: Wood for fuel. In: Boucher D, May-Tobin C, Lininger K, Roquemore S, Elias P, Saxon E, editors. The root of the problem: What's driving tropical deforestation today?: Union of Concerned Scientists; 2011.

xxxvii

Acknowledgements

“Umuntu ngumuntu ngabantu” (A person is a person through other persons)

– Zulu proverb

My gratitude to all the amazing people in my life goes far beyond what I can

write on these pages. So many of you have been part of this journey and

although I cannot mention you all, please rest assured that your impact has

not gone unnoticed or unappreciated.

First and foremost, my deepest thanks goes to my Heavenly Father, my Lord

and Saviour, Jesus Christ, and your guiding, comforting Holy Spirit. I am

because you are. Thank you for being with me through all the hills and

valleys. You are the firm foundation on which I stand; my constant hope and

assurance. All glory and honour goes to you.

To my family, who has always been there for me and helped shape me into

the person I am today: thank you. A big thank you to my sister, Dahlia, who

not only has done an amazing job proof-reading for me but has been a

constant source of encouragement in this venture, along with bringing so

much joy and support in the everyday. Thank you to my Mum for

continually championing me and encouraging me to never give up, and Dad

for inspiring me from an early age with the idea of harnessing science and

technology to help create a sustainable future. My sincere thanks to my Papa

for all your prayers, support from near and far, and continually reminding

me of just how proud you are of me. Thank you to my brothers, Jarod and

Luke, and brother-in-law David, for helping me not to take myself too

seriously and my nephew Noah for being the cutest distraction imaginable.

I am very grateful to my principal supervisor, Prof Parisa A. Bahri, for being

an outstanding mentor and friend, continually encouraging me to take hold

of the opportunities before me and be the best researcher I can be. A big

thank you also to my co-supervisors, Dr Karne de Boer and Dr Mark P.

McHenry, for all your valuable support and advice. I thank you all for having

xxxviii

so much faith in me to complete this work to a high standard, while also

never forgetting that I am still a fellow human being.

I am also grateful to Murdoch University for providing me with the

opportunity to complete this candidature and the Australian Government

for the funding through the Australian Postgraduate Award (APA).

Thank you to all my colleagues at the School of Engineering and Information

Technology, particularly those part of MUERI, EELS, MEEP, and the

administrative staff. It has been such a privilege to get to know you all over

the years and to call so many of you dear friends. I could not have wished

for a better academic support network! A special thank you to Dr Tania

Urmee for all the words of advice and encouragement at just the right

moment.

To all my friends throughout the beautiful region of Sub-Saharan Africa:

Thank you for your honesty in making me aware of the challenges ahead,

and yet still welcoming this pursuit – you have been such an inspiration.

To Dr Anastase Rwigema, Jean Paul Sibomana, Felix Usengimana from the

University of Rwanda, Mr Jaime Sologuren Blanco and the rest of the team

at SNV Rwanda: thank you so much for the privilege of working with you

and the opportunity to assist with your study. My sincere thanks also to all

the families that participated in the study and to those who I had the

pleasure of meeting: you have been a huge inspiration for this work.

Murakoze cyane.

A special thanks also to Mr Jan Lam and Mr Felix ter Heegde from SNV for

their advice and encouragement regarding this work – it has been

invaluable.

I am also grateful to the following people for providing me with

specifications on the biogas system designs available from their respective

organisations/programmes: Anthony Walter Okello (Biogas Solutions

Uganda Ltd), Aster Haile (SNV Ethiopia), Benedikt Maibaum (Biogaz

xxxix

Burundi), Christophe Chesneau (Bioeco Sarl), Kenan Lungu (SNV Zambia),

and Matar Sylla (Programme National de Biogaz domestique du Sénégal).

Thank you to all my dear friends here in Australia and across the globe –

you are like family to me and I have valued all your prayers and support

during this exciting and challenging time.

xl

1

Chapter 1 Introduction

Introduction

“For wisdom is better than rubies; all the things that may be desired are not to be compared to it.”

– Proverbs 8:11

Sustainable development can be defined as a pattern of development that is

enriching and nourishing, meeting the social, environmental, and economic

needs of an area, without having any detrimental impacts to the systems and

services that make development possible in the first place [1]. Energy is

complementary to and essential for development [2]. Its consumption is an

indicator of development, as energy is needed to complete essential

domestic, agricultural, and educational tasks, as well as in health, transport,

and communication services, and for initiating or developing income

generating activities [1, 2]. Sustainable energy has therefore been

recognised as an important part of eradicating poverty [3]. Currently, Sub-

Saharan Africa (SSA) is facing an energy crisis due to limited supply,

restricted consumption, high costs, and low quality for both commercial and

biomass sources, which is predicted to continue to worsen [4].

Socioeconomic development in the region and the livelihoods of the people

has been impaired as a result. Limited clean energy access impacts

negatively on household health, particularly through the use of solid fuels

2

for indoor cooking in open fires and basic stoves. This traditional cooking

method is one of the leading risk factors for death and disability-adjusted

life years (DALYs) in western, eastern and central SSA [5]. The installation

of biogas systems in rural regions has been one means of attempting to

address this energy crisis. The transfer of knowledge and training to its users

is essential for effective installation of sustainable energy systems like

biogas digesters.

Biogas is an attractive alternative energy technology for SSA, particularly in

rural communities, due to it being a technology that can be applied to

various contexts and scales, and having multiple benefits that address some

of the key issues in the region. Its benefits are wide-reaching, including

areas of gender equality, employment, energy, agriculture, health and

sanitation, as well as contributing to achieving the United Nations (UN)

Sustainable Development Goals (SDGs) [6]. The technology harnesses

anaerobic digestion (AD) in one or more reactor tanks to convert organic

materials into energy, in the form of biogas, heat, and other usable products

[7]. The most common feedstocks for biogas systems are agricultural,

human sewage, and organic municipal wastes. For example, some

household biogas systems have a latrine connection, providing effective

waste management and improved sanitation. The resultant biogas can be

used for cooking, heating, electricity generation, and as a vehicle fuel (post-

refining and compressing). Along with biogas, a nutrient rich slurry is also

produced, which may be used as a bio-fertiliser to obtain greater crop yields

and restore nutrients to the soil [8, 9]. Therefore, biogas technology can be

3

used as an integrated system; simultaneously providing energy, improving

sanitation and organic waste management, increasing food production

through improved soil nutrient return, reducing greenhouse gas emissions

and, in some instances, providing cleaner water supplies [4, 10]. There is a

wealth of potential biogas feedstocks in SSA, but little research on the

optimal method of harnessing these regionally-specific resources. This

research is intended to address this gap by assisting in improving the design

of biogas systems in selected countries in SSA to help increase its uptake

and secure a sustainable energy future for the region.

1.1 Thesis overview

The research commences with an introduction to the challenges and

opportunities with energy supply in SSA, specifically the role of biogas

technology and the contribution it can make to sustainable development.

The aim and objectives of the thesis, followed by details on the methodology

used to conduct and develop this research work, are also provided.

Chapter 2 sets the context of the research by summarising the energy

situation in SSA, particularly the dissemination of biogas technology in the

region, including its barriers and opportunities. Biogas dissemination in

selected SSA countries will also be discussed, along with country specific

examples from other regions of the world.

Chapter 3 presents a review of the factors that influence the design of biogas

systems and the different types of biodigester designs applicable to SSA.

4

The feedstocks available in SSA for biogas production and associated energy

production potential are discussed in Chapter 4.

Details on the development of the proposed optimal biogas system design

model (OBSDM) for SSA, including a literature review of relevant biogas

models and design tools and preliminary testing of the model with case

studies from rural households in Cameroon and Kenya, are given in Chapter

5.

Chapter 6 describes the OBSDM validation and sensitivity analysis using

data from households with biogas systems in the eastern and central

districts of Rwanda.

A review of the research findings and resulting conclusions are provided in

the final chapter, along with recommendations for further research and

development.

1.2 Research aim and objectives

Biogas technology is a well-known technology that has been used all over

the world for many decades, particularly in China, India, and Western

Europe. A wide range of biogas digester designs have been developed and

are commercially available. Studies have been carried out on different

feedstock types, improving biogas yields through specific feedstock

combinations and other techniques to optimise biogas production [11, 12].

In Europe, extensive research has been carried out on biogas technology,

including studies on the energy efficiency related to biogas production and

utilisation, as well as energy balances on the life-cycle of biogas systems [13,

5

14]. In SSA, biogas was first introduced in Kenya and South Africa in the

1950s [15]. Since then, a number of small-scale to medium biogas systems

(less than or equal to 100 m3), as well as a small number of large scale

digesters (above 100 m3), have been installed all over the region, including

in Benin, Botswana, Burkina Faso, Cameroon, Côte d’Ivoire, Ethiopia,

Ghana, Lesotho, Madagascar, Nigeria, Rwanda, South Africa, Swaziland,

Tanzania, and Zimbabwe [15-19]. Some of these implementations have been

successful, although a significant number of systems have operated poorly

or failed completely within a few years of installation, due to inappropriate

designs and/or implementation [20]. The failed plants have contributed to

the slow uptake of the technology in SSA, as confidence has been lost in the

system’s ability to provide a sustainable energy supply [21, 22]. Moreover,

while the domestic biogas programmes currently running in Burkina Faso,

Ethiopia, Kenya, Tanzania, and Uganda, have assisted many farming

households to install biogas systems, the designs and financing options

available through the programme are unaffordable to those with limited or

no disposable income [17, 23]. This has highlighted the need for

translational research on affordable methods of using biogas technology for

meeting energy needs and improving sanitation, whilst maximising the

economic and environmental benefits for the unique conditions in SSA [4].

While research on biogas production has been carried out in SSA, including

on the economics of biogas digesters, environmental assessments of farm-

scale plants, and the production potential from domestic wastewater, there

is still a need to translate the extensive research in Europe, as well as parts

of Asia, on optimal biogas system designs and use to the SSA context [24-

6

27]. Providing potential biogas users and biogas system installers with the

means of identifying the most appropriate system for their context is

essential for building sustainable biogas systems. The motivation behind

this research is to address this gap in translational and optimal biogas

system design research in SSA.

The aim of this research is to develop a model that determines the optimal

biogas system design based on a given set of inputs for particular

applications in selected countries in SSA to help improve biogas

dissemination. The model is intended to assist potential biogas users, biogas

installers, governments, Non-Governmental Organisations (NGOs) and

other stakeholders involved in implementing biogas systems, in making

appropriate design choices that will ensure long term sustainability of the

biogas system and derive maximum benefit to its user(s). The optimal

design and benefits to the user are considered in terms of the three pillars

of sustainability: economic, social, and environmental, in addition to

technical sustainability, which are commonly used for assessing the

sustainability of energy supply systems [28]. Multi-criteria decision analysis

(MCDA) and multi-criteria sustainability assessment methods will be

applied in the development of the optimal design criteria and the biogas

system design model [28, 29]. Five key objectives have been identified to

help meet the main aim of developing the model. The five objectives are:

1) Identify the biogas system designs suitable for and available in SSA.

2) Assess the biogas feedstocks available in SSA.

7

3) Develop the optimal biogas system design model – identify suitable

inputs and outputs for the biogas system model and develop a

method of determining the optimal biogas system design.

4) Test the biogas system design model by comparing model outputs to

data from existing biogas systems in selected countries in SSA.

5) Highlight any patterns identified in using the model and make

recommendations based on the findings for optimal biogas system

designs in SSA.

In addition to contributing to biogas research specific to the context of SSA,

the development of the biogas system model and optimal design criteria will

also present a holistic approach to biogas system design and modelling. The

model brings together research on biogas digester designs and different

types of sustainability assessments and analysis to determine optimal

system designs in a way that is understandable, not only to researchers and

biogas installers, but also to those without a strong technical background. It

enables optimal design choices to be made for specific applications and/or

locations and ensures sustainability aspects are considered at the beginning

of the design phase. The model is intended to be adaptable and changeable

so that it can be extended in the future as required for use in other parts of

the world.

8

1.3 Research Method

The development of a biogas system model that gives the optimal system

design requires six main stages which reflect the five objectives outlined in

Section 1.2: literature review, feedstock assessment, development of the

optimal biogas system design model, testing and validating the model, data

analysis, and final model with recommendations. The approach with the six

steps is summarised in Figure 1-1

1.3.1 Literature review

The first stage is a literature review on suitable biogas digester technologies,

as well as existing models and tools for designing biogas systems,

particularly those incorporating MCDA and sustainability assessments (as

referred to in Section 1.2 above). The digester technologies are categorised

according to their application, namely household-, farm/community- and

commercial- scale, as well as the design type. The categories may be

adjusted depending on the technology types identified in the literature

review.

1.3.2 Feedstock assessment

In the second stage, a feedstock assessment is carried out for the SSA region.

Information on specific parameters required to calculate the biogas and

energy production potential of different types of feedstock were collected

from existing literature as well as research institutes and organisations

involved with biogas technology in the SSA region. These parameters were

used in conjunction with data on the availability of different feedstock types

available in SSA from the existing literature and online database such as

those from the Food and Agricultural Organisation of the United Nations

9

(FAO). A range of data and maps related to land use, agricultural resources,

and climate has been made available by the FAO [30].

1. Literature Review 2. Feedstock Assessment

• Identify & categorise biogas

technologies applicable to SSA

• Review existing models and tools

for biogas technology

Assess feedstock types and

availability in SSA and their biogas

yield from existing literature,

research institutes and organisations

3. Develop the Model

Determine inputs, outputs &

method of optimisation

4. Test the Model

5. Data & Sensitivity

Analysis

6. Final Model &

Recommendations

Apply the model to case studies

from selected SSA countries and

compare with existing biogas

systems

Analyse output data of the model

from cases studies to identify any

trends and patterns in the optimal

biogas system design for the SSA

context

• Adjust biogas system model based

on findings from case studies

• Make recommendations on suitable

biogas system designs for SSA

Figure 1-1: Approach to PhD Project

10

The work by Salomon and Silva Lora [31] provides a good guide on

conducting biogas feedstock assessments for large areas. The annual biogas

potential production for five main feedstocks available in Brazil was

determined in their work from the product of the methane conversion

indicators of the feedstocks and their estimated per unit production. A

similar approach was applied to determine the biogas and energy

production potential from feedstocks available from households (municipal

wastes), as well as agricultural and food processing industries in the four

regions of SSA – Central, East, West, and Southern Africa. The feedstock

assessment is summarised on per capita production potentials for different

feedstock types in each of the SSA countries, dependent on data availability.

1.3.3 Develop the optimal biogas system design model

A preliminary biogas system model is developed in the third stage where the

inputs, outputs, and optimal design criteria are determined. A visual outline

of the biogas system model for determining optimal biogas system design is

given in Figure 1-2. The model requires a given set of inputs to be entered,

which are required for the design of an optimal biogas system. Some of the

inputs will be numerical values, such as ambient and soil temperatures,

amount of feedstock, energy requirements, and amount of water available.

The other inputs are selected from lists of different parameters that have

been predefined in the model. Example selection lists are feedstock types,

type of energy required (e.g. cooking, heating, electricity), energy unit (e.g.

kWh, kJ) and type of materials available. The model has specific data related

to the input lists integrated into it, e.g. biogas yields for each type of

feedstock. Information on the biogas system technologies, biogas yields, and

11

economic parameters (e.g. construction material costs, labour costs and

operational costs), social parameters (e.g. estimates of time saved for the

user for every unit of energy that is supplied by biogas rather than firewood),

and environmental parameters (e.g. GHG emission reductions) from the

literature review and feedstock assessment, is incorporated into the model

to assist in determining the optimal design.

Determining the optimal biogas system design requires the feasible system

designs to be identified based on the inputs and design equations used in

the model, and these feasible options to then be analysed using MCDA and

sustainability assessment approaches. These approaches help identify

which of the system design options best meet the optimal design criteria.

Along with the inputs, the priority weighting of optimal design criteria also

need to be selected before the analysis can begin. Design criteria are selected

from the four pillars of sustainability: environmental, economic, social and

technical, such as reducing GHGs, minimising capital and operational costs,

saving time for the user (e.g. through reducing/eliminating the need for

Figure 1-2: Outline of biogas system model for optimal design

Biogas system model for

determining the optimal design Inputs

Output

Optimal

biogas

system

design

Technology

options &

design

equations

Biogas

yields from

feedstock

Economic parameters,

social parameters &

environmental

parameters

System

design

calculations

Multi-criteria

analysis for

optimal

design

System

design

options

Optimal

design

criteria

12

firewood collection), and maximising biogas production. The optimal

design criteria used in this research is selected based on the findings in the

literature review, and may be adjusted after testing with case studies from

SSA, to ensure that they reflect what is considered most important for long-

term sustainability and maximising the benefit to the biogas system user(s)

in SSA.

The multi-criteria sustainability assessment of biogas systems in Kenya by

Nzila et al. [29] identified the most suitable digester design based on the

cumulative multi-criteria sustainability score. The tubular biogas digester

was found to be the most sustainable, using this approach. Although the

tubular digester scored high overall, it scored poorly in terms of reliability

compared to the other digester designs that were considered. In rural

regions of SSA, reliability is of particular importance; therefore, the tubular

digester may not be the most sustainable option for this context. In contrast,

the biogas system model developed in this research includes a weighting for

the optimal design criteria based on the priorities of the intended user(s).

This approach will increase the likelihood of the model identifying optimal

designs that score high in the criteria that are important to the intended

user(s).

1.3.4 Test the optimal biogas system design model by applying it

to case studies

In the fourth stage, the optimal biogas system model is tested by applying it

to case studies from selected SSA countries. Case studies with existing

biogas systems were chosen as these enable comparisons to be made

13

between the anticipated performance and design parameters of the

optimised biogas system design from the model and the actual system

installed. Data available from existing literature, as well as a study

completed in collaboration with research institutes and international NGOs

based in SSA, were considered for testing and validating the model. The type

of data sought from the case studies is reflective of the inputs required for

the optimal biogas system design model. This includes details on the

measured or anticipated energy consumption, conventional fuels used, time

associated with energy related activities (e.g. firewood collection), and the

amount and type of feedstock available for biogas production.

1.3.5 Data and sensitivity analysis

The output data from the model after applying it to the case studies, is

analysed in the fifth stage, identifying any trends and patterns on the

optimal biogas system designs given by the model for the SSA context. The

model’s sensitivity to input parameters with uncertainties or a large scope

of variability is also analysed in this stage. Some of the key questions that

were asked to identify the trends and patterns when analysing the output

data are:

• Is there a particular reoccurring optimal design given by the model

for the different scenarios from the case studies?

• Are there any specific design features that stand out as a priority for

SSA based on the model outputs from the case studies?

14

1.3.6 Final model and recommendations

In the final stage, minor adjustments were made to the preliminary model,

based on the data and sensitivity analysis, to develop the final model.

Recommendations are also made on suitable biogas systems designs and

priority design features for SSA for sustainable, long-term use of the

technology, based on the findings from this research.

The research aims to encourage further dissemination of biogas technology

in SSA, whereby the priorities and context of the intended user have been

carefully considered and integrated into the design of the systems that are

being installed and promoted.

15

Chapter 2 Energy situation and

biogas dissemination in SSA

Energy situation and biogas dissemination

in SSA

"Access to modern forms of energy is one of the most pressing challenges

facing Africa, and is central to the three dimensions of sustainable

development"

– Dr Kandeh K. Yumkella, CEO UN Sustainable Energy for All

Initiative

This chapter presents the context of biogas technology in SSA through

providing an overview of the energy situation in SSA, followed by a

discussion on the main barriers and opportunities to its dissemination in

the region. Experiences in selected SSA countries, specifically in Rwanda

and Tanzania, as well as in China, India, Nepal, and Europe, are explored to

identify the lessons that can be learned for successful biogas technology

dissemination.

2.1 Energy situation in SSA

The current energy situation in SSA can be described in short as an

abundance of resources but limited accessibility and uneven distribution

[32]. A number of SSA countries, mainly in the west and southern regions,

have modest to significant fossil fuel resources [21]. Nigeria, for example

has the eighth largest natural gas reserves and the tenth largest oil reserves

in the world, while South Africa has the ninth largest coal reserves in the

16

world [33]. Along with Nigeria, six other SSA countries are oil exporters,

including: Angola, Côte d’Ivoire, Equatorial Guinea, Gabon, Chad, Republic

of the Congo, and Cameroon [34]. A large proportion of the SSA population,

however, does not have access to these abundant conventional energy

resources due to the energy resources being largely underdeveloped with a

lack of infrastructure and appropriate distribution systems, as well as the

focus on exporting resources outside of the region [21, 32, 35]. The majority

of SSA countries, therefore, struggle to meet their energy demand and rely

on fuel imports which are costly to the countries’ economies, particularly for

those that are landlocked [21, 35]. Renewable energy resources are available

throughout the region with the warm sunny climate in many SSA countries

providing a significant potential for solar thermal and photovoltaic energy

along with wind resources along the coast, and hydroelectric potential in

some of the major waterways. Small-scale off grid applications of renewable

electricity have been introduced in the region, including: solar PV systems,

pilot hydropower systems, wind turbines, and biomass generators [32].

Biofuels in the form of bioethanol, biodiesel, and biogas are still in the early

developmental stages with few operational commercial biofuel plants in

selected parts of SSA [35]. Currently, the biodiesel market is concentrated

in the south, namely Zimbabwe, South Africa, and Mozambique, while

bioethanol production is more scattered through the region, including in

Mauritius, Tanzania, Zambia, Kenya, Angola, Swaziland, Ethiopia, Uganda,

Malawi, as well as Zimbabwe and Mozambique [36]. The region is said to

have the largest potential for bioenergy crops in the world but there is a need

to apply more sustainable agricultural practices, develop appropriate

17

policies, and collaborate with rural community members such as farmers

and landowners (including women), to ensure the industry is developed

sustainably [36]. Overall, there is a need for regional co-operation to

effectively harness the abundant renewable energy resources, and bring

about low-cost solutions with appropriate infrastructure [32].

Energy distribution in the form of electricity is said to be in crisis in SSA

with the generating capacity and the number of household connections

lower than in any other region in the world [37, 38]. The electricity

infrastructure in SSA is said to be “inadequate, unreliable and costly”, which

has led to a decline in the per capita consumption in the region (excluding

South Africa), unlike in any other developing region [37]. Fossil fuels

comprise 80% of the electricity generation, with coal fired power generation

in South Africa being a particularly large contributor, followed by natural

gas [39]. Hydropower is another significant electricity generator in SSA,

while nuclear electricity generation contributes around 2% [39]. The

electrification rate in SSA was around 32% in 2010, compared to 99% in

North Africa, and is anticipated to increase to a modest 58% in 2030 with

655 million people remaining without electricity access [40]. The average

electricity price in SSA is high, at twice the rate of that found in other

developing regions, with the additional cost of supply unreliability [38]. The

unreliability of the power supply has made expensive emergency and back-

up power commonplace, as well as private generation systems [37]. The

additional installations along with low economies of scale; low connection

density (especially in rural areas); inefficient utilities; and low levels of

18

regional integration with less than ideal designs; have all contributed to high

electricity prices [37]. The limited supply and lack of appropriate energy

distribution infrastructure contributes to over 766 million people choosing

wood fuels as their primary energy source [41]. Specifically, rural regions of

SSA have very low electrification rates (approximately 11%) and rely heavily

on traditional biomass resources such as cow dung, crop residues, fuelwood,

and charcoal for energy [42]. Low-cost, reliable, and decentralised

electricity or other equivalent energy services are required to improve

energy access in SSA, especially in its rural regions.

The extensive use of traditional biomass in SSA has negative impacts on

both health and the environment. Fuelwood and charcoal are the most

common traditional biomass materials used for cooking in both urban and

rural SSA (Figure 2-1 and Figure 2-2). The majority of households use

inefficient traditional open fire stoves that pose a number of health concerns

due to the lack of ventilation, leading to a build-up of thick smoke,

particulates, and hazardous pollutants in homes [43, 44]. The International

Energy Agency (IEA) estimates that 80% of the SSA population does not

have access to clean cooking facilities [40]. Exposure to indoor pollution

from wood fuel stoves is strongly linked to a number of diseases including:

pneumonia and acute infections of the lower respiratory tract for children

under the age of five; and chronic obstructive pulmonary disease (COPD)

such as chronic bronchitis or emphysema in women [45]. Moderate links

have also been found to: lung cancer, particularly in women; asthma in

children and adults; cataracts and tuberculosis in adults; adverse pregnancy

19

outcomes such as low birth weight; ischaemic heart disease; interstitial lung

disease; and nasopharyngeal and laryngeal cancers [45]. Household air

pollution from solid fuels (wood, crop residues, animal dung, charcoal, and

coal) has been recognised as one of the leading risk factors for death and

DALYs in western, eastern, and central SSA [5]. In 2012, according to the

World Health Organization (WHO), around 580,000 people died in SSA

from diseases that have been caused by exposure to indoor air pollution

[46]. Alternative, improved cooking fuels and stoves in SSA will address a

prevalent health concern and improve the household cooking environment.

Figure 2-1: Urban SSA population cooking fuel use in 2007 [42]

Electricity11.00%

Gas11.00%

Kerosene20.00%

Coal2.00%

Charcoal24.00%

Wood30.00%

Dung0.01%

Other1.99%

Urban SSA

20

Figure 2-2: Rural SSA population cooking fuel use in 2007 [42]

Aside from the negative health impacts from cooking with traditional

biomass, the collection and use of these fuel sources has other adverse

consequences. Women and children are often assigned the task of fuelwood

collection, exposing them to the risk of injury, violence, and snake bites, as

well as causing children to miss school and women from activities that

contribute to socio-economic development [42, 47]. From an environmental

and economic perspective, fuelwood collection in SSA on the whole has not

been found to cause any significant degradation, rather providing some

local benefits through the provision of low-cost energy as well as income

generating opportunities for fuelwood venders [48]. On a more localised

level, however, the impact of fuelwood collection differs. In Nigeria, for

example, the supply of desirable wood types has been declining in the

country, and as a result there has been an increase in the use of low quality

wood while the gathering time and distance walked to collect the wood also

has become longer for the mostly rural population that consumes fuelwood

[16]. Other countries like Senegal, Burkina Faso, and Uganda are seeing a

Electricity2.00% Gas

1.00%

Kerosene1.99%

Coal0.01%

Charcoal6.00%

Wood87.00%

Dung1.00%

Other1.00%

Rural SSA

21

strain on their forests as fuelwood resources become more scarce [19, 49].

The use of wood for charcoal production in SSA presents a more serious

environmental concern as it requires the cutting of trees, and has been

found to be a major contributor to land degradation in dry lands, and the

destruction of forests, leading to a loss of biodiversity [41, 50]. In

considering the impacts of both firewood and charcoal, 70% of the observed

deforestation for 46 African countries has been attributed to wood fuel

demand [51]. These contributions to deforestation are unsurprising given

that the majority of wood fuels are sourced from natural forests with at least

38% of this amount being unsustainably harvested [52]. Furthermore, the

consumption of highly inefficient traditional biomass in its uncontrolled use

is aggravating soil erosion and flooding as well as hindering development in

the region [32]. Soil erosion is of great concern in SSA as the majority of the

population practice subsistence farming. The dominance of traditional

biomass in SSA’s energy economies while modern energy remains

unaffordable for the majority of the population, is reflective of the region

being in the poorest continent in the world [35, 53]. Improved energy

services including more efficient energy sources and conversion

technologies can make a positive contribution to poverty alleviation. The

improvement to livelihoods through energy services can only be realised,

however, when a holistic approach is taken which focuses on the specific

needs of individual communities, and the role of energy within that context,

such as water and food supply, communication, health, education,

transportation, heating, and cooking [54].

22

The critical energy situation in SSA presents a unique opportunity to

develop sustainable energy infrastructure tailored to the needs of individual

communities. Appropriate sustainable energy resources can be used from

the very beginning rather than transitioning from unsustainable energy

resources to renewable energy resources; which is the challenge being faced

in developed regions.

Biogas technology inherently meets most of the key requirements for

addressing the energy access challenges facing SSA. The technology can be

applied anywhere where there is a sufficient supply of organic materials.

Biogas systems not only supply energy but can be integrated into household,

community, or commercial organic waste management systems for

improved sanitation as well as nutrient recycling. The two main products

from a biogas system are biogas and a slurry, known as digestate or

bioslurry, which can be applied as a fertiliser, either directly or after further

treatment. Biogas is comparable to natural gas, due to its high methane

content, and can be used in the same way – for cooking, heating, electricity

generation and as a transport fuel. It can also be fed into the natural gas

grid, which is done in some European countries including: Denmark,

Switzerland, Sweden, and Germany, but this requires pre-treatment and is

not always economically viable [55]. Biogas technology has the potential to

play an important role in improving energy access in SSA, particularly in

rural regions, in addition to helping improve sanitation and soil fertility.

Furthermore, biogas technology is scalable and can be made from local

construction materials, thereby enabling it to be adaptable to the specific

23

needs of the potential user(s). Within urban/peri-urban SSA there is

significant potential for biogas production from municipal organic solid and

sewage waste as well as agricultural residues by applying the AD process for

treatment [17, 56]. This is particularly important in major cities that have

limited or sometimes no infrastructure to safely manage waste, converting

a nuisance into a profitable, recyclable product [16, 17]. Households relying

on firewood for cooking and subsistence farming can also gain important

benefits from biogas through reducing or eliminating the need for firewood

collection, providing a smoke free cooking environment, helping improve

soil fertility and crop productivity through the application of the bioslurry,

while also offering the potential for improving sanitation [57]. The benefits

to households are particularly attainable through biogas being cost

competitive with firewood and charcoal [58]. Biogas technology is unique to

other renewable energy sources in that it can provide benefits to three

priority sectors for SSA: energy supply, sanitation, and food security (crop

productivity). This urges the question: what has prevented widespread

dissemination of biogas technology in SSA?

2.2 Biogas dissemination in SSA

2.2.1 Overview

Interest in biogas technology keeps resurging in SSA. Since its first introduction in

the 1950s until the launch of the “Biogas for Better Life –An African Initiative”

(Biogas for Better Life) in 2007, biogas dissemination in SSA and the rest of the

African continent has been sporadic. Biogas for Better Life aimed to develop a

commercial domestic biogas market throughout the continent that would offer

investment and business opportunities, market-orientated partnerships, and local

24

ownership with a goal of 2 million biogas installations by 2020 [59]. For the first

time, through the initiative, the potential of biogas was evaluated for the whole

continent of Africa. Biogas for Better Life provided a platform for biogas

dissemination programmes in SSA through establishing the Africa Biogas

Partnership Programme (ABPP). The ABPP is a partnership between two Dutch

non-profit organisations, Hivos and the Netherlands Development Organisation

(SNV), which currently supports domestic biogas programmes in five SSA

countries: Burkina Faso, Ethiopia, Kenya, Tanzania, and Uganda [23]. The

programme aimed to install 100,000 biogas plants to provide sustainable energy

to half a million people by 2017 and has met 57% of this target [23, 60]. Financial

assistance is provided by the Directorate General for International Cooperation of

the Dutch Ministry of Foreign Affairs (DGIS) along with SNV, while Hivos manages

the funds and the programme. Capacity building services in all five countries is

provided by SNV, which has had experience setting up large-scale domestic biogas

programmes in Asia [23]. The biogas systems installed under the domestic

programmes are designed for households that have four or more cows with cattle

dung as the main feedstock due to there being a strong ownership of cattle in rural

SSA communities [17]. The author notes that in SSA cattle ownership is often

linked to status and wealth in a community. The focus on households with cattle

has left biogas technology inaccessible under the programme to those with a lower

socioeconomic standing [17].

In addition to the five countries currently running domestic biogas programmes

through ABPP, several other SSA countries have some experience with biogas

technology, including: Benin, Cameroon, Lesotho, Madagascar, and Nigeria,

Rwanda, Senegal, South Africa, and Zimbabwe [16-19]. The oil crisis in the 1970s

along with the success of biogas use in China and India motivated many of these

25

countries to start development programmes for the technology which involved

scientific, technical, social, and economic studies [19]. The studies were carried out

precipitously and as a result brought disappointment, which led some

administrators to conclude that biogas is not suitable for the region [19]. More

recently, in 2001, a pilot biogas project was set up at Rumbek Secondary School in

South Sudan which involved the training of students and teachers on the

installation and operation of biogas systems, specifically the tubular plastic

biodigester [47]. After its successful implementation, the biogas system is being

used as a demonstration site to teach and equip community members and visitors

on the benefits and use of biogas technology [47]. Similarly in South Africa, a

demonstration biogas plant was installed in a rural school, which uses ‘night soil’

and cattle manure as feedstock and the generated biogas is consumed for cooking,

science experiments, and electricity generation [61]. The Ethiopian NGO,

Bioeconomy Africa (BEA), is another promoter of biogas technology in SSA by

making it part of their integrated bioeconomy system (IBS). The IBS is a farming

system developed by BEA to enable both urban and rural farmers with scarce

resources to significantly increase their agricultural yields and diversify their

production activities by applying a combination of low-cost bio-farming

techniques, such as composting, double-digging, biogas, and organic fertiliser

production [62]. BEA has seven demonstration operational research and

knowledge sharing centres (Integrated Biofarm Centres) located in various cities

and villages in Ethiopia, as well as in the Democratic Republic of the Congo (DRC),

Mozambique, and Côte D’Ivoire [62].

2.2.2 Main barriers

The main barriers to biogas dissemination can be categorised as financial,

technical, social-cultural, or institutional as shown in Table 2-1. The

26

installation costs of conventional biogas systems present a significant

barrier to increased adoption of biogas technology, particularly in rural

regions. Many of the rural farmers and households have little or no

disposable income, and most incomes are seasonal [47]. Flexible credit

schemes are also hard to come by both for those seeking to install biogas

systems and entrepreneurs wanting to set up biogas installation and

maintenance businesses. A survey conducted in Uganda found that

households with a higher income were more likely to adopt biogas

technology, and all the surveyed households with biogas systems had the

assistance of donor agencies for the installation [49]. Governmental or

donor agency support for the installation of biogas systems is not

uncommon in SSA, and has generated uncertainty over the ownership and

maintenance responsibility of the system [47, 49]. The biogas user, who has

paid a minimum amount for the installed system, often views it as being

externally owned and, therefore, expects government/donor support for

maintenance. In many instances, biogas systems break down or are

abandoned due to the user not being provided with technical support,

follow-up services, or sufficient training by the biogas installer/promoter,

as well as there being no suitable local technical expertise available [4, 18,

47, 63]. Poor design choices, mainly due to overlooking the users’ energy

needs and local conditions, are another major contributor to the short

lifespan of many installed biogas systems [4, 18, 19, 63]. The energy

requirements of the potential biogas user need to be considered when sizing

the system, while the amount, seasonal availability, and ease of collection of

27

both water and the feedstock material/s can be used as indicators to make

appropriate design choices.

A well-designed biogas system is of little use if it is not socially or culturally

acceptable. The inertia to change from traditional firewood stoves to biogas

stoves in SSA has been significant in some areas due to the perception that

food cooked the traditional way tastes better, or that the biogas flame is

small and cooking is slow, as well as the biogas stove not always being

suitable for the preparation of some traditional foods that require several

hours of cooking on the stove [16, 19, 63]. Firewood collection for household

energy is a social activity in some areas, for women in Burkina Faso for

example, and therefore, if firewood is replaced with biogas an alternative

social activity needs to be sought [63]. In other regions, firewood collection

is an important source of income either for selling to others directly or for

use in charcoal production. While some regions are used to using animal

dung for energy generation, others, such as in Zimbabwe, do not consider it

to be acceptable to cook food from energy generated from animal dung, let

alone from latrine waste [19]. It has also been suggested that traditional

gender roles in households can present a challenge to biogas adoption, as

often the decision to invest in a biogas system rest on the male head of the

household, while the women and children use the technology and benefit

from it most [63]. Many of these social barriers can be overcome through

considering social and cultural factors in the design of systems as well as

communicating effectively with potential biogas users on the appropriate

use and benefits of biogas technology to help meet their needs. The transfer

28

of knowledge and information regarding biogas technology, however, can

be more challenging in some parts of SSA due to a high illiteracy rate [16].

Biogas dissemination in SSA can be aided through appropriate government

policies and institutional support. Energy policies have only been

established in SSA since the mid-2000s and still need further development

[36]. Many of these energy policies consist of targets on increasing the use

of alternative energy sources and reducing dependence on wood resources,

but still require concrete strategies and institutional frameworks to

practically meet these objectives [16, 36, 64]. Strategies that national, state

and local governments can implement to help increase the uptake of biogas

technology in SSA include: appropriate financial incentives, such as loans

and subsidies; educational and promotional campaigns; institutional

frameworks to coordinate and stimulate interaction between stakeholders

in the biogas industry, as was suggested for Uganda; regulatory authorities

to coordinate research and development activities; standards and codes of

practice, and; development and funding agendas for research and

development [1, 16, 49]. SSA also faces the challenge of a low population

density compared to developing regions in southeast Asia, India, and China,

where biogas technology has been successful [18]. An increase in

collaboration and knowledge-sharing is therefore required within and

between SSA countries as well as internationally with countries that have

had a successful uptake of biogas systems. This will facilitate the necessary

translational research and capacity building for biogas technology suited to

the SSA context.

29

Table 2-1: Main barriers to biogas dissemination in SSA

Type Description Reference

Financial • Installation costs for conventional biogas systems

unaffordable for many rural farmers and other potential users with limited or no disposable income

[16, 19, 47, 49]

• Lack of flexible credit schemes and other financial support for potential biogas users and entrepreneurs to set up biogas businesses

[47]

• Competition from firewood – where wood collection is ‘free’ and available in abundance

[19, 63]

Technical • Lack of documentation on biogas system

performance in specific countries; result of short term use

[16]

• Low rate of functional installed biogas systems/short lifespan of installed systems

[18, 63]

• Gas leaks and cracking in digester [63]

• Lack of local capacity for maintenance [63]

• Incorrect operation and lack of maintenance due to lack of technical skills (especially in rural regions) and inadequate training and follow-up

[4, 18, 47, 63]

• Poor design and construction: unsuitable for local conditions and/or users

[4, 18, 19, 63]

• Lack of (permanent) water supplies [18, 47, 63]

• Reliance on expensive imported construction materials and spare parts

[19, 47]

• Insufficient feedstock and/or time [18, 19]

Social-cultural

• Preference of cooking the traditional way, with firewood stove instead of with biogas stove

[16, 63]

• Inertia towards change and new technology [16]

• Competition with traditional/other uses of feedstock materials such as cow dung

[19, 63]

• Social/cultural/religious objections to using animal or human waste for energy

[19]

• Nomadic cattle rearing practices, making dung collection for biogas unfeasible

[16]

• Biogas technology adoption may require a change in traditional energy use decisions: women and children of a household most likely to use the biogas system while men are most likely to make investment decisions

[63]

• Low literacy levels in some areas making adoption of the technology more difficult

[16]

• Lack of awareness about the technology and its benefits

[21, 63-67]

30


Institutional • Insufficient government and/or policy/regulatory

support

[18, 21, 47, 66]

• Low population density

• Ownership and responsibility of biogas system not well defined/understood

[18, 47, 49, 63]

• Lack of up to date information, knowledge sharing, and translational biogas research at national, continental, and international levels

[4, 47]

2.2.3 Main opportunities

SSA has a number of favourable conditions for the use of biogas technology.

The region is dominated by a tropical thermal climate with an average

monthly temperature above 18°C throughout the year, which is well suited

for anaerobic digestion [18, 68, 69]. Livestock rearing is practiced

throughout SSA and provides a significant potential for biogas production

from animal excreta, particularly if the livestock is zero-grazed or kept

overnight in cattle camps as is commonly practiced in countries like Kenya,

Malawi, South Sudan, Tanzania, and Uganda [47, 49, 70, 71]. The increasing

prices of fossil fuels and fertiliser has helped make biogas an attractive

alternative for energy and fertiliser production in some SSA countries, for

example Burkina Faso [63]. Increasing costs for fuel wood and other energy

sources for cooking as well as expensive lighting costs when using kerosene,

has also prompted interest in biogas as a cheaper, cleaner, and more

convenient alternative in Uganda and South Sudan [47, 49]. The large

number of people with limited or no access to national electricity grids,

particularly in rural regions, combined with a growing demand for energy

services, also makes biogas an attractive energy option for SSA [72]. A

survey in Uganda found that the majority of households with biogas systems

31

were in rural regions with limited or no access to the grid [49]. Therefore,

the climatic conditions, dominance of agriculture, and expensive energy

services have made biogas a suitable alternative energy technology in SSA.

The benefits of biogas to SSA are wide-reaching over the three main pillars

of sustainability: economic, social, and environmental as outlined in Table

2-2. The use of biogas systems produced from locally available materials,

including most fixed dome, as well as some floating cover and tubular

digester designs, assists in reducing the dependence on and need for aid for

construction and spare parts, along with creating jobs and encouraging

technical skills to be acquired locally [17, 19, 21, 47]. The implementation of

biogas systems also helps to improve energy security and reduces reliance

on expensive oil and other fuel imports by providing a stable, decentralised

energy supply from local, renewable sources [19]. Biogas produced from

agricultural residues, industrial and municipal waste/wastewater, is an

attractive option in developing countries as it does not compete with food

crops for land, water, and fertilisers, unlike other bioenergy sources such as

bioethanol and biodiesel [20]. Food security and nutrition can also be

increased through the use of the biogas output slurry on household

vegetable gardens or food croplands [9, 47]. Improvements to sanitation

and organic waste management practices in SSA can be made by directly

feeding animal excreta into biogas systems and connecting latrines to

household and community scale plants, as well as treating the large

amounts of waste/wastewater from food processing facilities [63]. Two

examples of biogas technology waste management facilities are located in

32

Nigeria. One is the ‘Cows to Kilowatt’ project at Bodija Abattoir in Ibadan,

which uses slaughterhouse waste to produce biogas and fertiliser, and the

other is a biogas waste to energy demonstration facility in Lagos, which uses

rotting fruit to produce biogas for electricity generation [73, 74]. The

technology can also be applied to treat wastewater in densely populated

areas, particularly major SSA cities, many of which have few or no adequate

wastewater treatment facilities [63]. Retrofitting household septic tanks

into biogas generators has been identified as a solution to address the waste

management issues in the Nigeria’s largest city, Lagos, which has a dense

population and a low quality sewage system that leaks directly into the city’s

drainage system [75]. Other social benefits of biogas are the reduction of

indoor pollution and associated risk of death and disease, through its use as

a clean, smokeless cooking fuel; while also easing burden on those

responsible for fuelwood collection, predominantly women and children,

allowing more time for productive activities or attending school [19, 49].

The eliminated or reduced need for traditional biomass resources through

the adoption of biogas also assists in combating the environmental concerns

of deforestation, land degradation, and reduced soil fertility discussed in

Section 2.1. The vast benefits that are realisable from the adoption of biogas

technology in SSA highlight the need to address the current barriers to help

improve sustainability in the region.

33

Table 2-2: The benefits that biogas technology can provide in Sub-Saharan Africa


Economic • Reduced aid dependence through local construction and materials

[19, 21]

• Low-cost energy for cooking and lighting [49, 58]

• Creation of jobs and technical skills [47]

Social • Improved energy security and reduced fuel imports

[19]

• Improved food security through use of bioslurry as fertiliser for food crops

[19, 47]

• Potentially improved sanitation, particularly through safer handling of organic wastes

[63]

• Improved quality of life through provision of clean, smokeless cooking fuel

[19]

• Enhanced productivity/reduced labour burden of women and children

[49]

Environmental • Sustainable source of energy and fertiliser, helping to enhance soil fertility and maintain the natural nutrient cycle

[19, 63]

• Encourages adoption of zero-grazing, helping prevent overgrazing

• Improved waste and wastewater management [17, 63]

• Reduced risk of deforestation and land degradation

[19, 49]

2.2.4 Country specific examples of biogas dissemination

2.2.4.1 Rwanda

Biogas technology has had a relatively short history in Rwanda with the

government promoting it as an alternative energy for cooking and lighting

since the late 1990s, and the first systems being installed in 2001 [76, 77].

The Kigali Institute of Science, Technology and Management (KIST) began

developing and installing large scale biogas plants through its Centre for

Innovations and Technology Transfer (CITT) to address the issue of sewage

disposal in overcrowded prisons in the aftermath of the genocide [78, 79].

The first system installed was a 600 m3 community-scale biogas plant at the

Cyangugu prison, which used toilet waste from 1,500 prisoners as the main

feedstock and supplied the energy for half of the 6,000 inmate prison’s

cooking needs [47, 77]. By 2008, there were 28 community-scale biogas

34

systems in operation in Rwanda and 8 more under construction including:

13 in secondary schools, 11 in prisons, 7 in community households, 2 in

military camps, 2 in training demonstration centres, and one in a hospital

[77]. The efforts of KIST to address the waste challenge and also reduce the

fuelwood demand in prisons, was recognised with the institutional biogas

plants winning the 2005 Global Ashden Award for Sustainable Energy [78,

79]. Household-scale systems were introduced into the country through the

National Domestic Biogas Programme (NDBP) which was implemented in

2007 by the Rwandan government in partnership with SNV and the German

Organisation for International Cooperation, GIZ [64]. The programme’s

main aim was to “establish a sustainable and commercial biogas sector in

Rwanda”, reduce the depletion of biomass resources in the country and

improve the quality of life of Rwandan families [80]. An overwhelming

majority of the Rwandan population rely on unsustainable wood and

charcoal as well as agricultural residues to meet their energy needs as

conventional fuels and electricity costs are high due to the country having a

small resource base and being landlocked [64]. To address the energy

supply and environmental issues, the Rwandan government set frameworks

and targets to promote renewable energy use and minimise the depletion of

natural resources [79]. The government recognised biogas technology as an

important part of improving its energy supply and reducing the country’s

waste and environmental problems and had implemented strict tree cutting

monitoring and zero-grazing policies which indirectly favoured biogas use

[79]. Under the NDBP, 2,600 family-sized biogas systems had been

35

installed by August 2012, which was well below the initial target of 15,000

and the revised target of 5,000 systems by 2011 [64].

The main barriers of biogas dissemination in Rwanda are similar to some of

the financial, technical, social-cultural, and institutional barriers outlined in

Table 2-1. Key financial challenges are potential biogas users being unable

to afford the installation costs due to limited subsidies and loans from banks

being difficult and lengthy to obtain, as well as firewood being considered

as a cheaper energy source [64, 79]. The biogas market in Rwanda also faced

a few setbacks as the market benefits were lower than expected, causing

many companies to withdraw quickly, and the industry also competing with

other industries such as housing and construction [79]. Large-scale biogas

systems faced technical malfunctions due to a lack of commitment to

managing the system and/or the biogas operator not having the required

skills, as well as there being a shortage of technical support to assist with

simple modifications and repairs [77]. The short term history of the

technology’s use has presented social barriers due to uncertainties about it

costs and benefits to the user, with indirect benefits not being recognised by

the user [64]. There is also a need to broaden the types of household systems

recognised and supported by the NDBP, and help encourage systems to be

designed for and tailored to local needs [79]. Significant institutional

barriers are present in Rwanda mainly due to: limited technical capacity

from a largely unskilled workforce and few entrepreneurs; limited

governmental capacity and budget for research and development, as poverty

and food security issues take precedence; limited infrastructure making

36

access to rural areas for construction difficult; lack of collaboration between

public agencies and the private sector (technology research centres and

training institutes are marginal partners in the biogas programme), and; a

need for greater unity among biogas companies [64, 79]. Despite these

setbacks, Rwanda has done well to promote and launch a domestic biogas

industry in a short amount of time given its limited resources and technical

capacity.

Rwanda stands as a unique example in SSA on the successful use of

community-scale biogas systems. The use of large biogas systems in

Rwandan schools, prisons, and community households has resulted in

financial, social (sanitation), and environmental benefits. In eight of the

prisons with biogas systems installed, an average firewood reduction of 19%

was achieved, which could be raised to 30% with some minor technical and

management improvements [77]. Community households experienced the

greatest reduction in firewood consumption – over 80% in some instances

– with biogas being able to meet all of the cooking energy needs [77]. The

reduction in firewood consumption not only has reduced the strain on the

surrounding environment but also provides annual financial cost savings,

resulting in high returns of investment on the biogas systems [77]. Living

conditions have been improved, due to reduced or eliminated odour and

improved hygiene in toilet waste systems (especially in prisons), as well as

a reduction in indoor pollution [77]. The knowledge and experience gained

with community-scale biogas systems are likely to be transferrable to other

SSA countries, which could then experience the same benefits. Part of this

37

knowledge transfer would involve the identification of suitable conditions

for community-scale plants.

2.2.4.2 Tanzania

Tanzania has a long history and experience with biogas technology [81, 82].

The technology was first introduced by the Small Industries Development

Organisation (SIDO), a Tanzanian parastatal organisation, who installed

floating-drum biogas digesters between 1975 and 1984 [82]. During this

time, the Arusha Appropriate Technology Project also began installing

biogas systems, both floating-drum and Chinese fixed-dome models in the

Arusha region [82]. In 1982, the Centre for Agricultural Mechanization and

Rural Technology (CAMARTEC), a government research organisation, was

established who continued the dissemination of biogas technology in

Arusha [82, 83]. CAMARTEC together with the German Organisation for

Technical Cooperation (GTZ), then set up the Biogas Extension Service

(BES) which disseminated biogas plants in the coffee and banana growing

regions of Arusha until 1994 [82]. Since the early 2000s, other Tanzanian

organisations (most notably the Evangelical Lutheran Church in Tanzania

and the Dodoma Biogas and Alternative Energies Organisation

(MIGESADO)) have assisted with biogas dissemination in other regions of

the country [82]. The majority of these biogas projects have been on a

domestic scale. Widespread use of the technology, however, did not occur

until the Tanzania Domestic Biogas Programme (TDBP) was implemented

under ABPP in 2009 [81]. TDBP is a partnership between SNV, Hivos, and

CAMARTEC and aims to develop a commercially viable domestic biogas

sector to help improve the livelihoods of rural Tanzanian farmers [81, 84].

38

Training and accreditation for biogas masons is provided through short-

term biogas system construction and supervision courses at regional

training institutes [81, 84]. Masons are encouraged to form informal

associations and working groups to enable them to become private-sector

based, biogas service-delivery providers [81]. The programme initially set a

target of 12,000 biogas installations by the end of 2013 [82]. While this

target was not achieved with total installations since 2009 only reaching

8,796, the actual installations for 2013 exceeded the target for the year [85].

Therefore, Tanzania has a relatively successful domestic biogas sector and

the adoption of the technology continues to grow.

There are a number of favourable conditions that have contributed to the

relative success of biogas adoption and technology dissemination in

Tanzania. The country’s climatic conditions are well suited for biogas

technology use with average air temperatures ranging between 26.5 to 30°C

[18, 86]. Its current energy situation calls for a shift from the dominant

reliance on traditional biomass (over 90% for cooking , heating, and

lighting) to more sustainable options [82, 83]. Demand for firewood has

been increasing while wood resources are declining [83]. The scarcity of

firewood along with the large number of households with indoor-fed cattle

and/or pigs in some parts of Tanzania, has enabled biogas to become an

attractive and suitable technology [83]. The Tanzanian government has

been supporting most of the biogas projects in the country, providing

funding in collaboration with donors, as part of its key policy objective to

increase access to affordable and reliable energy services and stimulate

39

productivity [83]. CAMARTEC has been a key driving force in developing

biogas technology for application in Tanzania and other parts of Africa. Its

modified CAMARTEC design (MCD)5 is being adopted by the Tanzanian

private sector under the TDBP as well as in other African countries [49, 82].

The organisation has had experience with providing training for technicians

on biogas system construction, and instructing users, particularly women,

on system management and operation, as well as advising on the use of

bioslurry, gas pipeline systems, burners and lamps [83]. In addition to the

CAMARTEC technology, research and development has been conducted in

Tanzania for a range of other biogas technologies, including: floating drum

systems, other fixed dome systems (e.g. MIGESADO fixed dome model),

tubular plastic digesters, modified plastic water tank systems, and compact

biogas systems for kitchen waste [82]. Aside from the TDBP, a number of

other biogas projects have been set up in Tanzania, including Biogas support

for Tanzania (BiogasST) and the ‘Best Ray’ project –Bringing Energy

Services to Tanzanian Rural Areas, which has contributed to the use and

development of the different types of systems [84, 87]. This wide knowledge

base in biogas system types has enabled biogas technology to be applicable

to different contexts throughout Tanzania.

While the uptake of biogas technology has been increasing in Tanzania, the

country still faces a few challenges for its widespread dissemination. The key

barrier of high installation and maintenance costs is evident in the way

5 The MCD was developed for the TDBP and is an amalgamation of the original CAMARTEC design and the MIGESADO model, both modified versions of the Chinese fixed dome design to suit the local context, particularly in terms of locally available construction materials.

40

dissemination has been focused in the northern part of the country as well

as the capital, Dar es Salaam, where there are large livestock numbers and

relatively higher income levels [83]. A study conducted in the Rungwe

district showed that households are willing to adopt biogas technology but

are held back from doing so due to being unable to meet the costs [83].

Other challenges experienced with different types of biogas systems in

Tanzania include: inadequate water availability; more expertise required in

construction and maintenance; low awareness about the technology and

potential feedstocks; low level of understanding on appropriate operation

due to insufficient operating instructions provided by the installer; poor

performance of the system due to a lack of maintenance; limited or no

follow-up services provided by installer, and; application of biogas limited

to cooking [83, 86, 88]. To overcome some of these challenges, it has been

recommended that more technicians are employed and trained to provide

follow-up services, including inspections and repairs, particularly in the

first few months after the systems have been installed [83, 86]. Other

recommendations include: preparing and distributing simple operation

and maintenance manuals in English and Kiswahili, as has already been

done in some areas; encouraging users to contact installers immediately

when problems arise, and; including additional gas connections for heating

and lighting in the installation [70, 83, 86]. To address the key challenge of

high system costs, further research and development is required to produce

more affordable biogas systems that are suitable to the users’ needs and

context, as well as sourcing more materials locally to lower costs and

increase local productivity [83].

41

Tanzania has experienced a number of benefits from the use of biogas,

particularly through its contribution to reducing wood fuel consumption,

and the negative effects associated with its use and that of kerosene for

household cooking and lighting [70, 83, 88]. Decreased firewood

consumption has freed up time for women and children to carry out other

activities, including growing vegetable gardens, and reduced the stress of

the wet season where firewood collection is difficult [70]. The integration of

cattle raising and farming with biogas production has contributed to

increasing the income of farming households [83]. The biogas sector also

offers potential for increased employment opportunities [83]. A study

conducted in two Tanzanian villages found that the adoption of biogas

technology has brought about significant changes to the division of labour

within households [88]. In more than half of the households in the study,

the men have taken on the responsibility of collecting the feedstock(s) for

biogas production, which has largely replaced firewood collection; a task

that was predominantly assigned to women [88]. The responsibility of

cooking also transitioned in just over half of the households from being

solely the mother’s responsibility, to being equally shared between the

father and mother, and in the remaining surveyed households, cooking was

found to be shared by all household members after the biogas system

installation [88]. In another region of Tanzania, the Arumeru District,

biogas users were particularly impressed with the reduced cooking time

biogas stoves provided, as well as the ability to regulate the output flame

compared to traditional wood stoves [84]. The biogas system was also found

to be a good replacement of traditional manure management practices as it

42

does not take any longer than traditional cow dung cleaning and disposal

activities [84]. The benefits that many Tanzanians have received from the

use of biogas technology through its replacement of wood fuels for cooking

and the improved manure management, are likely to be experienced by

many others if dissemination improves in SSA, given that the issues with

traditional biomass use and manure management practices are faced

throughout SSA.

Tanzania stands as an example in SSA of the progress that can be made with

domestic biogas dissemination. The strong government support through

funding and parastatal research organisations such as CAMARTEC to

promote the technology along with the collaborations between donors and

local/regional research and development organisations to implement the

technology has enabled its dissemination to continue to increase in the

country. It has also enabled different types of biogas technologies to be

developed and applied in different parts of the country; an experience and

knowledge base from which many SSA countries could benefit. Local and

regional research organisations also have been important in the provision

of training for both biogas installers and users. The similarity between

Tanzania’s climatic and environmental conditions to many other parts of

SSA, indicate the potential for other SSA countries to achieve similar

successes in domestic biogas system use. Overall, the experience in

Tanzania has demonstrated the important role the government and

local/regional research and developmental organisations play in biogas

dissemination.

43

2.3 Biogas dissemination in developing regions

outside of SSA

2.3.1 China

China is the largest biogas producer and consumer in the world with

between 30 to 40 million domestic scale biogas plants installed all over the

country [17, 18]. The country is also the largest and fastest growing

developing nation in the world [89]. Biogas technology has been used in

China for nearly 100 years with promotion of the technology commencing

in 1929 after the invention of the rectangular hydraulic (fixed dome)

digester [90]. In 1958, a second attempt was made to popularise the

technology and the first biogas research institutions were established [90].

Large-scale biogas development, however, only began in China in the 1970s

with the fixed dome digester being widely used in rural areas [18, 91]. Over

the past forty years the focus has been on utilising biogas to supply energy

and help alleviate environmental stresses in rural regions with household-

scale systems, while interest and application of medium to large scale

systems has been increasing rapidly since the early 2000s [89, 91, 92].

Biogas is an important energy resource for China’s large rural population,

as firewood resources have become scarce and hydroelectricity

infrastructure is unaffordable in some areas [93]. Rural biogas systems

typically consist of a 8 to 20 m3 digester, a stall, washroom, kitchen, and a

greenhouse or other temperature holding facility for the cold regions [91].

Typical feedstock materials for the system are ‘night soil’, animal manure,

and agricultural residues such as grain stalks, sweet potato vines, and weeds

[91, 92]. Standards have been developed for rural biogas systems under four

44

categories: basic standards, product standards, technical specifications, and

construction specifications, which stipulate the design, construction,

operation, and facility production [91]. The long history and consistent use

of the technology in the country has enabled it to become well-developed,

complete with standardised digester types for different climates, materials

and uses; integrated utilisation patterns in agricultural production, and;

engineering structures for appropriate installation [91].

Biogas technology for rural development has been strongly supported by the

Chinese government over the past forty years [18, 89]. Since 1986, the

government has been introducing and implementing energy policies that

support the development and increased use of renewable energy, including

biogas [89]. Chinese government regulations on environmental protection

recognise biogas technology as a suitable and efficient means of treating

organic waste [89]. The country has well-structured policies, legal

environment, and a relatively efficient work network that takes care of the

marketing, technical support, and maintenance of biogas systems. China

has 40,000 full-time staff members working in 8,000 rural energy offices in

over 1,900 counties and towns to oversee the administration of biogas in

rural areas [18]. Education, advocacy and training has been provided by the

Ministry of Agriculture through the publication of brochures with biogas

training materials, television and radio programs, as well as training courses

for technicians and farmers [18]. The biogas sector in China saw a rapid rise

from the early 2000s up to 2010 due to the government providing support

for the construction of rural household digesters as well as some medium

45

and large-scale systems, known as the “National Debt Project for Rural

Biogas Construction” [18, 89]. Some Chinese enterprises have also been able

to obtain biogas systems as government approved CDM projects to reduce

GHG emissions [89]. The widespread use of biogas technology has led to

employment in a number of areas including: manufacturing of biogas

equipment and appliances; research and development; rural energy

management and technology promotion; quality supervision and

inspection; training and vocational skills certification, and; services for

biogas users such as construction, operations management, maintenance,

and repair [91]. The biogas development plan for 2020 has set a target of

installing 10,000 large-scale biogas projects on livestock farms and 6,000

biogas plants that use industrial organic effluent [18]. By this time an

estimated 80 million rural households or 300 million people will use biogas

as their main fuel [18].

China still has significant potential to increase its biogas use with the

installed household-scale systems accounting for just over 30% of the total

potential for that size, and under 2% of the potential agricultural organic

waste currently being exploited [91]. The challenges facing the biogas

industry in China include: systems being underutilised or abandoned due to

migration of rural labour to the cities; popularisation of commercial energy

use; decline in backyard farming and unstable supply of feedstock due to

fluctuations in livestock breeding; technical problems due to faulty or low

quality materials and insufficient product support; inadequate policies,

regulations, and standards for construction and use of medium and large

46

scale systems, and; weak demand as the integrated benefits, particularly

direct economic benefits, have not been realised [91]. A number of

household digesters, particularly those built prior to the 1990s, failed as

many of these systems were unheated and led to low or unstable biogas

production, especially in Northern China where the mean temperature is

between 10 and 15°C for around half of the year6 [89]. Farmers with

household biogas systems in China also faced the issue of a lack of training

and follow-up by the installers leading to poor operation and maintenance

[89]. For large scale plants in China, the main challenge is effective use of

the biogas residues and slurries as the plants do not have sufficient

surrounding land on which it could be applied and transportation of the

residues is uneconomical, while discharge into the water systems would

cause water pollution and waste if sufficient treatment is not applied [91].

China is in its early stages of research and development of biogas systems

for power generation with three large-scale biogas plants currently

operating for power generation while development is also underway on the

use of biogas fuel vehicle with the first biogas plant for vehicle fuel built in

2011 [91]. The use of biogas technology for treatment of the large amounts

of municipal solid waste from cities is not widely practiced in China and

therefore there is significant potential to use the technology to improve

waste management [89]. The future projections of the Chinese biogas

industry are: diversification of feedstock materials used in biogas plants;

extending biogas system construction from villages to small towns; greater

6 This problem is unlikely to be faced in most parts of SSA due to the warm climate.

47

focus on efficient, high value and comprehensive use of biogas products,

and; specialisation of the operation and management of biogas plants [91].

The vast amount of knowledge and experience of China in the development

and use of biogas, particularly for rural applications, could be a valuable

resource for SSA and demonstrates the potential of what could be.

2.3.2 India

India is another major biogas producer with the number of total installed systems

reaching 4.8 million in 2014 [94]. Like many other developing countries, India has

a limited conventional energy supply and is heavily reliant on fuel wood as an

energy source for cooking, especially in rural regions, but this resource is becoming

increasingly scarce [95]. Biogas was recognised as one of the suitable cooking fuel

alternatives that leads to an improved quality of life [95]. Development of biogas

digesters commenced in India in 1939, with the first plants constructed on a mass

scale for dissemination in 1960 by the Khadi Village Industries Commission (KVIC)

[96]. Due to government policies being focused on supporting rural electrification

and the distribution of chemical fertilisers in villages at the time, it took another

20 years for widespread use of the technology to commence, motivated mainly by

the oil crisis and serious firewood shortages [96]. The National Programme on

Biogas Development (NPBD) was implemented in 1982 with the aim that biogas

could supply all the cooking energy requirements for rural households and is now

one of the two largest biogas programmes in the world, the other being in China

[95]. Substantial subsidies between 1985 to 1992 under the programme, enabled

biogas to become a well-established technology with dissemination continuing

even after the subsidies were reduced [97]. The successful dissemination also

boosted development of variations of floating cover and fixed dome systems with

at least seven different types being approved for the NPBD by the Ministry of Non-

48

Conventional Energy Sources [97, 98]. By the early 2000s, however, target-driven

dissemination led to unhealthy competition between the implementing agencies

resulting in lower standards of construction and materials, eligibility and

sustainability criteria being overlooked, inconsistencies in the reporting of

achievements, and a lack of follow-up services and accountability for maintenance

[95, 99]. To address these issues, the government merged NPBD with the manure

management initiative in 2005 to form the National Biogas and Manure

Management Programme (NBMMP) [99]. NBMMP aims to provide biogas for

cooking and other energy needs, reduce the use of chemical fertiliser with bio-

fertiliser, alleviate the drudgery for rural women and the pressure on forests,

improve sanitation in villages by providing toilet connections with biogas plants,

and mitigate climate change through preventing black carbon and methane

emissions [100]. The government has also set up a Biogas Based Distributed/Grid

Power Generation Programme in 2006, which focuses on promoting the use and

development of biogas systems for decentralised electricity generation [101]. The

Indian Government and local organisations such as KVIC have, therefore, been

essential to the widespread use and development of biogas technology in the

country.

The long history of biogas technology use and development in India has led to

successful experiences in some areas, while other parts of the country still face

challenges with its implementation. A key barrier in some areas is biogas users

having insufficient knowledge of how to maximise the benefits from their system,

including the use of a range of feedstocks for improved biogas yield, and the output

slurry for organic fertiliser [99]. This has highlighted the need for more direct

education and awareness programmes under NBMMP [99]. In rural India, the

technology was found to be unaffordable to some of the families either due to the

49

construction costs or an insufficient supply of cow dung, where families owned less

than the three adult cattle required for the systems used and promoted through the

biogas programme [102]. Greater involvement of NGOs and other associated

stakeholders in the biogas sector with installations was recognised as a priority

area as the appointed biogas installers under NBMMP are not fully equipped for

the task [99]. Part of the installation process that is lacking is the provision of

sufficient training and follow-up services for the users, particularly the

predominant female users, which may be greatly improved if more women are

trained and hired to be installers [95, 99]. One district of India, Uttara Kannada,

particularly the Sirsi block, has experienced a high success rate with all of the

installed biogas plants remaining in operation [95]. Sirsi has favourable conditions

for biogas dissemination due to the users having a high level of interest in and

awareness of the technology, along with a high literacy rate, possible higher

income, easy credit access from multiple agencies, no access to some conventional

fuels, relatively large cattle holdings, strong government support, awareness of

forest conservation due to regional conservation and afforestation programmes,

and good services provided by installers [95]. The link between installers’ incomes

and biogas construction activity has led to good competition between installers in

Sirsi with services such as installations despite delays in finance release, assistance

with procuring subsidies, six-month guarantee and three-year warranty for

repairs, and free follow-up services [95]. The installed systems were found to

provide sufficient gas for cooking and high quality fertiliser for the majority of

households [95]. A study in Assam, north-east India, showed that the NBMMP

provided improved energy service outcomes for the majority of households that

had biogas systems installed in the region [99]. Although India’s biogas

dissemination has been largely focused on household-scale systems, it has also had

some positive experiences with community-scale systems. A notable example is the

50

Pura community biogas project which ran successfully for almost a decade in the

late 1980s to 1990s [103]. The project consisted of community members supplying

cow dung as feedstock to the system and receiving electrical lighting, clean water,

and organic fertiliser in return, with associated tariffs [103]. A greater focus on

applying the technology on a community scale, including in small villages and

towns for waste water treatment, through community-focused finance schemes

and business models, has been recommended [99, 104].

For continued growth in biogas dissemination in India, more research and

development is required to improve the efficiency of systems, tailor system

designs to local conditions, diversify the types of feedstock used, improve

the ease of operation, and present more options on the uses of biogas and

the bioslurry [104]. India already has a number of research institutes

working on improving and applying the technology in the country, such as

KVIC, the Indian Biogas Association, and the Biogas Development and

Training Centre. The work of the institutes and organisations involved in

the Indian biogas sector as well as the government driven dissemination

programmes has enabled biogas technology to be applied throughout the

country. SSA can benefit from the lessons learned in India on biogas

dissemination, particularly the factors that have made some household and

community-scale projects more successful in some areas than others.

2.3.3 Nepal

The energy supply situation in Nepal can be likened to that in many SSA

countries. The country has no significant fossil fuel resources and relies on

expensive fuel imports [105]. While the potential for hydroelectricity is

significant in Nepal, technical and financial constraints have hampered its

51

use, particularly in rural regions which has a low electrification rate [105].

As a result, fuelwood, agricultural residues, and animal waste, are the

dominant energy resources in the country [105, 106]. The use of these

traditional biomass resources has caused many of the same environmental,

social (especially in relation to health), and economic concerns currently

facing SSA as discussed in Section 2.1. The environmental damage on

Nepalese forests due to unsustainable fuelwood exploitation has been

significant [105]. In response to the need for more sustainable and

affordable energy, the Nepali Government initiated the production and

distribution of renewable energy technologies [105]. Biogas was recognised

as a particularly viable technology, as it proved to be feasible within the

socio-physical conditions of the country and offered several environmental,

agricultural, economic, and health benefits [105, 106].

Biogas technology was first introduced to the country in 1955, although

large scale use of the technology did not occur until the establishment of the

Biogas Support Program (BSP) in 1992 [105, 107]. The BSP started as a

working partnership between governmental institutions of Nepal, Dutch,

and German development organisations, the private sector in Nepal and

rural Nepali farmers [107]. The Government of Nepal has been a particularly

strong advocate for biogas, having implemented initiatives to support

promotion and development of the technology since 1974 [108]. Nepal has

seen a successful development of its biogas sector with over 260,000

installed systems to date [109]. Its success has been attributed to seven main

factors: increasing level of awareness of the benefits among the rural

52

population; energy, health and environmental costs associated with

traditional energy sources; inaccessible and underdeveloped rural

communities with little or no modern fuel supplies; abundant organic waste

supplies on farms for use in biogas systems; technology available freely

without intellectual property rights issues; readily available raw

construction materials, and; the availability of loans and subsidies from the

government [107]. The main challenges for the biogas sector in Nepal

include: cold temperatures in many the country’s hilly areas making

conventional biogas systems unfeasible there; a need for greater private

sector capability for biogas system installations; remote locations of many

villages making implementation of the systems difficult; the technology

remains expensive for some rural households who are excluded from

government subsidies; a lack of adequate water supplies to operate biogas

plants in hilly and mountainous regions, and; increased mosquito

prevalence reported by biogas system users after installation and the

associated adverse publicity7 [105, 108, 110]. Nepal still has ample

opportunity to increase its biogas sector with its current use being estimated

to account for only 9% of the total potential [108]. An area of possible

expansion in the sector is diversifying the feedstock types used to include

kitchen waste, municipal waste, and slaughterhouse waste, as currently the

sector is dominated by household systems that use cow dung as the main

feedstock [105].

7 An increase in mosquito prevalence would be of great concern in SSA, particularly due to the high risk of malaria contraction (WHO 2013, Fact Sheet No. 94: Malaria). Therefore, the impact of biogas systems on mosquito presence in SSA needs to be investigated.

53

A survey conducted in 2007-2008 on the impact of biogas technology in 15

districts of Nepal, showed that biogas technology has increased the socio-

economic status of its users [105]. The main recorded benefits were: a

reduction in the workload and time spent on household activities, with

women as the main beneficiaries; improved health for families, especially

women and children, due to reduced indoor smoke and air pollution from

the replacement or reduction of firewood and dung cake use for cooking;

improved sanitation levels through connecting a toilet to the system;

increased productivity in crops and kitchen gardens, leading to increased

incomes, and; bioslurry replacing the use of raw dung and chemical

fertilisers on crops [105]. These benefits are particularly relevant to Nepal’s

large rural population which relies on agriculture for their livelihood. The

benefits of biogas identified in Nepal demonstrate the realisable benefits of

biogas for SSA due to the region also having a large rural population

dependent on agriculture.

2.4 Biogas dissemination in Europe

Biogas technology has a longstanding history in Europe with its first

application being for the treatment of wastewater and the production of gas

for lighting towards the end of the 19th Century [55]. Use of the technology

then progressed rapidly with more efficient systems being developed and

used for combined heat and power (CHP) generation, as well as other

feedstocks, particularly agricultural waste becoming popular, and research

being carried out on refining and compressing biogas as a vehicle fuel [55].

Simple biogas technology for farms was introduced into France from Algeria

54

before the Second World War, while Germany had developed both small-

and large-scale biogas systems running on agricultural waste by 1950 [55].

Between the mid-1950s and 1970, interest in the use of biogas diminished

due to the abundance of inexpensive oil as well as the widespread use of

mineral fertiliser [55]. During the oil crisis in the 1970s, biogas technology

gained more interest again in Europe, particularly for agricultural

applications in Denmark, Germany, and the Netherlands [55, 111, 112].

Biogas technology faced a few setbacks in Europe during the late 1980s to

early 1990s as centralised systems were being developed, due to lower oil

prices and the systems often not generating profit due to the high

construction costs and an unexpected lower operational efficiencies [55,

111]. The biogas industry came to a halt in some countries, such as the

Netherlands. Germany and Denmark, however, were able to continue

developing the technology and improve their performance through

government support and the networking between biogas stakeholders to

share knowledge and experiences [55, 111]. The international focus on

Climate Change mitigation in the late 1990s to early 2000s, including the

1997 Kyoto protocol, sparked a renewed interest in biogas technology in

parts of Europe under new national renewable energy policies, as well as to

combat agricultural methane emissions [111]. In Germany, it was the

Renewable Energy Act of 2000, which introduced valorisations on

electricity produced from biogas systems that led to significant increases in

the number and size of agricultural biogas system installations [55, 113]. For

the whole of Europe, current biogas use is well below the estimated potential

and the dissemination of the technology has been and is likely to continue

55

to be directly and indirectly affected by environmental, waste management,

and energy policies [92].

In 2015, there were more than 17,000 biogas and over 450 biomethane8

plants in the European Union (EU) with Germany, Italy, France and

Switzerland leading in the number of installations [114]. Other European

countries that have installed commercial and farm-scale biogas systems

include: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland,

Greece, Hungary, Ireland, Lithuania, the Netherlands, Norway, Poland,

Portugal, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom [113].

Biogas technology is applied in Europe by the agricultural sector, using both

crop residues and energy crops as fertiliser, as well as for waste management

in the food processing industry, commercial kitchens, and municipalities

(municipal solid waste and wastewater treatment) [55, 115]. Energy crops

have become more popular as a biogas feedstock over the last few decades,

due to an oversupply of food crops in Europe prompting farmers to seek

alternative options for making an income from their crops [115]. The main

uses of biogas produced in Europe is for CHP generation, injection into the

electricity grid (after treatment), and as vehicle fuel (after treatment). There

is a growing trend to move away from using biogas for on-site electricity

production and feeding it into the natural gas pipeline after treatment [55].

Sweden and Switzerland are at the forefront of this trend with treated biogas

not only being injected into the natural gas grid but also used as vehicle fuel

[7, 115]. In Austria, Denmark, and Germany, agricultural biogas plants

8 Biogas that has been scrubbed (upgraded) to almost pure methane gas

56

commonly have a CHP facility with the produced electricity being fed into

the electricity network and the heat being used onsite [115]. Research is

currently being carried out by several working groups and industries on the

use of biogas for fuel cells to be used in CHP applications [115]. A number

of national regulations as well as a unified EU regulation exists on the

biological treatment and recycling of organic waste to ensure treatment is

carried out safely, without adverse environmental effects, and that the use

of recycled organic waste provides agricultural and ecological benefits [116].

Biogas technology has and will continue to be popular in Europe due to the

opportunity it presents for simultaneously producing energy and managing

waste [116].

The longstanding history of biogas use in Europe provides a rich resource of

experience and knowledge for SSA on appropriate technologies and

development strategies for improving its dissemination. While the use of

energy crops for biogas production is unlikely to be applicable to SSA, as it

would compete with food crops and has been found to be uneconomical in

some situations, the use of various organic waste resources and agricultural

residues can be applied in SSA [113]. Experiences in Germany demonstrate

the opportunity biogas presents the agricultural sector to also become an

energy producer with farmers receiving additional income through

electricity production from biogas systems [115]. Biogas plants in Europe

are significantly more efficient than the systems used in developing

countries with approximately double the biogas production per m3 of

digester volume [115]. These biogas plants tend to be commercial-scale

57

systems between 500 and 3,000 m3 with agitation systems and temperature

controls to enable mixing of the digesting slurry and optimum temperature

to be maintained inside the tank [115]. The cost of European biogas systems

(approximately 200 – 300 € per m3 digester volume) along with complex

operation and maintenance requirements can be prohibitive for developing

regions [115]. Nevertheless, the vast range of experience in European biogas

technology development can be drawn upon and translated to the SSA

context to improve system performance. Environmental, waste

management, and energy policies from Europe also can serve as examples

on appropriate policies that could be adapted on national scales in SSA or

by the African Union (AU) to help boost the biogas industry. Aside from the

importance of appropriate policies, which may be difficult to translate to

some SSA countries due to the differences in political structures, the

European experience has highlighted the importance of networking to share

experiences and build on the knowledge gained on biogas technology. The

cooperation of farmers in Germany to share biogas technology experiences

and work together to improve system performance played a key role in

making systems run economically [55]. SSA farmers and other stakeholders

in the biogas sector have the opportunity to do the same.

2.5 Conclusions and Recommendations

2.5.1 Key recommendations for improving biogas dissemination

in SSA

Between the current barriers to improve biogas dissemination and the

significant contribution the technology could make to sustainability, lies the

opportunity to increase its uptake in SSA. Strategies to increase the uptake

58

of biogas in SSA range from economic, technical, policy, and social, as

summarised in Table 2-3. Financial incentives that can assist in making the

technology more affordable and financially attractive include: soft loans,

low-cost credit, financial aid for the user, direct and indirect subsidies,

international funding through the Clean Development Mechanism (CDM)

and Joint Implementation (JI) programme, and fee-for-service schemes

[16, 49, 117]. Some of these incentives, however, could contribute to the

issues with ownership and responsibility discussed in Section 2.2.2.

Improved designs of biogas systems and associated appliances, tailored to

the specific needs and conditions of the user (including energy

requirements, feedstock availability, local building materials,

environmental conditions, budget, etc.) are perhaps a more effective means

of making the technology more accessible and affordable to low income

earners in SSA. Collaboration between national and international research

institutions as well as governments is required to share the available

knowledge and experience in biogas use and appropriate designs in SSA,

along with continuing research, development, and demonstration to

overcome technical issues and stay up to date [16]. Institutions like

CAMARTEC in Tanzania and KVIC in India, have demonstrated the impact

they can have in partnership with the government to drive this type of

research and development. Establishing knowledge hubs could also assist

in facilitating collaboration between biogas stakeholders. This includes

utilising farmer cooperatives to exchange ideas and experiences with biogas

technology, similar to the approach of German farmers. National

governments also can assist with setting up institutional frameworks such

59

as a National Biogas Technology Development Programme or National

Integrated Biogas Development Programme as has been suggested for

Uganda [49]. These type of programmes coordinate and stimulate

interaction and the sharing of experience between biogas stakeholders along

with developing appropriate standards or best practice guidelines [16, 49].

Experience in Europe, China, India, Nepal, Rwanda, and Tanzania has

demonstrated the important role of regional and national governments in

setting up a policy framework that is supportive of biogas technology. Such

policies can include appropriate standards and best practice guidelines as

has been recommended for Nigeria and Uganda [16, 49]. Design standards

are of particular importance for biogas appliances in SSA, specifically cook

stoves to ensure they run efficiently, as incomplete combustion releases

poisonous carbon monoxide and soot particles [118]. Environmental

policies such as the tree cutting restrictions in Rwanda and energy policies

that place emphasis on renewable energy, as is the case in China and Nepal,

could be implemented throughout SSA. Many of the social-cultural barriers

can be overcome through appropriate educational and promotional

campaigns that demonstrate the benefits of biogas and also provide training

for potential users and installers [16, 49, 64]. A good example of this is the

use of biogas systems in schools as training and demonstration sites like

those described in Ethiopia, South Africa, and South Sudan [47].

Implementing training and demonstration sites in both rural and urban

centres will raise awareness and equip local biogas users. Applying these

suggested strategies is anticipated to lead to an overall increased uptake of

60

biogas technology and improved skills to operate and maintain systems

effectively.

Table 2-3: Recommended strategies for improving biogas dissemination in SSA

Area Objective Responsible body

Outcomes Recommended Action

Economic • Reduce biogas system installation cost barrier

• Biogas companies

• Banks/financial institutions

• National and state governments

• NGOs

• Increased uptake of biogas systems among low income earners

• Provide soft loans, low-cost credit

• Apply for international funding e.g. CDM & JI programme

• Direct and indirect subsidies

• Introduce fee-for-service schemes

Technical • Design biogas systems that are specific to user needs and local conditions

• Universities and other research institutes

• National, state, and local governments

• Biogas companies and entrepreneurs

• NGOs

• Increased uptake and efficient use of biogas systems, reduced abandonment of systems, increased productive use of systems

• Modify existing biogas system designs according to identified user needs and local conditions

• Establish biogas technology knowledge sharing hub

Policy • Establish policy framework that is supportive of biogas technology

• National governments

• Increased uptake of biogas technology for energy supply and waste management

• National energy policies with set targets for RE

• National standards and guidelines construction and operation

• Biogas technology mentioned in national waste management policies/standards

• Policies to restrict tree harvesting and grazing

Social • Effective long-term use and acceptance of biogas technology

• National, state & local governments

• Biogas companies

• NGOs

• Increased awareness of the benefits of biogas

• Increased skills to effectively operate and maintain biogas systems

• Training and demonstration centres located in both urban and rural regions

61

2.5.2 Conclusions on biogas dissemination in SSA

High installation costs, inadequate user awareness and training as well as

insufficient follow-up services are persistent barriers in biogas

dissemination throughout the world, particularly in Sub-Saharan Africa.

Improving the design choices of biogas systems is an important part of

improving biogas dissemination but improved design choices require more

than just technical considerations. It is not only about what is possible

practically, depending on the surrounding environmental conditions,

technical skills, and materials available, but also identifying which type of

biogas system technology is most suitable based on the socio-cultural

context and needs of the user. A model that applies this multi criteria

analysis is an important part of improving biogas dissemination. The level

of government support in the form of appropriate energy, waste

management, and environmental policies or incentives is another common

factor that can be the ‘make or break’ of the biogas sector in a country. What

is also evident is the positive impact collaboration between research

institutions, governmental departments and potential as well as current

biogas users has on increasing its dissemination. The sharing of knowledge

and experiences is a crucial part of ensuring biogas technology continues to

be developed and applied more efficiently and appropriately. The model

developed in this thesis aims to take some of the knowledge and experiences

gained over the many years of biogas use and translate this into a form that

can be easily understood and applied to the design of systems for the SSA

context. The following chapter will describe biogas technology in more

detail, including the types of systems that are applicable to the SSA region.

62

63

Chapter 3 Biogas technology:

influential factors and available

design types

Biogas technology: influential factors and

available design types

“Knowledge is like a garden: if it is not cultivated, it cannot be harvested.”

– African proverb

In this chapter, the key technical considerations for harnessing biogas are

discussed. Firstly, the chemical composition of biogas and the process by

which it is produced is described. This is followed by an overview of the key

factors which influence biogas production and digester design. The second

part of the chapter provides a review and comparison of the main types of

biogas systems and their applicability to SSA. This section concludes with a

discussion on the key features that are a priority for household-scale biogas

systems in SSA.

3.1 Anaerobic digestion and biogas production

Biogas is a mixture of 50-70% methane, 30-45% carbon dioxide, and other

trace gases, as show in Figure 3-1. It has a relative density around 0.86, and

a heating value of 21-25 MJ/m3 when the methane content is 65%, which is

approximately 30-40% lower than the heating value of natural gas [119].

The gas is created through a process known as anaerobic digestion (AD)

[55]. In the AD process, organic materials are broken down by several

groups of bacteria in the absence of free oxygen and converted into biogas

64

along with a nutrient rich slurry [18]. The general biochemical equation for

this process was developed by Buswell in 1930 and is given by Equation 3-1.

𝐶𝑐𝐻ℎ𝑂𝑜𝑁𝑛𝑆𝑠 + 𝑦𝐻2𝑂→ 𝑥𝐶𝐻4 + (𝑐 − 𝑥)𝐶𝑂2 + 𝑛𝑁𝐻3 + 𝑠𝐻2𝑆

Equation 3-1 [55]

Where

𝑥 =1

8(4𝑐 + ℎ − 2𝑜 − 3𝑛 + 2𝑠)

𝑦 =1

4(4𝑐 − ℎ − 2𝑜 + 3𝑛 + 2𝑠)

Figure 3-1: Biogas composition based on figures from [55]

The process has four main phases: hydrolysis, acidogenesis, acetogenesis,

and methanogenesis, which are depicted in Figure 3-2 [18, 43]. In

hydrolysis, complex organic molecules, namely proteins, fats, and

carbohydrates are converted into monomers (sugars, amino-acids, and long

chain fatty acids) [55]. In the second phase, acidogenic bacteria turn the

monomers into alcohols and volatile fatty acids, which also results in the

50-70% CH₄

30-45% CO₂

0-0.5% H₂S

0-0.05% NH₃

1-5% Water Vapour

0-5% N₂

Biogas Composition

65

release of hydrogen, and carbon dioxide, as well as traces of hydrogen

sulfide and ammonia [18, 55]. In acetogenesis, the alcohols and volatile fatty

acids (VFAs) are transformed into acetic acid by acetogenic bacteria, which

also releases hydrogen and carbon dioxide [18]. In the final phase,

methanogenic bacteria metabolise the acetic acids, hydrogen, and some of

the carbon dioxide under strict anaerobic conditions to produce methane

and release carbon dioxide [18, 55]. As the carbon dioxide released through

AD is part of the immediate carbon cycle, no additional carbon dioxide is

emitted into the atmosphere. AD naturally occurs in humid, anaerobic

environments such as swamps, marshes, digestive tracts of ruminants,

plants that are wet composted, and flooded rice fields [55]. Biogas

technology harnesses AD in one or more reactor tanks, commonly known as

anaerobic or biogas digesters, to convert organic waste materials into energy

and other usable products. The technology has been recognised as a

sustainable organic waste management system, scoring the highest in terms

of energy balance, economic feasibility, greenhouse gas emission reduction,

and life cycle analysis when compared to other conventional methods such

as composting and incineration [120].

66

Figure 3-2: The four phases of anaerobic digestion [18, 43]

3.1.1 Factors that influence biogas production and digester

design

Biogas digesters are designed to provide the optimum conditions for biogas

production. Ideally, the digester is oxygen free, however, in practice some

oxygen dissolved in water will be present in the digester [121]. The oxygen

content in the digester is lowered to a suitable level for the anaerobic

bacteria through microbes known as facultative anaerobes in the hydrolysis

phase, which can absorb some of the oxygen [121]. Biogas production from

AD is influenced by a combination of chemical and physical factors,

including: feedstock, nutrients, particle size, temperature, pH, organic

loading rate (OLR), inhibiting substances, water, and mixing [18, 121].

Hydrolysis

•Proteins, fats & carbohydrates converted into sugars, amino-acids and long-chain fatty acids

(monomers)

Acidogenesis

•Monomers turned into alcohols & volatile fatty acids, hydrogen & CO2 released

Acetogenesis

•Alcohol & fatty acids transformed into acetic acid, hydrogen & CO2 released

Methanogenesis

•Acetic acid, hydrogen & some CO2 metabolised to produce CH4 & CO2

67

Methanogenic bacteria are particularly sensitive to fluctuations in some of

these parameters, specifically pH levels, temperature, and OLR [122]. A key

challenge is to find the right conditions that will result in a balanced

interdependent relationship between the different bacteria, particularly

those from acidogenesis and methanogenesis [123, 124]. Hydrolysis is

generally considered as the rate limiting step as it provides the first step of

degradation of the complex organic materials [119, 125]. The conditions that

affect this phase are: the nature and size of the incoming feedstock, with

smaller particle size resulting in increased efficiency; temperature, with

higher temperatures enhancing the process and the process temperature

affecting the required SRT; pH levels with an ideal range of 4 to 6, and; the

OLR, with high rates potentially inhibiting hydrolysis if the pH drops

significantly due to VFAs accumulation [125-128]. Acidogenic bacteria

require complete anaerobic conditions [129]. An accumulation of hydrogen

and VFAs in the acetogenesis phase can hinder the metabolism of

acetogenetic bacteria as well as methanogenic microbes [130, 131].

Methanogenic bacteria also require a completely oxygen free environment

[129]. The first and second phases as well as the third and fourth phases of

AD are closely linked, and therefore some biogas systems are operated with

the four phases separated into two stages [55]. Two-stage systems are

recommended when highly degradable feedstocks such as fruit and

vegetable waste are used as it allows for higher loads in the digester [120].

The rate of degradation in the two stages need to be balanced as a faster first

stage will lead to higher carbon dioxide content in the product gas, and a

faster second stage will result in reduced methane production [55]. Biogas

68

systems are often optimised for methanogenesis at the cost of lower

efficiencies in the first two phases [18, 122]. This research will focus on

single stage systems as they are less complex to operate and better suited for

domestic and community-scale applications in SSA.

3.1.1.1 Feedstock

The type of feedstock/substrate used in a biogas system is the single most

influential factor that determines the characteristics and the amount of

biogas produced, the type of AD technology that can be used, and the system

operation [18]. The feedstock provides all the organic components required

for the AD process [18, 55]. Ideally the feedstock should have the highest

possible nutritional value, which also means a high potential for gas

formation, be free of harmful pathogens9, and have no limiting or inhibiting

substances [18, 55]. Substances suitable as feedstock for AD are ones that

have a high biodegradability such as fats, sugars, proteins, and starch based

compounds, while those with a lower biodegradability are less ideal,

including hemicellulose, cellulose, and lignin organic substances [18, 55,

132]. Feedstocks high in lignin such as wood have a particularly low

biodegradability and are not suitable for AD [133]. Common biogas

feedstocks include: organic fraction of municipal solid waste (OFMSW),

market waste, household kitchen waste, green waste; waste from the food

and beverage industry (such as spent fruits, apple mash, potato mash,

oilseed, residuals, and rumen); sludge and excreta including sewage sludge;

9 Here pathogens are considered harmful if as they inhibit AD or do not become innocuous through the AD process and thereby could have negative impacts on health and the surrounding environment.

69

wastewater from the livestock and dairy industry, and; agricultural residues

(such as crop residues as well as liquid and solid manure from cattle, pigs,

and poultry) [18, 125, 134]. Key parameters of a feedstock that influence its

biodegradability are: volatile solids (VS) or organic dry matter content

(oDM); biogas yield; methane content, and; the rate and reliability of supply

[134, 135]. Feedstocks applicable to SSA will be discussed in Chapter 4.

3.1.1.1.1 Total solids/dry matter content

The total solids (TS), also know as the dry matter content (DM) of the

feedstock, is an important parameter as it influences the type of pre-

treatment and feeding mode required, as well as the type of biogas system

types that are feasible based in their TS operating range [136]. It is measured

in mg/L or as a percentage of wet weight and denotes the residue that

remains after water or sludge is filtered and dried at 105°C [134]. A too high

DM can leads to clogging, and a too low DM from a high water/liquid

concentration can lower the gas yield and increase the amount of heating

energy required, depending on the type of biogas system used [134]. Biogas

digesters can be categorised as ‘wet’, ‘dry’ or ‘semi-dry’ types depending on

the TS range under which they operate [137]. Wet digesters are those with a

TS of 16% or less, dry digesters have a TS range between 22 and 40%, while

semi-dry digesters are those that operate between these two TS ranges [137].

The TS ranges for the digester types that are relevant to SSA are given in

Table 3-2 in Section 3.2.

70

3.1.1.1.2 Volatile solids/organic dry matter content

The percentage of VS or oDM in a given feedstock is an important parameter

for AD as it indicates the portion of solids that can be used for biogas

production [136]. The oDM is usually determined as the mass that is

evaporated when heating a dried sample to 550°C in a kiln for a minimum

of two hours until a constant mass is reached, which is the mineral (ash)

residue, and the oDM is the difference between the initial mass and the

mineral residue mass [55]. A minimum oDM content of 60% is usually

considered suitable for anaerobic digestion [134]. The oDM of selected

feedstocks is presented in Table 3-1.

3.1.1.1.3 Biochemical methane potential

Biochemical methane potential (BMP) is the most common indicator for the

energy production potential of a feedstock as it describes the maximum

volume of methane gas that can be produced per unit mass of solid or

volatile solid matter [134]. BMP is measured by incubating a small amount

of feedstock with an anaerobic inoculum, and then determining the

methane generation by simultaneous measurements of the produced gas

volume and composition [138]. There are some variations in the technical

approach to determining the BMP for a particular feedstock, including those

outlined by Owen et al. [139] and Hansen et al. [138]. The BMP provides a

useful means of comparing the biogas production potential of different

feedstocks [137]. Table 3-1 presents the BMP for a range of feedstocks.

71

Table 3-1: Average percentage of volatile solids and the biochemical methane potential of selected feedstocks

Feedstock Type oDM (% TS) BMP (m3/kg oDM or L/g oDM

added)

Reference

Cattle dung 82% 0.230 [140-147] Poultry manure 85% 0.195 [140, 142, 145,

148] Sewage sludge 75% 0.334 [149, 150] Vegetable waste 76% 0.280 [149, 151, 152] OFMSW 85% 0.291 [134, 153-155] Spent fruits 93% 0.330 [55, 151, 152] Millet/sorghum 92% 0.287 [149, 151, 152] Cassava pulp 98% 0.344 [148, 152] Maize straw 72% 0.318 [55, 142]

3.1.1.1.4 Nutrients

A specific combination of nutrients is required for growth and survival of

specific microorganisms in the AD process [121, 130]. Key nutrients

required are carbon, nitrogen, phosphorus, and sulphur with a C:N:P:S

ratio of 600:15:5:1 [130]. Traces of iron, nickel, cobalt, selenium,

molybdenum, and tungsten also aid the growth of microorganisms [130].

Using a combination of feedstocks, known as co-digestion, often provides

an appropriate balance of nutrients such as OFMSW and sewage sludge or

crop residues and animal manure [120, 134, 156]. OFMSW and crop

residues have high C:N ratios, while sewage sludge and manure have low

C:N ratios [121, 134]. The improved nutrient balance as well as positive

synergism through co-digestion can increase the methane yield by as much

as 60% [55, 120, 157]. Agricultural and commercial biogas systems in

developed countries commonly co-digest pig, cow, or chicken manure with

crop residues, food processing waste, household biowaste, or energy crops

[130, 156]. For biogas systems treating community wastes, recycling some

of the digester effluent has been suggested to help maintain healthy nutrient

72

levels [121]. A carbon to nitrogen ratio between 20:1 to 30:1 has been

recommended for biogas feedstocks due to carbon is generally being

consumed 30 to 35 times faster by microorganisms than nitrogen is

converted to ammonia in AD [126, 158]. A high ratio can result in rapid

consumption and lower gas production [134]. A low ratio (less than 8:1)

leads to ammonia accumulation and can cause the pH value to exceed 8.5

which destroys methanogenic bacteria unless it is gradual so bacteria have

time to adapt [134, 142, 159]. Ammonia concentrations below 200 mg/L

have been found to assist AD, while concentrations between 1.7 to 14 g/L

have inhibiting effects on methane production [159]. Sulphide can cause

inhibition in a biogas system either through sulphate reducing bacteria

(SRB) competing with acidogenic, acetogenic, or methanogenic bacteria for

acetate, hydrogen gas, propionate, and butyrate in the digester system, or

through non-dissociated hydrogen sulphide being toxic for both

methanogens and sulphate reducers [119].

3.1.1.1.5 Particle size and pre-treatment

In order to achieve a significant increase in the rate of biodegradation in a

digester tank, an extremely small particle size would be required which can

be costly [121]. Reduced particle size increases the surface area of the

incoming feedstock to undergo hydrolysis [137]. Recommended particle

sizes vary depending on the feedstock and type of biodigester used. Vögeli

et al. [134] recommended a particle size between 2 to 5 cm for biogas

systems treating OFMSW in developing countries, based on the diameter of

the inlet pipe. Reducing particle size to the mm range could lead to

73

significant increases in biogas production, although the benefits of the

improved yields would need to be compared to the costs of reducing the

feedstocks’ particle size. Batch experiments with recalcitrant organic matter

in manure, found that a particle size of 0.35 mm increased the biogas

potential by approximately 20% [160]. Similarly, reducing the size of sisal

fibre waste to 2 mm increased the methane yield by 23% in batch digeters

[161]. Reduction of feedstock particle size is achieved through applying pre-

treatment, which can include mechanical, thermal, chemical, and biological

techniques [119]. Practical techniques used to reduce particle size are

mixing to enhance pulping action, maceration of fibre, disintegrating

feedstock using a ball mill or screw press, and microbial action on fibre [119-

121]. Pre-treatment may be applied to feedstock to enhance AD, remove

inorganic particles, or to reduce pathogens. Certain animal products, for

example wastes coming from slaughterhouses or products that may contain

residues of veterinary drugs, need to undergo controlled pre-treatment such

as pasteurisation or pressure sterilisation to inactivate pathogens and break

their propagation cycles [116, 162]. MSW requires pre-treatment in the form

of separating inorganic materials from organic materials [158]. Inoculation

is required at the start-up of a biogas plant and may be applied with each

incoming fresh feedstock for batch-fed systems. The digested slurry or the

liquid fraction of the slurry is commonly used for inoculation[137]. Animal

manure and rumen fluid are also used to inoculate feedstock due to their

microbial populations with rumen fluid being found to significantly enhance

the biogas production from lignocellulosic substrates [137, 163-165].

Applying thermal pre-treatment to the feedstock, which can include heating

74

the incoming slurry using waste heat from the biogas system, has been

found to increase methane production [119, 137]. Other pre-treatment

techniques include pre-composting treatment, ultrasonic treatment, acid or

alkali treatment, cell lysate, addition of certain metals, and high pressure

homogenisation [119, 120, 137]. In SSA, pre-treatment techniques are

normally not applied to household-scale systems, apart from the initial

inoculation.

3.1.1.1.6 Hydraulic and solids retention time

Hydraulic retention time (HRT) is the average number of days a unit volume

of the liquid feedstock mixture remains in the digester while solids retention

time (SRT) is the average number of days the solid fraction of the feedstock

mixture remains in the digester [119, 166]. In systems where there is no

recycle or removal of the liquid fraction of the effluent, HRT and SRT are

the same [119]. HRT typically ranges between 10 to 60 days with the average

of well-controlled digesters being 20 to 25 days [123]. The minimum SRT

for a completely mixed high-rate digestesr treating municipal wastewater at

an operating temperature of 35°C is 10 days and after 12-13 days there is no

significant changes in the rate of VS reduction [119]. HRT and SRT have a

direct impact on the AD process of a system [119]. Short retention times run

the risk of washout; where active bacteria exit the digester too quickly and

reduce the population of bacteria in the digester to unstable levels [18].

Higher retention times require a larger digester with higher capital costs,

but the smaller the digester the less time there is for substrate conversion to

biogas with the system more likely to breakdown [18, 123]. HRT can only

75

be accurately defined in batch-type systems, while in continuously operated

systems the mean HRT is approximated by the ratio of digester volume to

the volumetric flow rate of the incoming effluent, given by Equation 3-2,

where HRT is in days, Vdig is the digester volume in m3, and �̇� is the daily

influent rate in m3/d [18, 166]. Retention times are highly dependent on

process temperature and the feedstock [18]. For biogas systems operating

at mesophilic temperatures, the HRT can range from 10 to 40 days

(specifically 20 to 30 days when liquid cow manure is used, 15 to 25 days

when liquid pig manure is used, and 20 to 40 days for liquid chicken

manure) [134, 167]. Biogas plants treating wastewater usually have a HRT

between 20 and 30 days, or up to 60 days in larger systems [18, 119].

Thermophilic systems can have a HRT between 10 to 15 days, while simple

biogas systems used in developing regions have high HRTs, between 150 to

200 days, or even over a year (although the HRT could be shorter if systems

are well designed and appropriately sized) [18].

𝐻𝑅𝑇 =𝑉𝑑𝑖𝑔

�̇� Equation 3-2 [18]

3.1.1.2 Temperature

The operating temperature of a biogas system influences the rate of the

microbial activity in the digester [119]. Methanogenic bacteria are

particularly sensitive to fluctuations in temperature and can inhibit biogas

production [18, 123]. AD can occur under three different operating

temperature ranges: psychrophilic (<20°C), mesophilic (35-42°C), and

thermophilic (50-60°C) [17, 55, 121, 130]. A limited number of biogas

systems operate in the psychrophilic temperature range, with biogas

76

production slow and unstable and a greater risk of long-chain fatty acid

accumulation [134, 158]. Mesophilic biogas systems are the most stable due

to the microorganisms for this temperature range being more tolerant to

changes in environmental conditions [134]. Fluctuations in temperature of

±3 °C can occur in mesophilic systems without there being any significant

reduction in gas production compared to thermophilic systems that are

more sensitive to temperature changes [130]. The lower free ammonia and

carbon dioxide concentrations cause less inhibition than thermophilic

biodigesters [134]. However, the thermophilic process is faster and more

efficient due to an increased growth rate of methanogenic bacteria, leading

to a higher methane gas production along with a higher rate of pathogen

removal [119, 130]. Thermophilic biogas systems require an external energy

source and a control system to maintain a constant high temperature in the

digester, while mesophilic systems may not require external heating or

temperature control, provided the ambient temperature is high enough and

there are no major fluctuations [119, 123]. Tubular and spiral heat

exchangers are commonly used in biodigesters, which have a counter-

current flow design and heat transfer co-efficients of 850 to 1000 W/m2K

[119]. For mesophilic biodigesters, external heating is usually only required

for systems that are installed in areas where the ambient temperature is

below 20°C with the optimal temperature being 35°C for maximum biogas

production in the mesophilic range10 [158]. Strategies that can be used to

10 Biogas production can also be maximised at lower ambient temperatures through adjusting the HRT/SRT, however, this could result in very long HRTs/SRTs and therefore heating may be applied to achieve the desired biogas production rate.

77

help maintain a constant temperature aside from external heating include:

preheating the feedstock; using warm recycled digestate as a major part of

the system’s water supply; installing the majority of the biodigester

underground, and; building a greenhouse around the digester [121, 123,

134]. The heat generation inside the digester by the microbial activity, and

the heat flows across the digester boundaries need to be taken into

consideration when deciding on the heat requirements for a system [158].

Biogas systems that are required to produce gas throughout the year need

to be designed based on the worst season of the year to make sure it will still

produce sufficient gas during this time [134]. The biogas production per unit

volume is lower in colder regions, resulting in larger biodigesters being

required [123]. The mean temperature as well as temperature variations

between night and day or different seasons are important parameters [134].

Biogas systems in SSA are commonly unheated, operating mostly in the

mesophilic temperature range, and experience fluctuations in biogas

production as the digestion temperature is affected by changes in the

ambient temperature [17].

3.1.1.3 pH

For hydrolysis and acidogenesis a pH between 4 up to 7 is ideal, depending

on the feedstock, while for methanogenesis a pH close to neutral (6.8 to 7.5)

is ideal [18, 168, 169]. The methanogenic bacteria required during

methanogenesis are sensitive to pH, and are inhibited when the pH drops

below 6.6-6.8 [121, 170]. High pH values can lead to instability due to an

increase in the free ammonia concentration, which can then result in an

accumulation of VFAs [159]. The accumulation of VFAs can result in a drop

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in pH, which can lower the methane yield, or sour the digester and stop the

process completely [159, 170]. The lowered pH through VFA production in

acidogenesis is normally countered by the presence of carbon dioxide,

ammonia, and bicarbonate in methanogenesis [119]. Appropriate

management of pH levels needs to be incorporated into the design and

operation of a biogas system in order to avoid serious damage occurring to

its microbial system. A recycle stream can be used to control the pH as well

as nutrients [121]. Lime is commonly added to the system when there is a

drop in the pH, but its addition needs to be limited as it can cause

precipitations and clog up pipes [134, 170]. Sodium bicarbonate and sodium

hydroxide are suitable alternatives as they are fully soluble and tend not to

lead to precipitations, but they are higher in cost [134]. Animal manure has

surplus alkalinity and is suitable for co-digestation to stabilise the pH value

and reduce VFA accumulation [130]. These issues related to pH levels and

VFA accumulation are unlikely to be common in household-scale (and well-

designed) biogas systems in SSA where cattle dung is the dominant

feedstock.

3.1.1.4 Organic loading rate

OLR is the weight of the VS or oDM components that are loaded into the

digester per day per unit digester volume [166]. It is limited by the biological

conversion capacity of a biogas system and depends on the biodigester type,

mode of operation, and feedstock that is used [18]. Continuous or near

continuous feeding is required where the loading rate is high and at lower

loading rates only daily feeding of the digester is required [123]. OLR is one

of the key control parameters in a continuous system and is closely linked

79

to HRT [18]. The OLR affects the ratio of feedstock to microorganisms with

the equilibrium point being where the amount of feedstock is in balance

with the microorganisms consuming them in the digester [158]. Feeding a

biogas system above the sustainable OLR will decrease the gas yield due to

accumulation of inhibitory substances such as fatty acids [18]. For biogas

systems without forced agitation, which is normally the case for household-

scale systems, an OLR below 2 kg VS/m3/d is recommended [134].

3.1.1.5 Toxins

The feedstock materials fed into the digester need to be carefully checked to

ensure they do not contain toxins, as could be the case in OFMSW. Most

MSW has a small amount of toxins which are well diluted amongst the large

quantities of waste [121]. Sources of toxins to AD include medications (such

as antibiotics), feed additives, pesticides, and herbicides [123]. Heavy

metals such as zinc, copper, chromium, nickel, cadmium, cobalt, iron, or

lead from industrial processes, or from leaching in domestic applications

can have inhibitory impacts on the microbial activity in biogas systems,

particularly through accumulation in the digester as they are not

biodegradable, although some heavy metals can be detoxified through

precipitation with sulphide [119, 159, 171-173]. Low concentrations of

sodium, potassium, and other cationic elements can enhance microbial

activity, however, they can also be toxic if the concentrations become too

high [119, 159]. Some organic compounds have also been found to inhibit

anaerobic processes, including cholorophenols, halogenated aliphatics, N-

substituted aromatics, long chain fatty acids and lignin derivatives, as well

as organic chemicals that have a low solubility in water and are not easily

80

absorbed by the solid sludge such as apolar pollutants in bacterial

membranes [159]. Household- and community-scale biogas systems in

developing regions can be prone to a sediment of gravel, sand, and soil and

other slow or non-degradable materials accumulating at the bottom of the

digester due to being inadvertently fed in with the feedstock. The digester

may need to be emptied to remove the sediment if it reduces the active

digester volume too much [174].

3.1.1.6 Water

The water requirements of AD is dependent on the type of feedstock used,

and its TS/DM content [121]. Water is required to transport the feedstock

to the bacteria and the resulting products from the bacteria, thereby

preventing a build-up of toxic concentrations around the bacteria, as well

as aiding the distribution of nutrients and heat around the digester tank

[121]. The amount of water required is dependent on the type of feedstock

and biogas system used. A dung to water ratio of 1:1 has been recommended

for cattle dung-fed fixed dome biogas systems in Ethiopia, Kenya, Rwanda,

and Tanzania [144, 145, 175, 176]. In SSA, fresh water requirements can be

reduced or eliminated by using cattle urine, grey water, or connecting a

toilet to the biodigester11 [17]. Urine can be directly fed into a biogas system

through using a solid stable floor which slopes towards the system’s

feedstock inlet [63]. Alternatively, rainwater harvesting tanks can be

implemented with domestic biogas systems, as has been done in Nepal [63].

11 Replacing fresh water with urine may not be suitable in some situations, depending on the combination of feedstocks used, as it can lead to a high ammonia production due to the high nitrogen content in urine.

81

In some regions, such as Fada N’Gourma, Burkina Faso, rainwater

harvesting tanks are already used [63]. Austin and Morris [17] recommend

distance of 1km is as the maximum distance a person should walk in order

to get water for a domestic biogas system and to ensure that water access is

not a limiting factor in the technology’s uptake. However, as highlighted by

Tucho et al. [177], the time associated with the travel and collection of water

for biogas systems needs to be considered in context and combination with

the individual collection and transportation requirements for all biogas

resources (water, feedstock, and bioslurry). Only then can the true labour

intensity and potential time savings of the technology be compared to

traditional household energy options such as firewood cookstoves.

3.1.1.7 Mixing

Mixing is important in AD to help maintain relative homogeneity of the

slurry in the digester tank in order to prevent channelling, facilitate the rise

of gas bubbles, and inoculate the fresh feedstock material with microbes

from the digestate [119, 121, 134]. Mixing is particularly important to

prevent the development of stagnation zones, accumulation of scum, and

temperature gradients within the digester [121, 134]. The accumulation of

heavy inorganic solids in stagnation zones as well as floating materials such

as plastics, reduce the effective volume of the digester and must be removed

[121]. Scum formation can occur as a result of filamentous microorganisms

in low loading rate and nutrient conditions as well as due to high

concentrations of fatty acids and grease [134, 178, 179]. The scum can cause

blockages in the gas and slurry pipes [134]. In large-scale systems a scum

layer of 20-60 cm is acceptable and easy to manage [134]. Some options for

82

mixing in a digester are a paddle, scraper, mechanical stirrer, piston,

pumped recirculation, or gas recirculation [119, 123, 158]. A mixing device

inside the tank is not always practical or necessary, particularly in systems

typical for developing countries (fixed-dome, floating dome, bag digester),

and passive mixing techniques can be applied (such as alternating slurry

removal from the top and bottom of the tank or recirculating the output

slurry, which also helps flush the inlet pipe and mixes the fresh feedstock

with bacteria-rich digestate) [121, 134]. Some passive mixing occurs

naturally in a digester through the rise of gas bubbles and the convection

currents created by the inflow of heated feedstock [119]. Advanced

recirculation systems pump out the digested sludge from the middle of the

digester to external heat exchangers where it is then heated and mixed with

fresh feedstock, and pumped back into the tank through nozzles at the base

or top of the digester to provide mixing and helping to break the scum layer

[119]. Recirculation system flow rates need to be high to ensure complete

mixing to minimise power consumption to 0.005 to 0.008 kW/m3 of

digester volume [119].

3.1.1.8 Operation and maintenance requirements

The operation and maintenance requirements of a biogas system vary from

site to site and depend on the system design, knowledge and skills of the

operator, characteristics of the feedstock, climatic conditions, and the

application of the system [18]. The feedstock parameters described in

section 3.1.1.1, including TS, biodegradability, C:N ratio, particle size, and

the type of pathogens present, are key influencing factors on the

maintenance and operation requirements of biogas systems.

83

Slaughterhouse waste not fit for human consumption, for example, require

pressure sterilisation (pre-treatment); while crop residues may require

mechanical pre-treatment to reduce the particle size or more complex

treatment to break the lingo-cellulosic molecules [126]. Feedstocks can be

fed in either batch, continuous, or semi-continuous mode. In batch systems,

the digester is filled with the feedstock in one sitting and the effluent is only

discharged once the feedstock has been anaerobically digested, as will be

further described in Section 3.2.1. Continuous systems have a constant flow

of incoming feedstock and there is no interruption to loading the fresh

material and unloading the effluent [18]. Systems that operate in the

continuous mode tend to require regular monitoring [18]. Operation and

maintenance activities common for biogas systems include regular checks

and inspections of digester and pipes, management of feedstocks, shredding

and pre-composting of crop residues and other fibrous feedstocks, control

of mixing, monitoring of biological process, and management of problems

[18]. Post-treatment may be applied to the output slurry (bioslurry) to

produce a standardised biofertiliser or to allow discharge into a sewage

system through the removal of nutrients and organic matter, similar to

wastewater treatment [126]. Bioslurry treatment often consists of solid-

liquid separation followed by drying or composting for the solid faction, and

ammonia stripping, membrane filtration, aerobic treatment, or evaporation

on the liquid phase [120, 126].For good management of biogas systems,

sufficient knowledge for adequate operation, appropriate skills, and access

to reliable support where it is crucial, is required [18].

84

3.2 Biogas system design options

Biogas digesters can be distinguished by: operation mode (batch, semi-

continuous, continuous), number of stages, operating temperature

(psychrophilic, mesophilic, thermophilic), feedstock solid content

(TS/DM), organic loading rate, hydraulic retention time, size range, and

application [136]. There are six main types of biogas digesters used to treat

organic slurries and solid waste; batch reactor, continuously stirred tank

reactor (CSTR), covered anaerobic lagoon, fixed dome digester, floating

cover digester, and plug flow digester. Table 3-2 summarises the differences

between the digester types based on some of these distinguishing

parameters. For wastewater treatment, fixed film systems, particularly

upflow anaerobic sludge blanket (UASB) reactors, are typically used.

85

Table 3-2: Comparison of parameters for six main types of biogas digesters used to treat organic slurries and solid waste

Parameter Batch reactors Continuous stirred tank reactors (CSTR)

Covered anaerobic lagoon (CAL)

Fixed dome digesters

Floating cover digesters

Plug flow digesters

Feeding mode Batch Continuous/ semi- continuous

Semi-continuous Semi-continuous, batch

Semi-continuous, batch

Semi-continuous

No. of stages ≥1 ≥1 ≥1 1 1 1

Operating temp.a P, M M, T P, M P, M P, M P, M

Typical feedstock TS/DM range

22-40% (dry) 3-14% 0.5-3% 5-12%

5-12% 10-15, ≤45% (dry)

HRT (days) ≥5 10-40 40-60 40-90 35-90 15-40 (high-tech), 60-90 (low tech)

Size range (m3) 0.5×10-3-0.1b, 15-20c, ≥100d

≥100 ≥2000 2-200 1-100 1-8

Application Large-scale, commercial: OFMSW

Large-scale, commercial: agriculture, food processing, OFMSW

Large-scale, commercial: livestock,

Small- to large-scale: rural household, community/ institution, agriculture

Small- to medium-scale: urban & rural household, agriculture

Small- to medium-scale: rural household, agriculture

References [18, 63, 122] [55, 91, 122, 123, 166]

[26, 180-182] [17, 18, 55, 63, 122, 123, 134, 183, 184]

[16-18, 55, 63, 86, 104, 122, 123, 134, 184-186]

[17, 18, 55, 122, 166, 187]

86

3.2.1 Batch systems

In batch systems, the feedstock is loaded into a digester tank and sealed

until the material is completely digested, after which it is emptied and the

process is repeated [18]. Some residue of the digested slurry is usually left

in the tank to inoculate the incoming feedstock [55]. Maximum (volumetric)

gas production may be reached at half the residence time, depending on the

inoculum to feedstock ratio, with production decreasing slowly for the

remaining time [55, 188]. Batch systems can easily be operated as two stage

systems where the second tank is fed with the output slurry from the first

tank and captures any additional biogas produced through the slurry [55].

Commercial systems typically use three or more tanks that are run off-set,

alternating between loading and unloading [18]. Batch systems can also be

used for dry digestion where solid waste is loaded into the digester along

with inoculum, and sometimes alkali to maintain the pH levels [18]. Dry

batch systems are used in both developing and developed regions, for

example in the Philippines and Germany [18]. Systems in Germany are

multi-stage and off-set for steady biogas production with total retention

times of four to six weeks, while the systems used developing regions are

loaded a few times a year [18]. The Philippines has had the most successful

batch system biogas programme [18].

Batch systems are simple in design and have lower investment costs and are

recommended for developing countries, however, there are some

limitations that may make them unsuitable [134]. A key disadvantage of

87

batch systems is that there are variations in the production and supply of

biogas, which can have damaging impacts on gas motors, deeming them

unsuitable for electricity production [18, 55, 134]. Another challenge is the

requirement of gas tight sealing for the inlet and outlet, that also needs to

be opened and closed after each batch sequence [134]. This challenge was

noted in the development of a dry batch system using a shipping container

in Kumasi, Ghana [189]. In addition, the study noted that safety precautions

need to be adhered to in order to prevent severe accidents, particularly gas

explosions when emptying the digester [189]. Its potential application is for

treating bulky organic wastes with a high dry content, such as the OFMSW,

to produce biogas and compostable digestate [189]. Aside from the

Ghanaian dry system, a batch system was developed in Burkina Faso in the

1980s based on the floating drum design, however, the design was found to

be too expensive and too demanding in operation and maintenance [63].

Further testing and development of batch systems is required in SSA before

they can be considered viable for commercial applications.

3.2.2 Continuously stirred12 tank reactors (CSTRs)

CSTRs stir the digester contents completely to produce a homogenised

mixture [122]. New feedstock is added regularly and in turn the output

slurry exits the digester regularly, enabling a continuous digestion process

[134]. The SRT in these systems is equal to the HRT, provided the digested

slurry is not recycled or some of the liquid components removed during the

process[119]. A minimum SRT of 10 days is recommended at a digestion

12 Also known as completely stirred tank reactor

88

temperature of 35°C, as shorter retention times lead to a washout of

methanogenic bacteria and a build-up of VFAs [119]. Some washout can still

occur, however, if a mix of feedstock types and/or varying particle sizes are

used due to their different rates of biodegradability [122, 190]. CSTRs

require a high level of monitoring and control over the mixing as well as

temperature to maintain uniform conditions, some of which can be

minimised through good design [119]. Constant mesophilic or thermophilic

temperature inside of the digester is commonly maintained by applying

heating through external heat exchangers [119, 124]. Key benefits of CSTRs

are that there is a high rate of manure stabilisation as well as a high biogas

production rate at a reduced HRT [166]. CSTRs are commonly operated as

single or two stage systems with single stage systems being considered

easier to operate but less efficient [190]. A study by Kaparaju et al. [190]

found that treating manure in two-stage series connected CSTRs resulted in

up to 17.8% higher biogas production when compared to single-stage CSTR

performance due to more optimal retention time distributions of particulate

matter compared with nominal retention time. In some two-stage systems,

the secondary digester is a simple floating cover or fixed dome reactor,

which is primarily used to store the produced gas [119].

A wide variety of feedstocks can be used for CSTRs, however, the technology

has been found to be uneconomical for communal wastewater treatment

[55]. CSTRs are widely used in systems that use co-digestion, such as the

livestock and agricultural industry, including medium to large scale systems

in China [91]. In SSA there is potential to use CSTRs in large-scale farming

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or food processing industries. The CSTR has been identified as a suitable

digester for agro-processing feedstocks in SSA including solid coffee waste,

sisal waste, and waste from fresh cut flowers [191, 192]. In Tanzania, a CSTR

has been installed for a demonstration project for biogas from sisal waste

[192]. High investment costs of CSTRs are likely to be hindering additional

applications in SSA. One study in South Africa estimated that an investment

subsidy of 53.8% combined with income from carbon credits and electricity

feed-in tariffs would be required to make CSTRs a viable option for

electricity generation at piggeries with 400 to 500 sows, while CSTRs were

not considered viable for dairy farms with 200 to 300 cows, even with the

implementation of incentives [193]. The average dairy farm in South Africa

has less than 300 cows, well below the theoretical size of 5,500 cows

identified in the study to result in a positive NPV and enable the installation

of CSTRs to be economically feasible [193]. Economies of scale in relation

to farm size, therefore, is a critical factor when considering CSTR

installations in SSA.

3.2.3 Covered anaerobic lagoons (CALs)

Covered anaerobic lagoons (CALs), also known as covered anaerobic ponds

or covered lagoon digesters, are usually used for treating flushed manure on

livestock farms with a TS range between 0.5 to 3% [26, 124]. These systems

are commonly applied in the United States of America (USA), which has

over 50 years of experience with the technology [181, 194]. They consist of a

lagoon or pond with a depth of 2 to 5 m, which may be lined if the soil is too

permeable, and an impermeable membrane flexible or floating cover to

capture the generated biogas [182]. The effluent from the CAL is normally

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fed into a secondary facultative pond for further treatment [182]. CALs are

passive systems that provide solid separation through gravitational settling

and waste stabilisation through the AD process [181]. The accumulated

sludge at the bottom of the lagoon needs to be emptied every 1 to 3 years

[182] Key advantages of these systems are their low-cost and simple

operation [181]. Its digestion temperature is normally close to the ambient

temperature and is best suited to temperate climates [26, 124]. Due to the

passive AD treatment of low TS feedstocks, biogas production in CALs is

slower compared to other biogas systems, this also results in significant land

footprints [26, 195]. Other disadvantages include inadequate gas capture

leading to methane emissions, poor odour control, limited nutrient capture,

and expensive desludging requirements [195] The lower investment costs of

CALs compared to CSTRs, improve their economic feasibility with the South

African study finding that piggeries required no investment subsidies for the

installation of CALs to be viable if carbon credit and feed-in electricity tariffs

were available [193]. The investment subsidies for dairy farms, however, is

still significant at 70% due to their small size with the ideal size for CALs

being 1,500 cows or more [193]. Similar to CSTRs, the applicability of CALs

in SSA is subject to economies of scale in relation to farm size.

3.2.4 Fixed film digesters and other anaerobic wastewater

treatment systems

Fixed film digesters are designed for wastewater or other dilute, low

strength organic waste streams [124, 166]. They are filled with an inert

material (such as wood chips, small plastic rings, gravel, ceramic rings, glass

beads, or baked clay), on which the anaerobic microbes from the influent

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can attach themselves to form a biofilm [124, 166]. The microbes in the

biofilm continue to grow as new feedstock flows in which ensures that there

is no washout [124, 166]. These digesters are compact and have the shortest

retention times out of all the anaerobic treatment systems –between three

to five days [124, 166]. The feedstock is usually pre-treated through the

application of physical separation to remove suspended solids and fibrous

materials, preventing clogging [166]. Fixed film digesters can be operated at

ambient temperature in hot climates, although they are commonly heated

to mesophilic or thermophilic temperatures [124]. Common types of fixed

film digesters are the expanded-bed reactor and the packed/fixed-bed

reactor [124]. In packed-bed also known as fixed-bed reactors, the influent

wastewater can be passed through the reactor either in upflow or downflow

mode, although systems operating in upflow mode perform the best in

terms of biogas production, chemical oxygen demand (COD) reduction, and

loading rate [124]. Two packed-bed digesters can be connected in series to

create a two-stage system which improves its performance [124]. In

expanded-bed reactors and fluidized bed reactors (FBRs), another type of

anaerobic wastewater treatment system, the feedstock is circulated in

upstream mode at high velocity using using a pump, causing small solid

inert particles in the reactor to become fluidised or expanded[55]. FBR are

often operated as two-stage systems with the digesters connected in series

[55]. Other anaerobic wastewater treatment systems include the upflow

anaerobic sludge blanket (UASB) reactor, the expanded granular sludge bed

(EGSB) reactor, and the anaerobic sequencing batch reactor (ASBR).

92

UASB reactors are well suited for treating domestic sewage in developing

regions with tropical climates, where no auxiliary heating is required, due

to their low investment costs13 as well as simple design and operation [150,

196, 197]. A sludge bed is formed at the bottom of these reactors through the

accumulation of incoming suspended solids, on which the biofilm then

forms (Figure 3-3) [196]. The EGSB is a modified UASB, operating at a

higher velocity, which has improved wastewater-biomass contact through a

fully or partially expanded sludge bed and intensified hydraulic mixing

[198]. It has been applied to low- and medium-strength wastewater

treatment [198]. ASBRs are typically used in sewage treatment plants. They

allow all the stages of sewage treatment to occur sequentially in one tank –

filling, biochemical reaction, sedimentation and decanting [55].

Some UASB systems are being used in developing regions, including SSA,

to treat abattoir waste, canteen/kitchen waste, and agro-processing

wastewater [199-201]. A UASB system is used to treat the waste from the

Bodija Municipal Abattoir in Ibadan, the second largest city of Nigeria [199].

In Zimbabwe and Ghana, UASB reactors are used to treat the wastewater

from breweries [202, 203]. Aside from UASB reactors, other anaerobic

wastewater treatment systems are currently not common in developing

regions, due to the limited resources and infrastructures in place for

wastewater treatment, however, there is potential for them to be applied as

large-scale systems for wastewater and liquid food processing waste [204-

206].

13 The estimated construction costs of a UASB reactor are 20-40 USD per inhabitant, in comparison, a septic tank - anaerobic filter system costs 30 – 80 USD per inhabitant to construct [181]

93

Figure 3-3: Schematic of a UASB reactor [207]

3.2.5 Fixed dome digester

The fixed dome digester, also known as the Chinese dome digester (CDD)

or Chinese model and hydro-pressure digester, is commonly used in

developing regions due to its low-cost design, long life span, and low

maintenance requirements [18]. It consists of an underground reactor with

a fixed cover where the gas and input slurry are stored and a displacement

tank with the outlet as shown in Figure 3-4. These systems are typically

loaded semi-continuously and as gas production increases inside the

reactor, the digested slurry is pushed into the displacement tank, and

likewise as the gas is used, the slurry in the digester tank flows back into the

reactor, creating agitation [18, 55]. Fixed dome systems originated from

China where they were first built in 1936 and by the 1980s an improved

model was introduced worldwide [63]. The systems are typically made from

bricks or stone with mortar, or poured concrete and a sealant for the inside

plastering, such as bees wax, engine oil mixture or acrylic emulsion [18,

94

134]. Plastic, fibreglass, fibre-reinforced plastic, and composite materials

are becoming more popular to use for prefabricated systems or

prefabricated parts [17, 183]. Most commonly, fixed dome digesters are

applied on a household scale with the generated biogas being used for

cooking and lighting. These systems are constructed underground to

minimise temperature fluctuations in the digester tank. In selected SSA

countries, including Rwanda and Ghana, fixed dome systems are also

applied on community/institutional-scale [77, 203]. Figure 3-5 shows a 60

m3 system installed at a Rwandan school.

Figure 3-4: Diagram of a fixed dome digester [208]

95

Figure 3-5: Community-scale fixed dome digester at a Rwandan school

[Photo by G.V. Rupf]

Several variations of the fixed dome digester have been developed to suit

local conditions in SSA, including the AGAMA BiogasPro from South Africa,

MCD from Tanzania, Kenya Biogas Model (KENBIM), LUPO and SINIDU

models from Ethiopia, SimGas GesiShamba used in Tanzania and Kenya,

TED design from Lesotho, and the Rwanda III digester [63, 82, 209-211].

The MCD design has been used in a number of SSA countries due to its long

lifespan of 20 or more years, low-cost, local construction materials (bricks,

clay, wood), and local employment creation through construction [17, 55].

In response to the need for a suitable digester for pastoralists living in dry

or semi-arid areas of Tanzania, the CAMARTEC solid state digester (SSD)

was developed. It includes a cylindrical inlet with a larger diameter, a larger

digester tank with a conical bottom to collect inorganic materials or debris,

and an expansion chamber with a manhole directly above the slurry outlet

96

opening to enable removal of inorganic solids [144]. The SSD requires less

water, operating at a TS range of 8-18% compared to 8-10% for the MCD

[144]. In Uganda, interlocking stabilised soil blocks (ISSB), which consist of

compressed local soil with 5% cement, have been introduced as replacement

building material to fired bricks in MCD biodigesters, achieving a 30% cost

reduction in addition, saving time and firewood normally needed to fire the

bricks [212]. The TED and LUPO designs are similar, both contain an

expansion channel rather than an outlet tank which enables more gas

storage and encourages effective use of the bioslurry as fertiliser through a

channel to a nearby compost pit [63]. Other features include minimised

water use through maximising the use of urine and grey water, a weak ring

and wire mesh to prevent cracking in the digester tank, a testing unit to

enable monitoring of the digester and piping system, and no manhole [63].

The KENBIM and Rwanda III models both have been designed for the

domestic biogas programmes in Kenya and Rwanda, respectively [175, 176].

Both systems have a cylindrical digester, gasholder dome, and

hemispherical outlet tank with the Rwanda III system being based on the

Nepalese GGC 2047 model and containing a concrete inlet with a mixer [175,

213]. In Ghana, a design of a biogas septic tank to treat household sewage

included a fixed dome digester, followed by a gas storage tank which acts as

a desilting unit, and an anaerobic baffled reactor (ABR) with packing

material in one chamber and gravel for filtration in the final chamber [214].

Prototypes of this design are yet to be constructed and tested on their

performance and practicality. In Lesotho decentralised wastewater

treatment systems (DEWATS) have been implemented to treat domestic

97

wastewater [215]. The DEWATS consist of a TED digester for primary

treatment followed by an ABR and a planted gravel filter [215].

Prefabricated fixed dome plastic biogas digesters have also been introduced

into SSA, including the PUXIN fixed dome system, AGAMA BiogasPro, and

the SimGas GesiShamba [17]. The PUXIN system contains a prefabricated

gasholder manufactured in China, while the digester tank is constructed

locally from concrete with a reusable 10 m3 mould for domestic applications

or 50, 75, and 100 m3 moulds for large-scale systems [216]. The AGAMA

BiogasPro system is designed and manufactured in South Africa from linear

low-density polyethylene (LLDPE) and consists of a single, transportable

unit that contains both the gasholder and digester [217]. It can be installed

as an alternative to septic tanks, and can be used with a variety of feedstocks

such as sewage, food waste, animal manure, and grass silage [217, 218]. The

SimGas GesiShamba is a modular and expandable system that was designed

and developed in the Netherlands in close collaboration with local partners

in East Africa [219]. The main drawbacks to fixed dome digesters are

significant fluctuations in gas pressure, leaks and cracks in the digester

(only relevant to built systems), and scum formation inside the digester,

which can cause blockages (although this is not a problem unique to fixed

dome digesters) [17, 63, 122].

3.2.6 Floating cover digester

The floating cover digester, also known as the floating dome, drum or cup

digester, was originally developed in the 1950s by the KVIC in India [18].

The digester has a flexible floating cover where the gas is stored, which

either floats directly on the slurry or in a water jacket surrounding the

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digestion tank [17]. The cover rises as gas production increases and its

weight enables the gas to be kept under constant pressure –usually between

0.7 and 0.9 kPa [17, 122]. A partition wall is included in systems that have

a high height to diameter ratio to prevent short-circuiting of the fresh feed

and partially digested slurry exiting the outlet [122]. The gasholder cover

was commonly made of mild steel but fibreglass and reinforced plastic

covers are becoming more popular [18, 55, 122]. The digestion tank can be

made of bricks, cement and mortar, or plastic or steel drums in simpler

versions [55]. Prefabricated digesters or partially prefabricated parts,

usually made from plastic or fibreglass, are increasingly being implemented

[17]. Along with fixed dome digesters, these systems are popular in

developing countries due to their simple operation and construction [17,

122]. Aside from maintaining the gas pressure, the floating cover also can

be used to break up scum and provide agitation through rotating it by hand

[122]. Compared to fixed dome digesters, these type of digesters can have

up to 50% higher construction and maintenance costs where a steel cover is

used as it has to be repainted each year [16, 122]. The rust-prone steel parts

also lead to a reduced lifespan of 15 year or as low as 5 years in tropical

regions [16].

Variations of the floating cover digester used in SSA include the ARTI

digester and the Botswana model [86, 185]. The ARTI digester was

developed in India by the Appropriate Rural Technology Institute (ARTI)

for small-scale, household applications, and is being promoted in Tanzania

and Uganda [86]. It consists of two standard high-density polyethylene

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water tanks that have been cut to size with the smaller one inverted and

placed in the larger one as the floating cover [86]. Organic solid waste, such

as kitchen scraps, can be used in the ARTI system as an alternative to

manure for urban and periurban households (who may not have access to

sufficient quantities of animal manure for their system) [86]. The Botswana

model has a similar design, it consist of two different sized steel drums with

the larger being used as the reactor and the smaller as the gasholder cover

[122, 185]. Steel guide bars of 10 mm thickness are used to assist with the

rise and fall of the cover [185]. The high installation cost of floating cover

digesters is a significant barrier for increasing their uptake in SSA. The high

installation cost has been mitigated by one Kenyan company that has

applied a fee-for-service model [220]. Its ‘pay-as-you-go’ scheme allows

rural households to pay for a certain amount of ‘credit’ to use the gas from

the company’s prefabricated floating dome system, which is set up near the

client’s home [220]. Prefabricated floating cover biodigesters are commonly

above ground systems, which can easily be moved from an installation site

compared to locally constructed systems that contain a permanent, partially

underground digestion tank as shown in Figure 3-6.

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Figure 3-6: Household-scale floating cover digester in South Africa [221]

3.2.7 Plug flow digester

Plug flow digesters consist of long, narrow tanks, usually with a 5:1 length

to height ratio [18]. The systems are fed batch-wise or semi-continuously

and have no internal agitation [18]. There is limited horizontal mixing which

ensures that minimum retention time is reached with the HRT ranging

between 15 to 40 days [18, 122, 166, 222, 223]. A variety of feedstocks can

be used in plug-flow digesters, generally in the range TS of 10% to 15%,

although some do handle up to 45% TS [18, 55, 166, 222, 223]. The systems

are quite versatile in their application, from modern cylindrical steel tanks

or storage flow-through systems in Europe, to small household plastic bag

systems commonly used in Vietnam and Taiwan [55, 122]. The bag digester,

also known as flexible balloon, tubular, ball-type, bladder, or sausage-type

digester, was developed in Taiwan and is used in Vietnam, Philippines,

Cambodia, and numerous countries in Southern and Central America [183].

The systems in Cambodia were installed as floating systems in a floating

101

village to help improve sanitation [224]. The systems can be made of

polyvinyl chloride (PVC), polyethylene (PE), neoprene coated nylon fabric,

UV resistant bags, industrial grade tarpaulin, or plastic silo bags (although

these need to be replaced after each batch) [18, 55, 134, 183, 225]. Plug flow

digesters are usually set up in a trench in the ground [55]. In colder regions

the digester is kept inside a greenhouse made of adobe walls and a plastic

sheet cover [226]. Materials for household-scale plug flow digesters are easy

to transport compared to fixed dome and floating cover digesters, which

makes them favourable for remote households [226]. Patch repair kits are

recommended with flexible balloon digester installations to address the

issue of damage from sharp objects along with a shelter to prevent plastic

degradation in UV [223]. Installation time and costs of household-scale plug

flow digesters are approximately half that of fixed dome models, but their

average life expectancy is 5 years [223, 226]. Flexible balloon digesters in

Uganda were found to have a payback period of 4 years based on savings

from reducing wood and compost requirements [223]. The lower cost and

short lifespan of these digesters makes them appropriate for households

with uncertain economic futures, variability of suitable feedstock for the

digester, and livestock farmers with fluctuating herds [223, 226].

Aside from Uganda, bag digesters have also been installed in Ethiopia,

Kenya, Rwanda, South Africa, Tanzania, and Uganda [70, 227, 228]. A

sustainability assessment by Nzila et al. [29] ranked bag digesters as the

most sustainable when compared to fixed dome and floating cover digesters

in Kenya, mainly due to its low cost, ease of installation, and high energy

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production rate. The digester, however, was noted as having the lowest

score in terms of reliability [29]. Cheaper plug flow designs (Figure 3-7)

have a small inlet which makes safe handling of manure more difficult.

Another alternative in the design for safer waste handling is including a

concrete inlet mixer (Figure 3-8). Both plug flow designs have a greenhouse

cover to help increase the digester temperature, however, the heating effect

is minimised by the translucent plastic used and it being left open near the

inlet. A study in Uganda found that the installation of flexible balloon

digesters led to increases in Escherichia coli and total coliform loads in local

environments due to spillage of manure during digester feeding [223]. To

assist with the safer handling of waste to feed into the digester it was

recommended that flexible balloon systems are placed in lower trenches to

enable ground level feedstock inlet [223]. In Ethiopia, plug flow digesters

are sold by one company as a business franchise suitable for households that

have sufficient feedstock to produce above their own biogas demand [229,

230]. The excess biogas is stored in a 1.3 m3 ‘biogas backpack’ (Figure 3-9),

which enables the gas to be sold to nearby households [231].

103

Figure 3-7: Low-cost plug flow digester in Rwanda with small inlet [Photo by G.V. Rupf]

Figure 3-8: Plug flow digester in Rwanda with an inlet mixer [Photo by G.V. Rupf]

104

Figure 3-9: ‘Biogas backpack’ used to test different types of cookstoves at a German university [Photo by G.V. Rupf]

3.2.8 Comparison of different digester designs

In assessing the different biogas digester designs for applicability to SSA (in

the context of organic slurry and solid waste treatment), their performance

needs to be compared in addition to the parameters presented in Table 3-2.

A comparison of the lifespan, construction time and cost, pressure, and

operation and maintenance difficulty for the six main types of biogas

systems is presented in Table 3-3. Large-scale batch, CSTRs, CALs, and

advanced plug flow digesters are commonly used for commercial

applications in developed regions. As floating cover, fixed dome, simple

batch, and simple plug flow digesters are commonly used on a household or

community-scale in developing regions, there is no existing literature that

compares all of these digester types together, but rather compares the two

groups. For commercial-scale systems treating livestock manure, CSTRs

were found to have the highest rate of biogas production, followed by plug

105

flow digesters, based on a series of reported biogas production values [166].

On a household-scale, as mentioned in section 3.2.7, low-cost plug flow

digesters were found to yield the highest scores for economic impact, as well

as in some environmental impacts (global warming reduction and energy

demand) and technical impacts (energy breeding ratio and energy payback)

when compared to fixed dome and floating drum digesters in Kenya.

However, the digester was also found to have the lowest reliability as

indicated by its short lifespan (Table 3-3) [29]. A study by Pérez et al. [187]

comparing the use of fixed dome and plastic bag digesters in rural Andean

communities in South America found that the bag digester had lower capital

costs and was simpler in its implementation and handling, although the

fixed dome digester had a lower environmental impact and a greater

lifespan [187]. These studies as well as Table 3-3, highlight that the ideal

biogas digester type is dependent on its specific application and context

(including type of feedstock used as well as environmental and operating

conditions). Where low-cost is a key priority for household systems, simple

plug flow digesters are likely to be the preferred option, but where

robustness is sought fixed dome digesters may be ideal. Floating cover

digesters are suitable where a small-scale digester is required with a gas

output at constant pressure. CSTRs and fixed film digesters are suitable

commercial-scale systems where a high volumetric14 and stable biogas

production is preferred, as required for continuous electricity generation,

for example. While dry batch systems are still in early development in SSA,

14 Volumetric biogas production is the volume of biogas produced per digester volume per day (m3/m3/d)

106

future applications are recommended where the intention is to sell the

digestate as compostable fertiliser.

Table 3-3: Comparison of performance of six main types of biogas systems used to treat organic slurries and solid waste

Performance Parameter

Batch reactor

CSTR CAL Fixed dome digester

Floating cover digester

Plug flow digester (simple model)

Lifespan (years)

>10* >20* 20* 20 5-15 3-5

Construction time (days)

≤20* ≤20 ≤20* 2-20 0.5-18 1-2

Construction cost

Low High Low Low Medium Low

Pressure Varying Constant Constant Varying Constant Low & varying

Gas quality Varying High Low Low Low Low to medium

Operation difficulty

Low High Low Low Low Low

References [18, 55] [91, 122, 166, 232]

[26, 180-182]

[17, 18, 29, 63, 122, 134, 183, 228]

[10, 18, 29, 63, 122, 134]

[17, 29, 134]

*Estimate

3.2.9 Key priorities for biogas systems

Key priorities for biogas systems applicable to SSA households are

affordability, water access, ease of operation, and reliability. The

affordability of a biogas system is dependent on the income of the intended

user, the model of service provision applied, the costs of a biogas system,

and the value of the products (biogas and fertiliser slurry) to the user [17,

25, 158]. The main costs of a biogas system are the construction and

maintenance costs, costs of obtaining feedstock, and costs of preparing

feedstock for AD [158]. Construction costs can be minimised by using local

materials as has been done for some fixed dome, floating cover, and simple

plug flow models used in SSA [63]. The two main models of service

provision for biogas systems are ownership-based and fee-for-service

models [17]. In ownership-based models, the assets that provide the services

107

are owned by the individual customers where as in the fee-for-service

models the assets are owned by service provider or utility [17]. In SSA, the

ownership based models are commonly used. An exception is the fee-for-

service model used by a biogas company in Kenya, as mentioned in section

3.2.6, which allows users to buy gas ‘credit’ with their mobile phone [220].

The use of fee-for-service models for biogas systems can assist in making

the technology accessible to those with limited or no disposable income.

Modified fixed dome digesters such as the CAMARTEC SSD, which use less

water could motivate increased uptake in rural households. To assess the

ease of operation of a particular biogas system design the literacy and other

skills of the intended user needs to be considered [63]. The reliability of a

biogas system is influenced by the quality of the materials and construction.

Pre-fabricated digesters often have a greater reliability than on-site

constructed digester systems as they are subject to quality control, as well

as being designed and built by trained technicians [183]. Reliability is also

improved through the provision of follow-up services and training local

masons and technicians [63].

For commercial applications, a biogas system’s financial viability is of

primary importance. The current and anticipated energy costs can be used

to help assess the financial viability of biodigester installations. Renewable

Energy Feed-in Tariffs (REFiTs) can assist commercial facilities to establish

power purchase agreements, and receive loans and finance for the

installation of biogas plants for electricity production [233]. Currently

REFiTs are established in Ghana, Kenya, Mauritius, Namibia, Nigeria,

108

Rwanda, Tanzania, and Uganda, while Botswana and Ethiopia are

developing REFiT schemes [233-238]. In South Africa, its REFiT has been

replaced with the renewable energy independent power producer

programme (REIPPP) [239]. Thus, SSA is well placed to increase the use

and application of commercial scale biodigesters. For all scales of biogas

technology applications (commercial, community/institutional, and

household), commitment from the system owner/operator to properly

maintain and operate the system is essential for its continued long-term use.

3.3 Conclusions on biogas system design selection

Biogas systems are designed to provide the optimum conditions for the

production of biogas through the AD process. The production of biogas and

design of systems is influenced by a range of chemical and physical factors,

with the feedstock being the single most influential factor. The type of

feedstocks available in SSA and the associated biogas and energy production

potential will be discussed in the following chapter. A range of different

biogas system designs exist for commercial, community/institutional, and

household applications. Commercial-scale biogas systems in SSA are few in

number with selected examples of batch, CSTR, and fixed film digester

types. Household-scale and community/institutional systems are more

common in the region with variations of fixed dome, plug flow, and floating

cover digesters applied at these scales. In comparing the different types of

biogas systems based on past studies as well as their performance and

operational parameters, it is evident that the ideal biodigester type is

dependent on its specific application. Water supply and affordability are of

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particular importance in identifying suitable digester designs for household

and community-scale systems. In commercial systems, the intended use of

the generated biogas and digestate assists in selecting the most suitable

digester design. REFiTs, which are either established or being developed in

a number of SSA countries, increase the opportunities for financing

commercial biodigester systems for electricity generation. The experience

with biogas technology at all scales in SSA, although limited, presents

opportunities for increased uptake.

110

111

Chapter 4 Biogas feedstock

assessment for SSA: unlocking

the energy production potential

from organic waste Biogas feedstock assessment for SSA:

unlocking the energy production potential

from organic waste

“All the human and animal manure which the world wastes, if returned to

the land, instead of being thrown into the sea, would suffice to nourish the

world.”

– Victor Hugo

4.1 Biogas feedstock assessments in SSA

As mentioned in previous chapters, biogas dissemination in SSA has

focused on using cattle manure and ‘night soil’ as the main feedstocks,

although a wide range of feedstocks can be used in biogas systems. Biogas

feedstocks can be broadly categorised according to their source –

agricultural, municipal, and industrial [134, 240]. Municipal feedstocks

include sewage and OFMSW; agricultural feedstocks include animal

manure, crop residues, and energy crops; feedstocks from industry include

wastewater and residues from food and agro-processing of both animal and

plant origin [134, 240]. To date there has been limited research on the

biogas production potential of these biogas feedstocks in SSA. In 2007,

under the Biogas for Better Life Initiative, 18.5 million households were

estimated to have the potential to install cattle manure-fed biogas systems

based on the domestic livestock population, an applied cattle holding factor,

112

and having access to water sources [241]. Aside from this estimate, biogas

feedstock assessments have largely been limited to a country level, with

figures on biogas and methane potentials from various feedstocks (Table

4-1). With the exception of the feedstock assessment for Kenya on the

potential from food processing industries by Fisher et al. [191] and agro-

processing wastes by Nzila et al. [242], estimates have only been provided

on livestock manure and domestic sewage. This chapter will present a

broader assessment of the feedstocks available in SSA, and highlight the

untapped potential for energy generation from biogas technology.

Table 4-1: Previous studies on biogas feedstocks in SSA

Country Feedstock type Biogas potential

(million m3/y)

Methane potential

(million m3/y)

Ref.

Burkina Faso

livestock manure and domestic sewage

3100 - [63]

Kenya coffee production waste - 22.6 [191]

Kenya chicken manure - 4.4 [191]

Kenya cut flower residues - 1.5 [191]

Kenya instant tea production waste - 1.5 [191]

Kenya tea factory residues - 22 [242]

Kenya maize residues - 1134 [242]

Kenya seedcotton residues - 9 [242]

Kenya sisal waste - 45.4 [191]

Kenya sugar production waste - 9.1 [191]

Kenya sugarcane residues - 138 [242]

Kenya milk processing waste - 1.2 [191]

Kenya pineapple waste - 5.3 [191]

Kenya barley residues (from brewery) - 11 [242]

Kenya distillery stillage - 2.4 [191]

Kenya slaughterhouse wastewater - 0.1 [191]

Kenya (Nairobi)

MSW - 84.6 [191]

Senegal cattle and pig manure 547.5 - [243]

Uganda livestock manure 1000 - [228]

113

4.2 Agro-processing and food production feedstocks

4.2.1 Biogas and energy production potential from the livestock

industry

4.2.1.1 Livestock Manure

Aside from the estimate of cow dung available for household scale systems

conducted under the “Biogas for Better Life –An African Initiative” [241],

other comparable country-level feasibility assessments are available for

household digesters. For example, the potential number of household

digesters from selected assessments are 1.8 million in Tanzania (where

cattle manure is the feedstock), 175,000-400,000 in Senegal (where cattle

manure is the feedstock), 110,267 in Burkina Faso (where cattle, poultry,

piggery, and goat manure is the feedstock), and 216,000 in Uganda (where

cattle, pig, and chicken manure is the feedstock) [63, 228, 243, 244].

Broadening the potential from assessments based primarily on cattle

manure, the FAO provides country specific data on the methane emissions

from the management of livestock manure on their Food and Agriculture

Organization Corporate Statistical Database (FAOSTAT) website, referring

to the emissions from aerobic and anaerobic manure decomposition

processes in the capture, storage, treatment, and utilisation of manure

[245]. These emissions are calculated based on the statistics of animal

numbers reported to FAOSTAT and the Tier 1 IPCC 2006 Guidelines on

National GHG Inventories [245, 246]. Livestock in SSA based on FAO data,

include dairy and non-dairy cattle, asses, camels, chickens, ducks, goats,

horses, mules, pigs, sheep, swine, and turkeys. The total estimated methane

production potential from livestock manure based on 2012 FAO data for the

114

whole of SSA is 462,890 t/y, which is equivalent to 681 million m3/y and

7,056 GWhth/y using a density of 0.6797 kg/m3 at 15°C and 1.013 bar, and

assuming a methane energy content of 37.3 MJ/m3 [245, 247]. This total

methane production potential was calculated by summing the total methane

emissions from livestock manure for each SSA country provided on

FAOSTAT. The manure from this data can be assumed to be feasible for use

as feedstock in biodigesters as it already is collected. Energy production

potentials of using manure for biogas generation according to livestock type

for the four main regions of SSA are given in Figure 4-1. The energy

production potential from ducks, mules, pigs, and turkeys has been

excluded from the graph as they are insignificant. The amount of manure

that can be collected is dependent on the type of grazing and livestock

rearing practices. Manure collection is most feasible where zero-grazing or

night-stabling occurs. Traditionally, cattle have been commonly kept by

nomadic herdsmen but an increase in land use for agricultural productivity

has reduced the area of rangelands and resulted in more intensive livestock

husbandry such as night-stabling [248, 249]. The livestock production

systems applied in SSA differ between the various agro-ecological zones

with humid/subhumid regions being more likely to apply mixed farming

systems where livestock husbandry is combined with crop farming and

manure is collected for fertilising [248]. West and East African households

commonly keep domestic livestock including sheep and goats tethered

during the day and in small enclosures overnight while chickens are

increasingly kept in coops [249, 250].

115

Figure 4-1: Energy production potential from using livestock manure as feedstock in anaerobic digestion for each SSA region (calculated using 2012 data from FAOSTAT [245])

East Africa is the region with the largest potential for methane production

from livestock manure with a total of 330 million m3/y, followed by West

Africa with 239 million m3/y, equivalent to 3,419 GWhth/y and 2,475

GWhth/y of energy, respectively, as indicated by Figure 4-1. Non-dairy cattle

manure makes up around half of the potential in all regions except West

Africa, where non-dairy cattle manure contributes to a third of the energy

production potential. In East Africa, camel and dairy cattle manure also

contribute a significant amount to the regional energy production potential

at 12% and 15%, respectively. Goat, sheep, and swine manure make up a

significant portion of the energy production potential in West Africa at 17%,

0

200

400

600

800

1000

1200

1400

1600

1800

Central Africa East Africa SouthernAfrica

West Africa

En

erg

y p

rod

uct

ion

po

ten

tia

l (G

Wh

th/y

)

SSA Region

Energy production potential from livestock manure

Asses

Camels

Cattle, dairy

Cattle, non-dairy

Chickens

Goats

Horses

Sheep

Swine

116

12%, and 11%, respectively. Similarly, in Central Africa, goat and swine

manure make up a large portion to the total energy production potential,

11% and 15%, respectively. In Southern Africa, chicken and sheep manure

make a significant contribution of 13% each, to the total energy production

potential. The differences in the total energy production potential between

the SSA regions, in part, are due to the differences in the geographic areas

with Southern Africa being the smallest region, as well as the socio-

economic conditions and population of the countries within each region.

The countries with the estimated top energy production potential from

livestock manure for each region were Nigeria (817 GWhth/y), Ethiopia

(1,131 GWhth/y), South Africa (379 GWhth/y), and Chad (233 GWhth/y), for

West, East, Southern, and Central Africa, respectively. On a per capita basis,

the country with the highest methane production potential is Somalia (42.8

kWhth/y) based on 2012 population data from the World Bank as depicted

in Figure 4-2 [251].

117

Figure 4-2: Per capita energy production potential from livestock manure for SSA countries (excluding South Sudan) (calculated using 2012 data from FAOSTAT [245] and 2012 World Bank population data [252])

7.44

5.07

8.06

6.40

8.13

9.90

7.24

42.80

4.82

3.45

9.99

2.56

3.92

4.86

19.47

23.75

2.73

3.88

35.35

20.69

4.75

8.61

2.15

3.85

12.10

12.54

7.79

3.19

5.06

3.50

12.27

12.06

0.59

12.33

0.65

2.63

1.99

1.66

18.33

21.22

6.40

10.96

2.39

18.53

23.80

5.78

5.62

0.00 10.00 20.00 30.00 40.00 50.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Somalia

Sierra Leone

Seychelles

Senegal

Sao Tome and Principe

Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Equatorial Guinea

Djibouti

Democratic Rep. Congo

Côte d'Ivoire

Congo

Comoros

Chad

Central African Rep.

Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola

Energy production potential per capita (kWhth/y)

Per capita energy production potential from livestock manure (kWhth/y)

118

4.2.1.2 Livestock product waste

Livestock product waste such as eggs, hides, and skin, as well as skimmed

and whole milk also has significant potential for biogas production in SSA

with an estimated yield of 81.3 million m3/y of biogas, equivalent to 48.8

million m3/y of methane and 505 GWhth/y. Kenya was found to have the

highest biogas production potential of 17.2 million m3/y or 107 GWhth/y out

of all the SSA countries. Out of the four SSA regions, East Africa has the

highest biogas production potential of 38.2 million m3/y equivalent to 238

GWhth/y of energy (Figure 4-3). These biogas and methane production

potentials were estimated using 2009 FAO data on livestock primary

equivalent waste [253]. The biogas production potential (BPP) from waste

egg, whole milk, skimmed milk, and raw animal fat was calculated using

Equation 4-1, where m is mass of livestock product waste, DM is percentage

mass of DM content, oDM is the percentage of oDM, and BY is the biogas

yield (average volume of biogas that can be produced per unit mass of oDM

for specific feedstocks).

𝐵𝑃𝑃 (𝑚3 𝑦)⁄ = 𝑚 (𝑘𝑔 𝑦)⁄ × 𝐷𝑀 × 𝑜𝐷𝑀 × 𝐵𝑌(𝑚3 𝑘𝑔 𝑜𝐷𝑀)⁄ Equation 4-1

The DM, oDM, and BP for the different types of livestock product waste are

given in Table 4-2. To estimate the methane yield for these waste types it

was assumed that the biogas would contain 60% methane. The potential

methane production (MPP) for hides and skins was estimated using

Equation 4-2, where BMP is the biochemical methane potential for leather

fleshing given in Table 4-2. The total BPP and MPP for each SSA country

was calculated as the sum of the BPPs and MPPs of each livestock product

119

waste. The total BPP and MPP was then calculated by summing all the

country totals.

𝑀𝑃𝑃(𝑚3/𝑦) = 𝑚(𝑘𝑔/𝑦) × 𝐷𝑀 × 𝑜𝐷𝑀 × 𝐵𝑀𝑃(𝑚3 𝑘𝑔 𝑜𝐷𝑀)⁄ Equation 4-2

To estimate the biogas yield for hides and skin, it was assumed that the

content of methane in biogas from these sources was 33% by volume, the

same as that for leather fleshing [254]. Hides and skin are unlikely to be

suitable as a main feedstock as these feedstocks have a high nitrogen content

when used in biogas systems, and require a long retention time, as well as

mincing and homogenisation pre-treatments [254]. The highest biogas and

methane yields for livestock product waste were estimated to be either from

milk or eggs in all four SSA regions (Figure 4-3). Waste milk contributes to

69% and 77% of the energy production potential from livestock product

waste for Central and East Africa, respectively, while waste eggs provide

84% and 54% of the energy production potential for Southern and West

Africa. The contribution to the energy production potentials from raw

animal fats was minor and limited to West Africa, therefore it was excluded

from the graph in Figure 4-3. On a per capita basis, Mauritania has the

highest energy production potential of 2.79 kWhth/y, respectively (Figure

4-4).

120

Table 4-2: Average dry matter and organic dry matter content, biogas and methane yields by mass for livestock product waste

Livestock Waste Type

DM oDM Biogas yield (m3/ kg oDM)

Biochemical methane potential (m3 CH4/kg oDM)

Reference

Eggs 25% 92% 0.98 - [55]

Hides & skins 22% 81% - 0.08 [254, 255]

Whole milk 8% 92% 0.90 - [55]

Skimmed milk 8% 92% 0.70 - [55]

Raw animal fats 100% 100% 1.00 - [55]

Figure 4-3: Energy production potential from using livestock product waste as feedstock in AD for each SSA region (calculated using 2009 data from FAOSTAT [253])

0

20

40

60

80

100

120

140

160

180

200


West Africa

En

erg

y p

rod

uct

ion

po

ten

tia

l (G

Wh

th/y

)

SSA Region

Energy production potential from livestock product waste

Eggs

Hides & Skins

Milk, Skimmed

Milk, Whole

121

Figure 4-4: Per capita energy production potential from livestock product waste for SSA countries (excluding South Sudan) (calculated using 2009 data from FAOSTAT [253] and 2012 World Bank population data [252])

0.75

0.35

0.82

0.33

0.55

0.90

1.29

0.00

0.47

2.10

0.63

0.24

0.37

0.35

1.75

1.23

0.14

0.13

2.79

0.87

0.25

0.67

0.29

0.56

2.52

0.35

0.65

0.14

0.20

0.15

0.29

0.65

0.00

0.48

0.00

0.14

0.07

0.24

0.58

0.38

0.31

0.53

0.15

1.25

1.98

0.29

0.28

0.00 0.50 1.00 1.50 2.00 2.50 3.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Somalia

Sierra Leone

Seychelles

Senegal


Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Equatorial Guinea

Djibouti


Côte d'Ivoire

Congo

Comoros

Chad


Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola


Per capita energy production potential from livestock product waste (kWhth/y)

122

4.2.2 Biogas and energy production potential from the crop

farming industry

4.2.2.1 Crop residues normally burned

Crop residues that are normally burned, specifically maize, wheat, and rice

from paddies, are estimated to have the potential to produce a total of 15.6

billon m3/y of biogas and 9.35 billion m3/y of methane for the whole of SSA,

equivalent to 96.9 TWhth/y of energy, based on 2012 FAO data [256]. The

FAO data is given as the total tonnes of crop residues burned on-site, which

is the amount left over after considering the fraction of crop residues

removed from the field before burning due to animal consumption, decay in

the field, and use in other sectors [257]. Sugar cane crop residues are also

included in the FAO data, although the methane and biogas production

potential from this crop is not considered in this assessment due to the high

cellulose, hemicellulose, and lignin content making it unsuitable for AD

unless it is pre-treated and co-digested with easily degradable substrates

like manure [258-260]. The biogas production potential for the crop

residues was calculated by applying Equation 4-1 with the DM, oDM, and

biogas yield values of maize, rice, 4mm wheat straw given in Table 4-3. To

determine the methane production potential of maize and rice crop residues

it was assumed that 60% of volume of the biogas produced from these

sources would be methane, while for wheat a methane content by volume of

52% was assumed [55, 140]. According to these assumptions, East Africa

has the largest biogas and methane production potential for crop residues

normally burned of 7.3 billion m3/y and 4.3 billion m3/y, respectively;

equivalent to 44.9 TWhth/y with 93% of this energy potential coming from

123

maize residues (Figure 4-5). The SSA country with the highest biogas

production potential from crop residues (that are normally burned) is

Nigeria with an estimated 2.5 billion m3/y of biogas or 15.7 TWhth/y of

energy. Per capita, Malawi has the highest energy production potential of

286 kWhth/y (Figure 4-6). Given that 70% of agricultural production in SSA

is subsistence farming, much of the methane production potential from crop

residues can be attributed to rural households [261].

Table 4-3: Dry matter and organic dry matter content, biogas yield by mass, and methane content by volume for crop residues that are normally burned

Crop residue type

DM oDM Biogas yield (m3/ kg oDM)

CH₄ content by volume

Reference

Maize straw 86% 72% 0.7 60% (estimate) [55]

Rice straw 38% 83% 0.59 60% (estimate) [55]

Wheat straw (4mm)

91% 92% 0.41 52% [140]

Figure 4-5: Energy production potential from crop residues normally burned used as feedstock in AD for each SSA region (calculated using 2012 data from FAOSTAT [256])

0

5000

10000

15000

20000

25000

30000

35000

40000

45000


West Africa

En

erg

y p

rod

uct

ion

po

ten

tia

l (G

Wh

th/y

)

SSA Region

Energy production potential from crop waste normally burned

Maize

Rice, paddy

Wheat

124

Figure 4-6: Per capita energy production potential from crop waste that is normally burned for SSA countries (excluding South Sudan) (calculated using 2012 data from FAOSTAT [256] and 2012 World Bank population data [252])

178.17

198.80

85.17

282.62

239.95

153.37

168.93

22.32

80.43

0.00

35.45

19.63

66.43

93.56

3.07

45.33

170.68

0.28

21.76

124.32

285.61

81.20

36.92

132.35

139.72

57.48

166.75

114.51

63.96

44.49

72.18

15.50

0.00

0.03

63.90

54.07

8.95

28.11

69.94

55.69

130.81

172.15

34.36

142.58

111.18

255.77

70.33

0.00 50.00 100.00 150.00 200.00 250.00 300.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Somalia

Sierra Leone

Seychelles

Senegal


Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Equatorial Guinea

Djibouti


Côte d'Ivoire

Congo

Comoros

Chad


Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola


Per capita energy production potential from crop waste normally burned

125

4.2.2.2 Crop primary equivalent waste

The feedstock group with the largest energy production potential from

biogas in SSA is crop primary equivalent waste, with an estimated potential

of 9.1 billion m3/y of biogas equivalent to 6.7 billion m3/y of methane and

69.6 TWhth/y of energy, respectively. These estimates are based on 2013

data on crop primary equivalent waste [262]. It includes over thirty different

types of vegetables, fruit, nuts, and other food crop wastes that are lost at all

stages between production and the household (e.g. processing, storage, and

transportation) [263]. The list of crop primary equivalent waste, and

associated dry matter and organic dry matter content, biogas yields, and

methane content used to determine the potential yields, are given in Table

4-4. Equation 4-1 and Equation 4-2 were used to calculate the biogas and

methane production potentials where the BY and BMP were known. For the

crop wastes where the BMP was not known, the volume of potential biogas

that can be produced was multiplied by the methane content by volume in

the biogas giving the potential volume of methane. Similarly, where the BY

was not known the volume of methane that can be potentially produced was

divided by the estimated percent methane content by volume in biogas for

the particular feedstock type, giving the volume of biogas. For some

feedstock types the biogas production potential was calculated based on the

volume of biogas per tonne of fresh matter (FM). As can be seen in Figure

4-7, West Africa has the highest biogas production potential from crop

equivalent waste out of the four SSA regions with cassava, maize, and

pineapple waste contributing 25%, 16%, and 15% of the 39.9 TWhth/y energy

126

potential, respectively. Cassava also provides the energy production

potential (41.5%) for an individual crop waste type in Central Africa, while

maize waste provides the greatest energy production potential from biogas

(24%) in Eastern Africa, and orange and mandarin waste provides the

largest contribution in Southern Africa (53%). Nigeria has the largest biogas

production potential from crop primary equivalent waste out of all the SSA

countries, with a total of 2.7 billion m3/y, equivalent to 20.1 TWhth/y of

energy. The country with the highest per capita energy production potential

is Ghana at 375 kWhth/y (Figure 4-8). The actual energy production

potentials will be lower as it is unlikely that the full amount of these crop

wastes can be collected for use in biogas systems in SSA.

127

Table 4-4: Dry matter, organic dry matter, biogas and methane yields for crop wastes

Crop Type DM (%)

oDM (%)

Biogas yield (m3/ kg oDM)

% CH₄ in biogas by volume

Biochemical methane potential

(m3/kg oDM)

Ref.

Apples (pomace) 22% 98% 0.52 52% 0.27 [149]

Bananas 18% 85% 0.41 60%* 0.24 [55, 264]

Barley 31% 92% 0.76 63% 0.48 [264]

Beans (broad beans)

18% 91% 0.50 55% 0.28 [149]

Beverages, fermented (brewer's yeast, boiled)

10% 92% 0.66 62% 0.41 [149]

Cassava (pulp) 31% 98% 0.57 60%* 0.34 [148]

Cereals, Other (dry spent grain)

90%

95% 0.60 60%^ 0.36 [55]

Cocoa Beans (shells dried)

90%

92% 0.41 55% 0.23 [149]

Coconuts – incl. Copra (waste from coconut extraction)

89%

93% 0.66 55% 0.36 [149]

Fruits, spent 35% 93% 0.55 60%^ 0.33 [55]

Grapes (vine pressings)

- 93% 0.28 60%^ 0.17 [55, 147]

Groundnuts (in shell, bruised)

91% 94% 0.63 63% 0.40 [149]

Groundnuts (shelled, bruised)

89%

94% 0.66 63% 0.42 [149]

Lemons (pressings, %DM based on orange & mandarin values)

22% 97% 0.47 60%^ 0.28 [55, 147]

Maize (dry grains)

87% 98% 0.69 53% 0.37 [149]

Millet (sorghum bicolour)

21% 92% 0.56 51% 0.29 [149]

Molasses N/A N/A 0.315* 60% ^ 0.19* [55, 265]

Oats (grain, two-rowed)

87% 97% 0.60 52% 0.31 [149]

Onion waste 13% 95% 0.65 59% 0.38 [149, 266]

Oranges & mandarins (whole rotten fruit)

23% 93% 0.56 60% ^ 0.34 [147, 264]

128

Crop Type DM (%)

oDM (%)

Biogas yield (m3/ kg oDM)

% CH₄ in biogas by volume

Biochemical methane potential

(m3/kg oDM)

Ref.

Palm oil (mill effluent)

5% 90% 0.997 60% 0.60 [264]

Peas (garden pea pods seeds removed, %DM based on broad bean)

18% 92% 0.39 60% ^ 0.23 [55, 147]

Pineapples N/A 94% N/A 60% ^ 0.36 [264]

Plantains (based on banana values)

18% 85% 0.41 60% ^ 0.24 [264]

Potatoes (high starch)

26% 93% 0.73 51% 0.37 [149]

Pulses (in buds) 12% 86% 0.58 56% 0.32 [149]

Rice (husk) N/A N/A 0.05* 60%^ 0.03* [55, 267]

Rice (straw) 38%

83% 0.59 60%^ 0.35 [55]

Roots & tuber (stubble)

12% 87% 0.70 53% 0.37 [149]

Roots (consumables)

17% 87% 0.65 52% 0.34 [149]

Sorghum (bicolour)

21% 92% 0.56 51% 0.29 [149]

Soybeans (peelings)

90%

95% 0.60 53% 0.32 [149]

Sunflower seeds 88%

97% 0.70 64% 0.45 [149]

Sweet potatoes (peel)

27% 87% 0.46 46% 0.21 [268]

Tomatoes 95% 95% 0.30 60%^ 0.18 [55, 147,

264]

Vegetables waste 15% 76% 0.50 56% 0.28 [149]

Wheat (shredded) 87% 98% 0.70 53% 0.37 [149]

Yams (wild cocoyam peel)

27% 85% 0.36 55% 0.20 [268]

*per kg of fresh matter ^estimate

129

Figure 4-7: Energy production potential from crop equivalent waste used as feedstock in AD for each SSA region (calculated using 2013 data from FAOSTAT [262])

0 2000 4000 6000 8000 10000 12000

CentralAfrica

East Africa

SouthernAfrica

West Africa

Energy production potential (GWhth/y)

SSA Region

Energy production potential from crop primary equivalent waste

Yams

Wheat

Vegetables, Other

Tomatoes

Sweet Potatoes

Sunflowerseed

Soyabeans

Sorghum

Roots, Other

Roots & Tuber Dry equiv.

Rice (Paddy)

Rice (Milled)

Pulses, Other

Potatoes

Plantains

Pineapples

Peas

Palm Oil

Oranges, Mandarines

Onions

Oats

Molasses

Millet

Maize

Lemons, Limes

Groundnuts (Shelled)

Groundnuts (in Shell)

Grapes

Fruits, Other

Coconuts - Incl. Copra

Cocoa Beans

Cereals, Other

Cassava

Beverages, Fermented

Beans

Barley

Bananas

Apples

130

Figure 4-8: Per capita energy production potential from crop primary equivalent waste for SSA countries (excluding the Democratic Republic of the Congo, Equatorial Guinea, Somalia, and South Sudan) (calculated using 2013 data from FAOSTAT [262] and 2012 World Bank population data [252])

59.33

30.39

60.21

107.56

58.16

239.01

101.42

32.34

20.37

69.08

102.03

44.96

119.53

20.47

39.45

40.94

46.16

23.74

42.56

122.45

87.71

40.02

92.08

51.76

25.17

144.04

375.02

38.43

106.19

31.42

12.51

105.28

81.72

48.02

28.64

49.60

76.60

100.27

42.29

15.19

29.88

28.45

296.29

134.44

0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Sierra Leone

Seychelles

Senegal


Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Djibouti

Côte d'Ivoire

Congo

Comoros

Chad


Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola


Per capita energy production potential from crop primary equivalent waste

131

4.3 Municipal feedstocks

4.3.1 Methane and energy production potential from domestic

wastewater

Applying AD for the treatment of sewage not only has significant potential

for energy production in SSA, but also for improving sanitation. In urban

centres wastewater is often treated in open sewers, which increases the risk

of contamination of water sources along with emitting GHGs [269]. It is

estimated that over 80% of the wastewater from large cities in SSA is

released into the soil either through direct discharge into rivers and lakes or

through on-site sanitation systems [270]. Eutrophication (increase of plant

biomass in water bodies as a result of an enhanced input of nutrients) is

common in fresh water bodies in inland SSA due to wastewaters from

sewage and industries in urban areas being discharged without treatment

[270]. In 2012, 34% of the total SSA population was estimated to have access

to improved sanitation facilities, with 40% of the population in urban

regions, and 22% of the rural population [251]. Improved sanitation

facilities include flush/pour flush systems to a piped sewer, septic tank, or

pit latrine, as well as ventilated improved pit (VIP) latrine, pit latrine with

slab, and composting toilet [251]. The majority of the urban SSA population

with access to improved facilities, use on-site sanitation systems that with

low wastewater volumes and high nutrient concentrations such as pit

latrines or septic tanks, while in rural areas pit latrines and open defecation

is common [269, 270].

132

The methane production potential from using biogas systems to treat

wastewater was estimated using the guidelines and equation from the

Intergovernmental Panel on Climate Change (IPCC) for estimating the

methane emissions from domestic wastewater [271]. This approach was also

applied by Salomon and Silva Lora [31] to estimate the electric energy

generating potential from biogas produced from domestic sewage in Brazil.

Equation 4-3 was applied to calculate the estimated methane potential from

wastewater (MPww) where Ui is the fraction of the population that is either

urban or rural, Ti is the fraction of the urban or rural population that has

improved sanitation facilities, Pop is the total population, BOD is the

country-specific per capita biological oxygen demand (BOD) in a given year,

BO is the maximum methane producing capacity, and MCF is the methane

correction factor. Data on the urban and rural population in 2012 as well as

access to improved sanitation was obtained from the World Bank [251]. The

population with improved sanitation facilities was chosen exclusively to

derive this estimate methane production potential as the sewage from these

facilities is most likely to be feasibly collected for AD. As the country specific

per capita BOD and BO was not available for SSA countries, the estimated

per capita BOD value for Africa of 0.037 kg/day and the default BO value of

0.6 kg CH4/kg BOD was used for each SSA country [271]. The MCF value

for anaerobic reactors of 0.8 given in [271] was used. The mass of methane

was then converted to volume by using a density of 0.6797 kg/m3 at 15°C

and 1.013 bar, as was described in Section 4.2.1.1 [247].

133

𝑀𝑃𝑤𝑤(𝑘𝑔 𝑦)⁄ = [ ∑ (𝑈𝑖 × 𝑇𝑖𝑖=𝑢𝑟𝑏𝑎𝑛, 𝑟𝑢𝑟𝑎𝑙

× 𝐵𝑂(𝑘𝑔 𝐶𝐻4 𝑘𝑔 𝐵𝑂𝐷)⁄ × 𝑀𝐶𝐹)] × (𝑃𝑜𝑝

× 𝐵𝑂𝐷(𝑘𝑔 𝑝𝑒𝑟𝑠𝑜𝑛 𝑑⁄⁄ ) × 365(𝑑𝑎𝑦𝑠 𝑦)⁄

Equation 4-3 (adapted from

[271])

The total estimated methane production potential from domestic

wastewater is 2.4 billion m3/y. The urban and rural populations contributed

51% and 49% to the total, respectively. This energy production potential can

best be realised through community-scale, institutional, or commercial-

scale AD treatment systems in dense urban or regional centres, and

household-scale biogas systems in rural areas with low population densities.

The region with the highest methane production potential from domestic

sewage was East Africa with a total of 869 million m3/y, with the rural

population contributing 66% of this total (Figure 4-9). The country with the

largest potential for methane production from domestic wastewater is

Nigeria with at total methane production potential of 480 million m3/y

equivalent to 5.0 TWhth/y of energy with sewage from the rural population,

making up 51% of the total. In 2012, only 33% of the urban population and

27% of the rural population in Nigeria have access to improved sanitation

systems. The methane production potential for Nigeria could be

significantly greater if more improved sanitation facilities were

implemented and connected to a biogas system. Per capita, the SSA country

with largest energy production potential from domestic wastewater is

Seychelles with 97.2 kWhth/y (Figure 4-10) as it also has the largest portion

of both its urban and rural population with access to improved sanitation

134

facilities (98.4%). The actual (recoverable) energy production potential

from domestic wastewater is likely to be lower given that these estimates are

based on the maximum methane production capacity per BOD (BO).

Figure 4-9: Estimated energy production potential from domestic wastewater use as feedstock in AD for each SSA region (calculated using 2012 World Bank population data on access to improved sanitation facilities [251])

0

1000

2000

3000

4000

5000

6000

7000

East Africa Central Africa Southern Africa West Africa

En

erg

y p

rod

uct

ion

po

ten

tia

l (G

Wh

th/y

)

SSA Region

Energy production potential from domestic wastewater

Urban region Rural region

135

Figure 4-10: Per capita energy production potential from domestic sewage for SSA countries (excluding South Sudan) (calculated using 2012 World Bank population data on access to improved sanitation facilities [251])

36.8742.74

18.3511.39

13.8956.84

63.9523.45

12.7197.23

45.4733.96

58.4229.54

10.0432.72

19.6191.90

37.7623.07

39.2211.48

15.9429.0129.31

19.9918.66

14.2258.05

40.9223.97

15.0874.09

46.7227.25

21.4014.40

33.8011.87

21.2444.65

67.3646.93

18.4361.65

18.1748.51

0.00 20.00 40.00 60.00 80.00 100.00 120.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Somalia

Sierra Leone

Seychelles

Senegal


Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Equatorial Guinea

Djibouti


Côte d'Ivoire

Congo

Comoros

Chad


Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola


Per capita energy production potential from domestic wastewater

136

4.3.2 Methane and energy production potential from municipal

solid waste

The dominant disposal method of MSW in SSA is open dumping, with an

average of 56% of MSW consisting of biodegradable materials [269, 272].

The provision of adequate urban waste management has been difficult and

is lacking in many SSA cities due to restricted funding to public services,

lack of technical and human resources, as well as a large number of residents

unable to contribute to the costs of waste management [272]. Current MSW

disposal methods in SSA have led to significant GHG emissions due to the

large organic component, and uncontrolled open dump sites directly release

methane gas [272]. AD has been recognised as a suitable process for treating

MSW in SSA, with its main advantage over composting being energy

recovery and smaller land area requirements [273]. The main disadvantage

of biogas systems treating MSW, however, is a greater technical complexity

and financial investment required relative to composting [273]. This

research estimates the methane production potential from MSW in SSA

available for biogas systems.

Accurate data on waste management is not available for many SSA

countries. To estimate the organic fraction of MSW in SSA and the methane

that could be produced from it, MSW figures between 2009 and 2010 were

used from studies in urban centres of Ethiopia, Namibia, Tanzania, South

Africa, and Nigeria, along with data collected in Mauritius and Botswana

from at least 10% of the population [272, 274, 275]. Waste generation rates

have been found to be influenced by the gross domestic product (GDP), with

waste generation increasing in lower income countries as their GDP

137

increases [275]. The study by Couth and Trois [272] found that the waste

generation in SSA cities are clearly linked with the GDP of the country, but

no direct links were evident with the waste composition. Based on these

findings, a per capita GDP range was assigned to each of the seven SSA

countries, based on 2012 population and GDP data from the World Bank

[276]. The per capita waste generation for each SSA country was then

approximated to be the same for all countries that fall within the GDP

ranges given in Table 4-5. The regional average of 56% for the OFMSW was

used for all SSA countries where no data was available on the proportion of

the generated waste that is biodegradable. Since most of the waste

generation data was collected in urban centres, the per capita organic waste

generation rate was assumed to be only applicable to the urban SSA

population. This is a reasonable assumption to make in estimating the

annual organic waste generation for each SSA country, as treatment of the

OFMSW in biogas systems is likely to be the most feasible in urban areas.

To calculate the potential methane that can be produced from the OFMSW,

Equation 4-2 was used with the average BMP of 360 L CH4/kg oDM, DM of

40%, and oDM of 82.5% [134, 277, 278]. A total methane generation of 6.8

billion m3/y, equivalent to 70.7 TWhth/y, was estimated for the whole of

SSA. As can be seen in Figure 4-11, West Africa has the largest methane

production potential from OFMSW for the urban population with a total of

2.5 billion m3/y, equivalent to 25.9 TWhth/y, which is to be expected given

it has the largest population, followed closely by East Africa with an energy

production potential of 25.5 TWhth/y. South Africa is the country with the

largest methane production potential of 0.9 billion m3/y from the OFMSW,

138

equivalent to 9.7 TWhth/y of energy, attributable to its large urban

population and relatively high GDP when compared with other populous

countries like Nigeria. On a per capita basis, Gabon has the greatest energy

production potential from the OFMSW of 282.5 kWhth/y, as evident in

Figure 4-12, with its urban population making up 86% of the total

population, the highest in SSA.

Table 4-5: Per capita GDP, GDP ranges, waste generation, and the organic fraction of MSW for selected SSA countries used to estimate the methane potential from the organic fraction of MSW

City/ Country

GDP per capita

(current US$/y)

GDP per capita

range min (current US$/y)

GDP per capita range

max (current US$/y)

Waste generation per capita

(kg/y)

% organic

Ref.

Mauritius 7,587 7,501 20,000 475 N/A [275]

Botswana 6,980 5,501 7,000 120 N/A [275]

Ethiopia1 337 0.00 450 64 55% [272]

Namibia2 5,113 3,001 5,500 242 47% [272]

Tanzania3 510 451 1,500 531 65% [272]

South Africa4

7,176 7,001 7,500 426 43% [272]

Nigeria5 2,311 1,501 3,000 202 49% [274]

1. Based on data for Addis Ababa and Arba Minch

2. Based on data for Windhoek

3. Based on data for Arusha

4. Based on data for Cape Town and Durban

5. Based on data for Lagos, Kano, Ibadan, Kaduna, Port Harcourt, Makurdi, Onitsha, Nsukka, and Abuja

139

Figure 4-11: Estimated energy potential of the organic fraction of MSW as feedstock in AD from the urban population of each SSA region (calculated using 2012 World Bank population and GDP data [276] and waste generation rates from [272, 274, 275])

0

5000

10000

15000

20000

25000

30000

Central Africa East Africa Southern Africa West Africa

En

erg

y p

rod

uct

ion

po

ten

tia

l (G

Wh

th/y

)

SSA Region

Energy production potential from OFMSW

140

Figure 4-12: Per capita energy production potential from MSW for SSA countries (excluding South Sudan) (calculated using 2012 World Bank population and GDP data [276] and waste generation rates from [272, 274, 275])

120.1855.22

107.9555.32

163.5429.92

185.58139.65142.33

173.07156.59

231.5694.78

54.687.96

61.13114.99

131.37212.29

137.557.00

14.7021.49

94.2989.20

171.7515.83

72.63211.22

282.488.04

78.15129.07

107.5518.13

190.48106.93

102.5981.00

17.38192.81

105.744.95

100.0947.08

156.1769.56

0.00 50.00 100.00 150.00 200.00 250.00 300.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Somalia

Sierra Leone

Seychelles

Senegal


Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Equatorial Guinea

Djibouti


Côte d'Ivoire

Congo

Comoros

Chad


Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola


Per capita energy production potential from the organic fraction of MSW

141

4.4 Summary of feedstock assessment for SSA

The total methane production potential from the feedstocks available in SSA

(excluding South Sudan) is estimated to be 26.1 billion m3/y, equivalent to

270 TWhth/y of heat energy. Crop waste normally burned makes up the

greatest portion of this potential (36%), and also presents the greatest

potential on a per capita basis evident in Figure 4-13 and Figure 4-14,

respectively. This highlights the importance of encouraging more rural

households to take advantage of this organic waste resource to improve local

energy supply. The country with the highest per capita methane production

potential for the combined total of all the feedstock types covered in this

assessment is Benin, with a potential of 732 kWhth/y attributable mainly to

high potentials for crop residues normally burned and crop primary

equivalent waste (Figure 4-15). Biogas technology has been identified as

having the potential to help reduce the demand for imported energy sources

and preserve forests in the country [279]. Data on crop primary equivalent

waste was not available for the Democratic Republic of the Congo,

Equatorial Guinea, and Somalia. Therefore, the per capita methane

potential for these countries is likely to be higher than the plot depicts. The

results from this feedstock analysis provide an indication on the scale of

biogas technology dissemination that may be most suitable in different

countries and regions of SSA. In Southern Africa, for example, there is great

potential for increasing renewable energy production and improving solid

waste management in urban communities through treating OFMSW in

biodigesters, as indicated by the net per capita energy production potential

plot in Figure 4-15. On a country level, the highest energy production

142

potential from OFMSW is in Gabon, indicating that community or

commercial biodigesters to treat this waste may be suitable. Significant

untapped feedstocks exist from SSA agro-processing and food production

industries that could be utilised in commercial-scale biogas systems,

particularly in West Africa, with Ghana being estimated to have the greatest

energy production potential from these feedstocks. Malawi, on the other

hand, has a large potential for rural household-scale biogas systems based

on its high per capita energy potential from crop waste normally burned of

286 kWhth/y, indicating suitability for rural household biodigesters. This

feedstock assessment, however, is preliminary as further research and field

testing in each SSA region is recommended to determine the availability of

the feedstocks and their specific biogas and biomethane production

potentials. Using BMP values that have been carefully measured within the

SSA region would enable more accurate figures of the energy production

potentials of these feedstocks to be calculated. It is likely that the present

estimated biogas production potentials are optimistic for some of the

feedstocks, particularly domestic wastewater. The impact of the feedstock

characteristics on the design and suitability of different types of biogas

systems will become further apparent in the following chapter on the

development of the optimal biogas system design model.

143

Figure 4-13: Total energy production potential based on the assessment of feedstocks suitable for AD in each SSA region

Figure 4-14: Per capita total energy production potential based on the assessment of feedstocks suitable for AD in each SSA region

WestAfrica

EastAfrica

SouthernAfrica

CentralAfrica

Livestock manure 2,475 3,419 504 658

Crop waste normallyburned

32,156 44,894 9,644 10,180

Domestic wastewater 8,201 8,932 3,683 4,423

Organic fraction of MSW 25,858 25,483 10,185 9,194

Crop primary equivalentwaste

39,873 17,176 5,943 6,602

Livestock product waste 168 238 77 23

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

To

tal

ener

gy

po

ten

tia

l (G

Wh

th/y

)

WestAfrica

EastAfrica

Southern Africa

CentralAfrica

Livestock manure 7.60 9.75 8.39 4.74

Crop waste normallyburned

98.80 128.03 160.59 73.33

Domestic wastewater 25.20 25.47 61.33 31.86

Organic fraction of MSW 79.45 72.67 169.59 66.23

Crop primary equivalentwaste

122.51 48.98 98.96 47.55

Livestock product waste 0.52 0.68 1.28 0.16

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

180.00

To

tal

ener

gy

pro

du

ctio

n p

ote

nti

al

per

ca

pit

a (

kW

h/y

)

144

Figure 4-15: Per capita net energy production potential based on feedstock assessment for AD for each SSA country (excluding South Sudan) (calculated using 2012 World Bank population data [252])

402.73

332.57

280.57

463.61

484.22

489.93

528.40

228.22

273.09

296.22

317.21

389.98

268.87

302.51

62.74

203.62

349.10

273.72

333.70

349.05

459.26

204.37

116.80

352.13

324.60

287.27

353.72

579.71

376.92

435.42

148.16

133.96

296.54

272.39

109.93

350.44

180.36

195.04

231.31

192.51

475.25

399.02

103.98

310.77

274.13

732.47

328.74

0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00

Zimbabwe

Zambia

Uganda

Togo

Tanzania

Swaziland

South Africa

Somalia

Sierra Leone

Seychelles

Senegal


Rwanda

Nigeria

Niger

Namibia

Mozambique

Mauritius

Mauritania

Mali

Malawi

Madagascar

Liberia

Lesotho

Kenya

Guinea-Bissau

Guinea

Ghana

Gambia

Gabon

Ethiopia

Eritrea

Equatorial Guinea

Djibouti


Côte d'Ivoire

Congo

Comoros

Chad


Cameroon

Cabo Verde

Burundi

Burkina Faso

Botswana

Benin

Angola


Per capita net energy production potential

145

Chapter 5 Development of the

Biogas System Design Model

Development of the Biogas System Design

Model

“The most sustainable way is to not make things. The second most

sustainable way is to make something very useful, to solve a problem that

hasn’t been solved.”

– Thomas Sigsgaard

This chapter outlines the development of the optimal biogas system design

model (OBSDM). The development of this model is the core objective of this

PhD thesis. The model sets out to create a synergy between what is

technically feasible and what is important to the intended user in the

function and design of biogas systems in the SSA context. It addresses the

gap in appropriate biogas system designs required to help overcome the

barriers to biogas dissemination in the SSA region discussed in Chapter 2.

The model is intended to be used as a decision-making tool, which increases

awareness of the technology’s potential for different applications in SSA.

The optimal design is identified based on user defined inputs related to the

essential parameters that influence biogas production, digester design, the

system’s sustainability, and the biogas technologies applicable to SSA

presented in Chapter 3. A range of feedstocks are considered in the model

based on those available for biogas production in SSA as identified in

Chapter 4. Based on these previous three chapters, the model consists of five

146

different input categories and one main output section. The input sections

are energy demand, feedstock, location, economics, and priorities. The

output section presents the design details of the recommended optimal

biogas systems. Each key component of the model will be described in the

following sections. The final section of this chapter describes the method

and results for preliminary testing of the model with case studies from rural

households in Cameroon and Kenya, as well as the limitations of the

OBSDM.

5.1 Interacting factors in the design of biogas systems

To assess and identify the optimum biogas digester design, four interacting

factors need to be considered: the amount and nature of feedstock available

on site; the energy demand and intended use of the system; the conditions

at the proposed installation site, and; the conditions and priorities of the

intended user [280]. The different aspects of these factors to consider in the

design of a biogas system is summarised in Figure 5-1. In SSA, the amount

of water available for use in a biogas system is a crucial factor (particularly

where there are technical and financial constraints on the type of digester

that can be installed), with 40% of the population living in water scarce

environments [281]. The four interacting factors make up the first four

input sections to the OBDM, with the final input section consisting of the

sustainability criteria, which are to be rated according to their importance

to the intended user. This final input section provides the weighting to the

147

sustainability criteria, thereby impacting on how the feasible biogas system

designs are ranked in the model.

5.2 Review of existing biogas models and design tools

The tools and models that are currently available and applicable to the

design and assessment of biogas systems can be broadly categorised into

four main types according to their analysis approach:

• Technical and economic assessment based on user-defined feedstock

supply;

• Economic feasibility assessment and planning considering

geographic location;

• AD process kinetic analysis, and;

• Multi-criteria feasibility assessment of AD technologies (examples

for each are listed in Table 5-1).

The tools and models that focus largely on the technical and economic

viability of biogas systems based on the feedstock supply often require the

calculation of costs based on detailed user inputs on installation and

operation costs, or limit the assessment to one particular biogas system type

(often for agricultural, farm-scale applications). Furthermore, the majority

of these type of tools are only applicable to the European and North

American context.

148

Figure 5-1: Factors influencing the design of biogas systems

149

For developing regions, the biogas calculation tool from the Nepalese

Alternative Energy Promotion Centre (AEPC) is a unique example, enabling

plant designers to conduct technical and financial assessments for biogas

projects in Nepal [282]. The tool is intended to be used for institutional,

community, commercial, or waste-to-energy applications where most of the

design parameters are already chosen and entered as inputs, including: the

biogas plant type, biogas plant costs (installation, operation and

maintenance), feedstock supply, and application of the generated gas. Based

on the inputs, it provides recommendation on whether it is cost-effective to

use biogas for onsite electricity generation to replace mains electricity or for

load shedding [282].

Table 5-1: Examples of existing models and tools applicable to the design and assessment of biogas systems

Type of analysis Examples of existing models and tools References

Technical and economic assessment based on user-defined feedstock supply

• Investment decision tool (IDT)

• Renewable Energy Concepts Biogas calculator

• KTBL Wirtschaftlichkeitsrechner Biogas

• AEPC Nepal Biogas Calculation tool

[282-285]

Economic feasibility assessment and planning considering geographic location

• Iowa biogas assessment model (IBAM)

• Mixed integer linear programming (MILP) model for biomass to energy supply chains

• Fuzzy multiobjective MILP model for design and management of anaerobic digestion based biomass to energy supply chains

[286-289]

Anaerobic digestion process kinetic analysis

• IWA Anaerobic Digestion Model No 1 (ADM1)

• Modified Gompertz model

• Logistic model

• Explicit temperature-based model for anaerobic digestion (using cardinal temperature model)

[290-294]

Multi-criteria feasibility assessment of anaerobic digestion technologies

• Feasibility assessment tool for urban anaerobic digestion in developing countries

• Multi criteria analysis (MCA) tools (Super Decisions, DecideIT, Decision Lab, NAIADE)

[152, 295, 296]

Economic viability is also a key objective for the models that include

consideration of the geographic location of potential feedstocks and biogas

150

plants, such as the Iowa biogas assessment model (IBAM) and fuzzy

multiobjective mixed integer linear programming (MILP) model [289, 297].

IBAM uses an online calculation spreadsheet along with the geographical

information system (GIS) to assess the potential of biogas projects based on

feedstock sources available in the state of Iowa in the USA [297]. The fuzzy

multiobjective MILP model developed by Balaman and Selim [289] uses

environmental and economic objectives to design and manage AD based

biomass to energy supply chains. The two models are applicable to

commercial and agricultural biogas systems in the USA and Europe.

Kinetic analysis models, including the IWA Anaerobic Digestion Model No

1 (ADM1), logistic model, and the explicit temperature-based model are

used for optimising operating conditions through modelling the complex

biological process dynamics of AD [298]. ADM1 is a generic AD model that

provides a useful framework for process design and dynamic simulations,

albeit with a large number of parameters [298]. The Logistic model presents

a simplified approach to model the impact of the moisture level on the

specific growth rate of the biogas forming population, and the amount of

accessible organic substrate [293]. Similarly, the explicit temperature-based

model is a simplified mechanistic model, which uses the cardinal

temperature model function to describe the temperature dependency on

anaerobic digester performance [294]. The detailed analysis on the

anaerobic digester process dynamics provided by these models are likely to

be superfluous in determining the biogas potential of household scale biogas

151

systems in SSA, which are usually a simple design without active heating or

process control.

Consideration of non-technical factors in feasibility assessments of biogas

systems addresses the discrepancy between the large implementation

potential for the technology in developing countries (based on the available

resources and favourable climate) and the comparatively small number of

successful operating systems [152]. It is on this premise that the feasibility

assessment tool for urban AD in developing countries was developed [152].

The tool is to be used by an expert to assess the feasibility of a proposed AD

project by providing a systematic analysis of strengths and weaknesses of

the proposed project [152]. Its feasibility assessment categories consist of

technical-operational, environmental, economic-financial, socio-cultural,

and institutional. The tool does not provide design parameters but rather

assess the sustainability of a proposed AD system with the potential for the

tool to be modified to evaluate the sustainability of an existing AD project

[152]. Similarly, multi criteria analysis (MCA) tools can be used to assess the

sustainability of bioenergy systems, often involving the analysis and

comparison of different scenarios on their performance for a given set of

criteria [296]. MCA tools can include qualitative and quantitative analysis,

and involve both stakeholders and technical experts in the weighting and

selection of criteria, as well as ranking of alternatives [296].

The above mentioned models and tools focus on different aspects of AD

technology with an emphasis on large-scale systems. Due to the difficulty in

translating these models and tools to assist in identifying suitable biogas

152

system designs for a range of scales and applications, particularly smaller,

household-scale systems, the OBSDM was developed. The OBSDM is

intended to provide a holistic first assessment of the biogas technologies

available for a wide range of applications specific to the SSA context,

including household- and community-scale plants. NGOs, government

entities and other stakeholders in the SSA biogas industry can use the model

to carry out initial assessments on the type of biogas technologies that are

suitable for specific applications. The OBSDM uses internal databases on

different biogas technologies, feedstocks, country-specific climate data,

construction materials to minimise user inputs, with the possibility of

altering the internal data as required. Details on the inputs, internal

databases, design, and decision-making approach applied by the model, as

well as the outputs are explained in the subsequent sections.

5.3 OBSDM Inputs

5.3.1 Energy demand

The energy demand section (Figure 5-2) is the first input section in the

OBSDM, and stipulates the intended purpose of the biogas system.

Estimating the energy demand of the intended user is an ideal starting point

when advising on biogas installations [142]. The user is given one or more

of energy options to choose from for the intended use of the biogas system:

cooking gas; lighting, and; electricity. Lighting has been listed as a separate

energy option than electricity as biogas lamps are commonly used in

domestic biogas systems. Although biogas lamps are not as efficient as

electric light globes, they provide low-cost lighting for SSA households that

153

previously were using kerosene lamps or had no lighting [118]. Waste

management has also been included as system use option to highlight to the

user the possible functions of the system most relevant to their situation to

tailor the design accordingly, though waste management will be an inherent

function of any biogas system that uses organic waste as feedstock. The user

is required to specify the number of units of each particular energy

application, specifically the number of cooking stoves, number of lamps,

and any electrical loads. Cooking requirements can be entered either

according to the number of cookstove and cooking hours, or if that is

unknown, the number of people for which cooked meals are required each

day (at a rate of 2 meals per person per day), as shown in Figure 5-3. The

electrical load, if applicable, is determined by entering the type of electrical

appliances that will be used along with their rating (in W), number of hours,

and time of use (morning – 5.30am to 11.29am, midday – 11.30am to

1.29pm, afternoon – 1.30pm to 5.29pm, evening – 5.30pm to 9.59pm, or

late night/early morning – 10.00pm to 5.29am). This information is used

to calculate the total amount of power required at each time of use interval,

and thereby the maximum amount of power required at any given time

throughout the day (in kW). The daily amount of electrical energy

anticipated to be consumed is the sum of the electricity consumption of each

appliance (product of power rating and hours of use in kWh). These energy

applications are applicable to households and are not exhaustive, thereby

additional applications and biogas appliances can be added to the model in

the future, e.g. biogas refrigerators, incubators, milk chillers etc. [145, 299,

300]. Based on this input information, the total daily volume of biogas

154

required in m³ and the total daily energy required in kWh is estimated using

the biogas and power consumption rates given in Table 5-2. For example, a

household intending to use biogas for cooking with a known number of

cookstoves and cooking hours, the daily biogas and energy demand, Bd and

Ed, respectively, would be calculated using the following equations:

𝐵𝑑(𝑚3 𝑑⁄ ) =

𝑛𝑐𝑜𝑜𝑘𝑠𝑡𝑜𝑣𝑒𝑠 × 𝑡𝑐(ℎ 𝑑⁄ ) × 𝐵𝑐(𝐿 ℎ⁄ )

1000(𝐿 𝑚3⁄ ) Equation 5-1

𝐸𝑑(𝑘𝑊ℎ 𝑑⁄ ) = 𝑛𝑐𝑜𝑜𝑘𝑠𝑡𝑜𝑣𝑒𝑠 × 𝑡𝑐(ℎ 𝑑⁄ ) × 𝑃𝑐(𝑘𝑊)

Equation 5-2

Where ncookstoves is the number of cookstoves, tc is the number of cooking

hours per day, Bc is the biogas consumption rate of the cookstove and Pc is

the power consumption of the cookstove.

Table 5-2: Estimated power consumption of household energy applications

Appliance type Biogas consumption (L/h)

Power consumption (kW)

References

Household burner (cooking stove) 461.3* 3.35* [145, 222, 299, 301]

Gas lamp, equivalent to 60W bulb 161.3* 0.89 [145, 299, 301]

1kWh electricity generation in biogas/diesel engine (based on an efficiency of 22.9%**)

988.4* 4.37 [299, 301-304]

*Average values from references **Alternatively specific electrical efficiency can be used by entering it by the user in the input

155

Figure 5-2: Energy demand input section of the OBSDM

Input cell Output/warning cell

1.1 System intended use

Check all that apply:

Cooking gas

Lighting

Electricity

Waste management (incl. toilet connection)

1.2 Application

1.2.1 Cooking requirements

Choose your input method:

No. of people in household

No. of cookstoves 1

No. of hrs cooking gas required/stove/day 3.53

Please state for how many people meals intend to be

cooked for using the biogas system per day (based on

an average of 2 meals/per person/day)

5

1.2.2 Lighting requirements

No. of lamps 0

No. of hrs lighting required/lamp/day 0

1.2.3 Electricity requirements

Choose as many as apply from the list below:

ApplianceRating

(W)

No. of hours of

use (h/day)Time of use

Electricity

consumption

(kWh)

0

0

0

0

0

0

0

Total consumption (kWh) 0

Maximum amount of electric

power required at any given

time (kW)

0

No. of hrs electricity required

(h/day)0

Electrical conversion efficiency (%) (default 22.9%)

Total daily volume of biogas required (m³/day) 1.50

Total daily energy required (kWh) 9.58

1.3 Current energy use and costs

Country Rwanda

Currency for country RWF

Select alternative currency if required:

Currency used for costs RWF

Use default currency exchange rate Yes

No

Enter exchange rate to USD (1=___RWF) 811.4

Exchange rate to USD (1=___RWF) used in tool 811.4

Fuel type

Amount of fuel used per day 60.33 kg 0 L 0 kg

Time spent collecting/preparing fuel 37 min 0 min 0 h

Cost per month 5000 RWF/month 0 RWF/month 0 RWF/month

Annual energy costs 60000 RWF/y

Annual consumption (kWh/y) 83922.38 kWh/y

Costs per kWh 0.71 RWF/kWh

Hours spent preparing current energy sources per year 225.08 h/y

Greenhouse gas emissions per year 367.52 t CO₂-e/y

Instruction cell

firewood kerosene

Cooking fuel used Lighting fuel used Electricity fuel used

Energy demand

Proceed to

Feedstock Input

Feedstock Location Economics Priorities Recommended DesignEnergy demand

Update currency exchange rate using online

converter (requires internet connection)

Clear all inputs

156

Figure 5-3: Cooking requirements input options in the OBSDM

Details on the current energy use is required in the second part of the energy

input section as it enables comparisons to be made between the potential

biogas system and current energy sources. These inputs consist of

dropdown menus where the type of energy used for cooking, lighting, and

electricity can be entered, followed by numeric inputs for the associated

amount, cost, and preparation time (hours) required. The entered

information is used to estimate the annual energy costs and consumption

(in kWh/y), total hours spent preparing current energy sources, annual

GHG emissions (in tonnes CO₂-e/y), and the estimated costs per kWh. Costs

are presented in the currency selected from the currency input dropdown

menu with the option to enter the currency exchange rate to USD or use the

model’s default currency exchange rate linked to an online currency

converter. The calorific values of each of the fuel types and the mass of CO₂

equivalent GHG emissions per kWh of delivered energy used to calculate the

annual energy consumption and greenhouse gas emissions in the OBSDM,

respectively, are given in Table 5-3.

1.2 Application



Quantity of cookstoves & duration (preferred)

No. of cookstoves 1

No. of hrs cooking gas required/stove/day 3




1.2 Application



No. of people in household

No. of cookstoves 1

No. of hrs cooking gas required/stove/day 3




157

Table 5-3: Calorific values and CO₂ equivalent GHG emissions per kWh of delivered energy for conventional fuel types used in SSA

Fuel type Calorific value (kWh/kg)

CO₂-e GHG emissions (g/kWh delivered energy)

Reference

Charcoal 8.31 2147 [174, 305-307]

Charcoal (improved stove)

8.31 1706 [174, 305-307]

Coal 8.74 2753 [174, 308-310]

Crop residues 4.78a 4144 [311, 312]

Firewood 3.81a,b 4379 [306-310]

Firewood (improved stove)

3.81a,b 2554 [305-307, 309, 310]

Dung 2.44 4381 [308, 313]

LPG 12.78a 513 [174, 308, 314]

Kerosene 12.17 638 [312, 315]

Electricity grid 1.00 293 [40]

Diesel 12.00 1700 [174, 316]

aAverage of values from references bAverage of air dried and fresh wood with moisture contents of 15% to 20% and 50%, respectively

5.3.2 Feedstock

As described in Chapter 3, the feedstock is the single most influential factor

in the choice and design of a biogas system. In SSA, the significant energy

production potentials from domestic sewage, crop residues, and OFMSW as

feedstock in biogas systems has remained largely untapped with domestic

biogas programmes placing emphasis on the use of cattle dung as feedstocks

(see Chapter 4). The biogas production potential of these feedstocks, along

with others can be estimated at a given site using the OBSDM. Up to eight

different types of feedstock can be chosen in the feedstock input section of

the model from a database of 40 feedstocks (Table A-1 in Appendix A),

grouped into eight categories shown in Figure 5-4. The feedstock categories

and related feedstock types can be selected from dropdown menus along

with the relevant unit of feedstock; generally, a choice between mass in kg

or number of animals with the associated unit conversions given in Table

5-4.

158

Table 5-4: Feedstock unit conversions used in the OBSDM

Feedstock Unit Conversion to kg (kg/unit)

Reference

Cattle dung cattle 12.25 [145, 241]

Eggs eggs 0.05

Poultry manure chickens 0.04 [145, 151, 184]

Pig manure pigs 4.05 [145, 151, 184]

Night soil (pit toilet waste)

people 0.31 [145, 317, 318]

*Average values from references

Remaining feedstock inputs – amount, rate of supply (e.g. daily), distance

from the proposed biogas system site, and the time required to collect and

transport the feedstock to the proposed installation site – are entered as

numerical inputs. Ideally, the feedstock should be located within 3 km of the

proposed installation site, and the model displays a warning if this distance

is exceeded for one or more feedstocks [295]. The model also displays

warning messages if the amount of feedstock entered is unable to meet the

full energy requirements specified in the energy input section, the C:N ratio

of a selected feedstock is exceptionally high or low, if the combination of

feedstocks is likely to yield an undesirable C:N ratio, or if the type of

feedstock used will require further treatment to ensure all dangerous

pathogens are removed, as shown in Figure 5.5.

159

Figure 5-4: Feedstock input section of the OBSDM

Figure 5-5: Feedstock inputs with warnings based on feedstock amount and

combination in the OBSDM

Input cell Instruction cell

2. Feedstock Input

Enter the details on the types of organic waste/other organic matter available to use in the biogas system in the table below:

Feedstock

Category

Type of

feedstock

Amount

of

feedstock

Unit of

feedstock

This

feedstock

is

available

every __

days

Feedstock

rate

Distance from

proposed biogas

system site (m)

Time required to

collect feedstock

& transport to

proposed biogas

installation site

(min)

Cattle manureCattle (dairy)

manure77 kg 1 77 kg/day 0 1

kg

kg

kg

kg

kg

kg

kg

Total daily biogas

production potential

(m ³/day)

2.92

Total daily energy


(kWh/day)

16.07

Output/warning cell

WARNING! Insufficient feedstock to meet the

entire daily energy demand

Proceed to Location Input

Return to Energy

Demand Input


Input cell Instruction cell

2. Feedstock Input

Enter the details on the types of organic waste/other organic matter available to use in the biogas system in the table below:

Feedstock Category Type of feedstock

Amount

of

feedstock

Unit of

feedstock

This

feedstock is

available

every __

days

Feedstock rateDistance from proposed

biogas system site (m)

Time required to

collect feedstock &

transport to proposed

biogas installation site

(min)

Other manure &

sewage

Poultry manure (with

straw)10 chickens 1 10 chickens/day 0 60

WARNING! This feedstock has a

low C:N ratio

Livestock food product

wasteEggs 10 eggs 1 10 eggs/day 0

WARNING! This feedstock has a

low C:N ratio

Other manure &

sewage

Night soil (pit toilet

waste)10 people 1 10 people/day 3005

WARNING! This feedstock

requires post-treatment to

ensure no dangerous pathogens

remain in the bioslurry. Without

post-treatment, the bioslurry

should only be applied to non-

consumable crops and/or fruit

trees.

Total daily biogas


(m ³/day)

0.27

Total daily energy


(kWh/day)

1.69

Output/warning cell

WARNING! This feedstock combination has a low C:N ratio,

consider adding more of the/a feedstock with a high C:N

ratio or lowering the amount of the feedstock with a low

C:N ratio

WARNING! Insufficient feedstock to meet the entire daily

energy demand

WARNING! One or more feedstocks are far away from the

proposed installation site. Consider an alternative

feedstock or closer installation site for the biogas system

Proceed to Location Input

Return to Energy

Demand Input


160

To determine if the full biogas requirements can be met, the maximum daily

biogas production potential (BPP) is calculated in the model as outlined in

the following equation:

𝐵𝑃𝑃 (𝑚3 𝑑)⁄ =∑1

2[(𝑚𝑖 (𝑘𝑔 𝑑)⁄ × 𝐷𝑀𝑖 (𝑘𝑔 𝐷𝑀 𝑘𝑔)⁄

× 𝑜𝐷𝑀𝑖 (𝑘𝑔 𝑜𝐷𝑀 𝑘𝑔 𝐷𝑀)⁄ × 𝐵𝑌𝑖(𝑚3 𝑘𝑔 𝑜𝐷𝑀⁄ )

+ 𝑚𝑖 (𝑘𝑔 𝑑)⁄ ×𝐵𝑌𝐹𝑀,𝑖(𝑚

3 𝑡 𝐹𝑀)⁄

1000 (𝑘𝑔 𝑡)⁄]

Equation 5-3

Where mi is the daily mass input of each chosen feedstock type and BYi and

BYFM,i are the corresponding biogas yields per kg of oDM and tonnes of FM,

respectively, from the database. The average of the two different methods of

calculating biogas production potential is used to derive a more accurate

estimate of the maximum daily biogas potential from the selected

feedstocks. Feedstock parameters in the database are recommended to be

revised and updated with region-specific data wherever possible as they can

be subject to significantly geographic variability. Cattle dung is a prime

example as its characteristics are largely dependent on cattle grazing

practices and diet. In Ethiopia, cattle were noted to have a poor diet which

meant that households required a minimum of four heads of cattle to have

sufficient dung available (over 20 kg/day) to produce above 1 m3 of biogas

[145].

The daily energy production potential (EPP) of the selected feedstock mix is

calculated as given in the expression below:

𝐸𝑃𝑃(𝑘𝑊ℎ𝑡ℎ 𝑑) =∑𝐵𝑃𝑃𝑖(𝑚3 𝑑) × 𝐸𝑌𝑖(𝑘𝑊ℎ𝑡ℎ 𝑚3⁄⁄⁄ )

Equation 5-4

161

Where EYi is the energy yield in kWh per m3 of biogas produced for each

chosen feedstock type from the database. These calculated biogas and

energy production values are based on ideal conditions and are likely to be

lower in practice since it is unlikely that a system will be operating under

ideal conditions at all times. For example, there may be some gas escaping

to pipe fittings if they have not been adequately sealed. The calculations for

adjusted BPP and EPP figures based on methane yields according to digester

operating temperatures and digester size are presented in section 5.4.3. The

energy and biogas demand and potential supply is compared in the model

as follows, displaying the warning message if false:

𝑂𝑅(𝐵𝑃𝑃 ≥ 𝐵𝑑 (𝑚3 𝑑)⁄ , 𝐸𝑃𝑃 ≥ 𝐸𝑑(𝑘𝑊ℎ𝑡ℎ 𝑑⁄ )) Equation 5-5

A C:N ratio between 10:1 and 40:1 for the feedstock mixture is deemed as

acceptable in the model as the bacterial activity is unlikely to be inhibited

within this range [11, 142, 319]. The C:N ratio of the mixture of feedstocks

selected in the input, C/Nmix, is estimated in the OBSDM using the approach

suggested by Werner et. al [142], as in the equation below:

𝐶/𝑁𝑚𝑖𝑥 =∑[𝑚𝑖 (𝑘𝑔 𝑑)⁄ × 𝐶 𝑁⁄ 𝑖]

∑𝑚𝑖 (𝑘𝑔 𝑑)⁄ Equation 5-6

Where mi is the daily mass input and C/Ni is the C:N ratio of each chosen

feedstock type.

5.3.3 Location

As previously mentioned, the location where a biogas system is to be

installed significantly influences the type of system that can be constructed

162

and the amount of biogas that can be produced. In the location input section

of the OBSDM (Figure 5-6), the first input is the water supply; specifically,

the amount of water and the time required to collect the water. The amount

of water available along with the DM of the feedstock is used to determine

the TS range that is possible with the feedstock and water mix, given by:

𝑇𝑆𝑖𝑛_𝑚𝑖𝑛(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ ) =∑𝐷𝑀𝑖(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ ) × 𝑚𝑖(𝑘𝑔)

∑𝑚𝑖 +𝑚𝑤 (𝑘𝑔)

𝑇𝑆𝑖𝑛_𝑚𝑎𝑥(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ ) =∑𝐷𝑀𝑖(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ ) × 𝑚𝑖(𝑘𝑔)

∑𝑚𝑖 (𝑘𝑔)

Equation 5-7

Where TSin_min is the minimum TS based on the input feedstock mix and

daily amount of water available, mw, and TSin_max is the maximum TS based

on the DM of the feedstock mix. This TS range is compared with the TS

range of the biogas systems in the model’s biodigester database, displaying

a warning if the feedstock is either too dry or too wet, as shown in Figure

5-7. The full list of biodigester types and the associated TS ranges considered

in the OBSDM are provided in Table A-2 (Appendix A). These TS values are

based on the common types of feedstocks used (normally cattle dung) and

the recommendations from the supplier, including HRT range. Therefore

other TS ranges may apply, which can be determined through field testing.

Water consumption of biogas systems can be reduced or replaced entirely

with cattle urine, grey water, a toilet connection, or by including an effluent

recycle15 [17]. Based on experiences in Ethiopia and other parts of Africa,

15 Replacing fresh water with urine may not be suitable in some situations, depending on the combination of feedstocks used, as it can lead to a high ammonia production due to the high nitrogen content in urine. The use of toilet waste will also limit where the bioslurry can be applied as some pathogens will remain, unless further treatment is applied.

163

distances of up to 1 km or a duration of up to 30 mins are considered

acceptable for a person to walk in order to collect water for a domestic biogas

system without the risk of deterring from the technology’s uptake [17, 145].

The model has not stipulated a maximum acceptable time or distance for

the water supply given that some biogas system designs may not require the

addition of water, and for those that do the time required to collect the water

is considered in the MCDA analysis. Rainwater harvesting is another means

mitigating issues with water supply for biogas systems in SSA. Annual

rainfall has been included as one of the climatic data inputs with the

potential to expand the model to include estimates of rainwater harvesting

potential for biogas systems in the future.

Following on from the water supply section, details on the climatic

conditions at the installation site are required, particularly the mean

ambient temperature, mean high temperature during the day, mean

temperature in the coldest month, and the maximum temperature

difference between day and night. Location-specific data can be entered by

the user, or if this is not possible, the country average climate data from the

internal SSA country database can be used (see Table A-3 in Appendix A).

This temperature data is used to estimate the temperature of the biogas

digester and to determine if heating would be required. As mentioned in

Chapter 3, the operating temperature and internal temperature fluctuations

significantly impact the microbial activity of biogas systems. The biodigester

temperature, Tdig, is estimated to be the average of Ta and Ta-max for

unheated underground or insulated systems and equivalent to Ta for

164

unheated above ground systems16, or for heated systems the digester

temperature is equal to the set temperature, Tset:

𝑇𝑑𝑖𝑔(°𝐶) = {

𝑇𝑎, 𝑓𝑜𝑟 𝑎𝑏𝑜𝑣𝑒 𝑔𝑟𝑜𝑢𝑛𝑑, 𝑛𝑜 𝑖𝑛𝑠𝑢𝑙𝑎𝑡𝑖𝑜𝑛

(𝑇𝑎, 𝑇𝑎_𝑚𝑎𝑥) 2, 𝑓𝑜𝑟 𝑢𝑛𝑑𝑒𝑟𝑔𝑜𝑢𝑛𝑑 𝑐𝑜𝑛𝑠𝑡./𝑖𝑛𝑠𝑢𝑙𝑎𝑡𝑖𝑜𝑛⁄

𝑇𝑠𝑒𝑡, 𝑓𝑜𝑟 ℎ𝑒𝑎𝑡𝑒𝑑 𝑑𝑖𝑔𝑒𝑠𝑡𝑒𝑟

Equation 5-8

Where Ta and Ta_max are the mean daily ambient temperature and mean

ambient high temperature, respectively.

Heating requirements are determined as in the expression below:

𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝑟𝑒𝑞.

{

𝑖𝑓 𝑇𝑑𝑖𝑔_𝑜𝑝 = 𝑃, 𝑎𝑛𝑑 (𝑇𝑎_𝑚𝑎𝑥 − 𝑇𝑎_𝑚𝑖𝑛) 12 ≥ 2 (°𝐶)⁄

𝑜𝑟 𝑖𝑓 𝑇𝑑𝑖𝑔𝑜𝑝 = 𝑀, 𝑎𝑛𝑑 (𝑇𝑎_𝑚𝑎𝑥 − 𝑇𝑎_𝑚𝑖𝑛) 12 ≥ 1⁄ (°𝐶)

𝑜𝑟 𝑖𝑓 𝑇𝑑𝑖𝑔𝑜𝑝 = 𝑇 (°𝐶)

𝑜𝑟 𝑖𝑓 𝑇𝑎 ≤ 𝑇𝑜𝑝_𝑚𝑖𝑛 (°𝐶)

Equation 5-9

Where Tdig_op is the digester operating temperature range, which can be

psychrophilic (<20°C), P; mesophilic (35-42°C), M; or thermophilic (50-

60°C), T [17, 55, 121, 130] and is specified in the biodigester database. Ta_min

denotes the mean ambient daily minimum temperature. The limits indicate

the hourly temperature fluctuations based on an average 12-hour period

between Ta_max and Ta_min. Top_min is the minimum outside (ambient)

16 These digester temperature calculations are approximations based on the close relationship between digester temperature and ambient temperature for conventional biogas systems in developing regions. The above ground systems may experience higher digester temperatures during the day due to exposure to sunlight, however, they may also experience lower temperatures in the evenings, relative to digesters installed underground. For this reason, underground digesters were estimated to have a higher average digester temperatures compared to above ground systems. Some examples of field studies that have investigated digester temperature of unheated systems include: Castano, J.M., J.F. Martin, and R.J. Ciotola, Performance of a small-scale, variable temperature fixed dome digester in a temperate climate. Energies, 2014. 7: p. 5701-5716. Nekhubvi, V. and D. Tinarwo, Long-term temperature measurement: Biogas digesters fermenting slurry. Journal of Energy in Southern Africa, 2017. 28: p. 99-106.

165

temperature in which the biodigester can operate. This differs from the

internal digester operating temperature range; e.g. underground fixed dome

biodigesters can operate in the mesophilic operating range (internal

temperatures of 35-42°C) with outside (ambient) temperatures ranging

between 10°C to 40°C, depending on the applied HRT [295].

As discussed in Chapter 3, biogas systems in SSA are commonly unheated

with many systems being constructed underground or insulated to reduce

digester temperature fluctuations. In some situations, underground

construction may not be possible due to the soil conditions or a shallow

groundwater depth. For this reason, the shallowest groundwater depth at

the installation site at any point throughout the year and the soil type

(selected from the list of 15 soil types found in SSA, see Table 5-5) make up

part of the location inputs. Underground construction is considered feasible

in the OBSDM if the soil type is considered suitable for specific digester

designs, and the maximum excavation depth required for the biogas system

installation is less than the shallowest groundwater depth. Other inputs on

land use in the location input section are the amount of dry fertiliser

required per year and the cost of the fertiliser. The fertiliser consumption

details are used to estimate the amount of fertiliser the anticipated bioslurry

from the recommended biogas system could replace, and the resulting

financial savings (if any)17. Other important location-specific considerations

17 In some applications, where the intended user does not normally buy fertiliser for crops and/or gardens, the benefits of applying the bioslurry are difficult to quantify. The increase in yield varies, depending on how it is applied and the type of crops that are used.

166

related to biogas system installations are listed as guidelines in the location

input section (see Figure 5-6).

Figure 5-6: Location input section of the OBSDM (excluding construction materials)

Figure 5-7: Warnings for water supply in the location input section of the OBSDM

3.1 Country where the system will be installed (from Energy Demand Input)

Country Kenya

3.2 Water supply

Amount of water available 63.5 L/day

Time required to collect water 60 mins/day

3.3 Default average/local climate data

Yes

No

Climate data description

Location specific

values (leave blank

if using default)

Default average

values

Value used in

tool Unit of measure

Mean daily temperature 27.66 20.80 20.8 °C

Mean high temperature during the

day31.31 26.90 26.9 °C

Mean temperature in the coldest

month15.30 16.10 16.1 °C

Maximum temperature difference

between day and night16.01 10.80 10.8 °C

Average annual rainfall 400 1929.8 1929.8 mm/year

3.4 Land use

Unit/description/

default value used

Shallowest groundwater table

depth at any point throughout the

year

3.00 m

Soil type Nitisols, Andosols

Red tropical soil

with/without volcanic

soil

Area available to install biogas

system30 m²

Underground construction possible? Yes

No

Amount of dry fertiliser required

per year 1386 kg DM/y

Currency for purchase of fertiliser KES KES


No

Exchange rate to USD (1=___KES) 101.32 101.32

Cost of fertiliser per kg 40.80 KES/kg

Use default average climate data

for this country?

Important guidelines on biogas system installation:

- Installation (including the bioslurry pit) should be at least 10 m away from a well/groundwater

source to avoid contamination- Installation should be at least 2 m away from

structures and trees- Installation should be no more than 20 m from the

point where the biogas will be consumed (e.g. kitchen with biogas cookstove)

3.2 Water supply

Amount of water available 0 L/day


WARNING! Feedstock is too dry and not suitable for any

biogas systems -add water/wetter feedstock or consider

using feedstock directly for combustion in an improved

wood stove, in pryolysis for energy production, or

composting for waste management

3.2 Water supply

Amount of water available 300 L/day


WARNING! Feedstock is too diluted, choose/add a drier

feedstock

167

Table 5-5: Soil types database in OBSDM based on [320, 321]

Soil type Code Definition Underground construction suitable

Arenosols AR Loose, sandy soil No

Calcisols, Cambisols, Luvisols

CL Limestone, sandy loam or high base clay soil

Yes

Gypsisols, Calcisols GY Soil with gypsum and/or limestone Yes

Acrisols, Alisols, Plithosols

AC Low base/highly acid soil susceptible to water erosion, clay-rich with/without iron minerals, may contain aluminium

No

Andosols AN Volcanic soils, high aluminium content, excellent water & nutrient holding capacity

Yes

Fluvisols, Gleysols, Cambisols

FL Marsh or wetland soil with/without sandy loam

No

Ferralsols, Acrisols, Nitisols

FR red/yellow soil with metal oxides (incl. tropical red soil), fine texture, may be clay rich

Yes

Gleysols, Hitosols, Fluvisols

GL Wetland/swamp/marsh soil No

Leptosols LP Shallow soil over continuous rock with gravel/stone

No

Lixisols LX Soil with high clay content in subsoil, common in tropical regions with dry season/s, high erodibility

No

Nitsols, Andosols NT Red tropical soil with/without volcanic soil

Yes

Plantosols PL Light-coloured soil, clay in subsurface, seasonal waterlogging and drought stress

No

Pozdols, Hitosols PZ Ash-grey top layer of coarse texture, subsurface of humus and metal oxides (common in humid tropics & light forest regions) with/without wetland soil

Yes

Solonchaks, Solonetz

SC Soil high in soluble salts, common in arid/semi-arid/coastal regions may have dense subsurface with high clay content

Yes

Vertisol (black cotton soil)

VR Heavy textured soil, high in expansive clay, unstable -shrinking and swelling

No

168

The final section in the location input of the OBSDM requires the details on

the local construction materials to be entered, as shown in Figure 5-8.

Construction materials that are available locally can be chosen from a

dropdown menu under seven categories: masonry; composite and

prefabricated; metals and wire; piping and sealants; biogas appliances;

labour, and; other. These categories and associated construction materials

were chosen for the model based on the construction materials required for

each of the biogas system types, with the complete construction material

database provided in Appendix A (Table A-4). Individual material costs can

be entered by the user, or alternatively default costs based on the average

costs of the selected materials in the country where the system is to be

installed. If the national costs are unavailable, the default average costs of

the material in SSA can be used. The table of construction material costs for

selected SSA countries and regional average costs are given in Appendix A

(Table A-5). Once again, the currency for the construction material costs can

be chosen by the user along with either the default or manually entered

exchange rate. The selected list of local construction materials are used to

determine percentage of construction materials that can be locally sourced

for each of the feasible biogas system types, while material costs are

considered in the estimated installation costs of the systems.

169

Figure 5-8: Construction materials section in location input of the OBSDM

3.5 Construction Materials

Currency for construction materials

Currency used (default shown if no

currency is chosen above)KES


NoEnter exchange rate to USD (1=___KES) 101.32

Exchange rate to USD (1=___KES)

used in tool101.32

Use default material costs Yes

No

Value added tax (VAT) (%) 0

Import tax (%) 0

Select which construction materials are available locally

Masonry Local Cost per unit Default cost per unit Cost used in toolUnit

Stone 0.60 0.60 kg

Bricks 20.00 20.00 pcs

Dressed quarry stone 40.00 40.00pcs

(390x190x150mm/pc)

Cement 800.00 800.00 bag (50 kg/bag)

Gravel (1x2) 1200.00 1200.00 tonne

Coarse sand 1.50 1.50 kg

Waterproof cement 200.00 200.00 bag (1 kg/bag)

Composite and prefabricated Local cost per unit Default cost per unit Cost used in toolUnit

Metals and wire Local cost per unit Default cost per unit Cost used in toolUnit

Welded square mesh (G8) -heavy

gauge3000.00 3000.00

pcs (1200mm x

2400mm, 3 mm dia,

12.9kg)

Steel rod/round bar 8 mm 394.05 394.05pcs (400 g/mm, 3m

length)

Binding wire 120.00 120.00 kg

Piping and sealants Local cost per unit Default cost per unit Cost used in toolUnit

Gas piping (PVC or galv. Steel) incl.

fittings, valves & water drain10000.00 10000

Per (household scale)

installation

Biogas appliances Local cost per unit Default cost per unit Cost used in toolUnit

Labour Local cost per unit Default cost per unit Cost used in toolUnit

Skilled Labour 1000.00 1000.00 person-day

Unskilled Labour 500.00 500.00 person-day

Other Local cost per unit Default cost per unit Cost used in toolUnit

170

5.3.4 Economics

The economics input section of the OBSDM, shown in Figure 5-9, requires

the monthly disposable income of the intended user, savings available for

capital expenditure, and any subsidies available for biogas system

installations to be entered. Subsidies can be entered either as a fixed amount

or as a percentage of the capital cost (Figure 5-10). As in the previous

section, the currency and exchange rate can be chosen by the user. These

economic inputs are used to calculated key low-cost criteria parameters,

including the installation costs, affordability (difference between monthly

disposable income and the monthly operation and maintenance costs),

additional savings required to meet installation costs, and the months of

savings required to meet the installation costs.

Figure 5-9: Economics input section of the OBSDM

Figure 5-10: Subsidy type options in economics input of the OBSDM

4.1 Income and savings

Currency RWF

Currency used (default shown if no currency is chosen above) RWF


No

Enter exchange rate to USD (1=___RWF) 811.4

Exchange rate to USD (1=___RWF) used in tool 811.40

Monthly disposable income of intended user(s) 5714.17

Savings available for capital expenditure 50000.00

4.2 Subsidies

Yes

No

Type of subsidy

Value of subsidy (if % input between 0 and 100) 300000

4.3 Loans

Are there any microfinance loans available for biogas

installations?

Loan amount ($)

Monthly interest

Are there any subsdies available for biogas installations?


Update currency exchange rate using online

converter (requires internet connection)

Proceed to Priorities

Input

Return to Location Input

4.2 Subsidies

Yes

No

Type of subsidy


Are there any subsdies available for biogas

installations?

4.2 Subsidies

Yes

No

Type of subsidy


Are there any subsdies available for biogas

installations?

171

5.3.5 User priorities

The final input section of the OBSDM is used to help determine the

priorities of the intended user in relation to key sustainability criteria. A

total of eight technical, economic, environmental, and social sustainability

criteria are included in the model. These criteria are rated on a scale of 1 to

5, with 1 being not at all important and 5 being extremely important (Figure

5-11). The rating then provides the weighting for the criteria in the MCDA

applied by the model. These priority criteria were included in the OBSDM

based on their relevance to biogas systems, many of which are common in

analysing the sustainability of renewable energy technologies [29, 295, 296,

322]. The list of parameters used to derive a score for each of the criteria are

given in Table 5-6 along with the source of the data. These parameters

thereby correspond to the objective hierarchy of the OBSDM. Details on the

MCDA method applied in the model to calculate the scores for each of the

criteria and rank the feasible biogas system designs are given in section 5.5.

Figure 5-11: Priorities input section of the OBSDM

5.1 Priorities of the intended user

Scale of importance key

Reliability 3 1=not at all important

Robustness 3 2=slightly important

Simple operation 5 3=moderately important

Low cost 5 4=very important

Technical efficiency 3 5=extremely important

Environmentally benign 3

Local materials & labour 3

Save time 5

Rate each criteria on a scale of 1 to 5

according to how important they are to

the intended user


172

Table 5-6: Priority criteria and associated parameters and source in the OBSDM

Priority Criteria Parameters Source

Reliability • Lifespan of digester • Gas pressure variability (constant or varying)

• Biodigester database

Robustness • Sensitivity to changes in ambient temperature

• Vulnerabilities to structural integrity of biogas system

• User input – local climatic conditions/ internal climate database for SSA countries


Simple operation & construction

• Daily operation time (h/d)

• Annual maintenance required (d/y)

• Level of expertise required for operation

• Construction time (d)


Low-cost • Installation costs (including & excluding subsidies)

• Operation & maintenance (O&M) costs

• Annual savings

• Net present value (NPV)

• Simple payback period (y)

• Affordability (monthly disposable income – monthly O&M costs)

• Additional savings required to meet capital costs

• Months of savings required to meet capital costs


• User input – energy demand (current fuel source costs), location (fertiliser & local construction material costs) & economics

• Construction materials database

Technical efficiency

• Biogas production efficiency (%)

• Proportion of energy requirements met (%)

• Volumetric biogas production (m³ biogas/m³ installed/d)


• User inputs – energy demand, feedstock & local climatic conditions

Environmentally benign

• GHG emissions avoided from waste management (t CO2-e/y)

• GHG emissions avoided from fuel replacement (t CO2-e/y)

• GHG emissions from construction (t CO2-e/y)

• Energy returned on energy invested (EROI)

• User input – energy demand, feedstock

• Construction materials database


Local materials & labour

• Employment generation (unskilled/skilled ratio for installation)

• Proportion of required construction materials available locally (%)


• User input – location (local construction materials)

Save time • Time saved from replacing current energy demand (h/d)

• Time required to operate & maintain the system (including feedstock and water collection) (h/d)

• User input – energy demand (current fuel sources), feedstock, location (water supply)


173

5.4 Digester sizing and design in the OBSDM

5.4.1 Determining the ideal digester size

In determining the type of biogas systems that are feasible and the

associated suitable sizes in the model, a number of factors are considered

based on the input values as well as the internal databases. The initial

screening of feasible biogas system types is carried out by comparing the TS

operating range of each biodigester type with the TS range that is likely to

occur with the selected feedstocks and specified water supply, as mentioned

in Section 5.3.3. A biogas system type will be considered feasible if the

following conditions are true:

{

𝑇𝑆𝑖𝑛_𝑚𝑖𝑛 < 𝑇𝑆𝑑𝑖𝑔_𝑚𝑎𝑥 (𝑘𝑔 𝐷𝑀 𝑘𝑔)⁄

𝑇𝑆𝑖𝑛_𝑚𝑎𝑥 > 𝑇𝑆𝑑𝑖𝑔_𝑚𝑖𝑛 (𝑘𝑔 𝐷𝑀 𝑘𝑔)⁄

𝑂𝑅(𝑚𝑤_𝑚𝑖𝑛 < 𝑚𝑤,𝑚𝑤_𝑚𝑎𝑥 ≤ 𝑚𝑤) (𝑘𝑔)

Equation 5-10

Where mw_min and mw_max are the minimum and maximum amounts of

water required to be added to the digester with the feedstock each day, and

TSdig_max and TSdig_min, denote the maximum and minimum total solids

content at which a digester can function properly, respectively. The range

for the required amount of water is calculated using the following equation:

𝑚𝑤_𝑚𝑖𝑛(𝑘𝑔/𝑑) = {

0, 𝑇𝑆𝑖𝑛_𝑚𝑎𝑥 < 𝑇𝑆𝑑𝑖𝑔_𝑚𝑎𝑥 (𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ )

∑𝐷𝑀𝑖 (𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ ) ×𝑚𝑖(𝑘𝑔 𝑑⁄ )

𝑇𝑆𝑑𝑖𝑔_𝑚𝑎𝑥(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ )−∑𝑚𝑖(𝑘𝑔 𝑑⁄ )

𝑚𝑤_𝑚𝑎𝑥(𝑘𝑔/𝑑) =∑𝐷𝑀𝑖(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ ) × 𝑚𝑖(𝑘𝑔 𝑑⁄ )

𝑇𝑆𝑑𝑖𝑔_𝑚𝑖𝑛(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ )−∑𝑚𝑖(𝑘𝑔 𝑑⁄ )

Equation 5-11

174

For each feasible biogas system type (based on the TS range), a theoretical

HRT range is defined in the OBSDM according to the recommended

digester and feedstock HRT ranges as follows:

Where HRTdig_min and HRTdig_max are the minimum and maximum HRTs of

a given biodigester in the model’s biodigester database, and HRTFS_min and

HRTFS_max are the minimum and maximum recommended HRTs of the

feedstock18 (given in the feedstock database, Table A-1, in Appendix A). For

a combination of feedstocks, HRTFS_min and HRTFS_max are determined by

calculating the sum-product of the minimum and maximum HRT of each

feedstock type relative to the mass of each feedstock and total mass,

respectively (Equation 5-13).

𝐻𝑅𝑇𝑚𝑖𝑥(𝑑) =∑[𝑚𝑖 (𝑘𝑔 𝑑)⁄ × 𝐻𝑅𝑇𝑖(𝑑)] ∑𝑚𝑖 (𝑘𝑔 𝑑)⁄⁄

Equation 5-13

The theoretical HRT ranges are used to derive a suitable digester volume

range, Vdig_min and Vdig_max for each digester type in the model:

𝑉𝑑𝑖𝑔_𝑚𝑖𝑛(𝑚3) = [

∑𝑚𝑖 +𝑚𝑤_𝑚𝑖𝑛(𝑘𝑔 𝑑)⁄

1000(𝑘𝑔 𝑚3)⁄] (𝑚3 𝑑)⁄ × 𝐻𝑅𝑇𝑡ℎ_𝑚𝑖𝑛(𝑑)

𝑉𝑑𝑖𝑔_𝑚𝑎𝑥(𝑚3) = [

∑𝑚𝑖 +𝑚𝑤_𝑚𝑎𝑥(𝑘𝑔 𝑑)⁄

1000(𝑘𝑔 𝑚3)⁄] (𝑚3 𝑑)⁄ × 𝐻𝑅𝑇𝑡ℎ_𝑚𝑎𝑥(𝑑)

Equation 5-14

18 The feedstock HRT range is based on the recommended SRTs for each feedstock, approximated to completely mixed scenarios where HRT=SRT.

𝐻𝑅𝑇𝑡ℎ_𝑚𝑖𝑛(𝑑) = max (𝐻𝑅𝑇𝑑𝑖𝑔_𝑚𝑖𝑛(𝑑), 𝐻𝑅𝑇𝐹𝑆min(𝑑))

𝐻𝑅𝑇𝑡ℎ_𝑚𝑎𝑥(𝑑) = min (𝐻𝑅𝑇𝑑𝑖𝑔_𝑚𝑎𝑥(𝑑), 𝐻𝑅𝑇𝐹𝑆_𝑚𝑎𝑥(𝑑))

Equation 5-12

175

These digester volume ranges and the theoretical HRT ranges are then used

to determine the resulting minimum and maximum organic loading rate

(OLR):

𝑂𝐿𝑅𝑚𝑖𝑛(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄ =[∑(𝑚𝑖 × 𝐷𝑀𝑖 × 𝑜𝐷𝑀𝑖) (𝑘𝑔 𝑜𝐷𝑀 𝑑⁄ )]

𝑉𝑑𝑖𝑔_𝑚𝑎𝑥(𝑚3)

𝑂𝐿𝑅𝑚𝑎𝑥(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄ =[∑(𝑚𝑖 × 𝐷𝑀𝑖 × 𝑜𝐷𝑀𝑖) (𝑘𝑔 𝑜𝐷𝑀 𝑑⁄ )]

𝑉𝑑𝑖𝑔_𝑚𝑖𝑛(𝑚3)

Equation 5-15

The derived OLR range is applicable to digesters operating in the digester

temperature for which the HRT range was assigned (THRT); however, the

actual digester operating temperature (Tdig) may differ from this depending

on the climatic conditions and the digester type. At lower temperatures, for

example, a lower OLR and higher HRT is required to achieve comparable

biogas production rates. If THRT is not specified for a given digester design,

it is estimated based on the average ambient temperature of the country

where the system is available in the same manner as Tdig (Equation 5-8).

To determine the adjusted OLR range, OLRmin,adj and OLRmax,adj, the

following equation is applied [323]:

𝑂𝐿𝑅𝑚𝑖𝑛,𝑎𝑑𝑗(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄ = 𝑒𝑝(𝑇𝑑𝑖𝑔−𝑇𝐻𝑅𝑇) × 𝑂𝐿𝑅𝑚𝑖𝑛(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄

𝑂𝐿𝑅𝑚𝑎𝑥,𝑎𝑑𝑗(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄ = 𝑒𝑝(𝑇𝑑𝑖𝑔−𝑇𝐻𝑅𝑇) × 𝑂𝐿𝑅𝑚𝑎𝑥(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄

Equation 5-16

Where p is the rate constant (1/°C), which is 0.10 for the temperature range

of 10°C to 30°C [323]. The p value for this temperature range is used in the

model due to all the biogas system types considered being unheated with

176

Tdig being approximated to the ambient temperature, as previously

mentioned.

The adjusted OLR range is used to recalculate the digester volume range,

Vdig_min,adj and Vdig_max,adj, and the resulting HRT range (Equation 5-17).

𝑉𝑑𝑖𝑔_𝑚𝑖𝑛,𝑎𝑑𝑗(𝑚3) =

[∑(𝑚𝑖 × 𝐷𝑀𝑖 × 𝑜𝐷𝑀𝑖) (𝑘𝑔 𝑜𝐷𝑀 𝑑⁄ )]

𝑂𝐿𝑅𝑚𝑎𝑥,𝑎𝑑𝑗(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄

𝑉𝑑𝑖𝑔_𝑚𝑎𝑥,𝑎𝑑𝑗(𝑚3) =

[∑(𝑚𝑖 × 𝐷𝑀𝑖 × 𝑜𝐷𝑀𝑖) (𝑘𝑔 𝑜𝐷𝑀 𝑑⁄ )]

𝑂𝐿𝑅𝑚𝑖𝑛,𝑎𝑑𝑗(𝑘𝑔 𝑜𝐷𝑀 𝑚3 𝑑⁄ )⁄

Equation 5-17

The ideal digester volume recommended by the OBSDM for each

biodigester type (Vdig_ideal), is the mean volume of the adjusted digester

volume range as this provides a compromise between minimising costs

(Vdig_min,adj), and maximising biogas production (Vdig_max,adj) (Equation

5-18).

𝑉𝑑𝑖𝑔_𝑖𝑑𝑒𝑎𝑙(𝑚3) =

𝑉𝑑𝑖𝑔_𝑚𝑎𝑥,adj (𝑚3) + 𝑉𝑑𝑖𝑔_𝑚𝑖𝑛,adj(𝑚

3)

2 Equation 5-18

5.4.2 Identifying the optimal available digester size

Once the ideal digester volume, Vdig_ideal, has been determined for each

biodigester type, each available digester size (as a volume, Vdig_avail), for a

given type is compared to the ideal volume (Vdig_ideal) in the model’s digester

size database (Table A-6 in Appendix A) to identify what digester volume for

that size is feasible (Vdig_avail_feas), as depicted in Equation 5-19.

177

𝑉𝑑𝑖𝑔𝑎𝑣𝑎𝑖𝑙𝑓𝑒𝑎𝑠(𝑚3) =

{

‖𝑉𝑑𝑖𝑔_𝑖𝑑𝑒𝑎𝑙

𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙‖ × 𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙 ,

𝑉𝑑𝑖𝑔_𝑖𝑑𝑒𝑎𝑙

𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙≥ 0.5

𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙, 0.5 <𝑉𝑑𝑖𝑔_𝑖𝑑𝑒𝑎𝑙

𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙≥ 0.15

0

Equation 5-19

Where the nearest integer is used to determine the multiples of Vdig_avail

required if Vdig_ideal is half of the available size or larger volume. If the ratio

of Vdig_ideal to Vdig_avail is less than half and greater than 0.15, Vdig_avail is

chosen as the feasible digester size. A ratio less than 0.15 indicates that

Vdig_avail is significantly larger than the ideal digester volume. Therefore, the

available digester size is not considered feasible. The 0.15 boundary is the

minimum ratio value that allows at least the smallest available size of each

biogas system type in the OBSDM to be considered based on a feedstock

supply of cattle dung from 1 cow (12.25 kg/d [145, 241]).

For each feasible digester volume, the average HRT (HRTavg), number of

digesters (ndig), and percentage change from the ideal volume are calculated

using Equation 5-20 to Equation 5-22, given below.

𝐻𝑅𝑇𝑚𝑖𝑛(𝑑) =𝑉𝑑𝑖𝑔𝑎𝑣𝑎𝑖𝑙𝑓𝑒𝑎𝑠

(𝑚3)

∑𝑚𝑖 +𝑚𝑤_𝑚𝑎𝑥(𝑘𝑔 𝑑⁄ )× 1000(𝑘𝑔 𝑚3)⁄

𝐻𝑅𝑇𝑚𝑎𝑥(𝑑) =𝑉𝑑𝑖𝑔𝑎𝑣𝑎𝑖𝑙𝑓𝑒𝑎𝑠

(𝑚3)

∑𝑚𝑖 +𝑚𝑤_𝑚𝑖𝑛(𝑘𝑔 𝑑⁄ )× 1000(𝑘𝑔 𝑚3)⁄

𝐻𝑅𝑇𝑎𝑣𝑔(𝑑) =𝐻𝑅𝑇𝑚𝑖𝑛(𝑑) + 𝐻𝑅𝑇𝑚𝑎𝑥(𝑑)

2

Equation 5-20

178

𝑛𝑑𝑖𝑔 =𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠 (𝑚

3)

𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙(𝑚3) Equation 5-21

% 𝑐ℎ𝑎𝑛𝑔𝑒 =𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠 − 𝑉𝑑𝑖𝑔_𝑖𝑑𝑒𝑎𝑙 (𝑚

3)

𝑉𝑑𝑖𝑔_𝑖𝑑𝑒𝑎𝑙 (𝑚3)× 100

Equation 5-22

The installation cost of each feasible digester size, excluding any subsidies

that may be available (costVdig_avail_feas), is estimated based on the average of

the recommended retail price (RRP) (costRRP-Vdig_avail_feas), and the total

costs of the required construction materials (costmat-Vdig_avail_feas),

considering the cost of value-added tax (VAT), if applicable (Equation 5-23).

The RRP and cost of the required construction materials per digester

(excluding VAT) are denoted as costRRP-Vdig_avail and costmat-Vdig_avail,

respectively.

𝑐𝑜𝑠𝑡𝑅𝑅𝑃−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠

= [𝑐𝑜𝑠𝑡𝑅𝑅𝑃−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙

+ (𝑐𝑜𝑠𝑡𝑅𝑅𝑃−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙 × 𝑉𝐴𝑇)] × 𝑛𝑑𝑖𝑔

𝑐𝑜𝑠𝑡𝑚𝑎𝑡−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠

= [𝑐𝑜𝑠𝑡𝑚𝑎𝑡−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙

+ (𝑐𝑜𝑠𝑡𝑚𝑎𝑡−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙 × 𝑉𝐴𝑇)] × 𝑛𝑑𝑖𝑔

𝑐𝑜𝑠𝑡𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠

=𝑐𝑜𝑠𝑡𝑅𝑅𝑃−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠 + 𝑐𝑜𝑠𝑡𝑚𝑎𝑡−𝑉𝑑𝑖𝑔_𝑎𝑣𝑎𝑖𝑙_𝑓𝑒𝑎𝑠

2

Equation 5-23

The optimal feasible digester size for each biodigester type in the model is

identified by applying the technique for order preference by similarity to

ideal solution (TOPSIS) method – a multi-criteria decision analysis (MCDA)

approach. In the TOPSIS method each option is ranked according to its

179

distance from the ideal solution, with the best option being identified as

having the shortest Euclidean distance from the ideal solution and the

longest Euclidean distance from the worst [322, 324]. The ideal solution for

the digester sizing is the best possible normalised score for each size

parameter (HRTavg, ndig, % change, costVdig_avail_feas), while the worst

solution is the worst possible normalised score for each sizing parameter, as

summarised in Table 5-7. Each sizing parameter is normalised using vector

normalisation as in Equation 5-24 [325]:

�̂�𝑠,𝑖 =𝑥𝑠,𝑖

√∑ (𝑥𝑠,𝑖)2

𝑖=1

Equation 5-24

Where x are the values of a given sizing parameter (s), and feasible digester

size (i).

Table 5-7: Equations to determine best and worst normalised scores for sizing parameters

Sizing parameter Best sizing score (ss+) Worst sizing score (ss-)

HRTavg, costVdig_avail_feas* 𝑠𝑠+ = max (�̂�𝑠,𝑖) 𝑠𝑠− = min (�̂�𝑠,𝑖)

ndig, % change 𝑠𝑠+ = min (�̂�𝑠,𝑖) 𝑠𝑠− = max (�̂�𝑠,𝑖)

* All costs are considered as negative values and all profit is given as positive values in the OBSDM, as is common practice in accounting, resulting in an objective function of maximising profits and thereby minimising costs.

The distance from the ideal sizing score (d+) and the worst sizing score (d-)

for each feasible biodigester type is determined by the square-root of the

squared sum of the difference between the ideal and worst scores,

respectively, from the normalised sizing scores of each feasible size [322]:

𝑑(𝑠𝑠𝑖)+ = √{∑|𝑠𝑠+ − �̂�𝑠,𝑖|

2

𝑖=1

} Equation 5-25

180

The overall sizing score (z) of each feasible size is determined as follows

[322]:

𝑧(𝑠𝑠𝑖) =𝑑(𝑠𝑠𝑖)

−

[𝑑(𝑠𝑠𝑖)− + 𝑑(𝑠𝑠𝑖)+] Equation 5-27

The optimal available digester size for each biogas system type is identified

in the model as the size which has received the maximum overall sizing

score.

5.4.3 Determining the required gasholder volume

The optimal available digester size will have an associated gasholder volume

based on the type of biogas system that it is. Gasholders are recommended

to be sized to cover the peak gas consumption rate to provide sufficient gas

storage for the longest zero-consumption period in a day [184]. The OBSDM

compares the available gasholder volume with the required gasholder

volume based on this recommendation to determine whether any additional

gas storage is required for a given biogas system type. The peak gas

consumption rate is the daily required gas consumption based on the energy

demand input. The maximum zero-consumption period is estimated to be

10 hours in a day. Gas production, and thereby required storage volume

during the zero-consumption period is estimated based on daily methane

production potential (MPP) and the fraction of methane in biogas (fCH4).

𝑑(𝑠𝑠𝑖)− = √{∑|�̂�𝑠,𝑖 − 𝑠𝑠−|

2

𝑖=1

} Equation 5-26

181

MPP is estimated using the kinetic model for steady state methane

production rates19 from Chen and Hashimoto [326-328] as given below:

𝑀𝑃𝑃 (𝑚3 𝑑⁄ ) = [(𝑚𝑖 (𝑘𝑔 𝑑)⁄ × 𝐷𝑀𝑖(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ )× 𝑜𝐷𝑀𝑖(𝑘𝑔 𝑜𝐷𝑀 𝑘𝑔 𝐷𝑀)⁄× 𝐵𝑀𝑃𝑖(𝑚

3𝐶𝐻4 𝑘𝑔 𝑜𝐷𝑀⁄ )]

× [1 −𝐾

𝐻𝑅𝑇 (𝑑) × 𝜇𝑚(1 𝑑⁄ ) − 1 + 𝐾]

Equation 5-28

Where BMPi is the methane yield for a chosen feedstock per kg of oDM, and

K is the relative substrate micro-organism binding constant, which can be

determined based on the following equations for cattle manure and swine

manure, respectively [327, 328]:

𝐾 = 0.8 + 0.0016𝑒0.06(𝐷𝑀×𝑜𝐷𝑀×1000) Equation 5-29

𝐾 = 0.6 + 0.0206𝑒0.051(𝐷𝑀×𝑜𝐷𝑀×1000) Equation 5-30

For all other feedstocks types the K value is estimated to be the same as that

for swine manure, which was also used by Abarghaz et al. [329] for a mixture

of feedstocks, due to the unavailability of K value data for other feedstocks.

The maximum specific growth rate per day (µm) is estimated based on the

digester temperature using the following equation, applicable to a digester

temperature range between 20°C and 60°C [327]:

𝜇𝑚 = 0.013𝑇𝑑𝑖𝑔 − 0.129 Equation 5-31

19 This kinetic model was chosen over alternatives like the first order hydrolysis constant, as it was identified as the most appropriate based on the type of feedstocks that would be used, i.e. cattle manure and household wastes like food scarps as well as crop residues. In this context, methanogenesis is the rate limiting step rather than hydrolysis, as stated in Bouallagui et. al (2005). Bouallagui, H., Y. Touhami, R. Ben Cheikh, and M. Hamdi, Bioreactor performance in anaerobic digestion of fruit and vegetable wastes. Process Biochemistry, 2005. 40(3): p. 989-995.

182

The estimated daily biogas production for a given biodigester (BP) is then

calculated as follows:

𝐵𝑃 (𝑚3 𝑑⁄ ) = (𝑀𝑃𝑃(𝑚3 𝑑⁄ ) 𝑓�̅�𝐻4⁄ ) × 𝜂𝐵𝑃 Equation 5-32

Where the mean fCH4 is used for a mixture of feedstocks and ηBP is the biogas

production efficiency for a given biodigester type, which is the fraction of

the total biogas produced by a biogas system that is available for use (i.e. the

gas produced minus any leakages as a portion of the total production) and

is taken from the biodigester database.

The required gasholder volume (Vgh) is then calculated based on the

estimated daily biogas consumption/demand (Bd) (as described in section

5.3.1), the maximum gas production during the zero-consumption period,

and a safety factor of 15% (Equation 5-33).

𝑉𝑔ℎ(𝑚3) =

{

1.15 × max [𝐵𝑑 (𝑚

3 𝑑⁄ ), (10(ℎ)

24(ℎ 𝑑⁄ )× 𝐵𝑃(𝑚3 𝑑⁄ ))] , 𝐵𝑑 ≤ 𝐵𝑃

1.15 × (10(ℎ)

24(ℎ 𝑑⁄ )× 𝐵𝑃(𝑚3 𝑑⁄ ))

Equation 5-33

In the model, the additional gas storage volume needed for a biodigester is

calculated as the difference between the required and available gasholder

volume. BP based on MPP is used to calculate the required gasholder

volume rather than BPP, as it enables the variation in methane production

according to digester temperature to be considered, whereas BPP is based

on ideal conditions.

183

5.4.4 Identifying feasible biogas system designs based on the

proposed installation site and intended system application

Once biodigester sizing is completed in the model, each biogas system

design is analysed based on location-specific and energy use input data to

determine which of the systems are feasible. The two main considerations

to determine each biogas system design’s feasibility are whether biodigester

construction at the proposed installation site is possible and whether the

system is suitable for the intended energy application.

Biogas system installation at the proposed construction site is determined

feasible if the following conditions are true:

𝑏𝑖𝑜𝑑𝑖𝑔. 𝑐𝑜𝑛𝑠𝑡. 𝑓𝑒𝑎𝑠.

{

𝐴𝑖𝑛𝑠𝑡(𝑚

2) ≤ 𝐴𝑎𝑣𝑎𝑖𝑙(𝑚2)

(𝑈𝑛𝑑𝑒𝑟𝑔𝑟𝑑. 𝑐𝑜𝑛𝑠𝑡. 𝑟𝑒𝑞.

𝐴𝑁𝐷 𝑢𝑛𝑑𝑒𝑟𝑔𝑟𝑑. 𝑐𝑜𝑛𝑠𝑡. 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒)

𝑂𝑅 𝑈𝑛𝑑𝑒𝑟𝑔𝑟𝑑. 𝑐𝑜𝑛𝑠𝑡. 𝑛𝑜𝑡 𝑟𝑒𝑞.

Where Ainst is the are required for the biogas system installation (land

footprint) and Aavail is the total area available at the site for the biodigester

installation. Underground construction at the site considered possible

provided the following conditions are true:

𝑈𝑛𝑑𝑒𝑟𝑔𝑟𝑜𝑢𝑛𝑑 𝑐𝑜𝑛𝑠𝑡. 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 {

ℎ𝑖𝑛𝑠𝑡(𝑚) < ℎ𝑔𝑟𝑜𝑢𝑛𝑑𝑤𝑎𝑡𝑒𝑟(𝑚)

𝑠𝑜𝑖𝑙 𝑠𝑢𝑖𝑡𝑎𝑏𝑙𝑒 𝑓𝑜𝑟 𝑢𝑛𝑑𝑒𝑟𝑔𝑟𝑜𝑢𝑛𝑑 𝑐𝑜𝑛𝑠𝑡."YES" 𝑖𝑛 𝑢𝑠𝑒𝑟 𝑖𝑛𝑝𝑢𝑡

Where hinst is the maximum depth below the ground level of the proposed

biogas system (maximum excavation depth required for the installation),

and hground_water is the shallowest groundwater depth at the installation site

at any point throughout the year. “YES” in user input refers to the input in

184

the location section, which asks whether underground construction is

possible at the proposed installation site as mentioned in section 5.3.3.

To test whether a biogas system design is suitable for the intended biogas

use, the estimated average pressure that can be generated from a given

system (provided in the biodigester database – Table A-2) and its state

(varying or constant) is compared with the gas pressure requirements

summarised in Table 5-8 for each of the three main energy applications of

biogas. Tumwesige et al. [118] state that the typical gas pressure range

suitable for biogas lighting is between 0.46 and 1.47 kPa, however, no

constraint has been set on the maximum gas pressure for lighting in the

model as lamp testing was carried out in one study at higher pressures

[330]. Furthermore, given that biogas lamps are normally used in the

evening, a biogas system’s gas pressure is likely to be lower. Electricity

generation is generally not recommended for household-scale biogas

systems due to gas pressure fluctuations and the need for a reliable gas

scrubber to remove trace gases that would otherwise harm engine parts

[145].

Table 5-8: Gas pressure requirements for different biogas technology applications [118]

Application Minimum pressure (kPa) Constant gas pressure required

Cooking N/Aa No Lighting 0.49 No Electricity generation N/Ab Yesc aDepends on cookstove type & dimensions bDepends on generator type & dimensions cConstant gas pressure or sound gas pressure control is required

185

5.5 Determining the optimal biogas system design

using MCDA

5.5.1 Calculating cost savings, GHG emissions avoided, EROI

and other sustainability criteria parameters

Once the feasible biogas systems designs have been identified using the

approach described in the previous section, the associated parameters

related to the sustainability criteria (Table 5-6 in section 5.3.5) are identified

from the relevant databases or calculated for each feasible design. Key

calculated parameters include the installation costs, annual financial

savings (from fuel and chemical fertiliser replacement), GHG emissions

avoided from fuel replacement, GHG emissions avoided from waste

management, GHG emissions from construction, and the energy returned

on energy invested (EROI). The net installation costs of each feasible design

are calculated as the difference between the installation costs (Equation

5-23) and any subsidies available. The net installation costs are used to

calculate the NPV, simple payback period, and the months of savings

required to meet the capital costs. A discount rate of 10% is used when

calculating the NPV based on the rate used by the World Bank to calculate

the net official development assistance (ODA) per capita in SSA [331]. The

annual estimated cost savings (ECS) from fuel replacement is calculated

based on the estimated daily energy production (EP) (Equation 5-34), the

annual energy consumption (Ed), the energy costs associated with the use of

current conventional energy resources (costsE-d), and the comparison of BP

to Bd, as given in Equation 5-35.

186

𝐸𝑃(𝑘𝑊ℎ𝑡ℎ 𝑑⁄ ) = 𝐵𝑃(𝑚3 𝑑)⁄ ×∑𝐵𝑃𝑃𝑖 (𝑚

3 𝑑)⁄ × 𝐸𝑌𝑖 (𝑘𝑊ℎ𝑡ℎ 𝑚3⁄ )

𝐵𝑃𝑃(𝑚3 𝑑)⁄

Equation 5-34

𝐸𝐶𝑆 (𝑈𝑆𝐷 𝑦⁄ )

{

𝐸𝑃(𝑘𝑊ℎ𝑡ℎ 𝑑⁄ ) × 365 (𝑑 𝑦⁄ ) 𝐸𝑑(𝑘𝑊ℎ𝑡ℎ 𝑦⁄ )⁄

× 𝑐𝑜𝑠𝑡𝑠𝐸−𝑑(𝑈𝑆𝐷 𝑦⁄ ), 𝐵𝑃 < 𝐵𝑑

𝑐𝑜𝑠𝑡𝑠𝐸−𝑑(𝑈𝑆𝐷 𝑦⁄ )

Equation 5-35

At present, there is no standard method of estimating the savings associated

with fertiliser replacement as literature on the performance of bioslurry

compared to other organic and chemical fertilisers and its economic value

is limited [8]. What is known, based on experience from domestic biogas

programmes such as in Tanzania and Vietnam, is that the utilisation of

bioslurry can provide significant financial benefits to biogas system owners

[8, 9]. In the model the amount of chemical fertiliser that can be replaced

with the bioslurry from a given biogas system is estimated on the

assumption that one kg DM of bioslurry (ignoring any organic DM

component) can replace one kg DM of chemical fertiliser, where the DM

mass of the bioslurry, mBS,DM, is calculated using Equation 5-36. Annual cost

savings from chemical fertiliser replacement are then determined based on

the annual chemical fertiliser consumption in kg DM (mF,DM) and costs

(costsF) from the location input (section 5.3.3) using Equation 5-37.

𝑚𝐵𝑆,𝐷𝑀(𝑘𝑔 𝐷𝑀 𝑑⁄ ) =∑(𝑚𝑖(𝑘𝑔) × 𝐷𝑀𝑖(𝑘𝑔 𝐷𝑀 𝑘𝑔⁄ )) × (1 − 𝑜𝐷𝑀𝑖)

Equation 5-36

187

𝐴𝑛𝑛𝑢𝑎𝑙 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑠𝑒𝑟 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 (𝑈𝑆𝐷 𝑦⁄ )

=

{

𝑐𝑜𝑠𝑡𝑠𝐹(𝑈𝑆𝐷 𝑦⁄ ) × 𝑚𝐹,𝐷𝑀(𝑘𝑔 𝐷𝑀 𝑦⁄ ),

𝑚𝐵𝑆,𝐷𝑀(𝑘𝑔 𝐷𝑀 𝑑⁄ ) × 365(𝑑 𝑦⁄ ) > 𝑚𝐹,𝐷𝑀

𝑚𝐵𝑆,𝐷𝑀(𝑘𝑔 𝐷𝑀 𝑑⁄ ) × 365(𝑑 𝑦⁄ ) × 𝑐𝑜𝑠𝑡𝑠𝐹(𝑈𝑆𝐷 𝑦⁄ )

Equation 5-37

The overall annual savings for each feasible biogas system design is the sum

of the annual energy and fertiliser savings. Given that these estimated

savings from bioslurry use are not dependent on the type of biogas system

applied, any uncertainties in the associated economic value do not

undermine the objectives of the model.

Overall GHG emission savings are determined in the model based on the

sum of the GHG emissions avoided from fuel replacement and waste

management, minus the estimated GHG emissions from construction. The

GHG emission savings from fuel replacement (GHGeavoidedeng) are

calculated based on the estimated total emissions from the consumption of

current fuels (GHGe_eng) and the portion of energy requirements met by the

biogas system based on BP and Bd, as given in the equation below:

𝐺𝐻𝐺𝑒𝑒𝑛𝑔(𝑡𝐶𝑂2 − 𝑒 𝑦⁄ )

=∑𝑚𝑖,𝑓𝑢𝑒𝑙 (𝑘𝑔 𝑑⁄ ) × 365(𝑑 𝑦⁄ ) × 𝐶𝑉𝑖(𝑘𝑊ℎ 𝑘𝑔⁄ )

×𝐺𝐻𝐺𝑖,𝑒 𝑒𝑛𝑔⁄ (𝑔 𝐶𝑂2 − 𝑒 𝑘𝑊ℎ⁄ )

(1 × 106)(𝑔 𝑡⁄ )

Equation 5-38

𝐺𝐻𝐺𝑒 𝑎𝑣𝑜𝑖𝑑𝑒𝑑𝑒𝑛𝑔(𝑡 𝐶𝑂2 − 𝑒 𝑦⁄ ) = {𝐺𝐻𝐺𝑒_𝑒𝑛𝑔 , 𝐵𝑃 ≤ 𝐵𝑑𝐺𝐻𝐺𝑒_𝑒𝑛𝑔 × (𝐵𝑃 𝐵𝑑⁄ )

188

Where mi,fuel is the daily consumption of a given fuel, CVi is the calorific

value of the fuel (in kWh/kg), and GHGi,e/eng is the GHG emission rate per

kWh of delivered energy from the fuel source (as given in Table 5-3).

The emissions avoided from the management of organic waste through the

AD process (GHGeavoidedWM) was estimated to be equivalent to the

methane content of the estimated biogas production for each feasible biogas

system with the conversion to tonnes of carbon dioxide equivalent

emissions as given by Equation 5-39.

𝐺𝐻𝐺𝑒 𝑎𝑣𝑜𝑖𝑑𝑒𝑑𝑊𝑀(𝑡 𝐶𝑂2 − 𝑒 𝑦) =⁄ 𝐵𝑃(𝑚3 𝑑⁄ ) × 𝑓�̅�𝐻4

× 365(𝑑 𝑦⁄ ) ×0.6797(𝑘𝑔 𝑚3⁄ )

1000 𝑘𝑔 𝑡⁄

× 21(𝑡 𝐶𝑂2 − 𝑒 𝑡 𝐶𝐻4)⁄

Equation 5-39

The emissions avoided through fertiliser replacement are not considered in

the model, as they are not dependent on the type of biogas system applied

and would require additional input data on the specific type of chemical

fertiliser used, where it is sourced from, and how it is distributed on the

field. Furthermore, these avoided emissions are likely to be small when

compared to the avoided emissions from the management of organic waste

and fuel replacement. Surendra et al. [332] estimated the GHG emission

mitigation potential through chemical fertiliser substitution to make up

2.4% of the net GHG emission potential from biogas for the whole of Africa.

To calculate the emissions associated with the construction materials for a

given biogas system design (GHGe_mat) the product of the required mass of

each construction material (mi,mat) and embodied carbon dioxide equivalent

189

emissions per kg of material (GHGi,e/mat) is summed (Equation 5-40). The

GHGi,e/mat for each of the construction materials is listed in the construction

materials database in Appendix A (Table A-4).

𝐺𝐻𝐺𝑒_𝑚𝑎𝑡(𝑡 𝐶𝑂2 − 𝑒)

=∑𝑚𝑖,𝑚𝑎𝑡(𝑘𝑔) × 𝐺𝐻𝐺𝑖,𝑒 𝑚𝑎𝑡⁄ ( 𝑘𝑔 𝐶𝑂2 − 𝑒 𝑘𝑔⁄ )

1000 𝑘𝑔 𝑡⁄

Equation 5-40

Similarly, to determine the embodied energy based on the construction

materials of a feasible biogas system, the required mass and embodied

energy per kg of material (EEi,mat) is considered as in Equation 5-41. EEi,mat

and GHGi,e/mat values are based on those provided in the ‘Carbon Inventory’

by Hammond & Jones [333].

𝐸𝐸𝑚𝑎𝑡(𝑀𝐽) =∑𝑚𝑖,𝑚𝑎𝑡(𝑘𝑔) × 𝐸𝐸𝑖,𝑚𝑎𝑡(𝑀𝐽 𝑘𝑔⁄ ) Equation 5-41

The EROI is then calculated for each feasible biogas system design based on

EEmat, EP and its lifespan (lifespandig) as given below. It denotes the ratio of

the usable energy that will be produced from the biogas system to its

embodied energy (excluding the energy associated with construction

labour).

𝐸𝑅𝑂𝐼 =

[𝐸𝑃(𝑘𝑊ℎ 𝑑⁄ ) × 365(𝑑 𝑦⁄ )

× 𝑙𝑖𝑓𝑒𝑠𝑝𝑎𝑛𝑑𝑖𝑔(𝑦) × 3.6 (𝑀𝐽 𝑘𝑊ℎ⁄ )]

𝐸𝐸𝑚𝑎𝑡(𝑀𝐽)

Equation 5-42

190

5.5.2 Applying the TOPSIS method to identify the optimal biogas

system design

The TOPSIS method was chosen in the OBSDM to help identify the optimal

biogas system design as it enables each design option to be analysed and

ranked simultaneously and the required computation is easily integrated

into a spreadsheet [334]. Other MCDA methods commonly used in energy

applications, such as PROMETHEE and ELECTRE, use outranking and

pair-wise comparisons of the different design options (alternatives), which

require more complicated programming and are better suited for

applications where there are few criteria and many design options [335]. A

major disadvantage with the TOPSIS method, however, is that it does not

consider the relative importance of each criterion’s distance from the best

and worst scores in the analysis [336, 337]. Therefore, the OBSDM output

includes graphical and tabular summaries of the top four biogas system

design options’ performance according to each criterion, allowing the user

to easily compare them, and make the final judgement on their suitability.

The MCDA analysis of the feasible biogas system design options is carried

out using a similar approach described in section 5.4.2. Each of the

parameter values (x) (as listed in Table 5-6 in section 5.3.5) for each

sustainability criterion (j) and a feasible biogas system design option (i) are

normalised using the following equation [325]:

�̂�𝑖𝑗 =𝑥𝑖𝑗

√∑ (𝑥𝑖𝑗)2

𝑖=1

Equation 5-43

191

The normalised values of each sustainability criterion (j) for every biogas

system design option are then added, with the summed value (X) being

normalised to derive an overall normalised score for each criterion

(Equation 5-44).

𝑋𝑖𝑗 =∑�̂�𝑖𝑗

�̂�𝑖𝑗 =𝑋𝑖𝑗

√∑ (𝑋𝑖𝑗)2

𝑖=1

Equation 5-44

The A weighting (w) is applied to each normalised score based on the user

rating of each sustainability criteria, as described in section 5.3.5. The

weighting for each sustainability criteria is calculated using Equation 5-45,

where rs is the rating given to each criterion.

𝑤𝑖𝑗 =𝑟𝑠𝑖𝑗

∑ 𝑟𝑠𝑖𝑗𝑖=1

Equation 5-45

The best and worst weighted scores for each of the sustainability criteria (s+

and s-, respectively) are determined using the equations summarised in

Table 5-9.

Table 5-9: Equations to determine the best and worst scores for sustainability criteria in the OBSDM

Priority criteria Best score (s+) Worst score (s-)

Reliability, robustness, low-costa, technical efficiency, environmentally benign, local materials & labour, save time

𝑠+ = max (𝑤𝑖𝑗 × �̂�𝑖𝑗) 𝑠− = min (𝑤𝑖𝑗 × �̂�𝑖𝑗)

Simple operation & constructionb 𝑠+ = min(𝑤𝑖𝑗 × �̂�𝑖𝑗) 𝑠− = max(𝑤𝑖𝑗 × �̂�𝑖𝑗)

aAll costs are considered as negative values, and all profit is given as positive values in the OBSDM, as is common practice in accounting, resulting in an objective function of maximising profits and thereby minimising costs. bThe objective function is to minimise the time required for construction, operation and maintenance, as well as the level of expertise required to operate the system.

192

The distance from the ideal score (d+) and the worst score (d-) for each

digester design option is calculated using the following equations [322]:

𝑑(𝑠𝑖)+ = √{∑|𝑠+ − 𝑤𝑖𝑗 × �̂�𝑖𝑗|2

𝑖=1

} Equation 5-46

𝑑(𝑠𝑖)− = √{∑|𝑤𝑖𝑗 × �̂�𝑖𝑗 − 𝑠−|2

𝑖=1

} Equation 5-47

The overall weighted score (z) of each option is then calculated using

Equation 5-48 [322]. Each option is ranked according its overall score with

the optimal design option being identified in the model as the one with the

highest overall score. The output of the model contains a summary of the

main design and estimated performance parameters, as shown in Figure

5-12, which are likely to be of interest to the intended biogas system user.

Graphical and tabular summaries of the estimated performance of the top

four biogas system designs are also provided in the model output, Figure

5-13 and Figure 5-14, respectively, as previously mentioned. This

comparative data can assist stakeholders to determine which biogas system

design is most suitable.

𝑧(𝑠𝑖) =𝑑(𝑠𝑖)−

[𝑑(𝑠𝑖)− + 𝑑(𝑠𝑖)+] Equation 5-48

193

Figure 5-12: OBSDM output section – summary of recommended biogas system design

6.1 Recommended Biodigester

Biodigester & size name Modified CAMARTEC

stabilised blocks (20 b/b)9 (size name)

Estimated daily biogas

production 1.75 m³

Estimated hours of energy

production per day 3.80

hrs of cooking

(single burner

cookstove)

hrs of cooking

(single burner

cookstove)

Estimated capital cost

(considering subsidy if avail.)$74,390.87 KES KES

Months of saving req to meet

capital cost (based on current

savings & disposable income

8.88 months

Estimated monthly running costs$309.96 KES

Annual savings (from fuel and

fertiliser replacement)$44,149.15 KES

Minimum amount of water

required to mix with feedstock 46.00 L/d

Estimated time saved per day

(negative number indicates

additional time rather than a

time saving)

-48.72 min

Closest supplier contact details Tanzania Domestic Biogas Programme/CAMARTEC P.O. Box

764, Arusha, Tanzania – Njiro, Tel: +255(0)27 2549214, Fax:

+255(0)27 2549000, E-mail: info@biogas-

tanzania.org, [email protected],

[email protected], [email protected]


Return to Priorities Input

Start over - go to

Energy Demand Input

Print the

recommendedbiogas system design

194

Figure 5-13: OBSDM output section, graphical summary of the scores for sustainability parameters for the top four ranked biogas system designs

6.2.1 Recommended biodigester and top 3 other feasible biodigesters

6.2 Details of recommended biodigester and comparison with top 3 other

feasible biodigesters

-0.1-0.08-0.06-0.04-0.02

00.020.040.060.08

Reliability

Robustness

Simple operation& construction

Low cost

Technicalefficiency

Environmentallybenign

Local materials &Labour

Save time

Modified CAMARTEC stabilised blocks (20

b/b)

-0.1-0.08-0.06-0.04-0.02

00.020.040.060.08

Reliability

Robustness


Low cost

Technicalefficiency



Save time

KENBIM

-0.1-0.08-0.06-0.04-0.02

00.020.040.060.08

Reliability

Robustness


Low cost

Technicalefficiency



Save time

Fiberglass (Prefabricated)

-0.1-0.08-0.06-0.04-0.02

00.020.040.060.08

Reliability

Robustness


Low cost

Technicalefficiency



Save time

Flexi biogas digester

195

Figure 5-14: OBSDM output section – tabular summary of the top four ranked biogas system designs

Biodigester & size name

Modified

CAMARTEC

stabilised blocks

(20 b/b)

4 KENBIM 4Fiberglass

(Prefabricated)4

Flexi biogas

digester6

6.2.2 Size specifications

Recommended digester

size3.37 m³ 2.99 m³ 2.17 m³ 5.27 m³

Recommended available

total digester size4.00 m³ 3.60 m³ 3.07 m³ 5.50 m³

Number of digesters 1.00 1.00 1.00 1.00

Total gasholder size 0.90 m³ 0.85 m³ 3.07 m³ 1.20 m³

Additional recommended

gas storage0.00 m³ 0.00 m³ 0.00 m³ 0.00 m³

6.2.3 Gas and energy

production


production0.78 m³ 0.87 m³ 0.82 m³ 0.90 m³

Estimated hours of energy

production per day1.70

hrs of

cooking

(single

burner

cookstove)

1.89

hrs of

cooking

(single

burner

cookstove)

1.79

hrs of

cooking

(single

burner

cookstove)

1.95

hrs of

cooking

(single

burner

cookstove)

Specific gas production per

dig. vol. 0.16

m³

biogas/m³

installed 0.20

m³

biogas/m³

installed 0.13

m³

biogas/m³

installed 0.13

m³

biogas/m³

installed

Estimated daily energy

production 4.31 kWh 4.79 kWh 4.53 kWh 4.95 kWh

Proportion of energy

requirements met 37% 41% 39% 43%

6.2.4 Operational

specifications

Minimum amount of water

required to mix with

feedstock

0.00 L/d 0.00 L/d 0.00 L/d 0.00 L/d

Maximum amount of water

required to mix with

feedstock

33.00 L/d 33.00 L/d 15.00 L/d 15.00 L/d

Average hydraulic

retention time (HRT)73.09 d 65.48 d 66.23 d 116.64 d

Organic loading rate (OLR)0.81

kg

oDM/m³/d0.91

kg

oDM/m³/d1.26

kg

oDM/m³/d0.52

kg

oDM/m³/d

6.2.5 Economics


(considering subsidy if

avail.)

$496.65 USD $662.69 USD $714.45 USD $711.82 USD


(excl. subsidy)$496.65 USD $662.69 USD $714.45 USD $711.82 USD

Additional funds required

to meet capital cost based

on intended user's current

$200.55 USD $366.60 USD $418.36 USD $415.73 USD

Months of saving req to

meet capital cost (based

on current savings &

disposable income)

4.06 Months 7.43 Months 8.48 Months 8.42 Months

Estimated monthly running

costs$2.07 USD/month $1.10 USD/month $2.55 USD/month $2.54 USD/month

Additional monthly income

required to meet running

costs

$0.00 USD $0.00 USD $0.00 USD $0.00 USD

Annual savings (from fuel

and fertiliser replacement)$245.75 USD/yr $245.75 USD/yr $245.75 USD/yr $245.75 USD/yr

Estimated simple payback

period2.0 years 2.7 years 2.9 years 2.9 years

Estimated NPV $1,384.15 USD $1,316.68 USD $922.20 USD $925.67 USD

Cost per kWh $0.032 USD/kWh $0.027 USD/kWh $0.047 USD/kWh $0.043 USD/kWh

Estimated greenhouse gas

emissions reduced 12.48 t CO₂-e./y 13.44 t CO₂-e./y 13.62 t CO₂-e/y 14.75 t CO₂-e/y

Energy returned on energy

invested (EROI)22.88 11.60 39.87 9.06

Estimated time saved per

day (negative number

indicates additional time

rather than a time saving)

-1.22 h/d -1.19 h/d -1.21 h/d -1.17 h/d

6.2.1 Recommended biodigester and top 3 other feasible biodigesters

6.2.6 Emissions reduction, energy economics & time savings

efficiencyefficiency

196

5.6 Applying data from rural households in Cameroon

and Kenya to the OBSDM

5.6.1 Model inputs based on Kenyan and Cameroonian

household survey data

To contextualise the OBSDM as well as carry out preliminary testing,

averaged data from rural households in Kenya and Cameroon were applied

to the model. The averaged data is from two different surveys conducted in

rural Kenya in January 2014 and between April and May 2015 in Cameroon

[338, 339]. The survey from Kenya included 240 households (120 biogas

users and 120 non-users) across the six counties of Kericho, Nakuru,

Kiambu, Murang’a, Machakos, and Kajiado, which are representative of the

Western, Eastern, and Central regions of Kenya [338]. It assessed the

quality of the services provided by the Kenyan National Domestic Biogas

Programme (KENBIP), the socioeconomic impact of household biogas

systems, and sought to determine a baseline for the fuel situation [338]. In

Cameroon, a total of 18 households in the Adamawa region, 8 with biogas

systems and 10 without, were interviewed and their household air quality

monitored between April and May 2015 [339]. The aim of the study was to

assess the impact of biogas systems on household energy, water, labour, and

indoor air quality [339]. Table 5-10 and Table 5-11 summarise the average

rural household data (from both biogas users and non-user households) on

energy use, water supply, fertiliser use, and income from both studies, which

were entered as inputs in the OBSDM.

197

Table 5-10: Energy demand and feedstock inputs to the OBSDM based on averaged survey data from rural households in Kenya and Cameroon [338, 339]

Input parameter Kenya Cameroon

(Adamawa region)

Energy demand Cooking h/d (per stove) 4.5 (3 meals for 4-5 people) 4.5 (3 meals for 9-10 people) No. of stoves 1 2 Daily volume of biogas required (m3)

2.10 4.2

Daily energy required (kWh) 13.4 26.8 Current daily cooking fuel consumption

4.8 kg firewooda 10.5 kg firewood

Current lighting fuel used N/A N/A Monthly Energy costs

0 12,133.33 FCFA (20.62 USD)b

Time spent preparing current energy sources (min/d)

51 28

GHG emissions per year (t CO2-e/y) 29 64 Feedstock Amount & type 77 kg/d dairy cattle manure 66 kg/d cattle manure Time required to collect & transport feedstock to biodigester (min/d)

1 12.25

Daily biogas production potential (m3)

2.92 3.52

Daily energy production potential(kWh)

16.07 22.09

aBased on an estimated consumption of 1.2 kg /d per person [307] bBased on an exchange rate of 1 USD = 588.44 FCFA (as of July 2016)

198

Table 5-11: Location and economic inputs to the OBSDM based on averaged survey data from rural households in Kenya and Cameroon [338, 339]

Input parameter Kenya Cameroon

(Adamawa region)

Location Amount of water available (L/d) 63.5a 63.0

Time required to collect water 60 mina 15 min Mean daily temperature (°C) 20.8b 23.8b

Mean high temperature during the day (°C)

26.9b 28.8b

Mean temperature in the coldest month (°C)

16.1b 18.8b

Maximum temperature difference between day and night (°C)

10.8b 10.0b

Shallowest groundwater table depth at any point throughout the year (m)

3c 2d

Soil type Nitsols, Andosolse Ferralsols, Acrisols, Nitisolsf

Area available to install biogas system (m2)

30 30

Amount of dry fertiliser required per year (kg DM/y)

1,386g 140.3h

Cost of fertiliser per kg 40.80 KSh (0.40 USD)g,i 360 FCFA (0.61 USD)h,j

Construction materials available locally

Stone, bricks, dressed quarry stones, cement, lime, gravel, coarse sand, waterproof cement, welded square mesh (G8) –heavy gauge, steel rod/round bar (8 mm), binding wire

Stone, bricks, cement, lime, gravel, coarse sand, fine sand, water proof cement, chicken wire (1800 mm wide), steel rod/round bar (8 mm), steel rod (6 mm), binding wire, feeding mixer

Economics

Monthly disposable income 5,000 KSh (49.35 USD)i 941.67 FCFA (1.60 USD)j,k

Savings available for capital expenditure

30,000 KSh (296.10 USD)i 5,650 FCFA (9.60 USD)j,k

Subsidies available None 5% installation cost aAverage from informal settlements of Nyalenda in Kisumu and Kibera in Nairobi [340] bOBSDM country database, climatic data from Weatherbase [341] cBased on the shallowest groundwater levels encountered for the Baricho Aquifer in the coastal strip, which is shallower than the groundwater levels of major aquifers in Kenya [342] dBased on shallowest depth to water below ground level in buffer zone for bauxite mining project in Adamawa region [343] eSoils found in several regions of Kenya [344] fBased on dominant soils in the ferralitic zone [345] gBased on cost of 2,480KSh and 1,600KSh per 50kg bag of diammonium phosphate (DAP) and calcium ammonium nitrate (CAN) fertilisers, respectively [346] hBased on an average annual spending of 50,500 FCFA for chemical fertiliser by farmers in Mezam division and a cost of 18,000 FCFA per 50 kg bag [347] i Based on an exchange rate of 1 USD = 101.32 KSh (as of July 2016) j Based on an exchange rate of 1 USD = 588.44 FCFA (as of July 2016) kBased on annual savings of 11,300 FCFA in ‘Njangis’ of farmers in Mezam division [347, 348]

199

Although there are notable differences in the two studies, the conditions of

the surveyed Kenyan and Cameroonian households are comparable. The

dominant cooking method in both study regions is the three-stone wood

stove, and the main feedstock available for biogas production is cattle

manure. No cost has been assigned to firewood use for the Kenyan rural

household scenario, as the study noted that over half of surveyed

households collect rather than purchase firewood. In Cameroon, over half

of the surveyed households spent between 600 and 5000 FCFA per week on

firewood [339]. The availability of cattle dung and time associated with

dung collection to feed the biodigester differed in the two household

scenarios as different cattle grazing practices are applied. Kenyan

households kept their cattle in one cattle holding area close to the house for

most of the year, while in Adamawa cattle are only kept in kraals (cattle

holding areas) close to homes overnight during the dry season and left to

graze away from their homes during the wet season [338, 339].

The model input details on local construction materials and the area

available for installing the biogas system were estimated based on the type

of biogas systems developed through the domestic biogas programmes in

Kenya and Cameroon. The KENBIP, which began in 2008 as part of the

Africa Biogas Partnership Programme (ABPP), developed the KENBIM

fixed dome model and has helped increase biogas dissemination to over

10,000 biodigesters installed since the programme began [175, 349].

Cameroon’s biogas dissemination has been more localised with pilot

domestic biogas projects in selected regions such as Adamawa, while a

200

national domestic biogas programme is being developed by the Ministry of

Water and Energy (MINEE) through partnerships with the Netherlands

Development Organisation (SNV), Heifer International, and Programme de

Développement Durable du Lac Tchad (PRODEBALT) [347, 350, 351]. SNV

has facilitated the promotion and construction of fixed dome designs based

on the Nepalese model GGC 2047 in Cameroon [350]. The OBSDM does not

include this Cameroonian fixed dome model in the digester database,

however, it does include the comparable Rwanda III model which also is

based on the GGC 2047 model. Rural households in Adamawa were

provided with subsidies of 5, 25, and 45 percent of the biodigester

installation costs as part of a study conducted by SNV and the Development

Economics Group from Wageningen University [352]. The minimum

subsidy of 5 percent was included as an input to the model. In Kenya,

government subsidies are no longer available for households under the

KENBIP. The final model input – the priorities of sustainability criteria –

were rated based on the Kenyan survey responses on the reasons for

installing biogas systems for both case studies, as this information was not

available from Cameroon. The primary reasons for installing biodigesters

were to make cooking more convenient as well as save money and time

[338]. Therefore, simple operation, low-cost, and save time received the

highest rating of 5 while all other criteria were given a moderate rating of 3.

201

5.6.2 Model outputs – optimal biogas system designs for Kenyan

and Cameroonian households

The OBSDM identified a 6 m3 modified CAMARTEC stabilised soil blocks

(SSB) digester to be optimal based on the specified conditions for both an

average Kenyan and Cameroonian rural household with the details

summarised in Table 5-12. Compared to the survey results the model’s

estimates on biogas production, proportion of cooking needs met, and the

time saved by applying these biogas systems are conservative. The volumes

of the majority of household biodigesters in the Kenyan study region were

8 m3, providing 3 hours of cooking for a double burner stove with no

households reporting a shortage of gas [338]. Comparatively, the model

estimated the biogas system to provide a total of 3.2 cooking hours for a

single stove, meeting 71% of the cooking energy needs of an average rural

household in Kenya. The lower biogas production estimates relative to the

energy required given by the model may be due to the average amount of

feedstock fed to the digesters being greater than what was entered in the

model. The Kenyan survey report provided figures for the average amount

of dairy cattle, other cattle, market pig, and breeding pig dung fed to the

biodigester per day. However, it did not specify the average per household,

and thus some households may be using a combination of animal dung to

feed their biodigesters. For an average household in Adamawa the biogas

system is estimated to provide 3.1 h of cooking each day on a single stove,

saving 3.6 kg/d in firewood and meeting 34% of the daily cooking

requirements. These estimated savings in firewood and amount of cooking

energy met with biogas are conservative compared to the Cameroonian

202

survey results of 5.5 kg/d firewood saved and 5 of the households stating

that biogas is the only cooking fuel that they use. Aside from the reasons

already mentioned for the Kenyan study, the low biogas production

estimates from the model for both case studies may be due to a number of

factors. Firstly, the biogas consumption rate for cooking in the model is high

at 150 L/meal for each person and is likely to be different in Kenya and

Cameroon, depending on the biogas stove design and how it is used by the

households [142]. Secondly, feedstock parameters such as DM, oDM, biogas

yields per unit mass and methane content are based on average values from

literature, which are likely to differ from the actual values for cattle dung in

Kenya and Adamawa. Finally, the climate data used in the model for both

case studies is based on country average climate data, while the

temperatures experienced in both study areas may be higher, and the higher

digester temperature would result in higher biogas production. A sensitivity

analysis for these and other model parameters with uncertainties will be

carried out in the following chapter.

Households in Kenya and Adamawa are estimated to spend an additional

56 and 49 minutes, respectively, to operate and maintain their biogas

system. This is within the range reported in the Cameroonian survey (2 to

59 minutes) and attributable to the additional time required to collect

feedstock [339]. The time Kenyan households spend on collecting feedstock

and operating the biogas system (including water collection) was not

reported in the survey; however, households did indicate that less time was

spent on cooking [338]. Thereby the additional time estimated by the model

203

for an average Kenyan household is likely to be overestimated, particularly

since a significant portion of this time is attributed to water collection,

which is based on data from informal settlements in Kenya. Reductions in

cooking time have not been included in the model and could lead to overall

time savings. The OBSDM estimated that all fertiliser requirements will be

met by the biogas system for Cameroon and 85% of the amount required by

Kenyan households. Estimated financial savings from replacement of

chemical fertiliser with bioslurry were within 0.3% of the estimated savings

from the Kenyan survey, a total of 26,773 KSh and 21,296 KSh for DAP and

CAN fertiliser replacement, respectively [338]. The larger fertiliser

consumption for rural Kenyan households compared to those in Adamawa

have resulted in higher annual savings and thereby a shorter payback period

and higher NPV. The installation costs of the recommended biogas systems

from the OBSDM for the Kenyan and Cameroonian case studies are based

on average construction material and labour costs in Kenya and SSA,

respectively, and would need to be revised based on local costs for more

reliable cost figures.

204

Table 5-12: Optimal biogas system design details from the OBSDM for rural Kenyan and Cameroonian households

Digester design details Kenya Cameroon (Adamawa region)

Recommended digester 6 m3 Modified CAMARTEC stabilised soil blocks

6 m3 Modified CAMARTEC stabilised soil blocks

Recommended digester size 6.5 m³ 5.3 m³

Available total digester size 6.0 m³ 6.0 m³

Number of digesters 1.0 1.0

Total gasholder size 1.6 m³ 1.6 m³

Additional recommended gas storage

0.0 m³ 0.0 m³

Minimum amount of water required to mix with feedstock

0.0 L/d 39.0 L/d

Maximum amount of water required to mix with feedstock

63.5 L/d 63.0 L/d

Average hydraulic retention time (HRT)

62 d 49 d

Organic loading rate (OLR) 0.8

kg oDM/m³/d

1.8 kg oDM/m³/d

Estimated daily biogas production

1.5 m³ 1.4 m³

Estimated daily energy production

8.2 kWh 9.0 kWh

Proportion of energy requirements met

71 % 34 %

Estimated daily cookstove hours

3.2 h 3.1 h

Estimated capital cost 69,301 KSh (684USD)a 369,278 FCFA (628 USD)c

Estimated monthly running costs

289 KSh (2.85 USD)a 1,620 FCFA (2.75 USD)c

Estimated simple payback period (years)

1.4 4.4

Estimated NPV 309,266 KSh (3,052 USD)a,b 173,113 FCFA (294 USD)b,c

Annual savings (from fertiliser and fuel replacement)

47,931 KSh (473 USD) 83,145 FCFA (141 USD)c

Estimated time saved -56 min/d -49 min/d

Estimated GHG emissions reduced

24 t CO₂-e/y 26 t CO₂-e/y

Energy returned on energy invested (EROI)

29.23

32.19

Estimated savings in firewood consumption 3.4 kg/d 3.6 kg/d

Closest supplier contact details

Tanzania Domestic Biogas Programme/CAMARTEC, Arusha, Tanzania

Tanzania Domestic Biogas Programme/CAMARTEC, Arusha, Tanzania

aBased on an exchange rate of 1 USD = 101.32 KSh (current July2016) bBased on a discount rate of 10% [331] cBased on an exchange rate of 1 USD = 588.44 FCFA (current July 2016)

205

5.7 Summary and conclusions on the development and

preliminary testing of the OBSDM

5.7.1 Comparison of top four biogas system designs for rural

households in Kenya and Cameroon

The modified CAMARTEC SSB design was found to have the highest score

for the low-cost and environmentally benign criteria in both the Kenyan and

Cameroonian case study (Figure 5-15 and Figure 5-16, respectively).

Appendix B provides details of the MCDA analysis, specifically the optimal

digester size selection, parameter values, standardised scores and weighted

scores for all feasible biogas system designs (Table B-1 to Table B-8).

Modified CAMARTEC SSBs are less expensive and energy intensive than

masonry fixed dome biogas systems due to the use of stabilised soil blocks

instead of bricks. As would be expected for the Kenyan case study, the

KENBIM fixed dome model was among the top scoring systems, with the

highest score for local materials and labour. Similarly, the Rwanda III model

which is the closest match to the type of system used in Cameroon, was

among the top four systems for the Cameroonian case study with the highest

local materials and labour score. The modified CAMARTEC solid state

digester (SSD) is designed to operate at a higher TS range compared to

conventional fixed dome models, thereby requiring less water. This

characteristic was not significant to the rural Kenyan household scenario

due to the model estimating a TS content for the dairy cattle feedstock which

enabled the minimum water requirements to be zero for all but one of the

feasible digester designs. In the Cameroonian rural household scenario, the

cattle manure was estimated to have a higher TS content than the dairy

206

cattle manure in Kenya, and thereby the modified CAMARTEC SSD was the

only system where the minimum water requirement was zero litres per day.

As previously mentioned, water supply can be a significant issue in SSA and

there is a need for biogas system designs with a low water consumption

(such as the modified CAMARTEC SSD). In the rural household scenario for

both Kenya and Cameroon the results also indicate that masonry fixed dome

systems (which could be constructed from a majority of local materials) are

more cost effective than prefabricated systems with high upfront costs. The

flexi biogas digester was the only system which was found to be cost-

competitive with the masonry fixed dome systems; however, its shorter

lifespan results in higher costs per kWh. In light of these results from the

preliminary testing of the model, it can be concluded that the model is able

to recommend biogas system designs that are appropriate according to the

context and priorities of the intended user.

207

Figure 5-15: Summary of the MCDA analysis of the top four biogas system designs identified by the OBSDM for an average rural Kenyan household

208

Figure 5-16: Summary of the MCDA analysis of the top four biogas system designs identified by the OBSDM for an average rural Cameroonian household in the Adamawa region

209

5.7.2 Limitations of the OBSDM

The OBSDM has been developed to consider the critical factors in the design

of biogas systems to provide reasonable estimates on recommended designs

applicable to a range of stakeholders in SSA. The outputs are instructive

rather than prescriptive, highlighting the type of biogas systems that are

most likely to yield the best performance for the intended user based on

their context and priorities. Furthermore, the model has been designed in

such a way that it can be expanded and modified as more regional-specific

data becomes available and the SSA biogas sector develops. In its current

state the model is best suited for identifying suitable household-scale

systems, with the majority of the biogas system types included in the

biodigester database being intended for this application. However, it can

also be a useful tool for designing community-scale systems. Given that

numerous techno-economic biogas tools exist that are well suited to

commercial-scale systems, developing a model for household- and

community-scale applications was considered a priority for this research.

All the biogas system design types included in the model’s biodigester

database are unheated, which is typical for household- and community-

scale systems in SSA due to the dominant temperate climate. Nevertheless,

provision has been made in the model for heated systems, particularly in

determining the digester temperature as mentioned in section 5.3.3.

Estimates of the digester temperature for unheated systems could be

improved through considering the soil temperature, which currently is not

part of the location inputs or climate database, as soil temperature data is

210

not readily available in SSA. The model also does not consider the effect of

altitude on biogas volume and pressure, however, the energy production

potential calculations for biogas in the model are independent of

temperature and pressure. Any discrepancies between the estimated biogas

volume produced and the required storage will be minimised through the

changes in the volumetric gas consumption of appliances with altitude.

While the OBSDM is able to direct design choices that are optimal according

to the intended user’s context and priorities, the benefits of the technology

can only be fully realised through its appropriate application. Therefore, it

is highly recommended that where the OBSDM is applied, the quality and

efficiencies of the biogas appliances intended to be used with system are

carefully assessed and improved, where required, and that a consultation is

carried out with the intended users to ensure that the use of these appliances

along with the bioslurry will be socially and culturally appropriate. Biogas

lamps and cookstoves are the only appliances considered in the OBSDM,

but other appliances could be integrated into the model as required. The

feedstock database in the model can also be expanded and updated as

regional-specific data becomes available, as mentioned in the analysis of the

two case studies. In the following chapter, study data from Rwanda will be

used to validate the model, and some of the parameters with uncertainties

or significant variability will be tested on their sensitivity.

211

Chapter 6 Validation and

sensitivity analysis of the OBSDM

using household data from

Rwanda Validation and sensitivity analysis of the

OBSDM using household data from

Rwanda

“If you are building a house and a nail breaks, do you stop building or do

you change the nail?”

– Rwandan proverb

The development and preliminary testing of the OBSDM, as described in the

previous chapter, provides the basis for testing and refining the model

further. This chapter seeks to validate the model and test its sensitivity to

parameters that carry uncertainties or have a large scope for variability. The

challenge with the OBSDM lies within the accuracy of the output being

dependent on the accuracy of the inputs and internal databases. In order to

test whether the model calculations and output are reasonable, survey data

from Rwandan households with biogas systems from the “Scientific

Comparative Performance Study of Fixed Dome Masonry, Fiber Glass and

Flexbag Biodigesters in Rwanda” (Comparative Biodigester Study) [353]

was used to compare the model’s analysis of different types of biogas system

designs with the study results. The influence of the geographical location,

household income, and water supply on the model’s output recommended

biodigester designs was also tested by applying the survey data. This was

212

followed by a sensitivity analysis of parameters with uncertainties, including

climate data, biodigester lifespan, biogas production efficiency, biogas

yields from feedstocks, and biodigester costs. A sensitivity analysis was also

carried out on the priority rating of sustainability criteria in the OBSDM.

The results were used to identify any critical or superfluous input

parameters as well as what recommendations need to be provided to the

model user to ensure they get the output that is most appropriate for their

intended use of the model.

6.1 Rwandan Comparative Biodigester Study

Background

The Comparative Biodigester Performance Study was carried out in 2015 by

the University of Rwanda (UR), as commissioned by the Netherlands

Development Organisation office in Rwanda (SNV Rwanda), with the

assistance of technical experts from SNV and academic researchers from

Murdoch University. The aim of the study was to compare the technical,

economic, social, and environmental performance of three different types of

household-scale biodigesters: fixed dome; fiberglass; and flexbag

biodigesters [353]. Through collaboration with the National Domestic

Biogas Programme (NDBP), 19 suitable biodigesters (11 fixed dome

masonry, 4 fiberglass, and 4 flexbag) were identified for the study in the

districts of Gasabo, Kayonza, Kicukiro, Kirehe, Ngoma, and Rwamagana in

central and eastern Rwanda (Figure 6-1). In April 2015, the households of

the 19 biodigesters were surveyed to inquire about economic and technical

aspects of the biodigester performance. The main feedstock used by the

households for the biodigesters is a combination of cow dung and water.

213

Usually, biodigesters are fed once a day and the produced biogas is used in

one or more biogas stoves for cooking. Some households also have a biogas

lamp that is used in the evenings for lighting. Subsidies were made available

to some households to help cover the installation costs of the biodigesters.

After the initial survey, data loggers were set up at six sites to collect

technical data on the biodigesters’ performance. Gas flow meters were also

installed at four more sites and all households were provided with a weekly

set of recording sheets. Data from each site was collected on a biweekly basis

between June and August 2015. Laboratory testing was carried out on

samples of cattle dung, bioslurry from the slurry pit, and the digested slurry

at the outlet collected from 14 of the surveyed biodigesters, to determine

their total TS and VS content.

Fixed dome masonry biodigesters were found to perform the best overall

compared to the fiberglass and flexbag biodigesters, particularly due to their

high technical and operational performance, as well as their social and

environmental impact [353]. The technical performance parameters

measured in the study included: sensitivity to ambient temperature

fluctuations; pH levels; gas flow (production) per kg of cow dung; gas

pressure in the biodigester; and VS degradation. The operational

performance parameters were: ease of use (mixing/slurry discharge

operations); cleanliness of the site; pathogen reduction; and robustness

(current state of the digester, maintenance history, and estimated

operational life expectancy). The proportion of cooking requirements met

(gas stove hours), nutrient content of the bioslurry, perceived impact on

214

crop yields (according to users’ observations), and fuelwood savings were

used to compare the social and environmental impact of the different

biodigester types. Economic performance parameters included investment

costs, operational costs (maintenance), and savings from replacing chemical

fertiliser. Flexbag digesters were found to have the best economic

performance due to the low investment and repair costs, while fiberglass

biodigesters were the least sensitive to ambient temperature fluctuations.

Figure 6-1: Map of provinces and districts in Rwanda [Source: Government of Rwanda 2009]

215

6.2 Applying Rwandan household biodigester study

data to the OBSDM

6.2.1 Inputs -Energy use, feedstock, climate data, water

availability, and financial situation

The relevant data from each of the surveyed households from the

Comparative Biodigester Study was entered in the OBSDM as inputs, as

given in Appendix C (Table C-1 to Table C-3). For the current energy

demand and intended use of the biogas system, the survey data on the

number of cookstoves, number of biogas lamps, average number of hours of

biogas used for cooking and lighting, and alternative fuels used if biogas is

not available (entered as current energy fuels used in the model), were

utilised. Most households used firewood for cooking when no biogas was

available, although some used charcoal or a combination of firewood and

charcoal. This is reflective of the Rwandan population, where traditional

biomass resources are the dominant fuel sources, highlighting the

important role of biogas to help reduce the demand of firewood and charcoal

for cooking in the country [354, 355]. Where the firewood consumption was

not stated by the householder in the survey, the consumption was estimated

using the mean firewood saved per hour of cooking with biogas (14.8 kg

firewood saved/h cooking with biogas), or the mean firewood saved per m3

of biogas consumed (30.4 kg firewood/ m3 biogas). These mean

consumption figures were determined from the firewood consumption

amounts provided by households participating in the study. Data on the

time spent collecting firewood was not available from the Comparative

Biodigester Study; therefore, an average time of 37 minutes was used, based

216

on the study by Bedi et al. [356], which surveyed 305 biogas users in

Rwanda. Fuel costs were provided in the survey in Rwandan Francs (FRw)

per m3 of firewood, or in bags of charcoal with one bag of charcoal weighing

43.6 kg on average in Rwanda [355]. Where the specific firewood cost for a

given household was not stated, the cost was estimated using the mean cost

per m3 from the surveyed households in the region. The mass of firewood

was approximated using an average specific gravity of 0.734, based on the

main fuelwoods used in Rwanda, namely Grevillea robusta (Australian silky

oak), Eucalyptus camaldulensis (River red gum), E. citriodora (Lemon

scented gum), and E. tereticornis (Forest red gum) [354]. All surveyed

households used cattle dung as feedstock for their biodigesters, and

reported the mass of cattle dung and water fed into the biodigester per day.

The usual time spent collecting the feedstock was reported by the household

in the initial study survey, along with the number of cattle and calves. Where

the feedstock collection time was not available, an average rate of 1.03

min/kg of cattle dung, calculated from the survey data from other

households, was used to estimate the time taken to collect cattle dung for

the biodigester. Similarly, some households did not report the time taken

for water collection, therefore, it was also estimated based on the mean time

taken to collect water for a given district.

Climatic data including mean daily, mean minimum, and mean maximum

ambient temperatures were determined based on the ambient temperature

data recorded by dataloggers. For the surveyed households without

dataloggers, the mean ambient temperature was estimated based on the

217

ambient temperature measurements recorded by the enumerators at each

site, while the mean maximum and minimum ambient temperatures were

approximated according to districts based on the datalogger records.

Groundwater table depths were not recorded in the study and are not known

in many parts of Rwanda [357]. For the households in Gasabo, Kayonza, and

Kicukiro, the groundwater level was estimated to be equivalent to the depth

of boreholes in these districts [358]. The groundwater depth for

Rwamagana was approximated to be 40 m, based on the Rugende II water

well depth [359]. For the remaining households in the districts of Kirehe

and Ngoma, the mean groundwater table depth from all other study districts

was used. The soil type dominant in each district based on the African soil

map [360] was entered in the model as follows: Acrisols, Alisols, Plithosols

for sites in Gasabo, Kicukiro, Kirehe and Ngoma, and; Ferralsols, Acrisols,

Nitisols for sites in Kayonza and Rwamagana. A conservative estimate of the

area taken up by the existing biogas system and bioslurry pit was used for

the area available at each site to install a biodigester. Underground

construction was assumed to be possible at all sites, given that a large

majority of the installed biodigesters were underground fixed dome

systems. The annual mass of dry fertiliser required, mCN, was estimated

based on the volume of the slurry pit, Vsp, and the amount of cattle dung and

water fed to the digester, mi and mw, respectively (with the density of the

feedstock approximated to that of water), yielding the following equation:

218

𝑚𝐶𝑁(𝑘𝑔 𝑦⁄ )= 365(𝑑 𝑦⁄ )

× [𝑉𝑠𝑝(𝐿)

𝑚𝑤(𝑘𝑔 𝑑⁄ ) + (1 − 𝑜𝐷𝑀𝑖 × 𝐷𝑀𝑖) × 𝑚𝑖(𝑘𝑔 𝑑⁄ )]

−1

× [𝐷𝑀𝑖 ×𝑚𝑖(𝑘𝑔 𝑑⁄ )

𝑚𝑤(𝑘𝑔 𝑑⁄ ) + 𝑚𝑖(𝑘𝑔 𝑑⁄ )] × 𝑉𝑠𝑝(𝐿) × 1(𝑘𝑔 𝐿)⁄

Equation 6-1

Where oDMi and DMi are the organic dry matter and the dry matter fraction

of the feedstock. The survey responses regarding use of the bioslurry were

also considered, with some households using all their bioslurry, while others

sold a portion to their neighbours. The price of the bioslurry quoted by

households in the survey was used to estimate the cost of dry fertiliser per

kilogram in the model. Use of bioslurry as fertiliser in Rwanda leads to

improved crop yields and soil fertility, reducing the need for land clearing

for farming, which is a major cause of deforestation in the country [354].

Construction materials used in the Rwandan fixed dome model and

associated costs were entered as construction materials available locally at

all sites. Value added tax (VAT) is entered in the model as zero due to

equipment used in the supply of biogas energy being exempted from VAT in

Rwanda [361]. The disposable income of each household was estimated

based on the on average consumption per adult per year in the Kigali City

and Eastern Province, the poverty line, and the household size. Average

consumption figures were extrapolated from figures given for 2000-2001,

2005-2006, and 2010-2011 by the National Institute of Statistics of Rwanda

(NISR) [362]. The poverty line set by the NISR was based on the minimum

food consumption basket, which is the required number of calories a

Rwandan needs who is likely to be involved in physically demanding work,

219

along with an allowance for non-food items [362]. The household size was

estimated from the mean number of people per household according to

region and sex of household head, as well as the number of economically

active household members from the Rwanda Household Census [363]. The

reported mean consumption and household size with the resulting

estimated disposable income figures are summarised in Table 6-1 and Table

6-2. The initial investment made by the households to install their system,

as reported in the Comparative Biodigester Study, was entered as the

savings available for capital expenditure in the model. Similarly, the

reported subsidy amount received from the Rwandan Government was

entered in the model as the subsidy available (300,000 FRw for households

with fixed dome and flexbag biodigesters, and 600,000 FRw for households

with fiberglass biodigesters). The households’ motivations for the

installation of a biogas system was not part of the study, therefore, all

criteria are rated equally important for each household to determine the

impact of geographic location, household income, and water supply. To

compare the model output with the installed biodigester systems, two

scenarios were applied; one where an equal priority criteria rating is used,

and the other where the priority criteria were rated according to what is

expected to be favourable for their installed biogas system (Table 6-3).

220

Table 6-1: Estimated consumption in 2015 for households in the Kigali City and Eastern Province of Rwanda based on the 4th Population and Housing Census and the Integrated Housing and Living Conditions Survey 2010-2011 [362, 363]

Table 6-2: Estimated disposable incomes in 2015 for households in the Kigali City and Eastern Province of Rwanda based on the 4th Population and Housing Census and the Integrated Housing and Living Conditions Survey 2010-2011 [362, 363]

Table 6-3: Priority criteria rating* according to biodigester type for Rwandan households based on the results from the Comparative Biodigester Study [353]

Priority criteria Fiberglass Fixed dome Flexbag

Reliability 4 5 3

Robustness 5 5 3

Simple operation 5 5 4

Low-cost 3 3 5

Technical efficiency 3 5 4

Reducing greenhouse gas emissions

3 3 4

Local materials & labour 3 5 3

Save time 3 3 3

*1=Not important, 2=slightly important, 3=moderately important, 4=very important, 5=extremely important

Province (district)

Average consumption per adult (FRw) Predicted

consumption (FRw)

2000/ 2001 2005/ 2006 2010/ 2011 2015/ 2016

Kigali city (Kicukiro, Gasabo)

253,243 289,504 324,844 360,798

Eastern Province (Rwamagana, Kayonza, Kirehe, Ngoma)

71,397 89,901 104,487 121,685

Essential items consumption

64,000 N/A 118,000 87,400

Note: 1 USD = 811.40 FRw as of 25 November 2016

Province (district)

Male headed household (HH) Female headed household (HH)

Mean HH size

No of adults (based on % economically

active)

Disposable income (FRw)

Mean HH size

No of adults (based on % economically

active)

Disposable income

Kigali city (Kicukiro, Gasabo)

4.0 2 45,566 3.6 1 22,783

Eastern Province (Rwamagana, Kayonza, Kirehe, Ngoma)

4.6 2 5,714 3.7 1 2,857

Note: 1 USD = 811.40 FRw as of 25 November 2016

221

6.2.2 Results and analysis

6.2.2.1 Comparison of installed biogas system with the model

output biogas system design

The prefabricated fiberglass and Modified CAMARTEC stabilised block

design (MCD SSB) systems were the dominant recommended systems by

the OBSDM, differing from the Comparative Biodigester Study, which found

the Rwanda III fixed dome biodigester (based on the GGC 2047 model) to

have the best overall performance. The 4 m³ prefabricated fiberglass biogas

system was recommended for 63% of the households when an equal rating

for priority criteria was used (Figure 6-2Figure 6-3), and the 4 m3 MCD SSB

was recommended for 58% of the households when the priority criteria

favouring the installed systems were used (Figure 6-3).The biodigester sizes

recommended by the model were consistently smaller (with the exception

of Household 10, where the recommended size was the same) compared to

the installed systems, resulting in a shorter HRT and larger OLR, as can be

observed from Table C-4 in Appendix C. The difference in sizing indicates

that installed systems were sized based on overestimated feedstock supplies

or regional/national climate data, rather than the average measured

feedstock supply and location specific data used in the model. Installed

systems may also have been oversized for improved system stability.

Further analysis on the impact of location specific and national climate data

will be discussed in section 6.2.3.1. The large variations between the daily

biogas production and cooking hours possible (based on the biogas

production) estimated by the model and those from the study, may be due

to the survey data being based on the hours of use and the daily biogas

222

consumption, rather than the total volume of biogas that is available for

cooking (and lighting) each day. One example is Household 3, which had an

average gas pressure reading of 4.8 kPa at the end of each day, indicating

that the total amount of available gas was not consumed. Some variation

between the biogas production estimated by the model and that from the

study is also expected due to the difference between the properties of the

cattle dung, particularly oDM, used in the model and that observed at each

site. The uncertainties in biogas yields in feedstocks in the model will be

discussed in section 6.2.3.4.

Figure 6-2: Recommended biodigester types using equal priority criteria rating, categorised according to the installed system (horizontal axis)

0

2

4

6

8

10

12

Fiberglass Fixed dome Flexi-bag

No

. o

f h

ou

seh

old

s

Installed biodigesters

Modified CAMARTECstabilised blocks

Kentainer BlueFlameBioSluriGaz


223

Figure 6-3: Recommended biodigester types using priority criteria favourable to installed biodigester types, categorised according to the installed system (horizontal axis)

A closer look at the MCDA analysis applied by the model in both scenarios

reveals that the exceptionally high Energy Return on Energy Invested

(EROI) for the fiberglass biogas system has enabled it to become the

preferred biogas system design for most of the households. The EROI is

calculated as the ratio of usable energy produced by the system over the

energy embedded in the construction materials, as described in Chapter 5.

The embedded energy values used in the model only consider the cradle-to-

gate boundary, that is all the energy required in the extraction and

manufacturing of the material until it leaves the factory gate [333]. In the

SSA context, the energy required to transport imported materials from the

overseas manufacturer to the commercial capital of a country can be

significant, and should be considered in the embodied energy figures, as

well as tonnes of carbon dioxide equivalent GHG emissions when

comparing different types of biogas systems (a method described as cradle-

to-site) [333]. To consider the environmental impact of the transportation

0

2

4

6

8

10

12


No

. o

f h

ou

seh

old

s





224

component of imported biodigester materials in the model, the impact

calculation coefficients for primary energy demand (MJ/km) and GHG

emissions (kg CO2-e/km) per tonne of material from Zabalza Bribián et al.

[364] was applied, considering the distances and modes of transport

required to get the imported product to Kigali, the commercial capital of

Rwanda (Table 6-4). The primary energy demand and GHG emissions from

intra-country transport was not included in the model as it is expected to

have minimal variation between different construction materials at a given

site. Application of the cradle-to-site method for imported materials has

resulted in a 28% and 25% increase in GHG emissions and embodied

energy, respectively, for the fiberglass biogas system. The emissions and

embodied energy for five other types of biodigesters also increased due to

their use of imported materials, as summarised in Table 6-5. The increase

in embodied energy results in a lower EROI for the biodigester. The amount

of GHG emissions avoided through the capture of methane is proportionally

larger than the estimated emissions from the construction materials.

Therefore, consideration of emissions from transporting imported

construction materials does not yield to significant differences between the

overall GHG emissions avoided for each feasible biogas system.

225

Table 6-4: Revised GHG emissions and embodied energy for biodigester materials imported to Rwanda

Construction Material Flexi-biogas PVC tarpaulin

bag

Puxin Biogas storage bag

AGAMA BiogasPro (LLDPE plastic)

Kentainer BlueFlame tank (LLDPE plastic)

PUXIN gasholder (fiberglass

reinforced plastic)

Fiberglass biodigester

Unit m² m³ digester digester pce (1 m³) digester (6 m³)

kg/unit 0.7 2.8 120 111 30 120

Emissions manufacturing (kg CO₂-e/unit)

1.7 7.3 226.8 209.5 45.9 183.6

Embodied Energy manufacturing (MJ/unit)

45.3 192 8676 8012 840 3360

Place/port of origin Nairobi, Kenya Shenzhen, China Cape Town, South Africa Nairobi, Kenya Shenzhen, China Tianjin, China

Distance by sea freight to nearest port (Dar es Salaam, Tanzania) (km)a

N/A 11,414 4,832 N/A 11,414 14,353

Distance by lorry (road freight) to commercial capital (Kigali) (km)b

1,198 1,530 1,530 1,198 1,530 1,530

Emissions transport (kg CO₂-e/unit)c

0.2 1.1 41.2 25.6 12.2 52.1

Embodied energy transport (MJ/unit)d

2.6 18.8 689.1 433.6 201.9 856.7

Emissions total (kg CO₂-e/unit)

1.9 8.4 268.0 235.1 58.1 235.7

Embodied Energy total (MJ/unit)

47.9 210.9 9365.1 8445.9 1041.9 4216.7

aDistances based on sea route distance in nautical miles from [365] and a conversion factor of 1.852 km/nm. bDistances based on transport route and distances stated in [366, 367]. c Calculated using impact calculation coefficients of 0.011 and 0.193 kg CO2-e/km for the transportation of 1 tonne of goods via transoceanic freight ship and lorry (road freight), respectively. dCalculated using impact calculation coefficients of 0.170 and 3.266 MJ/km for the transportation of 1 tonne of goods via transoceanic freight ship and lorry (road freight), respectively.

226

Table 6-5: Comparison of greenhouse gas emissions and embodied energy of biodigesters in OBSDM with and without consideration of transport for imported construction materials

Biodigester Name

Digester volume

(m³)

Size name

GHG &EE boundary

GHG emissions (kg

CO₂-e)

Embodied energy (MJ)

AGAMA BiogasPro

3.00 3 Cradle-to-gate 226.8 8,676

Cradle-to-site 268.0 9,365

% change 18. 18% 7.94% Fiberglass (Prefabricated)

3.07 4 Cradle-to-gate 122.4 2,240


% change 28.36% 25.50% Flexi biogas digester

3.50 4 Cradle-to-gate 147.3 6,282


% change 0.10% 0.04% KENBIM* 3.60 4 Cradle-to-gate 1,091 10,853

Cradle-to-site N/A N/A Kentainer BlueFlame BioSluriGaz

1.80 1.8 Cradle-to-gate 117.8 4,507

Cradle-to-site 132.2 4,751 % change 12.23% 5.41%

Modified CAMARTEC*

4.00 4 Cradle-to-gate 1,017 10,062

Cradle-to-site N/A N/A Modified CAMARTEC stabilised blocks*

4.00 4 Cradle-to-gate 595.5 4,949 Cradle-to-site N/A N/A

Modified CAMARTEC solid state digester (SSD)*

7.87 9 Cradle-to-gate 2,448 24,988

Cradle-to-site N/A N/A

PUXIN (Bioeco Sarl)

10.00 10 Cradle-to-gate 1,238 10,173

Cradle-to-site 1,256 10,469 % change 1.45% 2.91%

Puxin (Biogas Burundi)

10.00 10 Cradle-to-gate 1,804 16,061

Cradle-to-site 1,816 16,263 % change 0.68% 1.26%

RW III (based on GGC 2047)*

3.04 4 Cradle-to-gate 1,454 18,367


Senegal GGC 2047*

8.00 8 Cradle-to-gate 1,292 8,723


Sinidu model (modified GGC-2047)*

4.00 4 Cradle-to-gate 1,627 18,846


Zambdigester* 3.10 4 Cradle-to-gate 604.0 4,267


The revised EROI figures have addressed the bias toward the fiberglass

biodigesters, as can be observed in Figure 6-4, which shows the

recommended biodigester types using equal priority criteria rating. The

MCD SSB was recommended instead of a fiberglass system for all but two

227

of the households with fiberglass and fixed dome systems installed (one

fiberglass system recommended for each installed type), and for a

household with a flexbag system installed, a Kentainer BlueFlame

BioSluriGaz (Kentainer BlueFlame) system was recommended instead of a

fiberglass biodigester. The Kentainer BlueFlame system has been

recommended for half of the households with flexbag systems under both

priority scenarios due to its high reliability rating, particularly its 30-year

lifespan, and modest land footprint. Since the lifespan of biogas systems are

often estimated by the manufacturer, and vary depending on the type of

environmental conditions, as well as the operation and maintenance habits

carried out by the owners, biodigester lifespans are subject to a degree of

uncertainty, which will be discussed in section 6.2.3.2. In the priority

criteria rating scenario favourable to the installed systems, the number of

MCD SSB systems recommended for households with fiberglass and fixed

dome systems also increased, as can be seen in Figure 6-5. The use of

stabilised soil blocks as an alternative to bricks in the MCD SSB system

enabled it to be less expensive and more environmentally benign than the

standard Rwandan III fixed dome system. It also has a lower capital cost

than the prefabricated fiberglass system. Details on the biogas systems

recommended by the OBSDM with the updated EROI values are given in

Appendix C (Table C-5 to Table C-8). These results affirm the findings from

the preliminary testing in Chapter 5; the model recommends biogas system

designs that suit the context and priorities of the intended user. For most

households with fiberglass or fixed dome systems installed, the MCD SSB

model was considered to be optimal as it provided the benefits of

228

conventional fixed dome systems with the advantage of lower installation

costs, through the use of less energy intensive construction materials. The

recommended system for households with the low-cost flexbag systems

installed was the more reliable and robust Kentainer BlueFlame or

Fiberglass system, with both also having modest land footprints, or the

MCD SSB with a low capital cost.

Figure 6-4: Revised recommended biodigester types using equal priority criteria rating with updated EROI figures, categorised according to the installed system (horizontal axis)

Figure 6-5: Revised recommended biodigester types using priority criteria rating favourable to installed biodigester types with updated EROI figures, categorised according to the installed system (horizontal axis)

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229

6.2.2.2 Geographic influence on biogas system design

As can be seen in Figure 6-6, there are apparent trends on the type of biogas

system recommended by the model for some of the districts. Prefabricated

systems (the fiberglass and Kentainer BlueFlame) are recommended for the

majority of households in the district of Rwamagana. For households in

Gasabo, Kicukiro, and Ngoma, as well as most of the households in Kirehe,

the MCD SSB system was recommended. No definite trend is apparent in

the biogas system recommendations for the district of Kayonza. Given the

small sample size, however, these results are insufficient to prescribe any

specific types of biogas systems to promote in each of the districts. Several

other factors including household income, availability of subsidies, and

water supply have significant influence on the type of biodigester the model

recommends, as will be discussed in the sections that follow. In order to use

the OBSDM to identify the most suitable biogas system designs in a

particular region, data from a sample size of households that is

representative of the regional population would need to be applied to the

model. The model outputs could then be used to direct and assist

promotional, training, and implementation activities.

230

Figure 6-6: Comparison of installed and recommended biogas systems according to district with mean daily temperature

6.2.2.3 Impact of household income on biogas system design

Household income and subsidies influence the affordability of a biogas

system design and thereby the score of the low-cost criteria. To determine

the impact of the subsidies provided to Rwandan households, the survey

data was entered in the model again without the subsidies associated to the

installed biodigesters. As can be observed from the summarised model

outputs in Figure 6-7, the absence of a subsidy has only changed the

recommended system for one of the households. This is likely due to the

recommended systems under the subsidy scenario, particularly the MCD

SSB system, having lower installation costs than other high scoring systems.

For a household with a flexbag system installed (Household 19), the

fiberglass system was recommended instead of the the Kentainer BlueFlame

0.00

5.00

10.00

15.00

20.00

25.00

30.00

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Mea

n d

ail

y t

emp

era

ture

(°C

)

No

. o

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ou

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old

s

Modified CAMARTEC stabilised blocks Kentainer BlueFlame BioSluriGaz

Flexi-bag Fixed dome

Fiberglass (Prefabricated) Mean daily temperature (°C)

231

system, despite having higher installation costs, due to the decrease in low-

cost score for the Kentainer BlueFlame system in the absence of the subsidy

and the fiberglass system having a higher estimated biogas production

potential. The recommended biodigesters according to district and funds

available for capital expenditure (sum of savings and one month of

disposable income), excluding subsidies, are depicted in Figure 6-8, with no

clear trend on the type of systems recommended by the model according to

district and capital funds. When subsidies are considered, the Kentainer

BlueFlame system is recommend for households with lower subsidy values,

the fiberglass system is recommended for households with the highest and

lowest subsidy values in Kayonza and the lowest subsidy value in the district

of Rwamagana, and the MCD SSB systems is recommended for households

with the median and lowest subsidies levels across all districts, as shown in

Figure 6-9. The financial viability of recommended biogas systems for the

surveyed households is significantly impacted by the availability of

subsidies, reducing the months of savings required to 0 for 47% of the

households, and reducing the simple payback period by 54% to 100% (Table

C-9 in Appendix C). In the absence of subsidies, it would take households

an average of 3.7 years to save up enough capital to install the recommended

biodigesters, highlighting the need for more affordable biodigesters and

access to appropriate financial services in the region. The minimal impact

of the subsidies on the type of biogas system design recommended by the

OBSDM, demonstrates the effectiveness of the MCDA approach in the

model, where, under the equal priority scenario, the optimal system is

232

identified as the one with the highest score, considering all sustainability

criteria, rather than a single criterion such as low-cost.

Figure 6-7: Recommended biodigester types when no subsidies are available using equal priority criteria rating, categorised according to the installed system (horizontal axis)

Figure 6-8: Recommended biodigester types per district and amount available for capital expenditure (excluding subsidies)

0

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100000

200000

300000

400000

500000

600000

Av

era

ge

tota

l a

mo

un

t a

va

ila

ble

fo

r ca

pit

al

exp

end

itu

re (

FR

w)

District




233

Figure 6-9: Recommended biodigester type per district and subsidy amount available

6.2.2.4 Impact of water supply on biogas system design

The surveyed households fed their biodigesters with an approximate equal

ratio of cattle dung and water, resulting in a TS range that is well within the

TS range specified for all the recommended biodigester types. Therefore, the

amount of water available at each surveyed household did not have a

significant impact in the output of the OBSDM with no observable trend

between the amount of water available and the recommended biodigester

system, except for the Kentainer BlueFlame system being recommended for

households with 21 to 30 L of water available per day (Figure 6-10). The

OBSDM does not include water consumption in its comparative analysis of

the different types of feasible biogas system designs. Low water

consumption can be added as a priority criterion to the model, given that

water is a precious resource in many parts of SSA.

0

100000

200000

300000

400000

500000

600000

700000

Av

era

ge

sub

sid

y a

mo

un

t (F

Rw

)

District




234

Figure 6-10: Recommended biodigester type according to household water supply

6.2.3 Sensitivity analysis

6.2.3.1 Country average climatic data vs. local climate data

Using default climate data in the OBSDM, based on country average

ambient temperatures rather than local regional climate data, may result in

inappropriately sized biogas system designs. The default ambient mean

temperature of Rwanda was found to be lower than the recorded mean daily

temperatures in the six surveyed districts (Figure 6-11). Mean temperature

values from the survey were limited to the dry season, which is normally

also warmer; however, the actual mean ambient temperature in the districts

are expected to be only slightly lower, given that Rwanda is an equatorial

country with minimal temperature variation. The model recommended a

larger digester volume for all surveyed households, up to 87% higher, where

default climate data was used instead of local temperature data (Figure

6-12). Recommended available digester sizes remained unchanged for all

but four of the households, due to the increased digester volumes still falling

within the volume of available digester sizes (refer to Table C-10 in

0

1

2

3

4

5

6

7

8

10-20 21-30 31-40 41-50 51-60 61-70

No

. o

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Water availablity range (L/d)




235

Appendix C). The type of biogas systems recommended by the model

remained the same when default climate data was used for the majority of

households. The exceptions were Households 2, 18, and 19. For Household

2, a fiberglass system was recommended due to a larger MCD SSB system

not being feasible under the default climate scenario, with its size

dimensions going beyond the estimated available installation area. The

fiberglass system was also preferred over the Kentainer BlueFlame model

for households 18 and 19, with the larger Kentainer BlueFlame system being

less cost-effective. Biogas production estimates were lower when the default

data was used, if the available digester size remained unchanged. For

households where a larger available digester size was recommended by the

model under the default climate data scenario, the biogas production was

also estimated to be higher. Oversizing biogas systems, as mentioned in

Chapter 5, leads to longer retention times and higher biogas outputs

(production); however, it also results in higher installation costs. Conversely

under-sizing systems reduces HRT, and increases OLR, which can result in

the washout of essential microorganisms and reduced system stability (if

the HRT is too short). Therefore, wherever possible, local climate data is

recommended to be used rather than default climate data to ensure the

model recommends the most appropriate size and provides reasonable

biogas production estimates. Practical examples of the issues with

oversizing biogas systems are provided by the recent study from

Tumutegyereize et al. [368].

236

Figure 6-11: Comparison of recommended biogas systems using local and default climate data

Figure 6-12: Change in biodigester size recommended by the OBSDM when using default and local (measured) climate data

0

5

10

15

20

25

30

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Tem

per

atu

re (

°C)

No

. o

f h

ou

seh

old

sModified CAMARTEC stabilised blocks Kentainer BlueFlame BioSluriGaz

Fiberglass (Prefabricated) Mean daily temperature (°C)

Default mean daily temperature (°C)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

% c

ha

ng

e (d

efa

ult

-m

easu

red

da

ta)

Household No.

Recommended digester size (m³)

237

6.2.3.2 Uncertainties in biogas system lifespan

The lifespan of a biogas system or total period of time that a biogas system

remains fully functional, is influenced by the quality and lifespan of the

construction materials used, quality of construction/installation,

operational and maintenance habits of the user(s), and local climatic

conditions. As a result, variation and uncertainty exists in the lifespan of

each type of biogas system. To investigate the sensitivity of the OBSDM to

uncertainties in biodigester lifespan, the changes in the output of the model

were observed when the Rwandan survey data was used with equal priority

criteria rating as the lifespan values of the biogas system types were altered

according to the upper and lower lifespan ranges given in Table 6-6. The

comparison of recommended biogas system designs, according to installed

biodigester types using standard lifespan values, and each of the upper and

lower lifespan scenarios, are summarised in Figure 6-13 to Figure 6-14.

Lifespan values influence the reliability, low-cost, and environmentally

benign priority criteria scores through its use as a parameter to measure

reliability, and to calculate NPV (a low-cost parameter) as well as EROI (an

environmentally benign parameter). When the maximum lifespan values

were used in the OBSDM, the recommended systems for households with

fiberglass and fixed dome biodigesters did not differ from the systems

recommended under the standard, manufacturer recommended lifespan

scenario (Figure 6-13). For households with flexbag biodigesters installed,

specifically Households 18 and 19, the fiberglass system was preferred over

the Kentainer BlueFlame systems when the maximum lifespan values were

used, due to the increased lifespan value for the fiberglass system (20 years

238

instead of 15 years), lowering the relative distance between its reliability and

environmentally benign scores and that of the Kentainer BlueFlame system.

The same observations were made under the second highest lifespan

scenario, with the only distinction being the preference of the fiberglass

system over the MCD SSB for Household 3, resulting from an increase in

the environmentally benign and low-cost scores of the fiberglass system. For

the remaining lifespan scenarios (the third highest lifespan values, the

second lowest lifespan values, and the minimum lifespan values as shown

in Figure 6-14), the lifespan of fixed dome designs were no longer higher

than that of the fiberglass system and, thereby, the increased reliability and

environmentally benign scores of the fiberglass system caused it to become

the recommended system type for all but one of the households. For these

three lifespan scenarios, Household 10 was the exception, where the MCD

SSB system remained the recommended system, while the ranking of the

fiberglass system improved from 5th to 2nd best based on the overall score.

Details on the recommended biogas system designs for the different lifespan

scenarios are provided in Appendix C (Table C-13 to Table C-18). These

results indicate that the lifespan of biogas system types can influence how

the systems are rated in the OBSDM and, therefore, reasonable lifespan

values need to be used in the model. Uncertainties in the lifespan of biogas

system designs can be reduced by adjusting lifespan values for each SSA

country or region based on what has been the observed lifespan of the

systems or systems of similar design and construction materials.

239

Table 6-6: Lifespan ranges for biodigester types used for sensitivity analysis in OBSDM

General digester type

Associated specific digester types

Maximum lifespan

(y)

Minimum lifespan (y)

References

Fixed dome -masonry

KENBIM, Modified CAMARTEC, Modified

CAMARTEC SSB, Modified CAMARTEC

SSD, Rwanda III (based on GGC 2047), Senegal

GGC 2047, Sinidu model (modified GGC-2047),

Zambdigester

50, 30, 20 15, 10 [29, 63, 134,

183, 244]

Fixed dome - composite (prefabricated)

AGAMA BiogasPro, Fiberglass

(prefabricated) 25, 20 15, 10 [183, 369]

Plug flow (bag) Flexi biogas 20, 15, 10 5, 2 [29, 183, 226, 370,

371]

Floating cover

Kentainer BlueFlame BioSluriGaz, Puxin (Bioeco Sarl), Puxin

(Biogas Burundi)

30, 20, 15 12, 10 [29, 63, 371,

372]

Figure 6-13: Comparison of recommended biodigester types using standard lifespan values and maximum lifespan values, categorised according to the installed system (horizontal axis)

0

2

4

6

8

10

12

Rec

om

men

ded

Rec

om

men

ded

(m

ax

life

spa

n)

Rec

om

men

ded

Rec

om

men

ded

(m

ax

life

spa

n)

Rec

om

men

ded

Rec

om

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(m

ax

life

spa

n)


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240

Figure 6-14: Comparison of recommended biodigester types using standard lifespan values and the lowest lifespan values, categorised according to the installed system (horizontal axis)

6.2.3.3 Uncertainties in biogas production efficiency

Biogas production efficiency in the OBSDM refers to the amount of biogas

that is captured by the biodigester and available to the user, that is the total

biogas production minus leakages, as mentioned in Chapter 5. Gas leakages

will vary from system to system, depending on the quality of the

construction/installation, gasholder materials, and gas piping equipment

and installation; however, some design types are more susceptible to leaks

than others. Traditional masonry fixed dome systems have been reported to

be prone to gas leakages through the inlet and outlet, as well as cracks in the

gasholder, highlighting the need for quality, skilled construction to ensure

the gasholder is gastight [102, 308, 373, 374]. Gas leaks were also reported

and quantified in floating cover systems through the inlet and outlet in one

study in India, although these leaks were lower than those reported for fixed

dome systems in a similar study [308, 375, 376]. Prefabricated systems

reduce the risk of leakages in the gasholder through quality control in the

0

2

4

6

8

10

12

Rec

om

men

ded

Rec

om

men

ded

(lo

wes

tli

fesp

an

va

lues

)

Rec

om

men

ded

Rec

om

men

ded

(lo

wes

tli

fesp

an

va

lues

)

Rec

om

men

ded

Rec

om

men

ded

(lo

wes

tli

fesp

an

va

lues

)


No

. o

f h

ou

seh

old

s





241

manufacturing process; however, leaks can still occur through the gas

piping [308]. Similarly, gas leaks are minimised in tubular bag digesters

through the use of appropriate gastight materials and quality gas piping,

although leaks have been reported from damaged or deteriorated gasholder

bags [203]. In the OBSDM, the gas production efficiency of each biogas

system type was estimated based on the rate of usable gas production

reported by the manufacturer/implementing agency. Where this figure was

not available, the upper value of the gas leakage range from the anaerobic

digestion of organic waste of 10%, as recommended by the IPCC, was used

to derive the production efficiency [278]. The gas leakage ranges given in

Table 6-7 were used with the Rwandan household data to test the model’s

sensitivity to uncertainties in biogas production efficiency values.

Table 6-7: Gas leakage ranges for biodigester types used to determine biogas production efficiency for sensitivity analysis in OBSDM

General digester type

Associated specific digester types

Maximum gas leakage (out of total production)

range

Minimum gas leakage (out of total production)

range

Ref.

Fixed dome -masonry

KENBIM, Modified CAMARTEC, Modified CAMARTEC SSB, Modified CAMARTEC SSD, Rwanda III (based

on GGC 2047), Senegal GGC 2047, Sinidu model (modified GGC-2047),

Zambdigester

17%, 10% 5%, 0% [278, 308, 375]

Floating cover

Kentainer BlueFlame BioSluriGaz, Puxin (Bioeco Sarl), Puxin (Biogas

Burundi) 10%, 8% 5%, 0%

[278, 308, 376]

Other AGAMA BiogasPro, Fiberglass

(prefabricated), Flexi biogas 10% 5%, 0% [278]

The biogas production efficiency directly impacts the estimated biogas

production in the OBSDM, which in turn influences several comparative

parameters, namely annual savings, simple payback period, NPV,

242

proportion of energy requirements met, specific gas production per digester

volume, greenhouse gas emissions avoided, EROI, and the time saved from

replacing the current energy demand. Thereby, the low-cost, technical

efficiency, environmentally benign, and save time priority criteria are

affected by changes in biogas production efficiency for each biogas system

type in the OBSDM.

When the minimum biogas production efficiency values were used

(maximum gas losses), the production efficiency of the Kentainer

BlueFlame was equal to the standard values used for this biodigester type in

the OBSDM, while the values used for the MCD SSB and Fiberglass

biodigesters were higher than the values normally used in the OBSDM by a

factor of 1.02 and 1.06, respectively. This resulted in the fiberglass system

being preferred over the Kentainer BlueFlame biodigester for Household 19.

The fiberglass system was also preferred over the MCD SSB for Household

3, due to the greater increase in production efficiency when compared to the

standard scenario. A comparison of the recommended biogas systems under

standard conditions and when the lowest production efficiency values are

used, is summarised in Figure 6-15 . Similarly, the second lowest, second

highest, and 100% biogas production efficiency values resulted in the

fiberglass system being preferred over the Kentainer BlueFlame biodigester

for Household 19; however, the MCD SSB was now preferred over the

fiberglass system for Household 3, due to the greater increase in its technical

efficiency, environmentally benign, and low-cost scores (Figure 6-16 for the

no gas loss scenario). Aside from these two households, the recommended

243

biogas system types under the four different biogas production efficiency

scenarios did not alter from the systems recommended under the standard

scenario.

The results indicate that the biogas production efficiency values have a

marginal effect on how the different types of biodigesters are compared and

ranked by the model, despite its influence on the technical efficiency,

environmentally benign, and low-cost parameters. Given the impact of

these values on comparative parameters, however, it is recommended that

these values are revised according to region-specific observations on the

biogas production efficiency of biodigester types that are to be compared in

the OBSDM. This would improve the accuracy of biogas production and

economic performance estimates of the biodigester designs recommended

by the model.

Figure 6-15: Comparison of recommended biodigester types using standard production efficiency values and minimum production efficiency values (maximum gas loss), categorised according to the installed system (horizontal axis)

0

2

4

6

8

10

12

Rec

om

men

ded

Rec

om

men

ded

(m

ax

ga

s lo

ss)

Rec

om

men

ded

Rec

om

men

ded

(m

ax

ga

s lo

ss)

Rec

om

men

ded

Rec

om

men

ded

(m

ax

ga

s lo

ss)


No

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244

Figure 6-16: Comparison of recommended biodigester types using standard production efficiency values and maximum production efficiency values (no gas loss), categorised according to the installed system (horizontal axis)

6.2.3.4 Uncertainties in biogas yields of feedstocks

The limited research on biogas yields of feedstocks specific to SSA has

resulted in values being used from outside the region, as discussed in

Chapter 4. Along with the specific biogas or methane yield per kg of oDM of

a given feedstock, its DM and oDM (or TS and VS) content also has a

significant impact on its biogas production potential. To determine the

impact of uncertainties in location-specific biogas yields of a given

feedstock, measured TS and VS values of the feedstock (fresh cattle dung)

from 14 of the surveyed households were used in the OBSDM and compared

with the model’s output when default DM and oDM values were used. The

TS and VS values are based on single samples analysed in a laboratory and

are therefore not sufficient in sample size to be considered representative of

typical TS and VS values for cattle dung in Rwanda. The use of these values

in the analysis is to demonstrate the impact of variations in TS and VS

values, rather than prescribing suitable biogas system designs based on

0

2

4

6

8

10

12

Rec

om

men

ded

Rec

om

men

ded

(n

o g

as

loss

)

Rec

om

men

ded

Rec

om

men

ded

(n

o g

as

loss

)

Rec

om

men

ded

Rec

om

men

ded

(n

o g

as

loss

)


No

. o

f h

ou

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old

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245

measured VS and TS values. The difference between the measured and

model TS and VS values varied between the households, as can be seen in

Table 6-8. In addition to the TS and VS values, data on the number of cows

at each surveyed household was also used in the OBSDM to analyse the

accuracy of estimated dung and resulting biogas production estimates based

on the number of cattle in the model compared to using the exact mass (kg)

of cattle dung figures.

Table 6-8: Measured TS and VS values for cattle dung from surveyed Rwandan households and comparison to values in OBSDM

HH No.

District Installed bio-digester type

Feed-stock type

Type of value

TS % (kg

TS/kg)

VS% (kg

VS/kg)

VS% of TS (kg

VS/kg TS)

1 Kayonza Fiberglass Cattle dung

Measured 15.1% 13.2% 87.1%

Default in model

18.0% - 82.0%

% difference 17.5% - 6.0%

3 Kirehe Fiberglass Cattle dung

Measured 26.6% 18% 66.7%

Default in model

18.0% - 82.0%

% difference 38.4% - 20.5%

4 Kicukiro Fiberglass Cattle dung

Measured 20.4% 16% 79.2%

Default in model

18.0% - 82.0%

% difference 12.5% - 4%

5 Kayonza Fixed dome

Cattle dung

Measured 15.1% 13% 88.5%

Default in model

18.0% - 82.0%


6 Kicukiro Fixed dome

Cattle dung

Measured 25.4% 21% 82.5%

Default in model

18.0% - 82.0%


7 Gasabo Fixed dome

Cattle dung

Measured 20.2% 17% 85.6%

Default in model

18.0% - 82.0%


8 Rwamagana Fixed dome

Cattle dung

Measured 17.0% 14% 82.4%

Default in model

18.0% - 82.0%


246

HH No.

District Installed bio-digester type

Feed-stock type

Type of value

TS % (kg

TS/kg)

VS% (kg

VS/kg)

VS% of TS (kg

VS/kg TS)

9 Rwamagana Fixed dome

Cattle dung

Measured 16.4% 12% 74.0%

Default in model

18.0% - 82.0%


10 Kicukiro Fixed dome

Cattle dung

Measured 16.3% 14% 88.0%

Default in model

18.0% - 82.0%


13 Ngoma Fixed dome

Cattle dung

Measured 22.7% 12.2% 53.7%

Default in model

18.0% - 82.0%

% difference 23.0% - 41.8%

15 Kirehe Fixed dome

Cattle dung

Measured 15.9% 15% 92.1%

Default in model

18.0% - 82.0%

% difference 12.4% - 11.6%

16 Gasabo Flexi-bag Cattle dung

Measured 15.7% 13% 82.9%

Default in model

18.0% - 82.0%


17 Rwamagana Flexi-bag Cattle dung

Measured 16.6% 13% 77.0%

Default in model

18.0% - 82.0%


19 Kirehe Flexi-bag Cattle dung

Measured 19.3% 12.9% 66.8%

Default in model

18.0% - 82.0%


The biogas system designs recommended by the OBSDM when the

measured TS and VS values of cattle dung from 14 of the surveyed

households were used, did not vary significantly in available size and type,

as can be observed in Figure 6-17. The MCD solid state digester (SSD) was

recommended for Household 3, as it was the only digester with a TS range

high enough to treat the drier cattle dung; however, this design requires a

larger installation area when compared to the installed fiberglass system.

For Households 6 and 13, the measured TS was high resulting in the

247

previously recommended MCD SBB no longer being feasible and therefore

the fiberglass system was recommended instead. Similarly, for Household

7, the Senegal GGC 2047 system was recommended as the previously

recommended MCD SSB model was no longer feasible, based on the TS of

the dung and water mix. Contrary to the variation in size and biogas system

type, variations in the estimated biogas production, minimum amount of

water required each day, average HRT, and OLR, as well as the

recommended digester size, were significant when comparing the use of the

measured and default TS and VS values in the model. Biogas production

estimates varied between 1.8% to 55.4%, and, thereby, the actual biogas

production of a system could differ significantly from what the model has

predicted, if TS and VS values are not adjusted to local/regional conditions.

The minimum water requirement figures varied from 9.5% up to 50.0%

between the default and measured TS and VS values. Given that water is a

precious resource in SSA, uncertainties in the water requirements

recommended by the model need to be minimised through adjusting TS and

VS values of a given feedstock to what is measured or observed locally or

regionally, wherever possible. Furthermore, the TS and VS operating range

of the biogas systems listed in the database (Table A-2) should be tested,

verified, and adjusted as required, wherever possible, to ensure they are

reflective of what is observed in practice. Ideally, the TS and VS ranges for

the biogas systems in the database would be standardised with rates

according to ambient temperatures, so that they can be applicable to any

region. Details on the comparison of the recommended biogas system

248

designs using default and measured TS and VS values in the OBSDM are

given in Table C-17 (Appendix C).

Figure 6-17: Comparison of recommended biodigester types using default and measured VS and TS values for cattle dung in the OBSDM, categorised according to the installed system (horizontal axis)

The amount of dung available per head of cattle can vary significantly

depending on the breed of cattle, fodder used, where the cattle are kept, and

the method applied to collect the dung. This is apparent in the variation

between the average daily amount of dung that is fed to the surveyed biogas

systems and the amount of dung estimated to be available based on the

number of cattle at each household, summarised in Figure 6-18. A range of

fodder is used for the cattle from the surveyed households, including plant

and crop residues, while dung collection is aided through the practice of zero

grazing [353]. Using the number of cattle to estimate the amount of

feedstock available resulted in dung amounts being underestimated in 79%

of the surveyed households. This indicates that the dung production

estimates in the model are conservative. The underestimated feedstock

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Senegal GGC 2047

Modified CAMARTEC solidstate digester (SSD)




249

amount led to a different type of biogas system design being recommended

for Households 4, 16, 17, and 18, as can be seen in Figure 6-19. A different

biogas system design was recommended for 3 of these households due to

significantly overestimated feedstock amounts (between 39.4% and 87.4%

difference) with the fiberglass system being preferred over the MCD SSB

and Kentainer BlueFlame system for Households 4 and 18, respectively, and

the flexi biogas digester over the MCD SSB for Household 16. The number

of feasible digester designs became limited in the scenarios of overestimated

feedstock amounts due to the amount of water available, resulting in higher

estimates for the TS of the input stream to the biodigester. For Household

17, the Kentainer BlueFlame was recommended over the Fiberglass system

due to the feedstock being significantly underestimated (119.6% difference),

resulting in improved low-cost, technical efficiency, and environmentally

benign scores for the smaller Kentainer BlueFlame design. The Kentainer

BlueFlame system was recommended by the model for the households with

1 and 2 cows along with the MCD SSB system, which was the most

commonly recommended design for households with 1 to 4 cows, as can be

observed in Figure 6-20. Details on the recommended biogas system types

and the comparison of the output with estimated and measured feedstock

amounts are provided in Appendix C (Table C-18, to Table C-20). To ensure

recommended system designs are not over- or undersized in the OBSDM,

feedstock amounts measured at the proposed installation site should be

used with adjustments based on future projections of the feedstock supply.

250

Figure 6-18: Difference in feedstock amounts (kg cattle dung/d) estimated based on the number of cattle and amounts measured on site, and resulting difference in biodigester size recommended by the OBSDM

Figure 6-19: Comparison of recommended biodigester types using location specific cattle dung supply and estimated cattle dung supply based on number of cattle in the OBSDM, categorised according to the installed system (horizontal axis)

-150.00%

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% d

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Amount (kg/d) Recommended digester size (m³)

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251

Figure 6-20: Recommended biodigester types according to the number of cattle (where the amount of cattle dung was estimated according to the number of cattle) in the OBSDM

6.2.3.5 Uncertainties in biogas system cost

Most uncertainties in the cost of biogas systems in the OBSDM are related

to imported products since local construction material costs are part of the

model input. The import and export of goods in SSA comes with

exceptionally high costs, time, and uncertainties for global standards,

particularly for landlocked countries like Rwanda [377]. Numerous factors

including low road density, regulation, market structure, administrative

barriers, and corruption contribute to these import and export challenges

[377]. A study by Christ and Ferrantino [377] estimated the range of

trucking costs; customs, paperwork, and shipping costs; and time costs of

road transport, as a percentage of cost, insurance and freight (CIF) for the

export of selected commodities, from farm/factory to port, in seven

landlocked countries using four land transport corridors. The study

included export costs for coffee, tea, and tungsten from Rwanda using the

eastern transport corridor. The CIF percentages for export from Rwanda

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252

were adjusted to estimate the average CIF percentages for import to

Rwanda, noting that trucking costs for export were estimated to be two

thirds of import costs in the study, with the resulting values given in Table

6-9 [377]. The estimated total CIF percentage for import was applied as an

increase to the total cost of construction materials not available locally to

determine the impact of uncertainties in import costs.

Table 6-9: Estimated costs of import to Rwanda as a percentage of cost, insurance, freight, based on the average CIF percentages of export for selected commodities [377]

Type of cost % of CIF

Trucking costs 4.1%

Customs, paperwork and shipping costs 0.8%

Time cost of road transport 5.9%

Total 10.8%

Considering the uncertainties in cost of imported construction materials in

the application of the Comparative Biodigester Study, data in the OBSDM

had a minimal impact on the recommended biodigester types where the

same priority criteria rating was applied (Figure 6-21). This is likely due to

the dominance of the MCD SSB system in the recommended systems under

the standard scenario. Most of the construction materials required for the

MCD SSB are available locally, 90% compared to 71% and 50% for the

Fiberglass and Kentainer BlueFlame systems, respectively, resulting in the

MCD SSB being less affected by import costs. For Household 19, the

fiberglass system was preferred over the Kentainer BlueFlame system due

to its higher share of local construction materials. The import costs still have

a significant impact on the fiberglass system costs, as its most expensive

component – the fiberglass gas holder and digester – is imported from

China. As can be seen in Figure 6-22, the NPV and months of savings

253

required to meet the capital cost were most affected by the import costs for

the two prefabricated systems. Minimal changes to costs were estimated for

households with fiberglass systems installed due to the large subsidy these

households received. Details on the differences in the economic parameters

for the recommended biogas system designs with and without consideration

of import costs are given in Appendix C (Table C-21). The results indicate

that while uncertainties in the costs of imported construction materials may

not always affect the type of biogas system recommended by the model,

some admission needs to be made in the model for the cost of imported

construction materials to provide more realistic cost estimates. Two

approaches to consider the cost of imported materials in the model are to

alter the cost of these materials in the internal database to include import

costs for a given country where the model is applied in the total cost or,

alternatively, the model user can enter the estimated percentage increase in

cost of imported materials as an input (with suggested percentages based on

the Christ and Ferrantino [377] study).

254

Figure 6-21: Comparison of recommended biodigester types using equal priority criteria rating with and without consideration of import costs in the OBSDM, categorised according to the installed system (horizontal axis)

Figure 6-22: Percentage change in economic parameters for biogas system designs recommended by the OBSDM when considering import costs for construction materials

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Fiberglass(Prefabricated)







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% Change (considering import costs - no import costs)

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% change - Average of Costper kWh

% change - Average ofEstimated NPV

% change - Average of Monthsof saving req to meet capitalcost (based on current savings& disposable income)

% change - Average ofEstimated simple paybackperiod

% change - Average ofEstimated capital cost (excl.subsidy)

% change - Average ofEstimated capital cost(considering subsidy if avail.)

255

6.2.3.6 Sensitivity of priority criteria rating

The OBSDM is expected to preference biogas system designs with high

scores for those priority criteria that have been given a high importance

rating and, conversely, rate those system designs lower that have low scores

for high importance priority criteria. To determine the extent to which the

priority criteria rating influences the recommended biogas system design in

the OBSDM, each priority criteria was tested with the Comparative

Biodigester Study data by applying a rating of 5 to one priority criterion and

a rating of 1 for all other criteria and then running the data through again

with all other criteria being rated as 3, while it maintains a rating of 5. The

priority criteria rating was found to have a significant impact on the type of

biogas system design that is recommended by the OBSDM when a priority

criterion was rated as 5, while all others had a rating of 1 (Figure 6-23).

When the rating of all other criteria was increased to 3, the impact of the

top-rated priority criterion was minimised, as many of the recommended

designs were equivalent to those designs recommended by the model when

equal priority criteria was used (Figure 6-24). These results indicate that the

rating system for the priority criteria in the model is effective in ranking the

recommended biogas system designs according to the user’s preference. A

comparison of the highest scoring biogas system designs for each priority

criteria and the system recommended by the OBSDM when a rating of 5 is

used and 1 for all other priority criteria is provided in Appendix C (Table C-

23 to Table C-30).

256

The Kentainer BlueFlame biodigester was the recommended biodigester for

all but one of the surveyed households when reliability was the highest

priority due to its high lifespan, as discussed in section 6.2.2.1. The

preference for the Kentainer BlueFlame system was reduced when the

rating for all other priority criteria was increased to 3, with higher scores in

other criteria resulting in MCD SSB or fiberglass designs being

recommended instead. For the robustness criterion, the high score in the

fiberglass system is a result of the large ambient operating range under

which it can function.

Figure 6-23: Comparison of OBSDM output using equal priority rating for all criteria and the highest rating (5) for each criterion at a time while all others are given the lowest rating (1)

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No

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Installed & recommended biodigesters

Senegal GGC 2047 RW III (based on GGC 2047)

PUXIN (Bioeco Sarl) KENBIM

Flexi biogas digester Modified CAMARTEC solid state digester (SSD)

Modified CAMARTEC stabilised blocks Kentainer BlueFlame BioSluriGaz


257

Figure 6-24: Comparison of OBSDM output using equal rating for all priority criteria and the highest rating (5) for each criterion at a time while all others are given a moderate rating (3)

The combination of high scores in robustness, simple operation and

environmentally benign resulted in the fiberglass system also being the

dominant recommended system when environmentally benign was a top

priority. The short construction times and minimal maintenance required

for prefabricated biogas systems, specifically the Kentainer BlueFlame, flexi

biogas and fiberglass systems, resulted in these types being recommended

when simple operation and construction was the most important criterion.

Installation costs, costs per kWh, and NPV values were most influential in

the low-cost scores and resulted in the MCD SSB system being

recommended by the OBSDM for the majority of households. Some of the

highest scoring low-cost systems, particularly the Rwanda III fixed dome

and KENBIM, were outranked in the OBSDM due to other systems

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Installed & recommended biodigesters

Fiberglass (Prefabricated) Kentainer BlueFlame BioSluriGaz

Modified CAMARTEC stabilised blocks

258

performing better in other priority criteria. The AGAMA BiogasPro system

had the highest technical efficiency score for 84% of the households;

however, it was outranked in the model by other biodigesters with higher

scores in reliability, environmentally benign, low-cost, and save time.

The KENBIM system was the most common recommended system

preferred over the AGAMA BiogasPro when technical efficiency was a top

priority, often having the second highest technical efficiency score due to its

high specific gas production per digester volume. The top rating for

technical efficiency became less significant when the rating of all other

criteria was increased to 3 with the recommended biogas system designs for

the majority of households being identical to those recommended under the

equal priority criteria scenario. The RWIII fixed dome biodigester had the

best local materials and labour score for half of the surveyed households

with the fiberglass, MCD and Senegal GGC 2047 systems making up the

rest. This was not reflected in the systems recommended by the model when

local materials and labour was a priority with the MCD SSB being preferred

over the RWIII fixed dome system due to its higher simple operation and

construction, low-cost, and environmentally benign scores. A range of

different systems were recommended by the OBSDM and found to score

high in the save time criterion, highlighting the interaction of local

conditions (particularly energy requirements and time spent on preparing

current energy sources) and biodigester-specific technical and operational

performance (portion of energy requirements met and time required for

operation and maintenance) in determining the score. The MCD SSB

259

outranked several systems that scored higher for save time for some of the

households, namely the flexi biogas digester, MCD SSD, PUXIN (Bioeco

Sarl), and Senegal GGC 2047 system, as it had the best environmentally

benign score, as well as a high save time score.

The analysis has highlighted the priority criteria that are largely influenced

by biodigester-specific parameters such as reliability, robustness, simple

operation and construction, technical efficiency, and environmentally

benign, as well as the criteria with scores that vary according to location-

specific conditions, namely low-cost and save time. From these results, it is

also apparent that the ranking of recommended systems in the model is in

line with user priorities through the rating input, while still considering the

biodigester performance for all criteria through the MCDA. The advantages

of different biodigester designs relative to the priority criteria have also been

highlighted through this analysis.

6.3 Conclusions on model validation and sensitivity

analysis

The model validation and sensitivity analysis discussed in this chapter have

been an important part of refining the OBSDM, as well as comparing the

outputs with existing systems applied in Rwandan households, enabling the

final two research objectives to be achieved. Results from the model

validation were consistent with the findings from the preliminary testing in

Chapter 5, in that recommended biodigester designs are becoming of the

intended user(s)’ specific context and priorities. Recommended biogas

system designs were smaller for the majority of households compared to the

260

installed systems, highlighting the importance of the use of the OBSDM in

planning and feasibility studies for biogas system installations to minimise

the potential of oversizing systems, which results in unnecessarily high costs

for the system owner. Appropriate sizing, as well as reasonable biogas

production estimates in the model, are enhanced through the use of local

climate data rather than using default climate data from the internal

database. Furthermore, the feedstock amounts entered in the model also

need to be as accurate as possible, based on measured and future projections

of feedstock supply at a given site. The need for more affordable biodigesters

in Rwanda was demonstrated through the comparison of recommended

system costs with and without subsidies. In the absence of subsidies, an

average of four years of savings is required to have sufficient capital to install

the biodigesters recommended by the model. To improve the accuracy of

system costs, the model needs to consider the cost of importing construction

materials or biodigester parts and thus an input was added, allowing a user

to enter the estimated percentage increase in cost for imported materials.

Alternatively, import costs can be considered through adjusting the cost of

imported materials in the model’s internal database. Similarly,

uncertainties in lifespan and biogas yields of feedstocks in the model can be

minimised by adjusting lifespan, TS and VS values according to what is

measured, observed, or reported locally or regionally, wherever possible.

The accuracy of TS and VS values also influence the accuracy of estimated

water requirements for biogas systems. Water consumption was identified

as a relevant biogas system design parameter in the SSA context, and

therefore assigning low water consumption as an additional priority

261

criterion in the model is recommended. In analysing the sensitivity of

priority criteria; reliability, robustness, simple operation and construction,

technical efficiency, and environmentally benign were found to be the most

susceptible to biodigester-specific parameters, whereas the low-cost and

save time criteria varied according to location-specific conditions. The

sensitivity analysis also highlighted the strengths of different biodigester

types according to the priority criteria and demonstrated that the priority

criteria rating input in the model functions as expected, influencing the

ranking of recommended system designs according to user preferences. To

increase the likelihood of biodigester designs with the best performance for

top priority criteria receiving the highest ranking in the model, the

difference between the rating given to top priority criteria and all other

criterion needs to be maximised. Overall, the model output comparisons

with the survey data and the associated sensitivity analysis has

demonstrated that the OBSDM is effective in providing an optimal biogas

system design that is tailored to the specific context and priorities of the

intended user.

Patterns have emerged through the model validation and sensitivity

analysis on optimal system designs for SSA. The MCD SSB has stood out in

this model analysis as the most suitable biogas system design for the

majority of the surveyed Rwandan households. It is a well-rounded design,

striking a balance between the benefits of a standard fixed dome model, as

identified in the Rwandan Comparative Biodigester Study, and reduced

investment costs and embodied energy, through the use of stabilised soil

262

blocks as an alternative to fired bricks. This has enabled the system to score

well for all priority criteria. Prefabricated biodigesters were found to score

high for robustness, reliability, simple operation and construction, as well

as environmentally benign (particularly the fiberglass system); however,

their reliance on imported parts and materials can make their costs

prohibitive for households in the absence of subsidies. Furthermore, there

are fewer opportunities for local job creation along with sufficient

maintenance and follow-up services, which is essential for the sustainable

development of the biogas sector in SSA. Thus, biogas system designs like

the MCD SSB, which feature local, low-cost materials, and a robust design

with a reliable performance, are ideal for households-scale applications in

SSA. Further conclusions and recommendations regarding the OBSDM and

biogas dissemination in the region will be discussed in the final chapter that

follows.

263

Chapter 7 Conclusions and

recommendations for future

work

Conclusions and recommendations for

future work

“The harvest is plentiful but the workers are few. Ask the Lord of the

harvest, therefore, to send out workers into his harvest field.”

– Matthew 9:37b-38

7.1 Conclusions

Access to affordable and reliable energy sources is an important part of

development, and essential for improving the livelihoods of millions of

people in SSA. The number of people dying and suffering as a result of using

traditional cookstoves and unsafe sanitation facilities can be reduced

through appropriate technologies. However, such technologies can only aid

the development of a sustainable future to the extent in which they are

useful and applicable. They are not ‘the solution’, but rather a tool or a

means to improve livelihoods. The biogas industry in SSA is still in its early

stages despite the technology being first introduced to the continent over

half a century ago. Its potential to improve the cooking environment for

millions of people in SSA is great, along with the significant number of other

energy access and environmental benefits through the safe treatment of

organic waste. With the increase in domestic biogas programmes and biogas

entrepreneurs in the region, there is traction for industry growth. However,

264

to ensure growth continues, the benefits to intended users of the systems

must be the focus at all stages of the planning, designing, and

implementation of biogas systems and programmes. This research has

aimed to help improve biogas technology dissemination in SSA through the

development of the OBSDM. To help meet this aim, five main research

objectives were identified. The first two were to identify the biogas system

designs that are available in, and suitable for, SSA, and assess the biogas

feedstocks that are available in the region. Following on from these

objectives was the development of the model through identifying suitable

inputs and a method for determining the optimal biogas system design. The

final two objectives were to test the model by applying data from existing

biogas systems from selected SSA countries, and to highlight any patterns

identified through applying the model to existing biogas system data and

make recommendations on biogas system designs that are optimal for SSA.

The model presents a holistic approach to designing biogas systems by

considering the context, needs, and priorities of the intended user(s)

according to relevant sustainability criteria, and presenting the

recommended designs in a way that is understandable, not only to experts,

but all the key stakeholders in the biogas industry. In doing so, the model

can be used to help increase awareness about the potential of biogas

technology in SSA, as well as ensuring sustainability aspects are considered

at the beginning of the design phase.

A thorough review of literature from a range of sources has helped identify

suitable biogas system designs for the SSA region and develop databases in

265

the OBSDM with their technical details, required construction materials,

available sizes and costs. The collated information on biogas system designs

embedded in the OBSDM, as well as the feedstock assessment carried out

as part of this research, is a useful point of reference when considering the

implementation of biogas technology in SSA. As the feedstock assessment

has shown, rural SSA households are particularly well suited for the uptake

of biogas technology, provided that sufficient training and technical support

is given. Therefore, the OBSDM has been developed with a focus on

household-scale biogas system designs and agricultural as well as organic

waste feedstocks, although it can be applied for assessing community-scale

biogas systems. Preliminary testing of the model using data from household

biodigester surveys has confirmed that the model is able to recommend

biogas system designs that are appropriate according to the context and

priorities of the intended user.

Further testing of the model output with data of existing biogas system

installations confirmed that the model is effective in providing an optimal

biogas system design that is tailored to the specific context and priorities of

the intended user. It also showed that the majority of surveyed sites were

oversized, highlighting the importance of applying the OBSDM in planning

and feasibility studies of future installations to minimise the likelihood of

significantly oversizing systems with higher installation costs. Capital costs

of biogas systems still present a significant barrier to many SSA households,

as indicated by both the literature and the model output for the scenarios

where capital subsidies were excluded. The modified CAMARTEC design

266

that uses stabilised soil blocks (MCD SSB) was the most frequently

recommended system by the model, particularly where the weighting of the

model’s eight sustainability criteria was equal. It demonstrated that, from a

sustainability perspective, a system made of less energy intensive local

materials outweighs the benefits of prefabricated systems manufactured

overseas. Based on the domestic biodigester designs currently available in

SSA, there is a need to develop more local designs like the MCD SSB, which

are affordable to households, and have the potential to contribute to local

economies. Further development is also recommended on systems that

require less water, utilise wastewater/recycled water, or incorporate

rainwater harvesting to improve viability in water scarce regions.

The notion of the accuracy of the outputs being dependent on the accuracy

of the inputs was emphasised in the sensitivity analysis of the OBSDM.

Entering local climate data rather than using default national average

climate data in the model will result in more appropriate digester sizes and

biogas production estimates in the output. The internal climate database of

the model can be improved by expanding it to include climatic data for

states or provinces, or major towns in each SSA country. Alternatively, the

model could link to a detailed external climate database, much like the

RETScreen software uses NASA weather data. Soil temperatures should also

be considered in an improved climate database for more accurate estimates

of digester temperatures for underground systems. For improved biogas

production estimates in highland regions as well as other areas in SSA that

experience more temperate climates, the bacterial growth rate (μm) used in

267

the model to calculate the methane production potential of a given feedstock

would need to be adapted to psychrophilic conditions. The model could also

be expanded to include heating and recycle (where part of the effluent is fed

back into the digester) options, which could be compared to standard

unheated systems in the output. Aside from these predominately climatic

considerations, the accuracy of the model outputs would also be improved

by updating the feedstock database with local or regional data as it becomes

available. Currently, data on the biochemical methane potentials and other

key characteristics of organic wastes in SSA is very limited. Therefore,

increasing the measurement and reporting of these characteristics is an

important part of reducing the knowledge gap for regional biogas

dissemination.

The synergy between technical feasibility and user priorities and context

created through the OBSDM is an example of the type of planning and

design approach required for securing a sustainable future. The OBSDM

outputs are instructive rather than prescriptive, highlighting the type of

biogas systems that are most likely to have the best performance with

respect to the intended users’ context and priorities. It can be used as a

promotional tool, raising awareness among households about the energy

and fertiliser potential from their organic waste through biogas by applying

the OBSDM to their situation and then sharing with them the key

information on the recommended system. The OBSDM can also be used as

a policy or programme implementation tool. Government bodies and NGOs

can use the OBSDM to help determine what are the most suitable systems

268

in a given region/community based on the average local household

conditions. The OBSDM can also be used as a design tool, making it quick

and easy for a biogas installer to determine what type of system they should

install at a given site and what its design parameters should be. These types

of applications of the OBSDM can aid increasing awareness and

implementation of appropriate biogas systems, and thereby the success-rate

of installed systems will be improved, which will likely create more demand

for the technology.

As the SSA biogas industry grows and develops, making more regional-

specific data available, the OBSDM can be refined and expanded with it.

There is also potential to expand the model to include biogas applications

other than cooking and lighting, such as incubators, gas refrigeration, and

different types of biogas engines; technologies particularly relevant to

potential users in the local agricultural industries. For application in both

urban and rural households, the model could be modified to consider fuel

stacking and recommend the optimal mix of fuels for cooking where there

may be insufficient feedstock to meet daily energy needs for cooking. While

the OBSDM has focused on the suitability of the biogas system itself to the

context of the intended user, the quality of the end-use biogas appliances is

also of vital importance. Further development is required in SSA on building

efficient biogas appliances, particularly stoves and suitable pots and pans to

maximise the heat transfer during cooking. Unfortunately, even the most

technically efficient biogas system cannot compensate for a poorly designed

stove and pot with a low heat transfer coefficient. Similarly, a lack of

269

appropriate operation and maintenance of biogas systems also can

undermine biodigester performance. Thereby, increasing support and

resources to train biogas users on best practices for all aspects of biogas

system use – including the use of appliances like biogas stoves and the

application of bioslurry – is vital to increasing the potential of the SSA

biogas industry. The foundations have been set, it is now up to governments,

NGOs, private entities, communities, and all other stakeholders in the SSA

industry to further the effective dissemination of biogas technology

throughout SSA through collaboration and making use of resources like the

OBSDM.

7.2 Recommendations for future work

The recommendations for future work arising from the main conclusions of

this thesis are summarised below.

7.2.1 Recommendations for biogas research and system designs

• Development of more affordable biogas system designs like the MCD

SSB, which use local, low-cost materials and can contribute to local

economies.

• Development of more biogas system designs suited to water scarce

regions through using less water, wastewater, or recycled water,

and/or incorporating rainwater harvesting.

• Increase the measurement and reporting of biochemical methane

potentials and other key characteristics of organic wastes in SSA to

help reduce the knowledge gap for regional biogas dissemination.

270

7.2.2 Recommendations for OBSDM development

• Improve the climate database in the OBSDM by expanding it to

include climatic data for states, provinces, or major towns in each

SSA country, or linking it to detailed external databases such as

NASA weather data. The improved climate database should also

include soil temperature data for more accurate estimates of digester

temperatures for underground systems.

• Improve biogas production estimates for highland regions and other

areas that experience more temperate climates in the OBSDM by

adjusting the bacterial growth rate (μm) value to model psychrophilic

conditions.

• Expand the OBSDM to include heating and recycle in the design

considerations, and enable these features to be compared to standard

unheated systems in the model output.

• Update the feedstock database in the OBSDM with local or regional

data, as it becomes available, to improve the accuracy of the biogas

production estimates in the model output.

• Expand the OBSDM to include biogas applications other than

cooking and lighting, such as incubators, gas refrigeration, different

types of biogas engines, and other technologies used in local

agricultural industries.

• Expand the OBSDM to consider fuel stacking in urban and rural

household applications, whereby the optimal mix of available fuels

for cooking is recommended when there is insufficient feedstock to

meet the daily energy needs for cooking.

271

• Update and expand calorific and CO2 equivalent GHG emissions per

kWh of delivered energy from conventional fuels, particularly

firewood, used in the OBSDM (Table 5.3), based on local/regional

values and their moisture content. The moisture content of firewood

can be an additional input in the Energy Demand section of the

model to then enable the calorific values and GHG emissions to be

approximated.

• Expand the economics section of the OBSDM to include the

availability and pricing associated with microfinance loans/schemes

in the input section and the cost calculations of the different biogas

system design options.

• Test other types of MCDA methods in the OBSDM, particularly to

assess how they impact the sensitivity to the priority rating of criteria

in order to identify how the model can become more responsive to

the priority ratings in the mid-range.

• Investigate the effectiveness of the OBSDM as a promotional tool to

raise awareness among SSA households of the benefits and potential

adopting biogas technology.

• Investigate the extent to which the OBSDM can be used as a policy or

programme implementation tool in SSA.

7.2.3 Recommendations for the application of biogas

technology:

• Development of more efficient biogas appliances for the SSA context,

specifically biogas stoves and suitable cooking pots and pans to

272

maximise heat transfers. This should include clear guidelines and

standards for quality-controlled manufacturing.

• Develop tools and resources to train biogas users in SSA on best

practices for all aspects of biogas system use – including use of biogas

stoves and bioslurry application.

273

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training material: improved VACVINA model, 2010. http://www.ease-

web.org/wp-content/uploads/2010/10/Biogas-training-material-for-

technicians-and-users.pdf. (Accessed 30.07.2013).

[374] T. Bond, M.R. Templeton, History and future of domestic biogas

plants in the developing world, Energy for Sustainable Development 15(4)

(2011) 347-354.

[375] R.S. Khoiyangbam, S. Kumar, M.C. Jain, N. Gupta, A. Kumar, V.

Kumar, Methane emission from fixed dome biogas plants in hilly and plain

regions of northern India, Bioresource Technology 95(1) (2004) 35-39.

[376] R. Khoiyangbam, S. Kumar, M. Jain, Methane losses from floating

gasholder type biogas plants in relation to global warming, JOURNAL OF

SCIENTIFIC AND INDUSTRIAL RESEARCH 63(4) (2004) 344-347.

[377] N. Christ, M.J. Ferrantino, Land transport for export: the effects of

cost, time, and uncertainty in Sub-Saharan Africa, World Development

39(10) (2011) 1749-1759.

http://www.ease-web.org/wp-content/uploads/2010/10/Biogas-training-material-for-technicians-and-users.pdf



322

Appendices Appendix A – Databases and details from the OBSDM

Appendix A

Table A-1: Feedstock database in the OBSDM

Feedstock Category Feedstock DM (%)

oDM (% of DM)

Biogas yield (m³/ kg oDM)

CH₄ yield (m³/ kg oDM)

CH₄ content by vol (%)

Biogas yield (m³/t FM)

Energy yield (kWh/m³)

C:N ratio

Min recomm. RT (d)

Max recomm. RT (d)

Comments Unit of meas-ure (excl. kg)

Ref.

Cattle manure

Cattle (dairy) liquid

manure

8% 80% 0.350 0.186 53% 32.3 5.50 20 20 30

[1-3]

Cattle (dairy) manure

11% 62% 0.350a 0.210 53%a 52.0a 5.50a 20a 40a 75a

[4]

Cattle dung 18% 82% 0.380 0.230 61% 52.0 6.28 19.33

40 75

cattle [1, 2, 5-8]

Buffalo manure

14% N/A N/A N/A 60% 35.0 6.22 19.33

40 75

[2, 9]

Livestock food product waste

Eggs 25% 92% 0.975 0.585 60%a N/A 6.22a 5a 3a 30a WARNING! This feedstock has a low C:N

ratio

eggs [10]

Milk (whole) 8%b 92%b 0.900c 0.540 60% N/A 6.22 5.9 3 10 WARNING! This feedstock has a low C:N

ratio

[10]

Skim/low-fat milk

8% 92% 0.700 0.420 60% N/A 6.22 5.9 3 10 WARNING! This feedstock has a low C:N

ratio

[10, 11]

Other manure & sewage


sawdust)

55% 85% 0.250 0.140 56% 81.3 5.80 11.67 30 80 WARNING! This feedstock has a low C:N

ratio

[1, 2, 6, 11]

323


oDM (% of DM)






C:N ratio

Min recomm. RT (d)

Max recomm. RT (d)


Ref.


straw)

70% 85% 0.380 0.213 56% 230.0 5.80 11.67 30 80a WARNING! This feedstock has a low C:N

ratio

chickens

[1, 2, 6, 11]

Sewage sludge

5% 75% 0.630 0.334 53% 95.0 5.50 10.5 30a 100 WARNING! This feedstock requires post-treatment to


remain in the bioslurry.

Without post-treatment, the

bioslurry should only be applied to non-

consumable crops and/or

fruit trees.

[1, 12]

Night soil (pit toilet waste)

18% 84% 0.241 0.158 66% 37.0 6.79 7.98 70 100 WARNING! This feedstock requires post-treatment to


remain in the bioslurry.

Without post-treatment, the

bioslurry should only be applied to non-

consumable crops and/or

fruit trees.

people [2, 3, 13-15]

324


oDM (% of DM)






C:N ratio

Min recomm. RT (d)

Max recomm. RT (d)


Ref.

Pig manure 20% 85% 0.310 0.195 63% 56.8 6.53 15 50 55

pigs [1, 2, 6,

16] Pig manure,

liquid 7% 75% 0.360 0.227 63% 19.0 6.53 15 20 40

[1, 11, 16]

Sheep/goat manure

25% 80% 0.450 0.248 55% 108.0 5.70 18.33

50a 60a

[1, 2]

Vegetable & food waste

Beans 18% 91% 0.504 0.277 55% 82.7 5.70 15 10 40

[17]

Canteen Food waste

100%d

100%d 0.264 0.150 57% 264.4 5.89 15 10 40

[1, 11, 18, 19]

Vegetable waste

15% 76% 0.500 0.280 56% 57.0 5.80 15 10 40

[11, 17, 18]

Kitchen/ food waste

23% 90% 0.318 0.173 54% N/A 5.63 17a 10 40

[11, 18, 20]

Coffee pulp 28% N/A 0.375 0.225 60%a N/A 6.22a 30 10 40

[9, 11, 18, 21]

Organic fraction

MSW

31% 85% 0.406 0.291 72% 130.0 7.43 18.09

15 50a

[15, 22-24]

Roots, tuber & market waste

Potatoes 26% 93% 0.729 0.375 51% 177.1 5.33 18 10 40

[11, 17, 18]

Wild cocoyam

peels

27% 85%e 0.360 0.198f 55% 360.0 5.68 18 10 40

[11, 18, 25]

Market waste 22% 77% 0.520 0.332 64% 42.7 6.63 25 10 40

[1, 11, 18-20]

325


oDM (% of DM)






C:N ratio

Min recomm. RT (d)

Max recomm. RT (d)


Ref.

Root consumables

/residues

17% 87% 0.649 0.336 52% 95.9 5.37 25a 10 40

[11, 17, 18]

Roots stubble 12% 87% 0.700 0.372 53% 72.8 5.51 25 10 40

[11, 17, 18]

Fruit & nut waste

Spent fruits 35% 93% 0.550 0.330 60% N/A 6.22 39.67

8 40

[10, 11, 18]

Bananas 100%d

100%d 0.062 0.037 60%a 62.2 6.22 35 8 40

[10, 11, 18, 26]

Groundnuts with shells (bruised)

91% 94% 0.634 0.397 63% 538.6 6.49 35a 10 40

[11, 17, 18]

Groundnuts, shelled

(bruised)

89% 94% 0.663 0.416 63% 549.0 6.50 35 10 40

[11, 17, 18]

Crops & residues

Barley 31% 92% 0.756 0.479 63% 134.6 6.57 50 10 40 WARNING! This feedstock has a high C:N ratio

[11, 18, 26]

Maize 87% 98% 0.690 0.364 53% 590.3 5.47 60 10 40 WARNING! This feedstock has a high C:N

ratio

[11, 17, 18]

Millet/ sorghum

21% 92% 0.563 0.287 51% 107.2 5.28 63 10 40 WARNING! This feedstock has a high C:N

ratio

[11, 17, 18]

Cassava pulp 31% 98% 0.573 0.344 60% N/A 6.22 100a 10 40 WARNING!

This feedstock has a high C:N

ratio

[16, 18]

Corn stalk 79% 54% 0.415 0.249 60% N/A 6.22 66.5 10 40 WARNING! This feedstock has a high C:N

ratio

[9]

326


oDM (% of DM)






C:N ratio

Min recomm. RT (d)

Max recomm. RT (d)


Ref.

Water hyacinth

7% N/A 0.250 0.150 60% N/A 6.22 25 10 40

[9]

Straw & grass

Grass 88% 58% 0.415 0.249 60% N/A 6.22 17 10 40

[9]

Young grass 50% 58%a 0.415a 0.249 60% N/A 6.22 12 10 40

[2, 11, 18]

Wheat straw (4mm)

77% 92% 0.410 0.213 52% N/A 5.39 87.55

10 40 WARNING! This feedstock has a high C:N

ratio

[1, 2]

Maize straw 86% 72% 0.700 0.318 45% N/A 4.70 51.67 10 40 WARNING! This feedstock has a high C:N

ratio

[6, 10]

Rice straw 59% 83% 0.585 0.351 60% N/A 6.22 75 10 40 WARNING! This feedstock has a high C:N

ratio

[2, 10]

aEstimate bBased on DM and oDM for low-fat milk

cBased on the maximum biogas yield for whey dBiogas and methane yield given per kg of FM therefore using 100% for DM and oDM

eoDM calculated by subtracting ash and crude fibre content fMethane yield based on a rate per kg FM

327

Table A-2: Biodigester database in the OBSDM

Digester Name AGAMA BiogasPro

Fiberglass (Prefab.)


KENBIM Kentainer BlueFlame

BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised soil blocks (SSB)

Modified CAMARTEC solid state

digester (SSD)

PUXIN (Bioeco Sarl)

Country Botswana, Lesotho, Mozambique, Namibia, South Africa, Swaziland, Zambia, Zimbabwe

Rwanda Kenya, Rwanda

Kenya Kenya, Uganda, Rwanda, South Sudan, Burundi, Tanzania, Ethiopia

Ghana, Kenya, Tanzania, Uganda

Ghana, Tanzania, Uganda

Tanzania Democratic Republic of the Congo, Madagascar, Senegal, Togo, Ivory Coast, Cameroon, Mauritius

Digester Type Fixed dome Fixed dome Plug flow Fixed dome Floating cover Fixed dome Fixed dome Fixed dome Floating cover

TS min 6%^ 8%^ 8%^ 6%^ 6%^ 5% 6% 8%^ 0%

TS max 11%^ 12%^ 14%^ 11%^ 11%^ 11% 11% 18% 14%

HRT min (days) 35^ 50^ 40 40 40^ 40 40 40 40

HRT max (days) 90^ 60^ 100 60 90^ 60 60 60 90

Avg. digester temp on which

HRT range is based (°C)

20.05 21.85 26.9 23.85 20.8 29 25.05 25.05 25

Feeding mode semi-continuous

semi-continuous

semi-continuous

semi-continuous

semi-continuous semi-continuous semi-continuous semi-continuous semi-continuous

No of stages 1 1 1 1 1 1 2 1 1

Op temp type** M M M M M M M M M

Min ambient op temp (°C)

10 10^ 25 10^ 18^ 20 15^ 15^ 15^

Max ambient op temp (°C)

40 45^ 36 40^ 40^ 45^ 40^ 40^ 42^

Active system with heating

No No No No No No No No No

Lifespan (years) 10^ 15^ 15 20 30 20 20 20 15

328





BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC



digester (SSD)

PUXIN (Bioeco Sarl)

Gas pressure (1=varying, 2=constant)

1 1 1 1 2 1 1 1 2

Average gas pressure (kPa)

4.43 3.21 0.20 7.50^ 0.80 7.50 7.50* 7.50^ 6.33

Vulnerability to structural integrity*

2^ 3^ 1^ 3^ 2^ 3 3^ 3^ 3^

Unsuitable soil types

VR^ VR^

FL, GL, LP, VR^

FL, GL, LP, VR^ FL, GL, LP, VR^ FL, GL, LP, VR^ FL, GL, LP,

VR^ Underground

const. req. Yes Yes No Yes No Yes Yes Yes Yes

Daily operation req. (h/d)

0.5 0.5^ 0.5 0.5 0.75^ 0.5 0.5^ 0.5^ 0.55

Maintenance required (d/y)

1 1^ 1 1 0.5^ 4 1 1 1

Level of expertise

required for op. (1=basic, 2=

intermediate, 3=expert)

1^ 1^ 1 1^ 1^ 1 1^ 1^ 1

Construction time (d)

2 5.5 1.5 20 0.5 14 8 8 12

Annual running costs (%

installation costs)

4.3%^ 4.3%^ 4.3%^ 2.0% 4.3%^ 5.0% 5.0% 5.0%^ 4.5%

Gas production efficiency (%)^^

90%^ 85%^ 90%^ 90% 90%^ 80% 80% 80% 90%

329





BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC



digester (SSD)

PUXIN (Bioeco Sarl)

Unskilled to skilled labour

ratio

1.50 3.00 2.00 2.50 1.00 2.50 2.50 2.50 1.75

Application Cooking & lighting

Cooking & lighting

Cooking Cooking & lighting


Cooking & lighting

Cooking & lighting

Cooking, lighting,

electricity

Company contact details

AGAMA Biogas (Pty) Ltd,

Cape Town, South Africa

E-mail: admin@biogas

pro..com Website:

http://biogaspro.com/contact.

html

Rwanda Energy Development

Corp. Ltd (EDCL),

Kigali, Rwanda E-mail

[email protected]

Website: www.edcl.reg.r

w

Biogas Inter-national Ltd

Nairobi, Kenya

E-mail: info@biogas.

co.ke Website:

http://biogas.co.ke/en/

Kenya Biogas Program E-mail:

[email protected]

Website: http://kenyabiogas.com

Kentainers Limited,

Nairobi, Kenya, www.kentainers.c

o.ke

Tanzania Domestic Biogas

Programme (TDBP), Arusha,

Tanzania E-mail:

[email protected]

Website: http://www.bioga

s-tanzania.org Biogas Solutions

Uganda Kampala, Uganda

E-mail: info@biogassoluti

ons.co.ug Website:

http://www.biogassolutions.co.ug



Tanzania E-mail:

[email protected]


s-tanzania.org Biogas Solutions

Uganda Kampala, Uganda

E-mail: info@biogassoluti

ons.co.ug Website:

http://www.biogassolutions.co.ug



Tanzania E-mail:

[email protected]


s-tanzania.org

Bioeco Sarl, Solutions de

valorisation de la biomasse,

41500 MAVES-FRANCE,

http://www.bio-e-co.fr Reps.

in: Benin, Cameroon,

Cote d'Ivoire, Democratic

Republic of the Congo,

Madagascar, Mali,

Mauritius, Senegal

Reference [27-29] [30-33] [34-36] [37] [38-40] [5, 14, 41-43] [44] [45] [46-49]

*Vulnerabilities to structural integrity: 1=Major parts prone to rust or damage from exposure to the elements within 2 years since installation, major components can be easily damaged by children or animals

2=Major parts easily damaged by heavy rains and/or wind 3=Major parts can only be damaged by earthquakes, fires, and other natural disasters

**M=mesophilc ^Estimate

^^ Production efficiency in this context is the fraction of the total biogas produced by a biogas system that is available for use (i.e. the gas produced minus any leakages as a portion of the total production)

mailto:[email protected]


http://biogaspro.com/contact.html





http://www.edcl.reg.rw/

http://www.edcl.reg.rw/







http://kenyabiogas.com/

http://kenyabiogas.com/

http://www.kentainers.co.ke/

http://www.kentainers.co.ke/



http://www.biogas-tanzania.org/




http://www.biogassolutions.co.ug/














http://www.bio-e-co.fr/

http://www.bio-e-co.fr/

330

Table A-3: Country database in OBSDM with climate data from Weatherbase and currency conversion rates as at 06.05.2017 from Google currency converter [56, 57]

Country ISO Country Code

Currency Exchange rates (to USD)

Default exchange rates (to USD)

Exchange rates used (to USD)

Mean daily temperature (°C)

Mean high temperature (°C)

Mean low temperature (°C)

Average annual rainfall


Angola AGO AOA 0.006027 0.006027 0.006027 21.2 27.6 15.50 992.3 12.1

Benin BEN XOF 0.001677 0.001677 0.001677 26.9 31.8 22.30 1108.5 9.5

Botswana BWA BWP 0.095552 0.095552 0.095552 21 28.8 13.70 441 15.1

Burkina Faso BFA XOF 0.001677 0.001677 0.001677 28.2 33.6 22.10 789.6 11.5

Burundi BDI BIF 0.000584 0.000584 0.000584 20 25.3 15.10 1232.1 10.2

Cameroon CMR XAF 0.001677 0.001677 0.001677 23.8 28.8 18.80 1929.8 10

Cape Verde CPV CVE 0.009935 0.009935 0.009935 22.6 26.6 21 178.7 5.6

Central African

Republic

CAF XAF 0.001677 0.001677 0.001677 24.9 31.4 19.10 1461.1 12.3

Chad TCD XAF 0.001677 0.001677 0.001677 27.3 35.3 20.50 695.5 14.8

Comoros COM KMF 0.002236 0.002236 0.002236 23.7 28.7 20.80 2335.4 7.9

Republic of the Congo

COG XAF 0.001677 0.001677 0.001677 24 28.5 20.50 1531.1 8

Democratic Republic of

the Congo

COD CDF 0.000711 0.000711 0.000711 23.5 29 18.60 1500.3 10.4

Cote d'Ivoire CIV XOF 0.001677 0.001677 0.001677 26.2 30.2 21.40 1379.8 8.8

Djibouti DJI DJF 0.005588 0.005588 0.005588 28.5 32.3 23.80 177.1 8.5

Equatorial Guinea

GNQ XAF 0.001677 0.001677 0.001677 24.4 29.2 19.80 2459.9 9.4

Eritrea ERI ERN 0.065189 0.065189 0.065189 23.3 31 20.80 347 10.2

Ethiopia ETH ETB 0.043524 0.043524 0.043524 19.3 25.8 13.90 1091.4 11.9

Gabon GAB XAF 0.001677 0.001677 0.001677 24.7 28.4 21.30 1930.5 7.1

331










The Gambia GMB GMD 0.021692 0.021692 0.021692 28 33.9 21.20 926.5 12.7

Ghana GHA GHS 0.237530 0.237530 0.237530 26.7 29.2 24.60 1184.1 4.6

Guinea GIN GNF 0.000110 0.000110 0.000110 25.5 29.5 22.60 2155.8 6.9

Guinea-Bissau

GNB XOF 0.001677 0.001677 0.001677 26.9 31 22.50 1818.4 8.5

Kenya KEN KES 0.009699 0.009699 0.009870 20.8 26.9 16.10 1084.1 10.8

Lesotho LSO LSL 0.074488 0.074488 0.074488 13.6 21.5 7.60 734.9 13.9

Liberia LBR LRD 0.010638 0.010638 0.010638 25.1 29.7 22.20 2771.1 7.5

Madagascar MDG MGA 0.000313 0.000313 0.000313 21.8 26.5 17.70 1701.8 8.8

Malawi MWI MWK 0.001378 0.001378 0.001378 21.4 26.4 15.60 1111.1 10.8

Mali MLI XOF 0.001677 0.001677 0.001677 28.2 33.6 22.90 656.4 10.7

Mauritania MRT MRO 0.002780 0.002780 0.002780 27.5 33 22.10 187.4 10.9

Mauritius MUS MUR 0.028944 0.028944 0.028944 23.7 26.2 20.70 1548.7 5.5

Mozambique MOZ MZN 0.015625 0.015625 0.015625 23.6 29 19.00 968.6 10

Namibia NAM NAD 0.074488 0.074488 0.074488 20.1 25.5 13.60 309.7 11.9

Niger NER XOF 0.001677 0.001677 0.001677 27.9 34.3 21.80 585 12.5

Nigeria NGA NGN 0.003155 0.003155 0.003155 26.7 31 21.90 1283.6 9.1

Rwanda RWA RWF 0.001211 0.001211 0.001211 19 24.7 14.80 1192 9.9


STP STD 0.000045 0.000045 0.000045 25.2 27.6 22.7 1744 4.9

Senegal SEN XOF 0.001677 0.001677 0.001677 27.4 33 21.40 632.8 11.6

Seychelles SYC SCR 0.073206 0.073206 0.073206 26.7 29.1 24.4 2277.7 4.7

Sierra Leone SLE SLL 0.000133 0.000133 0.000133 26.1 30.1 22.50 2718.9 7.6

Somalia SOM SOS 0.001734 0.001734 0.001734 26.6 31.7 21.30 312.9 10.4

South Africa ZAF ZAR 0.074507 0.074507 0.074507 17 23.1 10.60 669.9 12.5

South Sudan SSD SDG 0.150376 0.150376 0.150376 26.7 33.7 21.30 1041.6 12.4

332










Sudan SDN SDG 0.150376 0.150376 0.150376 27.9 34.6 20.60 326.7 14

Swaziland SWZ SZL 0.074488 0.074488 0.074488 19.7 23.9 14.10 923 9.8

Tanzania TZA TZS 0.000448 0.000448 0.000448 22.3 27.8 17.30 1073.1 10.5

Togo TGO XOF 0.001677 0.001677 0.001677 26.4 31.3 22.30 1262.6 9

Uganda UGA UGX 0.000277 0.000277 0.000277 21.5 27 15.40 1277.9 11.6

Zambia ZMB ZMW 0.108331 0.108331 0.108331 21 27.4 14.20 1044.1 13.2

Zimbabwe ZWE USD 1.000000 1.000000 1.000000 19.7 25.9 13.10 764.4 12.8

333

Table A-4: Construction material database in OBSDM with prices and local availability for Kenya [37]

Category Construction Material

Unit Unit conversion

Std unit Default Cost

(USD)

Cost (USD)/unit

Emissions (kg

CO₂/unit)

Embodied Energy

(MJ/unit)

Local avail.

Ref.

Biogas appliances

Stoves - single burner pcs 1.00 pcs 26.86 26.86 - - No -

Biogas appliances

Stoves - double burner pcs 1.00 pcs 25.23 25.23 - - No -

Biogas appliances

Lamp pcs 1.00 pcs 16.44 16.44 - - No -

Biogas appliances

Pressure gauge pcs 1.00 pcs 5.27 5.27 - - No -

Biogas appliances

Desulphurizer pcs 1.00 pcs 0.00 0.00 - - No -

Biogas appliances

Feeding mixer pcs 1.00 pcs 24.38 24.38 - - No -

Composite and prefabricated

PVC tarpaulin kg (660 g/m²)

1.00 kg (660 g/m²) 0.00 0.00 2.60 68.60 No [58]


Flexibiogas PVC tarp. bag

digester 0.66 kg (660 g/m²) 454.01 454.01 1.72 45.28 No [58]


Puxin Biogas storage bag m³ 2.80 kg 32.45 32.45 7.28 192.08 No [58]


HDPE plastic m³ 928.50 kg 0.00 0.00 1485.60 71215.95 No [59, 60]


LLDPE plastic m³ 923.50 kg 0.00 0.00 1745.42 66769.05 No [58]


AGAMA BiogasPro (LLDPE plastic)

digester 120.00 kg 1202.37 1202.37 226.80 8676.00 No [58]



digester 110.82 kg 937.62 937.62 209.45 8012.29 No [58]


PE plastic sheet m ( 3.75m wide, 6 mm

thick)

21.44 kg (3.75m wide, 6 mm

thick)

0.00 0.00 41.60 1781.87 No [58]


PUXIN Gasholder (fiberglass reinforced

plastic)

pce (1 m³) 30.00 kg 243.37 243.37 45.90 840.00 No [58, 61]

334




(USD)

Cost (USD)/unit

Emissions (kg

CO₂/unit)

Embodied Energy

(MJ/unit)

Local avail.

Ref.


Fiberglass biodigester digester (6 m³)

120.00 kg 731.27 731.27 183.60 3360.00 No [33]

Masonry Stone kg 1.00 kg 0.01 0.01 0.06 1.00 Yes [58]

Masonry Bricks pcs 1.00 pcs 0.20 0.20 0.62 8.40 Yes [58]

Masonry Stabilized blocks pcs (8-10 kg/pc)

9.00 kg 0.10 0.10 0.23 2.53 No [58]

Masonry Dressed quarry stone pcs (390x190x 150mm/pc)

28.34 kg 0.39 0.39 1.59 28.34 Yes [58]

Masonry Concrete blocks pcs (200x100x

70mm)

1.96 kg 0.30 0.30 0.12 1.18 No [58]

Masonry Cement bag (50 kg/bag)

50.00 kg 7.90 7.90 41.50 230.00 Yes [58]

Masonry Lime bag (25 kg/bag)

25.00 kg 2.47 2.47 18.50 132.50 Yes [58]

Masonry Gravel (1x2) tonne 1000.00 kg 11.84 11.84 17.00 300.00 Yes [58]

Masonry Coarse sand kg 1.00 kg 0.01 0.01 0.01 0.10 Yes [58]

Masonry Fine sand kg 1.00 kg 0.01 0.01 0.01 0.10 No [58]

Masonry Waterproof cement bag (1 kg/bag)

1.00 kg 1.97 1.97 0.83 4.60 Yes [58]

Metals and wire

Chicken wire (1800mm wide)

m (230g/m²/

m)

0.41 kg 1.22 1.22 1.17 14.90 No [58]

Metals and wire

Welded square mesh (G8) -heavy gauge

pcs (1200mm x 2400mm, 3mm dia,

12.9kg)

12.90 kg 29.61 29.61 79.34 731.43 Yes [58]

Metals and wire

Iron bars ø 6 mm kg 1.00 kg 33.93 33.93 1.91 25.00 No [58]

Metals and wire

Steel rod/round bar 8 mm

pcs (400 g/mm, 3m

length)

1.20 kg 3.92 3.92 2.05 29.52 Yes [58]

335




(USD)

Cost (USD)/unit

Emissions (kg

CO₂/unit)

Embodied Energy

(MJ/unit)

Local avail.

Ref.

Metals and wire

Steel rod 6 mm pcs (230 g/m, 3 m length)

0.69 kg 2.96 2.96 1.18 16.97 No [58]

Metals and wire

Binding wire kg 1.00 kg 1.18 1.18 2.83 36.00 Yes [58]

Piping and sealants

Acrylic emulsion paint L 1.07 kg 3.14 3.14 3.81 72.76 No [58]

Piping and sealants

Gas piping (PVC or galv. Steel) incl. fittings,

valves & water drain

Per (household

scale) installation

1.00 Per (household

scale) installation

98.70 98.70 0 0 Yes -

Labour Skilled Labour person-day 1.00 person-day 9.87 9.87 0 0 No -

Labour Unskilled Labour person-day 1.00 person-day 4.93 4.93 0 0 No -

Labour Semiskilled Labour person-day 1.00 person-day 0.00 0.00 0 0 No -

Other Company overheads/installation

fee

lumpsum 1.00 lumpsum 88.83 88.83 0 0 No -

Other After sales service fee/warranty

lumpsum 1.00 lumpsum 14.80 14.80 0 0 No -

Other PUXIN mould hire lumpsum 1.00 lumpsum 81.12 81.12 0 0 No -

336

Table A-5: Construction material cost database in the OBSDM with prices given in local currency and the regional average prices in USD based on currency conversion rates as at 06.05.2017 [57]

Construction Material

Category Unit Burundi (BIF)

Ethiopia (ETB)

Kenya (KES)

Rwanda (RWF)

Senegal (XOF)

South Africa (ZAR)

Uganda (UGX)

Zambia (ZMW)

SSA average (USD)

Stoves - single burner

Biogas appliances

pcs 73000.00 431.25 - 15000.00 - - - - 26.53

Stoves - double burner

Biogas appliances

pcs -

- - - - - 250.00 27.08

Lamp Biogas appliances

pcs - 550.00 - 7000.00 - - - - 16.21

Pressure gauge Biogas appliances

pcs - - - 7000.00 - - - 20.00 5.32

Desulphurizer Biogas appliances

pcs - - - - - - - - 3.35

Feeding mixer Biogas appliances

pcs - - - 20000.00 - - - - 24.21

Flexibiogas PVC tarp. bag


digester - - 46000.00 - - - - - 454.01

Puxin Biogas storage bag


m³ - - - - 20000.00 - - - 33.54

AGAMA BiogasPro

(LLDPE plastic)


digester - - - - - 16250.00 - - 1210.74



digester - - 95000.00 - - - - - 937.62

PUXIN Gasholder (fiberglass reinforced

plastic)


pce (1 m³) - - - - 150000.00 - - - 251.53

Fiberglass biodigester


digester (6 m³)

- - - 600000.00 - - - - 726.39

Stone Masonry kg - 0.14 - 4.90 - - - - 0.01

Bricks Masonry pcs 50.00 - 20.00 40.00 - - - - 0.09

Stabilized blocks Masonry pcs (8-10 kg/pc)

- - - - - - 350.00 - 0.10

337



Ethiopia (ETB)

Kenya (KES)

Rwanda (RWF)

Senegal (XOF)

South Africa (ZAR)

Uganda (UGX)

Zambia (ZMW)

SSA average (USD)

Dressed quarry stone

Masonry pcs (390 x 190 x 150 mm

/pc)

- - 40.00 - - - - - 0.39

Concrete blocks Masonry pcs (200 x 100 x 70 mm)

- - - - - - - 3.00 0.32

Cement Masonry bag (50 kg/bag)

28000.00 133.5 800.00 11000.00 4000.00 - - 80.00 9.79

Lime Masonry bag (25 kg/bag)

-

250.00 2000.00 - - - - 2.44

Gravel (1x2) Masonry tonne 2976.19 163.10 1200.00 9157.51 8982.80 - - 83.33 9.31

Coarse sand Masonry kg 3.22 0.35 1.50 9.65 2.83 - - 0.06 0.01

Fine sand Masonry kg - - - 8.92 - - - 0.05 0.01

Waterproof cement

Masonry bag (1 kg/bag)

- - 200.00 800.00 - - - - 1.47

Chicken wire (1800mm wide)

Metals and wire

m (230g/m²/m)

- - - 1000.00 - - - - 1.21

Welded square mesh (G8) -heavy

gauge

Metals and wire

pcs (1200mm x 2400 mm, 3 mm dia., 12.9

kg)

- - 3000.00 - - - - - 29.61

Iron bars ø 6 mm Metals and wire

kg - - - 1000.00 - - - - 33.51

Steel rod/round bar 8 mm

Metals and wire

pcs (400 g/mm, 3m

length)

- 102.35 - 5600.00 - - - 4.00 3.89

Steel rod 6 mm Metals and wire

pcs (230 g/m, 3 m length)

- - 300.00 2500.00 - - - - 2.99

Binding wire Metals and wire

kg - 36.00 120.00 1000.00 - - - 10.00 1.26

Acrylic emulsion paint

Piping and sealants

L - 39.10 - 3000.00 - - - 40.00 3.22

Gas piping (PVC or galv. Steel)

incl. fittings, valves & water

drain

Piping and sealants

per (household

scale) installation

- 1331.69 10000.00 85800.00 - - - 570.00 80.57

Skilled Labour Labour person-day - 250.00 1000.00 4000.00 - - - 100.00 9.11

338



Ethiopia (ETB)

Kenya (KES)

Rwanda (RWF)

Senegal (XOF)

South Africa (ZAR)

Uganda (UGX)

Zambia (ZMW)

SSA average (USD)

Unskilled Labour Labour person-day - 80.00 500.00 1500.00 3037.33 - - 60.00 4.37

Company overheads/

installation fee

Other lumpsum - 3360.00 9000.00 80000.00 - - - 1000.00 110.06

After sales service

fee/warranty

Other lumpsum - - 1500.00 5000.00 - - - 200.00 14.17

PUXIN mould hire

Other lumpsum - - - - 50000.00 - - - 83.84

References [50] [62] [63] [51] [46, 48, 53]

[27] [64] [65]

339

Table A-6: Biodigester size database in the OBSDM with costs and recommended sizes based on an average rural Kenyan household with 77 kg of cattle manure available as feedstock per day

Digester Name Reactor volume

(m³)

Gas holder volume

(m³)

Size name

Land footprint

(m²)

Max system depth/ height

(m)

Cost RRP

(USD)

Vdig recomm./

Vavail

Recomm. avail size

HRT avg.

(days)

No. of dig

% change

from ideal

Cost based

on cost mat

(USD)

Cost (USD)

AGAMA BiogasPro

3.00 0.60 3.00 3.14 2.15

1.5521 6.00 60.3 2 28.86% -2703.43 -2703.43

AGAMA BiogasPro

4.05 0.95 6.00 3.46 2.30

1.1497 4.05 40.7 1 -13.02% -2562.46 -2562.46


3.07 3.07 4.00 7.22 2.20

1.3625 3.07 34.4 1 -26.61% -714.45 -714.45


4.60 4.60 6.00 10.83 3.30 -945.00 0.9083 4.60 51.6 1 10.09% -1045.41 -995.20


6.14 6.14 8.00 14.44 4.40

0.6813 6.14 68.8 1 46.79% -1198.71 -1198.71


3.50 0.80 4.00 9.00 0.20 -454.01 2.9000 10.50 117.7 3 3.45% -1774.72 -1568.37


5.50 1.20 6.00 11.25 0.20 -602.05 1.8455 11.00 123.3 2 8.37% -1643.19 -1423.65


9.00 0.70 9.00 13.50 0.20 -750.10 1.1278 9.00 100.9 1 -11.33% -1188.63 -969.37

KENBIM 3.60 0.85 4.00 15.00* 2.04*

1.5986 7.20 72.4 2 25.11% -1325.39 -1325.39

KENBIM 5.28 1.33 6.00 20.00* 2.22*

1.0900 5.28 53.1 1 -8.25% -798.95 -798.95

KENBIM 7.20 1.64 8.00 23.00* 2.40*

0.7993 7.20 72.4 1 25.11% -988.83 -988.83

KENBIM 9.60 1.75 10.00 28.00* 2.50*

0.5995 9.60 96.5 1 66.81% -1118.62 -1118.62

KENBIM 12.48 1.91 12.00 34.00* 2.60*

0.4611 12.48 125.5 1 116.85% -1287.39 -1287.39

Kentainer BlueFlame

BioSluriGaz

1.80 1.50 1.80 2.00 0.00

3.7826 7.20 72.4 4 5.75% -2235.52 -2235.52

Kentainer BlueFlame

BioSluriGaz

3.20 3.00 3.20 2.00 0.00

2.1277 6.40 64.3 2 -6.00% -1992.34 -1992.34

Modified CAMARTEC

4.00 0.90 4.00 13.50* 1.55*

2.4079 8.00 80.4 2 -16.94% -1234.57 -1234.57

340


(m³)

Gas holder volume

(m³)

Size name

Land footprint

(m²)


(m)

Cost RRP

(USD)

Vdig recomm./

Vavail

Recomm. avail size

HRT avg.

(days)

No. of dig

% change

from ideal

Cost based

on cost mat

(USD)

Cost (USD)

Modified CAMARTEC

6.00 1.60 6.00 18.00 1.65* -520.00 1.6053 12.00 120.6 2 24.59% -1526.98 -1283.49

Modified CAMARTEC

9.00 2.35 9.00 24.00 1.80* -650.00 1.0702 9.00 90.5 1 -6.56% -946.40 -798.20

Modified CAMARTEC

13.00 3.56 13.00 28.00 1.90* -740.00 0.7409 13.00 130.7 1 34.97% -1140.33 -940.16

Modified CAMARTEC

stabilised blocks

4.00 0.90 4.00 13.50* 1.55*

1.6222 8.00 80.4 2 23.29% -1072.73 -1072.73

Modified CAMARTEC

stabilised blocks

6.00 1.60 6.00 18.00 1.65*

1.0815 6.00 60.3 1 -7.53% -684.36 -684.36

Modified CAMARTEC

stabilised blocks

9.00 2.35 9.00 24.00 1.80*

0.7210 9.00 90.5 1 38.70% -735.36 -735.36

Modified CAMARTEC

stabilised blocks

13.00 3.56 13.00 28.00 1.90*

0.4991 13.00 130.7 1 100.35% -885.15 -885.15

Modified CAMARTEC solid

state digester (SSD)

7.87 2.10 9.00 24.00* 1.63

0.6762 7.87 88.2 1 47.88% -1061.86 -1061.86



11.37 3.03 13.00 28.00* 1.84

0.4681 11.37 127.4 1 113.61% -1272.08 -1272.08

PUXIN (Bioeco Sarl)

10.00 6.00 10.00 7.00 3.00 -1484.66 0.7639 10.00 100.5 1 30.91% -1105.83 -1295.25

PUXIN (Bioeco Sarl)

100.00 51.00 100.00 30.00 5.00 -8083.14 0.0764 0.00 0.0 0

PUXIN (Bioeco Sarl)

200.00 101.00 200.00 60.00 5.00 -16496.21 0.0382 0.00 0.0 0


10.00 1.00 10.00 16.00 4.00 -3506.72 0.5949 10.00 100.5 1 68.10% -3362.76 -3434.74

RW III (based on GGC 2047)

3.04 1.38 4.00 15.24 1.65

1.3744 3.04 34.1 1 -27.24% -870.80 -870.80

341


(m³)

Gas holder volume

(m³)

Size name

Land footprint

(m²)


(m)

Cost RRP

(USD)

Vdig recomm./

Vavail

Recomm. avail size

HRT avg.

(days)

No. of dig

% change

from ideal

Cost based

on cost mat

(USD)

Cost (USD)


4.51 2.04 6.00 19.38 1.75

0.9262 4.51 50.6 1 7.97% -994.47 -994.47


5.94 2.70 8.00 22.82 1.85

0.7031 5.94 66.6 1 42.23% -1144.66 -1144.66


7.55 3.28 10.00 27.37 2.00

0.5538 7.55 84.6 1 80.58% -1271.97 -1271.97

Senegal GGC 2047

8.00 1.20 8.00 30.71* 1.85* -900.00 1.1665 8.00 89.7 1 -14.28% -759.77 -829.88

Senegal GGC 2047

10.00 2.40 10.00 36.26* 2.00* -965.28 0.9332 10.00 112.1 1 7.16% -856.67 -910.98

Senegal GGC 2047

12.00 2.34 12.00 44.79* 2.15* -1158.33 0.7777 12.00 134.5 1 28.59% -948.25 -1053.29

Senegal GGC 2047

14.00 2.73 14.00 52.26* 2.30* -1351.39 0.6666 14.00 156.9 1 50.02% -1016.94 -1184.16

Senegal GGC 2047

16.00 3.12 16.00 59.73* 2.45* -1544.44 0.5833 16.00 179.4 1 71.45% -1081.35 -1312.90

Senegal GGC 2047

18.00 3.51 18.00 67.19* 2.60* -1450.00 0.5185 18.00 201.8 1 92.88% -1123.68 -1286.84

Sinidu model (modified GGC-

2047)

4.00 0.96 4.00 23.80 1.65 -557.10 1.3687 4.00 42.4 1 -26.94% -671.61 -614.36


2047)

6.00 1.44 6.00 32.20 1.75 -631.09 0.9125 6.00 63.6 1 9.59% -992.44 -811.77


2047)

8.00 1.20 8.00 46.80 1.85 -709.44 0.6843 8.00 84.8 1 46.13% -876.11 -792.77


2047)

10.00 2.40 10.00 50.40 1.95 -835.65 0.5475 10.00 106.0 1 82.66% -965.31 -900.48

Zamdigester 3.10 0.90 4.00 23.00* 1.70 -757.12 3.7636 12.40 139.0 4 6.28% -2932.47 -2980.48

Zamdigester 4.65 1.35 6.00 32.00* 1.84* -873.79 2.5091 13.95 156.4 3 19.57% -2537.19 -2579.29

Zamdigester 6.98 2.03 9.00 48.50* 2.05* -1045.17 1.6727 13.95 156.4 2 19.57% -2021.10 -2055.72

Zamdigester 10.96 3.04 14.00 52.00* 2.41* -1213.84 1.0643 10.96 122.9 1 -6.04% -1170.63 -1192.24

Zamdigester 16.44 4.56 21.00 56.00* 2.90 -1535.80 0.7096 16.44 184.3 1 40.93% -1483.90 -1509.85

*estimate

342

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351

Appendix B – Detailed results for the Kenyan and Cameroonian case studies

Appendix B

Table B-1: MCDA parameters for biodigester size selection in the OBSDM for a rural Kenyan household based on average survey data


(m³)

Gas holder volume

(m³)

Size name

Vdig recomm./

Vavail

Recomm. avail size

HRT avg. (days)

No. of dig

% change from ideal

Cost based on cost mat

(USD)

Cost (USD)

AGAMA BiogasPro 3.00 0.60 3 1.5521 6.00 60.3 2 0.2886 -2682.87 -2682.87

AGAMA BiogasPro 4.05 0.95 6 1.1497 4.05 40.7 1 -0.1302 -2543.80 -2543.80


3.07 3.07 4 1.3625 3.07 34.4 1 -0.2661 -718.38 -718.38


4.60 4.60 6 0.9083 4.60 51.6 1 0.1009 -1050.97 -997.98


6.14 6.14 8 0.6813 6.14 68.8 1 0.4679 -1205.90 -1205.90

Flexi biogas digester 3.50 0.80 4 2.9000 10.50 117.7 3 0.0345 -1775.70 -1568.86


Flexi biogas digester 9.00 0.70 9 1.1278 9.00 100.9 1 -0.1133 -1187.10 -968.60

KENBIM 3.60 0.85 4 1.5986 7.20 72.4 2 0.2511 -1326.58 -1326.58

KENBIM 5.28 1.33 6 1.0900 5.28 53.1 1 -0.0825 -797.37 -797.37

KENBIM 7.20 1.64 8 0.7993 7.20 72.4 1 0.2511 -987.57 -987.57

KENBIM 9.60 1.75 10 0.5995 9.60 96.5 1 0.6681 -1117.36 -1117.36

KENBIM 12.48 1.91 12 0.4611 12.48 125.5 1 1.1685 -1286.13 -1286.13

Kentainer BlueFlame BioSluriGaz

1.80 1.50 1.8 3.7826 7.20 72.4 4 0.0575 -2236.83 -2236.83


3.20 3.00 3.2 2.1277 6.40 64.3 2 -0.0600 -1989.29 -1989.29

Modified CAMARTEC 4.00 0.90 4 2.4079 8.00 80.4 2 -0.1694 -1235.89 -1235.89

Modified CAMARTEC 6.00 1.60 6 1.6053 12.00 120.6 2 0.2459 -1524.59 -1282.29



352


(m³)

Gas holder volume

(m³)

Size name

Vdig recomm./

Vavail

Recomm. avail size

HRT avg. (days)

No. of dig

% change from ideal


(USD)

Cost (USD)


4.00 0.90 4 1.6222 8.00 80.4

2 0.2329 -1070.42 -1070.42


6.00 1.60 6 1.0815 6.00 60.3 1 -0.0753 -683.98 -683.98


9.00 2.35 9 0.7210 9.00 90.5 1 0.3870 -734.22 -734.22


13.00 3.56 13 0.4991 13.00 130.7 1 1.0035 -884.03 -884.03

Modified CAMARTEC solid state digester

(SSD)

7.87 2.10 9 0.6762 7.87 88.2 1 0.4788 -1060.86 -1060.86


(SSD)

11.37 3.03 13 0.4681 11.37 127.4 1 1.1361 -1271.08 -1271.08

PUXIN (Bioeco Sarl) 10.00 6.00 10 0.7639 10.00 100.5 1 0.3091 -1089.91 -1263.42

PUXIN (Bioeco Sarl) 100.00 51.00 100 0.0764 0.00 0.0 0



10.00 1.00 10 0.5949 10.00 100.5 1 0.6810 -3355.43 -3452.86


3.04 1.38 4 1.3744 3.04 34.1 1 -0.2724 -871.70 -871.70


4.51 2.04 6 0.9262 4.51 50.6 1 0.0797 -995.45 -995.45


5.94 2.70 8 0.7031 5.94 66.6 1 0.4223 -1146.06 -1146.06


7.55 3.28 10 0.5538 7.55 84.6 1 0.8058 -1273.46 -1273.46

Senegal GGC 2047 8.00 1.20 8 1.1665 8.00 89.7 1 -0.1428 -761.14 -830.57

Senegal GGC 2047 10.00 2.40 10 0.9332 10.00 112.1 1 0.0716 -858.05 -911.66

Senegal GGC 2047 12.00 2.34 12 0.7777 12.00 134.5 1 0.2859 -949.70 -1054.02

Senegal GGC 2047 14.00 2.73 14 0.6666 14.00 156.9 1 0.5002 -1018.39 -1184.89

Senegal GGC 2047 16.00 3.12 16 0.5833 16.00 179.4 1 0.7145 -1082.88 -1313.66

Senegal GGC 2047 18.00 3.51 18 0.5185 18.00 201.8 1 0.9288 -1125.21 -1287.60

353


(m³)

Gas holder volume

(m³)

Size name

Vdig recomm./

Vavail

Recomm. avail size

HRT avg. (days)

No. of dig

% change from ideal


(USD)

Cost (USD)

Sinidu model (modified GGC-2047)

4.00 0.96 4 1.3687 4.00 42.4 1 -0.2694 -673.07 -619.97


6.00 1.44 6 0.9125 6.00 63.6 1 0.0959 -993.94 -818.05


8.00 1.20 8 0.6843 8.00 84.8 1 0.4613 -877.82 -799.85


10.00 2.40 10 0.5475 10.00 106.0 1 0.8266 -967.12 -908.72

Zamdigester 3.10 0.90 4 3.7636 12.40 139.0 4 0.0628 -2888.32 -2854.66

Zamdigester 4.65 1.35 6 2.5091 13.95 156.4 3 0.1957 -2493.96 -2467.87

Zamdigester 6.98 2.03 9 1.6727 13.95 156.4 2 0.1957 -1981.22 -1964.17

Zamdigester 10.96 3.04 14 1.0643 10.96 122.9 1 -0.0604 -1146.20 -1138.44

Zamdigester 16.44 4.56 21 0.7096 16.44 184.3 1 0.4093 -1450.49 -1440.53

354

Table B-2: MCDA normalised and overall scores for biodigester size selection in the OBSDM for a rural Kenyan household based on average survey data

Digester Name HRT norm No. of dig norm

% change from ideal norm

Cost (USD) norm

Dist. from best Dist. from worst

Overall sizing score

AGAMA BiogasPro 0.8288 0.8944 0.9115 -0.7257 0.6721 0.2694 0.2861

AGAMA BiogasPro 0.5595 0.4472 0.4112 -0.6880 0.2694 0.6721 0.7139

Fiberglass (Prefabricated) 0.3714 0.5774 0.4859 -0.4171 0.4784 0.4647 0.4927



Flexi biogas digester 0.5942 0.8018 0.2377 -0.6738 0.5941 0.5500 0.4807



KENBIM 0.3696 0.7071 0.1800 -0.5296 0.5077 0.6652 0.5672

KENBIM 0.2710 0.3536 0.0592 -0.3183 0.3696 0.8810 0.7044

KENBIM 0.3696 0.3536 0.1800 -0.3943 0.3063 0.7654 0.7142

KENBIM 0.4928 0.3536 0.4791 -0.4461 0.4631 0.5567 0.5459

KENBIM 0.6407 0.3536 0.8379 -0.5135 0.8028 0.5117 0.3893

Kentainer BlueFlame BioSluriGaz 0.7474 0.8944 0.6915 -0.7472 0.4548 0.0886 0.1631

Kentainer BlueFlame BioSluriGaz 0.6644 0.4472 0.7224 -0.6645 0.0886 0.4548 0.8369

Modified CAMARTEC 0.3738 0.6325 0.3647 -0.5706 0.4955 0.3887 0.4396





0.4276 0.7559 0.2112 -0.6249 0.5344 0.7067 0.5694


0.3207 0.3780 0.0683 -0.3993 0.3742 0.9496 0.7174


0.4811 0.3780 0.3509 -0.4286 0.3556 0.7207 0.6696


0.6949 0.3780 0.9097 -0.5161 0.8495 0.5429 0.3899

355

Digester Name HRT norm No. of dig norm

% change from ideal norm

Cost (USD) norm

Dist. from best Dist. from worst

Overall sizing score

Modified CAMARTEC solid state digester (SSD)

0.5692 0.7071 0.3884 -0.6408 0.2530 0.5480 0.6842


0.8222 0.7071 0.9215 -0.7677 0.5480 0.2530 0.3158

PUXIN (Bioeco Sarl) 1.0000 1.0000 1.0000 -1.0000 0.0000 0.0000 1.0000

PUXIN (Bioeco Sarl)

PUXIN (Bioeco Sarl)

Puxin (Biogas Burundi) 1.0000 1.0000 1.0000 -1.0000 0.0000 0.0000 1.0000

RW III (based on GGC 2047) 0.2754 0.5000 0.2858 -0.4027 0.4555 0.5897 0.5642




Senegal GGC 2047 0.2430 0.4082 0.1085 -0.3049 0.3085 0.6233 0.6689

Senegal GGC 2047 0.3037 0.4082 0.0544 -0.3347 0.2448 0.6709 0.7327

Senegal GGC 2047 0.3645 0.4082 0.2173 -0.3870 0.2578 0.5126 0.6653

Senegal GGC 2047 0.4252 0.4082 0.3802 -0.4350 0.3713 0.3763 0.5034

Senegal GGC 2047 0.4860 0.4082 0.5431 -0.4823 0.5235 0.2925 0.3585

Senegal GGC 2047 0.5467 0.4082 0.7060 -0.4727 0.6729 0.3039 0.3111

Sinidu model (modified GGC-2047) 0.2722 0.5000 0.2724 -0.3906 0.4443 0.5921 0.5713




Zamdigester 0.4058 0.7184 0.1252 -0.6161 0.6671 0.6923 0.5093

Zamdigester 0.4566 0.5388 0.3900 -0.5326 0.5391 0.4798 0.4709

Zamdigester 0.4566 0.3592 0.3900 -0.4239 0.3786 0.5974 0.6121

Zamdigester 0.3588 0.1796 0.1205 -0.2457 0.1794 0.9545 0.8418

Zamdigester 0.5382 0.1796 0.8159 -0.3109 0.6984 0.6447 0.4800

356

Table B-3: MCDA parameters for biodigester size selection in the OBSDM for a rural Cameroonian household based on average survey data


(m³)

Gas holder volume

(m³)

Size name

Vdig recomm./

Vavail

Recomm. avail size

HRT avg. (days)

No. of dig

% change from ideal

Cost based on cost

mat (USD)

Cost (USD)

AGAMA BiogasPro 3.00 0.60 3 1.2378 3.00 25.9 1 -0.1921 -1320.32 -1320.32

AGAMA BiogasPro 4.05 0.95 6 0.9169 4.05 35.0 1 0.0907 -2522.68 -2522.68


3.07 3.07 4 1.3098 3.07 27.9 1 -0.2365 -690.90 -690.90


4.60 4.60 6 0.8732 4.60 41.8 1 0.1452 -1043.12 -994.06


6.14 6.14 8 0.6549 6.14 55.7 1 0.5269 -1178.41 -1178.41




KENBIM 3.60 0.85 4 1.2980 3.60 31.1 1 -0.2296 -635.61 -635.61

KENBIM 5.28 1.33 6 0.8850 5.28 45.6 1 0.1300 -763.00 -763.00

KENBIM 7.20 1.64 8 0.6490 7.20 62.2 1 0.5408 -943.75 -943.75

KENBIM 9.60 1.75 10 0.4867 9.60 82.9 1 1.0545 -1062.28 -1062.28

KENBIM 12.48 1.91 12 0.3744 12.48 107.8 1 1.6708 -1227.16 -1227.16


1.80 1.50 1.8 2.8552 5.40 46.6 3 0.0507 -1635.70 -1635.70


3.20 3.00 3.2 1.6061 6.40 55.3 2 0.2453 -1940.97 -1940.97






(SSB)

4.00 0.90 4 1.3171 4.00 34.6 1 -0.2408 -514.24 -514.24


(SSB)

6.00 1.60 6 0.8781 6.00 51.8 1 0.1388 -660.58 -660.58

357


(m³)

Gas holder volume

(m³)

Size name

Vdig recomm./

Vavail

Recomm. avail size

HRT avg. (days)

No. of dig

% change from ideal

Cost based on cost

mat (USD)

Cost (USD)


(SSB)

9.00 2.35 9 0.5854 9.00 77.7 1 0.7083 -714.61 -714.61


(SSB)

13.00 3.56 13 0.4053 13.00 112.3 1 1.4675 -868.08 -868.08


(SSD)

7.87 2.10 9 0.5820 7.87 90.1 1 0.7183 -905.00 -905.00


(SSD)

11.37 3.03 13 0.4029 11.37 130.2 1 1.4820 -1085.01 -1085.01

PUXIN (Bioeco Sarl) 10.00 6.00 10 0.5705 10.00 99.0 1 0.7527 -1061.83 -1249.38




10.00 1.00 10 0.4443 10.00 99.0 1 1.2505 -2540.41 -3045.35


3.04 1.38 4 1.3212 3.04 27.6 1 -0.2431 -742.80 -742.80


4.51 2.04 6 0.8903 4.51 41.0 1 0.1232 -847.74 -847.74


5.94 2.70 8 0.6759 5.94 54.0 1 0.4795 -979.54 -979.54


7.55 3.28 10 0.5323 7.55 68.6 1 0.8785 -1087.35 -1087.35

Senegal GGC 2047 8.00 1.20 8 1.0930 8.00 76.0 1 -0.0850 -774.37 -837.18

Senegal GGC 2047 10.00 2.40 10 0.8744 10.00 94.9 1 0.1437 -872.45 -918.86

Senegal GGC 2047 12.00 2.34 12 0.7286 12.00 113.9 1 0.3724 -961.44 -1059.89

Senegal GGC 2047 14.00 2.73 14 0.6245 14.00 132.9 1 0.6012 -1034.12 -1192.76

Senegal GGC 2047 16.00 3.12 16 0.5465 16.00 151.9 1 0.8299 -1098.50 -1321.47

Senegal GGC 2047 18.00 3.51 18 0.4858 18.00 170.9 1 1.0586 -1143.41 -1296.70


4.00 0.96 4 1.3589 4.00 32.7 1 -0.2641 -676.92 -621.89


6.00 1.44 6 0.9059 6.00 49.1 1 0.1039 -919.15 -780.66

358


(m³)

Gas holder volume

(m³)

Size name

Vdig recomm./

Vavail

Recomm. avail size

HRT avg. (days)

No. of dig

% change from ideal

Cost based on cost

mat (USD)

Cost (USD)


8.00 1.20 8 0.6794 8.00 65.5 1 0.4718 -859.42 -790.65


10.00 2.40 10 0.5435 10.00 81.9 1 0.8398 -939.98 -895.14

Zamdigester 3.10 0.90 4 3.7373 12.40 107.1 4 0.0703 -2836.93 -2828.96

Zamdigester 4.65 1.35 6 2.4916 9.30 80.3 2 -0.1973 -1635.27 -1631.56

Zamdigester 6.98 2.03 9 1.6610 13.95 120.5 2 0.2041 -1958.88 -1953.00

Zamdigester 10.96 3.04 14 1.0569 10.96 94.7 1 -0.0538 -1136.19 -1133.43

Zamdigester 16.44 4.56 21 0.7046 16.44 142.0 1 0.4192 -1442.83 -1436.70

359

Table B-4: MCDA normalised and overall scores for biodigester size selection in the OBSDM for a rural Cameroonian household based on average survey data

Digester Name No of dig norm % change from ideal norm

Cost (USD) norm Dist. from best Dist. from worst Overall sizing score

AGAMA BiogasPro 0.7071 0.9044 -0.4637 0.5211 0.4223 0.4476

AGAMA BiogasPro 0.7071 0.4267 -0.8860 0.4223 0.5211 0.5524

Fiberglass (Prefabricated) 0.5774 0.3972 -0.4090 0.4018 0.5666 0.5851



Flexi biogas digester 0.8018 0.6075 -0.6718 0.8119 0.2036 0.2005



KENBIM 0.4472 0.1112 -0.2993 0.4836 0.7513 0.6084

KENBIM 0.4472 0.0629 -0.3592 0.3947 0.7827 0.6648

KENBIM 0.4472 0.2619 -0.4443 0.3775 0.5960 0.6122

KENBIM 0.4472 0.5106 -0.5001 0.5149 0.4481 0.4653

KENBIM 0.4472 0.8090 -0.5778 0.7963 0.4812 0.3766

Kentainer BlueFlame BioSluriGaz 0.8321 0.2024 -0.6444 0.3020 0.7861 0.7225

Kentainer BlueFlame BioSluriGaz 0.5547 0.9793 -0.7647 0.7861 0.3020 0.2775

Modified CAMARTEC 0.7559 0.0320 -0.6493 0.5495 0.8964 0.6199




Modified CAMARTEC stabilised blocks (SSB)

0.5000 0.1457 -0.3668 0.5215 0.7839 0.6005


0.5000 0.0840 -0.4712 0.4161 0.8254 0.6648


0.5000 0.4285 -0.5098 0.4383 0.5529 0.5578


0.5000 0.8878 -0.6193 0.8425 0.5179 0.3807

360

Digester Name No of dig norm % change from ideal norm

Cost (USD) norm Dist. from best Dist. from worst Overall sizing score


0.7071 0.4362 -0.6405 0.2530 0.4809 0.6553


0.7071 0.8999 -0.7679 0.4809 0.2530 0.3447

PUXIN (Bioeco Sarl) 1.0000 1.0000 -1.0000 0.0000 0.0000 1.0000

PUXIN (Bioeco Sarl)

PUXIN (Bioeco Sarl)

Puxin (Biogas Burundi) 1.0000 1.0000 -1.0000 0.0000 0.0000 1.0000

RW III (based on GGC 2047) 0.5000 0.2344 -0.4021 0.4242 0.6403 0.6015




Senegal GGC 2047 0.4082 0.0556 -0.3053 0.3037 0.6608 0.6851

Senegal GGC 2047 0.4082 0.0940 -0.3351 0.2478 0.6192 0.7142

Senegal GGC 2047 0.4082 0.2436 -0.3865 0.2741 0.4747 0.6339

Senegal GGC 2047 0.4082 0.3932 -0.4350 0.3815 0.3535 0.4809

Senegal GGC 2047 0.4082 0.5428 -0.4819 0.5218 0.2853 0.3535

Senegal GGC 2047 0.4082 0.6924 -0.4729 0.6585 0.3039 0.3158

Sinidu model (modified GGC-2047) 0.5000 0.2630 -0.3996 0.4383 0.5996 0.5777




Zamdigester 0.7845 0.1367 -0.6700 0.7268 0.6875 0.4861

Zamdigester 0.3922 0.3839 -0.3864 0.4383 0.6487 0.5968

Zamdigester 0.3922 0.3970 -0.4625 0.4112 0.6312 0.6055

Zamdigester 0.1961 0.1048 -0.2684 0.1908 1.0081 0.8409

Zamdigester 0.1961 0.8157 -0.3403 0.7146 0.7188 0.5015

361

Table B-5: Identifying feasible biogas system types and digester sizing in the OBSDM in a rural Kenyan household based on average survey data



Flexi biogas

digester


BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks (SSB)


digester (SSD)

Digester type fixed dome fixed dome plug flow fixed dome floating cover fixed dome fixed dome fixed dome

TS min 0.06 0.08 0.08 0.06 0.06 0.05 0.06 0.08

TS max 0.11 0.12 0.14 0.11 0.11 0.11 0.11 0.18

FS DM Check TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE


10 10 25 10 18 20 15 15

Op temp type M M M M M M M M

Min water req (L/d) 0 0 0 0 0 0 0 0

Max water req th (L/d) 64 29 29 64 64 92 64 29

Sufficient Water available

TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Max water req (L/d) 63.5 29 29 63.5 63.5 63.5 63.5 29

Avg water req (L/d) 31.75 14.5 14.5 31.75 31.75 31.75 31.75 14.5

HRT th min (d) 40 50 40 40 40 40 40 40

HRT th max (d) 75 60 75 60 75 60 60 60

Avg. digester temp on which HRT range is

based (°C)

20.05 21.85 26.90 23.85 20.80 29.00 25.05 25.05

V dig min (m³) 3.08 3.85 3.08 3.08 3.08 3.08 3.08 3.08

V dig max (m³) 10.54 6.36 7.95 8.43 10.54 8.43 8.43 6.36

OLR max (kg oDM/m³/d)

1.71 1.36 1.71 1.71 1.71 1.71 1.71 1.71

OLR min (kg oDM/m³/d)

0.50 0.83 0.66 0.62 0.50 0.62 0.62 0.83

Digester temp (°C) 23.85 23.85 20.8 23.85 20.8 23.85 23.85 23.85

362



Flexi biogas

digester


BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC



digester (SSD)

OLR max adj 2.49 1.67 0.93 1.71 1.71 1.02 1.51 1.51

OLR min adj 0.73 1.01 0.36 0.62 0.50 0.37 0.55 0.73

Vdig min adj 2.11 3.15 5.67 3.08 3.08 5.15 3.47 3.47

Vdig max adj 7.21 5.21 14.63 8.43 10.54 14.11 9.50 7.17

V dig recomm (m³) 4.66 4.18 10.15 5.76 6.81 9.63 6.49 5.32

Best overall sizing score

0.71 0.74 0.52 0.71 0.84 0.80 0.72 0.68

Vdig tot (m³) 4.05 4.60 9 7.2 6.4 9 6 7.87

No. of dig 1 1 1 1 2 1 1 1

Size name 6.0 6.0 9.0 8.0 3.2 9.0 6.0 9.0

HRT min(d) 28.83 43.41 95.75 40.96 48.46 68.55 46.18 50.21

HRT max (d) 52.60 59.76 116.88 93.51 83.12 116.88 77.92 102.21

HRT (d) 40.71 51.58 106.32 67.23 65.79 92.72 62.05 76.21

OLR (kg oDM/m³/d) 1.13 1.26 0.52 0.91 0.77 0.55 0.81 0.99

Max. specific growth rate (µm)

0.1811 0.1811 0.1414 0.1811 0.1414 0.1811 0.1811 0.1811

MPP (m³/d) 0.92 0.96 1.01 0.99 0.96 1.02 0.98 1.00

Biogas pp (m³/d) 1.74 1.81 1.91 1.87 1.81 1.93 1.85 1.89

Biogas production (m³/d)

1.56 1.54 1.72 1.68 1.62 1.54 1.48 1.51

Gas holder req (m³) 0.75 0.74 0.82 0.81 0.78 0.74 0.71 0.73

Gas holder per dig (m³)

0.95 4.60 0.70 1.64 3.00 2.35 1.60 2.10

Gas holder tot (m³) 0.95 4.60 0.70 1.64 6.00 2.35 1.60 2.10

Additional gas storage req (m³)

0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00

Land footprint (m²) 3.46 10.83 13.50 23.00 4.00 24.00 18.00 24.00

363



Flexi biogas

digester


BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC



digester (SSD)

Max system depth/height (m)

2.30 3.30 0.20 2.40 0.00 1.80 1.65 1.63

Underground construction suitable

TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE

Dig construction possible

TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE

Application suitable TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Dig type suitable TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE

Digester Name PUXIN (Bioeco Sarl)



Senegal GGC 2047


2047)

Zamdigester

Digester type floating cover floating cover fixed dome fixed dome fixed dome fixed dome

TS min 0 0.05 0.08 0.08 0.08 0.08

TS max 0.14 0.14 0.12 0.13 0.1 0.11

FS DM Check TRUE TRUE TRUE TRUE TRUE TRUE

Min ambient op temp (°C) 15 15 10 19 10 15

Op temp type M M M M M M

Min water req (L/d) 0 0 0 0 8 0

Max water req th (L/d) 770 92 29 29 29 29

Sufficient Water available TRUE TRUE TRUE TRUE TRUE TRUE

Max water req (L/d) 63.5 63.5 29 29 29 29

Avg water req (L/d) 31.75 31.75 14.5 14.5 18.5 14.5

HRT th min (d) 40 40 50 40 40 45

HRT th max (d) 75 75 60 60 60 60

364




Senegal GGC 2047


2047)

Zamdigester

Avg. digester temp on which HRT range is based (°C)

25.00 22.50 21.85 30.67 25.00 32.50

V dig min (m³) 3.08 3.08 3.85 3.08 3.40 3.47

V dig max (m³) 10.54 10.54 6.36 6.36 6.36 6.36

OLR max (kg oDM/m³/d) 1.71 1.71 1.36 1.71 1.54 1.52

OLR min (kg oDM/m³/d) 0.50 0.50 0.83 0.83 0.83 0.83

Digester temp (°C) 23.85 23.85 23.85 23.85 23.85 23.85

OLR max adj 1.52 1.95 1.67 0.86 1.38 0.64

OLR min adj 0.44 0.57 1.01 0.42 0.74 0.35

Vdig min adj 3.46 2.69 3.15 6.09 3.81 8.23

Vdig max adj 11.82 9.21 5.21 12.57 7.14 15.11

V dig recomm (m³) 7.64 5.95 4.18 9.33 5.47 11.67

Best overall sizing score 1.00 1.00 0.74 0.73 0.72 0.84

Vdig tot (m³) 10 10 4.51 10 6 10.962

No. of dig 1 1 1 1 1 1

Size name 10.0 10.0 6.0 10.0 6.0 14.0

HRT min(d) 54.37 42.34 39.43 88.04 51.65 110.07

HRT max (d) 129.87 129.87 58.61 129.87 70.59 142.36

HRT (d) 92.12 86.11 49.02 108.96 61.12 126.22

OLR (kg oDM/m³/d) 0.69 0.88 1.26 0.56 0.96 0.45

Max. specific growth rate (µm) 0.1811 0.1811 0.1811 0.1811 0.1811 0.1811

MPP (m³/d) 1.02 1.01 0.95 1.03 0.98 1.04

Biogas pp (m³/d) 1.93 1.91 1.79 1.95 1.85 1.97

Biogas production (m³/d) 1.73 1.72 1.61 1.75 1.66 1.46

Gas holder req (m³) 0.83 0.83 0.77 0.84 0.80 0.70

365




Senegal GGC 2047


2047)

Zamdigester

Gas holder per dig (m³) 6.00 1.00 2.04 2.40 1.44 3.04

Gas holder tot (m³) 6.00 1.00 2.04 2.40 1.44 3.04

Additional gas storage req (m³) 0.00 0.00 0.00 0.00 0.00 0.00

Land footprint (m²) 7.00 16.00 19.38 36.26 32.20 52.00

Max system depth/height (m) 3.00 4.00 1.75 2.00 1.75 2.41


TRUE FALSE TRUE TRUE TRUE TRUE

Dig construction possible TRUE FALSE TRUE FALSE FALSE FALSE

Application suitable TRUE TRUE TRUE TRUE TRUE TRUE

Dig type suitable TRUE FALSE TRUE FALSE FALSE FALSE

Table B-6: Identifying feasible biogas system types and digester sizing in the OBSDM in a rural Cameroonian household based on average survey data



Flexi biogas

digester


BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD) Digester type fixed dome fixed dome plug flow fixed dome floating cover fixed dome fixed dome fixed dome

TS min 0.06 0.08 0.08 0.06 0.06 0.05 0.06 0.08

TS max 0.11 0.12 0.14 0.11 0.11 0.11 0.11 0.18

FS DM Check TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE


10 10 25 10 18 20 15 15

Op temp type M M M M M M M M

Add. heating/insulation req.

FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Min water req (L/d) 39 30 17 39 39 39 39 0

Max water req th (L/d) 127 78 78 127 127 165 127 78

366



Flexi biogas

digester


BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD) Sufficient Water

available TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Max water req (L/d) 63 63 63 63 63 63 63 63

Avg water req (L/d) 51 46.5 40 51 51 51 51 31.5

HRT th min (d) 40 50 40 40 40 40 40 40

HRT th max (d) 75 60 75 60 75 60 60 60

Avg. digester temp on which HRT range is

based (°C)

20.05 21.85 26.90 23.85 20.80 29.00 25.05 25.05

V dig min (m³) 4.2 4.8 3.32 4.2 4.2 4.2 4.2 2.64

V dig max (m³) 9.68 7.74 9.68 7.74 9.68 7.74 7.74 7.74

OLR max (kg oDM/m³/d)

2.26 1.97 2.85 2.26 2.26 2.26 2.26 3.59

OLR min (kg oDM/m³/d)

0.98 1.22 0.98 1.22 0.98 1.22 1.22 1.22

Digester temp (°C) 26.3 26.3 23.8 26.3 23.8 26.3 26.3 26.3

OLR max adj 4.21 3.08 2.09 2.88 3.04 1.72 2.56 4.07

OLR min adj 1.83 1.91 0.72 1.56 1.32 0.93 1.39 1.39

Vdig min adj 2.25 3.08 4.53 3.29 3.11 5.50 3.71 2.33

Vdig max adj 5.18 4.96 13.19 6.06 7.17 10.14 6.83 6.83

V dig recomm (m³) 3.71 4.02 8.86 4.67 5.14 7.82 5.27 4.58

Best overall sizing score 0.5524 0.7236 0.8933 0.6648 0.7225 0.7449 0.6648 0.6553

Vdig tot (m³) 4.05 4.60 9.00 5.28 5.40 9.00 6.00 7.87

No. of dig 1 1 1 1 3 1 1 1

Size name 6.0 6.0 9.0 6.0 1.8 9.0 6.0 9.0

HRT min(d) 31.40 35.67 68.67 36.22 39.84 60.62 40.84 35.51

HRT max (d) 38.57 47.93 108.43 50.29 51.43 85.71 57.14 119.24

HRT (d) 34.98 41.80 88.55 43.25 45.63 73.17 48.99 77.37

367



Flexi biogas

digester


BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD) OLR (kg oDM/m³/d) 2.55 2.36 1.07 2.03 1.84 1.21 1.80 2.07

Max. specific growth rate (µm)

0.21 0.21 0.18 0.21 0.18 0.21 0.21 0.21

MPP (m³/d) 0.88 0.99 1.33 1.01 0.94 1.32 1.08 1.35

Biogas pp (m³/d) 1.45 1.63 2.19 1.66 1.55 2.17 1.78 2.22

Biogas production (m³/d)

1.30 1.38 1.97 1.49 1.39 1.74 1.43 1.78

Gas holder req (m³) 0.62 0.66 0.95 0.72 0.67 0.83 0.68 0.85

Gas holder per dig (m³) 0.95 4.60 0.70 1.33 1.50 2.35 1.60 2.10

Gas holder tot (m³) 0.95 4.60 0.70 1.33 4.50 2.35 1.60 2.10

Additional gas storage req (m³)

0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00

Land footprint (m²) 3.46 10.83 13.50 20.00 6.00 24.00 18.00 24.00

Max system depth/height (m)

2.30 3.30 0.20 2.22 0.00 1.80 1.65 1.63


FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE

Dig construction possible

FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE

Application suitable TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Dig type suitable FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE




Senegal GGC 2047


2047)

Zamdigester

Digester type floating cover floating cover fixed dome fixed dome fixed dome fixed dome

TS min 0 0.05 0.08 0.08 0.08 0.08

TS max 0.14 0.14 0.12 0.13 0.1 0.11

FS DM Check TRUE TRUE TRUE TRUE TRUE TRUE

368




Senegal GGC 2047


2047)

Zamdigester

Min ambient op temp (°C) 15 15 10 19 10 15

Op temp type M M M M M M

Add. heating/insulation req. FALSE FALSE FALSE FALSE FALSE FALSE

Min water req (L/d) 17 17 30 23 50 39

Max water req th (L/d) 1089 165 78 78 78 78

Sufficient Water available TRUE TRUE TRUE TRUE TRUE TRUE

Max water req (L/d) 63 63 63 63 63 63

Avg water req (L/d) 40 40 46.5 43 56.5 51

HRT th min (d) 40 40 50 40 40 45

HRT th max (d) 75 75 60 60 60 60

Avg. digester temp on which HRT range is based (°C)

25.00 22.50 21.85 30.67 25.00 32.50

V dig min (m³) 3.32 3.32 4.8 3.56 4.64 4.725

V dig max (m³) 9.68 9.68 7.74 7.74 7.74 7.74

OLR max (kg oDM/m³/d) 2.85 2.85 1.97 2.66 2.04 2.00

OLR min (kg oDM/m³/d) 0.98 0.98 1.22 1.22 1.22 1.22

Digester temp (°C) 26.3 26.3 26.3 26.3 26.3 26.3

OLR max adj 3.25 4.17 3.08 1.72 2.32 1.08

OLR min adj 1.11 1.43 1.91 0.79 1.39 0.66

Vdig min adj 2.92 2.27 3.08 5.51 4.07 8.78

Vdig max adj 8.50 6.62 4.96 11.98 6.80 14.39

V dig recomm (m³) 5.71 4.44 4.02 8.74 5.44 11.59

Best overall sizing score 1.0000 1.0000 0.7281 0.7142 0.7204 0.8409

Vdig tot (m³) 10.00 10.00 4.51 10.00 6.00 10.96

No. of dig 1 1 1 1 1 1

369




Senegal GGC 2047


2047)

Zamdigester

Size name 10.0 10.0 6.0 10.0 6.0 14.0

HRT min(d) 44.23 34.44 31.15 67.78 42.13 89.81

HRT max (d) 120.48 120.48 47.01 112.36 51.72 104.40

HRT (d) 82.36 77.46 39.08 90.07 46.93 97.11

OLR (kg oDM/m³/d) 1.66 2.13 2.36 1.08 1.74 0.82

Max. specific growth rate (µm) 0.21 0.21 0.21 0.21 0.21 0.21

MPP (m³/d) 1.38 1.35 0.94 1.43 1.06 1.47

Biogas pp (m³/d) 2.28 2.22 1.56 2.36 1.74 2.42

Biogas production (m³/d) 2.05 2.00 1.40 2.12 1.57 1.79

Gas holder req (m³) 0.98 0.96 0.67 1.02 0.75 0.86

Gas holder per dig (m³) 6.00 1.00 2.04 2.40 1.44 3.04

Gas holder tot (m³) 6.00 1.00 2.04 2.40 1.44 3.04

Additional gas storage req (m³) 0.00 0.00 0.00 0.00 0.00 0.00

Land footprint (m²) 7.00 16.00 19.38 36.26 32.20 52.00

Max system depth/height (m) 3.00 4.00 1.75 2.00 1.75 2.41


FALSE FALSE TRUE TRUE TRUE FALSE

Dig construction possible FALSE FALSE TRUE FALSE FALSE FALSE

Application suitable TRUE TRUE TRUE TRUE TRUE TRUE

Dig type suitable FALSE FALSE TRUE FALSE FALSE FALSE

370

Table B-7: MCDA parameter values and standardised scores in the OBSDM for feasible biogas systems for a rural Kenyan household based on average survey data




BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD)

PUXIN (Bioeco

Sarl)


Lifespan 10 15 20 30 20 20 20 15 20

Std. lifespan 0.1703 0.2554 0.3405 0.5108 0.3405 0.3405 0.3405 0.2554 0.3405

Gas pressure variability 1 1 1 2 1 1 1 2 1

Std. Gas pressure 0.2582 0.2582 0.2582 0.5164 0.2582 0.2582 0.2582 0.5164 0.2582

Sum of std. reliability scores 0.4285 0.5136 0.5987 1.0272 0.5987 0.5987 0.5987 0.7718 0.5987

Reliability 0.2172 0.2604 0.3035 0.5208 0.3035 0.3035 0.3035 0.3913 0.3035

Sensitivity changes in ambient temp.

30 11 30 22 25 25 25 27 35

Std. sensitivity 0.3800 0.1393 0.3800 0.2786 0.3166 0.3166 0.3166 0.3420 0.4433

Vulnerabilities to structural integrity

2 1 3 2 3 3 3 3 3

Std. vulnerabilities 0.2520 0.1260 0.3780 0.2520 0.3780 0.3780 0.3780 0.3780 0.3780

Sum of std. robustness scores 0.6319 0.2653 0.7579 0.5306 0.6946 0.6946 0.6946 0.7199 0.8213

Robustness 0.3173 0.1332 0.3806 0.2664 0.3488 0.3488 0.3488 0.3615 0.4124

Daily operation req. (h/d) 0.5 0.5 0.5 0.75 0.5 0.5 0.5 0.55 0.5

Std. daily operation 0.3092 0.3092 0.3092 0.4638 0.3092 0.3092 0.3092 0.3401 0.3092

Maintenance required (d/y) 1 1 1 0.5 4 1 1 1 1

Std. maintenance 0.2074 0.2074 0.2074 0.1037 0.8296 0.2074 0.2074 0.2074 0.2074

Level of expertise req. 1 1 1 1 1 1 1 1 1

Std. level expertise 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333

Construction time (d) 2 1.5 20 0.5 14 8 8 12 11

Std. construction time 0.0634 0.0475 0.6339 0.0158 0.4437 0.2536 0.2536 0.3803 0.3486

371




BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD)

PUXIN (Bioeco

Sarl)


Sum of std. simple operation & construction scores

0.9133 0.8975 1.4838 0.9167 1.9158 1.1035 1.1035 1.2612 1.1986


0.2457 0.2415 0.3992 0.2466 0.5155 0.2969 0.2969 0.3393 0.3225

Installation costs ex. subsidy (USD)

-2543.80 -968.60 -987.57 -1989.29 -797.72 -683.98 -1060.86 -1263.42 -995.45

Installation costs (USD) -2543.80 -968.60 -987.57 -1989.29 -797.72 -683.98 -1060.86 -1263.42 -995.45

Std. installation costs -0.6141 -0.2338 -0.2384 -0.4802 -0.1926 -0.1651 -0.2561 -0.3050 -0.2403

O&M costs (USD) -108.86 -41.45 -19.75 -85.13 -39.89 -34.20 -53.04 -56.85 -42.26

Std. O&M -0.6105 -0.2325 -0.1108 -0.4774 -0.2237 -0.1918 -0.2975 -0.3188 -0.2370

Annual savings (USD) 473.07 473.07 473.07 473.07 473.07 473.07 473.07 473.07 473.07

Std. Annual 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333 0.3333

NPV -305.90 2314.32 2871.78 1667.78 2890.21 3052.37 2515.07 1902.34 2672.28

Std. NPV -0.0427 0.3233 0.4012 0.2330 0.4038 0.4264 0.3514 0.2658 0.3733

Simple payback period 5.4 2.0 2.1 4.2 1.7 1.4 2.2 2.7 2.1

Std. simple payback period 0.6141 0.2338 0.2384 0.4802 0.1926 0.1651 0.2561 0.3050 0.2403

Cost per kWh -0.12 -0.03 -0.02 -0.05 -0.03 -0.02 -0.03 -0.04 -0.03

Std.cost per kWh -0.7845 -0.2082 -0.1387 -0.3144 -0.1746 -0.1558 -0.2363 -0.2747 -0.1925

Affordability (monthly disp income - cost)

40.28 45.89 47.70 42.25 46.02 46.50 44.93 44.61 45.83

Std. afford 0.2987 0.3404 0.3538 0.3134 0.3414 0.3449 0.3332 0.3309 0.3399

Additional savings req. for installation (USD)

-2247.71 -672.51 -691.48 -1693.20 -501.63 -387.89 -764.76 -967.33 -699.36

Std. additional sav -0.6698 -0.2004 -0.2061 -0.5046 -0.1495 -0.1156 -0.2279 -0.2883 -0.2084

Months of savings req. for purchase

45.55 13.63 14.01 34.31 10.17 7.86 15.50 19.60 14.17

Std. months savings 0.6698 0.2004 0.2061 0.5046 0.1495 0.1156 0.2279 0.2883 0.2084

Sum of std. low-cost scores -3.3735 -0.3121 -0.0500 -1.8817 -0.0040 0.1957 -0.4838 -0.8501 -0.2803

372




BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD)

PUXIN (Bioeco

Sarl)


Low-cost -0.8409 -0.0778 -0.0125 -0.4690 -0.0010 0.0488 -0.1206 -0.2119 -0.0699

Biogas production efficiency (%)

0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.9 0.9

Std. biogas production efficiency

0.3456 0.3456 0.3456 0.3456 0.3072 0.3072 0.3072 0.3456 0.3456

Proportion of energy req. met (%)

74.4% 81.8% 80.1% 77.4% 73.4% 70.5% 72.1% 82.5% 76.8%

Std. proportion energy 0.3234 0.3556 0.3483 0.3364 0.3191 0.3067 0.3136 0.3588 0.3340

Specific gas production per dig. vol. (m³ biogas/m³

installed)

0.3124 0.1771 0.1903 0.1310 0.1358 0.1949 0.1519 0.1083 0.2461

Std. spec. gas production 0.5405 0.3063 0.3292 0.2267 0.2349 0.3372 0.2628 0.1873 0.4257

Sum of std. technical efficiency scores

1.2095 1.0076 1.0231 0.9087 0.8612 0.9511 0.8837 0.8917 1.1054

Technical efficiency 0.4078 0.3398 0.3450 0.3064 0.2904 0.3207 0.2980 0.3007 0.3727

GHG emissions avoided from waste management (t CO₂-

e/y)

4.31 4.74 4.64 4.49 4.25 4.09 4.18 4.78 4.45

GHG emissions avoided fuel replacement (t CO₂-e/y)

21.75 23.92 23.42 22.62 21.46 20.62 21.09 24.13 22.46

GHG emissions from construction (t CO₂-e/y)

0.45 0.38 1.72 0.42 1.58 0.54 2.45 1.24 1.83

Total GHG emissions avoided (t CO₂-e/y)

25.61 28.28 26.34 26.69 24.13 24.17 22.82 27.67 25.08

Std. total GHG avoid 0.3321 0.3668 0.3417 0.3462 0.3130 0.3135 0.2960 0.3589 0.3254


6.51 11.54 14.21 21.98 14.19 29.23 8.76 18.46 10.40

Std. EROI 0.1316 0.2335 0.2876 0.4448 0.2871 0.5915 0.1773 0.3736 0.2105

Sum of std. environmentally benign scores

0.4638 0.6002 0.6293 0.7910 0.6001 0.9050 0.4733 0.7325 0.5358

Environmentally benign 0.2372 0.3070 0.3218 0.4045 0.3069 0.4628 0.2420 0.3746 0.2740

373




BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


digester (SSD)

PUXIN (Bioeco

Sarl)


Employment generation (unskilled/skilled ratio for

installation)

1.5 2 2.5 1 2.5 2.5 2.5 1.75 2.5

Std. employ generation 0.2327 0.3102 0.3878 0.1551 0.3878 0.3878 0.3878 0.2714 0.3878

Proportion of const. materials avail. locally (%)

20.0% 20.0% 78.6% 0.0% 72.7% 50.0% 72.7% 55.6% 58.8%

Std. proportion construction mat.

0.1226 0.1226 0.4818 0.0000 0.4459 0.3066 0.4459 0.3406 0.3607

Sum of std. local materials & labour scores

0.3553 0.4329 0.8695 0.1551 0.8337 0.6944 0.8337 0.6121 0.7485

Local materials & Labour 0.1799 0.2192 0.4403 0.0785 0.4221 0.3516 0.4221 0.3099 0.3790

Time saved from replacing current energy demand (h/d)

0.63 0.70 0.68 0.66 0.62 0.60 0.61 0.70 0.65

Time req. for system O&M (h/d)

1.54 1.54 1.54 1.78 1.60 1.54 1.54 1.59 1.54

Time saved (h/d) -0.91 -0.84 -0.86 -1.12 -0.98 -0.94 -0.93 -0.89 -0.89

Save time -0.3246 -0.3021 -0.3072 -0.4012 -0.3512 -0.3363 -0.3315 -0.3178 -0.3172

374

Table B-8: MCDA parameter values and standardised scores in the OBSDM for feasible biogas systems for a rural Cameroonian household based on average survey data

Digester Name Flexi biogas digester

Kentainer BlueFlame

BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks



RW III (based on

GGC 2047) Lifespan 15 30 20 20 20 20

Std. lifespan 0.2873 0.5747 0.3831 0.3831 0.3831 0.3831

Gas pressure variability 1 2 1 1 1 1

Std. Gas pressure 0.3333 0.6667 0.3333 0.3333 0.3333 0.3333

Sum of std. reliability scores 0.6207 1.2414 0.7165 0.7165 0.7165 0.7165

Reliability 0.3111 0.6223 0.3592 0.3592 0.3592 0.3592

Sensitivity changes in ambient temp. 11 22 25 25 25 35

Std. sensitivity 0.1807 0.3614 0.4107 0.4107 0.4107 0.5750

Vulnerabilities to structural integrity 1 2 3 3 3 3

Std. vulnerabilities 0.1562 0.3123 0.4685 0.4685 0.4685 0.4685

Sum of std. robustness scores 0.3369 0.6738 0.8792 0.8792 0.8792 1.0435

Robustness 0.1690 0.3379 0.4410 0.4410 0.4410 0.5234

Daily operation req. (h/d) 0.5 0.75 0.5 0.5 0.5 0.5

Std. daily operation 0.3714 0.5571 0.3714 0.3714 0.3714 0.3714

Maintenance required (d/y) 1 0.5 4 1 1 1

Std. maintenance 0.2222 0.1111 0.8889 0.2222 0.2222 0.2222

Level of expertise req. 1 1 1 1 1 1

Std. level expertise 0.4082 0.4082 0.4082 0.4082 0.4082 0.4082

Construction time (d) 1.5 0.5 14 8 8 11

Std. construction time 0.0709 0.0236 0.6618 0.3782 0.3782 0.5200

Sum of std. simple operation & construction scores 1.0728 1.1001 2.3303 1.3800 1.3800 1.5219

Simple operation & construction 0.2876 0.2949 0.6247 0.3699 0.3699 0.4080

Installation costs ex. subsidy (USD) -935.81 -1635.70 -736.26 -660.58 -905.00 -847.74

Installation costs (USD) -889.02 -1553.92 -699.45 -627.55 -859.75 -805.35

Std. installation costs -0.3799 -0.6641 -0.2989 -0.2682 -0.3674 -0.3442

O&M costs (USD) -40.05 -70.00 -36.81 -33.03 -45.25 -35.99

375


Kentainer BlueFlame

BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks



RW III (based on

GGC 2047) Std. O&M -0.3612 -0.6314 -0.3320 -0.2979 -0.4081 -0.3246

Annual savings (USD) 162.55 139.94 153.31 141.30 154.89 140.31

Std. Annual 0.4455 0.3835 0.4201 0.3872 0.4245 0.3845

NPV 42.75 -894.60 292.35 294.19 73.71 82.80

Std. NPV 0.0430 -0.9007 0.2944 0.2962 0.0742 0.0834

Simple payback period 5.5 11.1 4.6 4.4 5.6 5.7

Std. simple payback period 0.3408 0.6920 0.2843 0.2768 0.3459 0.3577

Cost per kWh -0.02 -0.04 -0.02 -0.02 -0.02 -0.02

Std.cost per kWh -0.3616 -0.6288 -0.2972 -0.3244 -0.3569 -0.3910

Affordability (monthly disp income - cost) -1.74 -4.23 -1.47 -1.15 -2.17 -1.40

Std. afford -0.3116 -0.7592 -0.2632 -0.2067 -0.3893 -0.2509

Additional savings req. for installation (USD) -879.41 -1544.32 -689.84 -617.95 -850.15 -795.75

Std. additional sav -0.3794 -0.6663 -0.2976 -0.2666 -0.3668 -0.3433

Months of savings req. for purchase 549.54 965.03 431.08 386.15 531.25 497.26

Std. months savings 0.3794 0.6663 0.2976 0.2666 0.3668 0.3433

Sum of std. low-cost scores -2.0255 -5.2253 -1.3564 -1.2237 -2.1026 -1.8871

Low-cost -0.3099 -0.7994 -0.2075 -0.1872 -0.3217 -0.2887

Biogas production efficiency (%) 90% 90% 80% 80% 80% 90%

Std. biogas production efficiency 0.4315 0.4315 0.3836 0.3836 0.3836 0.4315

Proportion of energy req. met (%) 47.0% 33.2% 41.4% 34.0% 42.3% 33.4%

Std. proportion energy 0.4934 0.3480 0.4339 0.3567 0.4441 0.3503

Specific gas production per dig. vol. (m³ biogas/m³ installed)

0.2036 0.1407 0.1530 0.1879 0.1783 0.2139

Std. spec. gas production 0.4581 0.3166 0.3443 0.4227 0.4012 0.4814

Sum of std. technical efficiency scores 1.3830 1.0960 1.1618 1.1630 1.2289 1.2632

Technical efficiency 0.4630 0.3669 0.3890 0.3893 0.4114 0.4229

GHG emissions avoided from waste management (t CO₂-e/y)

6.24 4.40 5.49 4.51 5.62 4.43

GHG emissions avoided fuel replacement (t CO₂-e/y) 30.08 21.21 26.45 21.74 27.08 21.36

GHG emissions from construction (t CO₂-e/y) 0.38 0.35 1.58 0.54 2.45 1.83

Total GHG emissions avoided (t CO₂-e/y) 35.94 25.26 30.36 25.71 30.24 23.96

376


Kentainer BlueFlame

BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks



RW III (based on

GGC 2047) Std. total GHG avoid 0.5082 0.3572 0.4293 0.3636 0.4277 0.3388

Energy returned on energy invested (EROI) 15.15 25.51 18.27 32.19 11.75 10.33

Std. EROI 0.3034 0.5108 0.3658 0.6444 0.2352 0.2068

Sum of std. environmentally benign scores 0.8115 0.8679 0.7951 1.0080 0.6628 0.5456

Environmentally benign 0.4165 0.4454 0.4080 0.5173 0.3402 0.2800

Employment generation (unskilled/skilled ratio for installation)

2 1 2.5 2.5 2.5 2.5

Std. employ generation 0.3651 0.1826 0.4564 0.4564 0.4564 0.4564

Proportion of const. materials avail. locally (%) 20.0% 0.0% 72.7% 58.3% 72.7% 82.4%

Std. proportion construction mat. 0.1375 0.0000 0.4999 0.4010 0.4999 0.5661

Sum of std. local materials & labour scores 0.5026 0.1826 0.9564 0.8574 0.9564 1.0225

Local materials & Labour 0.2546 0.0925 0.4845 0.4344 0.4845 0.5180

Time saved from replacing current energy demand (h/d) 0.22 0.15 0.19 0.16 0.20 0.16

Time req. for system O&M (h/d) 0.98 1.22 1.04 0.98 0.98 0.98

Time saved (h/d) -0.76 -1.06 -0.85 -0.82 -0.78 -0.82

Save time -0.3622 -0.5076 -0.4063 -0.3913 -0.3727 -0.3926

377

Table B-9: MCDA with weighted scores in OBSDM for rural Kenyan households based on average survey data (best scores in green, worst sores in red, overall best scores in bold)

Weighted scores AGAMA BiogasPr

o



BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC



digester (SSD)

PUXIN (Bioeco

Sarl)

RW III (based on

GGC 2047)

Reliability 0.0217 0.0260 0.0304 0.0521 0.0304 0.0304 0.0304 0.0391 0.0304

Robustness 0.0317 0.0133 0.0381 0.0266 0.0349 0.0349 0.0349 0.0362 0.0412


0.0410 0.0402 0.0665 0.0411 0.0859 0.0495 0.0495 0.0566 0.0537

Low-cost -0.1401 -0.0130 -0.0021 -0.0782 -0.0002 0.0081 -0.0201 -0.0353 -0.0116

Technical efficiency 0.0408 0.0340 0.0345 0.0306 0.0290 0.0321 0.0298 0.0301 0.0373

Environmentally benign 0.0237 0.0307 0.0322 0.0404 0.0307 0.0463 0.0242 0.0375 0.0274

Local materials & Labour

0.0180 0.0219 0.0440 0.0079 0.0422 0.0352 0.0422 0.0310 0.0379

Save time -0.0541 -0.0503 -0.0512 -0.0669 -0.0585 -0.0561 -0.0553 -0.0530 -0.0529

Dist. Best score 0.1556 0.0518 0.0389 0.0968 0.0558 0.0280 0.0451 0.0521 0.0382

Dist. Worst score 0.0526 0.1372 0.1476 0.0850 0.1464 0.1588 0.1326 0.1166 0.1399

Overall score 0.2526 0.7260 0.7912 0.4675 0.7239 0.8501 0.7465 0.6911 0.7856

Rank 9 5 2 8 6 1 4 7 3

378

Table B-10: MCDA with weighted scores in OBSDM for rural Cameroonian households based on average survey data (best scores in green, worst sores in red, overall best scores in bold)

Weighted scores Flexi biogas digester

Kentainer BlueFlame

BioSluriGaz

Modified CAMARTEC

Modified CAMARTEC

stabilised blocks


(SSD)


Reliability 0.0311 0.0622 0.0359 0.0359 0.0359 0.0359

Robustness 0.0169 0.0338 0.0441 0.0441 0.0441 0.0523


0.0479 0.0491 0.1041 0.0617 0.0617 0.0680

Low-cost -0.0516 -0.1332 -0.0346 -0.0312 -0.0536 -0.0481

Technical efficiency 0.0463 0.0367 0.0389 0.0389 0.0411 0.0423

Environmentally benign 0.0416 0.0445 0.0408 0.0517 0.0340 0.0280

Local materials & Labour 0.0255 0.0092 0.0485 0.0434 0.0485 0.0518

Save time -0.0604 -0.0846 -0.0677 -0.0652 -0.0621 -0.0654

Dist. Best score 0.0586 0.1153 0.0646 0.0331 0.0425 0.0446

Dist. Worst score 0.1046 0.0674 0.1117 0.1228 0.1049 0.1097

Overall score 0.6408 0.3690 0.6338 0.7877 0.7117 0.7111

Rank 4 6 5 1 2 3

379

Appendix C – Details from the validation and sensitivity analysis of the OBSDM

Appendix C

Data from Rwandan Comparative Biodigester study entered as inputs in the OBSDM

Table C-1: Inputs to the OBSDM for households with fiberglass biogas systems installed from the Comparative Biodigester Study

Household No. 1 2 3 4

District Kayonza Kicukiro Kirehe Kicukiro

Installed biodigester type Fiberglass Fiberglass Fiberglass Fiberglass

System intended use Cooking Cooking Cooking Cooking

No. of cookstoves 1 2 1 1

No. of lamps 0 0 0 0

No. of hours of cooking (h/stove/d) 1.01 1.24 0.54 2.59

No. of hours of lighting (h/lamp/d) 0 0 0 0

Daily volume of biogas req. (m³/d) 0.47 1.14 0.25 1.19

Daily energy req. (kWh/d) 3.38 8.27 1.81 8.68

Avg. daily biogas production recorded from survey (m³/d) 0.49 0.96 0.14 1.04

Current energy use

Fuel type Firewood Charcoal Firewood Firewood

Amount 15 1.43 8.02 24.13

Time spent collecting/preparing fuel (min/d) 37 18.5 37 37

Cost per month (FRw/month)* 3,729.48 7,000.00 1,661.65 7,000.00

Annual energy costs (FRw/y)* 44,753.76 84,000.00 19,939.80 84,000.00

Annual consumption (kWh/y) 20,865.83 4,339.92 11,156.27 33,566.17

Costs per kWh (FRw/kWh)* 2.14 19.36 1.79 2.5

Hours spent preparing current energy source (h/y) 225.08 112.54 225.08 225.08

380


Annual GHG emissions (t CO₂-e./y) 91.38 9.03 48.86 147

Feedstock

Type of feedstock Cattle dung Cattle dung Cattle dung Cattle dung

Amount (kg/d) 33.77 53.07 19.58 41.45

Time req. to collect feedstock & transport it to the proposed installation site (time req. for biodigester feeding and maintenance) (min/d)

30 30 20.11 30

Total daily biogas production potential (m³/d) 1.8 2.83 1.04 2.21

Total daily energy production potential (kWh/d) 11.3 17.76 6.55 13.87

Location

Amount of water available (L/d) 23.33 41.96 17.55 41.23

Time req. to collect water (min/d) 50 30 37.5 0

Mean daily temperature (°C) 21.27 22.96 21.85 25.34

Mean high temperature during the day (°C) 30.07 29.34 31.99 29.34

Mean temperature in the coldest month (°C) 15.3 18.08 14.56 18.08

Maximum temperature difference between day and night (°C) 14.76 11.27 17.43 11.27

Shallowest groundwater table depth at any point throughout the year (m) 43 15 29.5 15

Soil type Ferralsols, Acrisols, Nitisols




Area available to install biogas system (m²) 13.23 13.98 13.83 16.83

Underground construction possible? Yes Yes Yes Yes

Amount of dry fertiliser required per year (kg DM/yr) 1717.51 3337.42 1720.12 2837.72

Cost of fertiliser per kg (FRw/kg)* 0 29.96 34.88 24.67

Construction materials available locally & costs Stone, bricks, cement, lime, gravel (1x2), coarse sand, fine sand, waterproof cement, chicken wire, steel rod/round bar 8 mm, steel rod 6 mm, binding wire, gas piping & fittings, stoves (single), biogas

lamp, pressure gauge, concrete feeding mixer Economics

Monthly disposable income (FRw)* 5,714.17 22,783.21 5,714.17 45,566.42

Savings available for capital expenditure (FRw)* 150,000.00 420,000.00 50,000.00 490,000.00

Type of subsidy available Amount Amount Amount Amount

381


Value of subsidy (FRw)* 600,000.00 600,000.00 600,000.00 600,000.00

Rating of priorities

Reliability 4 4 4 4

Robustness 5 5 5 5

Simple operation 5 5 5 5

Low-cost 3 3 3 3

Technical efficiency 3 3 3 3

Environmentally benign 3 3 3 3

Local materials & labour 3 3 3 3 Save time 3 3 3 3

*Note: 1 USD = 811.40 FRw as of 25 November 2016

382

Table C-2: Inputs to the OBSDM for households with fixed dome biogas systems installed from the Comparative Biodigester Study

Household No. 5 6 7 8 9 10 11 12 13 14 15

District Kayonza Kicukiro Gasabo Rwamagana Rwamagana Kicukiro Ngoma Ngoma Ngoma Ngoma Kirehe

Installed biodigester type

Fixed dome

Fixed dome

Fixed dome

Fixed dome Fixed dome Fixed dome

Fixed dome

Fixed dome

Fixed dome

Fixed dome

Fixed dome

System intended use


Cooking Cooking Cooking & lighting


Cooking & lighting

Cooking & lighting

Cooking Cooking

No of cookstoves 1 1 1 1 1 2 1 1 1 1 1

No of lamps 0 1 0 0 1 0 1 1 1 1 0

No of hours of cooking (h/stove/d)

2.44 2.49 4.76 1.5 4.7 2.89 3.2 2.73 2.2 8.18 1.33

No of hours of lighting (h/lamp/d)

0 2 0 0 1.61 0 0.86 2.94 2.17 0 0

Daily volume of biogas req. (m³/d)

1.13 1.47 2.20 0.69 2.43 2.66 1.61 1.73 1.36 3.77 0.61

Daily energy req. (kWh/d)

8.17 15.04 15.95 5.03 21.14 19.33 13.60 18.99 14.64 27.40 4.46

Avg. daily biogas production recorded from survey (m³/d)

0.82 1.00 1.95 0.58 1.14 2.65 1.35 1.51 0.89 3.27 0.43

Current energy use

Fuel type Firewood Firewood Firewood Firewood Firewood Firewood Firewood Firewood Firewood Firewood Firewood

Amount 36.24 37.79 70.69 22.28 43.12 72.39 51.26 57.35 33.72 124.17 19.75

Time spent collecting/preparing fuel (min/d)

37 37 37 37 37 37 37 37 37 37 37

Cost per month (FRw/month)*

7,508.20 15,661.02 11,717.71 5,406.96 10,464.79 18,000.00 10,621.13 14,259.45 8,383.39 15,436.52 4,092.58

Annual energy costs (FRw/y)*

90,098.40 187,932.24 140,612.52 64,883.52 125,577.48 216,000.00 127,453.56 171,113.40 100,600.68 185,238.24 49,110.96

Annual consumption (kWh/y)

50,411.85 52,567.99 98,333.72 30,992.72 59,982.32 100,698.51 71,305.51 79,777.04 46,906.39 172,727.37 27,473.35

Costs per kWh (FRw/kWh)*

1.79 3.58 1.43 2.09 2.09 2.15 1.79 2.14 2.14 1.07 1.79

Hours spent preparing current energy source (h/y)

225.08 225.08 225.08 225.08 225.08 225.08 225.08 225.08 225.08 225.08 225.08

383

Household No. 5 6 7 8 9 10 11 12 13 14 15

Annual GHG emissions (t CO₂-e/y)

220.77 230.21 430.64 135.73 262.68 440.99 312.27 349.37 205.42 756.43 120.31

Feedstock

Type of feedstock Cattle dung

Cattle dung

Cattle dung

Cattle dung Cattle dung Cattle dung Cattle dung

Cattle dung Cattle dung Cattle dung

Cattle dung

Amount (kg/d) 28.47 21.58 59.32 36.39 40.00 75.81 50.50 39.39 24.74 33.16 29.88

Time req. to collect feedstock & transport it to the proposed installation site (min/d)

90.00 30.00 60.00 37.38 41.09 20.00 7.00 15.00 15.00 20.00 30.69

Total daily biogas production potential (m³/d)

1.52 1.15 3.16 1.94 2.13 4.04 2.69 2.10 1.32 1.77 1.59

Total daily energy production potential (kWh/d)

9.53 7.22 19.85 12.18 13.39 25.37 16.90 13.18 8.28 11.10 10.00

Location

Amount of water available (L/d)

34.83 24.33 48.12 34.26 20.00 67.10 47.97 28.61 24.68 33.00 30.72

Time req. to collect water (min/d)

5.00 10.00 46.50 37.50 22.36 15.00 30.00 20.00 17.50 60.00 37.50


22.61 24.77 22.69 29.45 25.82 25.98 26.54 27.25 25.54 25.57 22.31

Mean high temperature during the day (°C)

31.27 29.34 31.56 30.81 30.81 29.34 30.81 30.81 30.81 30.81 30.63

Mean temperature in the coldest month (°C)

16.05 18.08 16.61 16.11 16.11 18.08 16.11 16.11 16.11 16.11 16.04


15.22 11.27 14.95 14.70 14.70 11.27 14.70 14.70 14.70 14.70 14.59


43 15 20 40 40 15 29.5 29.5 29.5 29.5 29.5

384

Household No. 5 6 7 8 9 10 11 12 13 14 15

Soil type Ferralsols, Acrisols, Nitisols

Acrisols, Alisols,

Plithosols

Acrisols, Alisols,

Plithosols



Acrisols, Alisols,

Plithosols

Acrisols, Alisols,

Plithosols

Acrisols, Alisols,

Plithosols

Acrisols, Alisols,

Plithosols

Acrisols, Alisols,

Plithosols

Acrisols, Alisols,

Plithosols Area available to install biogas system (m²)

23.73 25.88 38.88 25.38 25.38 25.38 21.66 35.48 28.13 22.03 25.38

Underground construction possible?

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Amount of dry fertiliser required per year (kg DM/yr)

0.00 1376.19 3957.86 2096.29 2200.61 4164.90 3198.84 2468.93 1923.25 2104.33 1608.91

Cost of fertiliser per kg (FRw/kg)*

N/A 28.21 20.21 28.62 54.53 28.21 28.21 28.21 26.00 28.21 28.21

Construction materials available locally & costs

Stone, bricks, cement, lime, gravel (1x2), coarse sand, fine sand, waterproof cement, chicken wire, steel rod/round bar 8 mm, steel rod 6 mm, binding wire, gas piping & fittings, stoves (single), biogas lamp, pressure gauge, concrete feeding mixer

Economics

Monthly disposable income (FRw)*

5,714.17 45,566.42 45,566.42 2,857.08 5,714.17 45,566.42 5,714.17 5,714.17 5,714.17 2,857.08 5,714.17

Savings available for capital expenditure (FRw)*

300,000 480,000 250,000 50,000 283,333 500,000 120,000 400,000 300,000 150,000 50,000

Type of subsidy available

Amount Amount Amount Amount Amount Amount Amount Amount Amount Amount Amount

Value of subsidy (FRw)*

300,000 300,000 300,000 300,000 300,000 300,000 300,000. 300,000 300,000 300,000 300,000


Reliability 5 5 5 5 5 5 5 5 5 5 5

Robustness 5 5 5 5 5 5 5 5 5 5 5

Simple operation 5 5 5 5 5 5 5 5 5 5 5

Low-cost 3 3 3 3 3 3 3 3 3 3 3

Technical efficiency 5 5 5 5 5 5 5 5 5 5 5

Environmentally benign

3 3 3 3 3 3 3 3 3 3 3

Local materials & labour

5 5 5 5 5 5 5 5 5 5 5

Save time 3 3 3 3 3 3 3 3 3 3 3


385

Table C-3: Inputs to the OBSDM for households with flexbag biogas systems installed from the Comparative Biodigester Study


District Gasabo Rwamagana Rwamagana Kirehe

Installed biodigester type Flexi-bag Flexi-bag Flexi-bag Flexi-bag

System intended use Cooking Cooking Cooking Cooking

No of cookstoves 1 1 1 1

No of lamps 0 0 0 0

No of hours of cooking 1.18 2.89 1.97 3.53

No of hours of lighting 0 0 0

Daily volume of biogas req (m³/d) 0.54 1.33 0.91 1.63

Daily energy req (kWh/d) 3.95 9.68 6.60 11.83

Avg. daily biogas production recorded from survey (m³/d)

0.47 1.16 0.05 1.20

Current energy use

Fuel type Firewood Firewood Firewood Firewood

Amount 24.13 42.92 29.26 60.33

Time spent collecting/preparing fuel (min/d) 37 37 37 37

Cost per month (FRw/month)* 7,000.00 10,417.40 7,101.14 5,000.00

Annual energy costs (FRw/y)* 84,000.00 125,008.80 85,213.68 60,000.00

Annual consumption (kWh/y) 33,566.17 59,704.10 40,702.29 83,922.38

Costs per kWh (FRw/kWh)* 2.50 2.09 2.09 0.71

Hours spent preparing current energy source (h/y) 225.08 225.08 225.08 225.08

Annual GHG emissions (t CO₂-e/y) 147.00 261.46 178.25 367.52

Feedstock

Type of feedstock Cattle dung Cattle dung Cattle dung Cattle dung

Amount (kg/d) 24.00 48.73 16.44 32.6

386


Time req. to collect feedstock & transport it to the proposed installation site (time req for biodigester feeding and maintenance) (min/d)

60.00 15.00 16.89 60.00

Total daily biogas production potential (m³/d) 1.28 2.60 0.88 1.74

Total daily energy production potential (kWh/d) 8.03 16.31 5.50 10.91

Location

Amount of water available (L/d) 24.22 56.27 26.64 27.83

Time req. to collect water (min/d) 2.00 60.00 22.36 40.00

Mean daily temperature (°C) 24.83 28.35 29.83 27.66

Mean high temperature during the day (°C) 31.56 30.81 30.81 31.31

Mean tempertaure in the coldest month (°C) 16.61 16.11 16.11 15.30


14.95 14.70 14.70 16.01


20 40 40 29.5

Soil type Acrisols, Alisols, Plithosols




Area available to install biogas system (m²) 14.93 11.25 11.25 11.25

Underground construction possible? Yes Yes Yes Yes

Amount of dry fertiliser required per year (kg DM/yr)

1287.01 2772.68 1062.95 2132.76

Cost of fertiliser per kg (FRw/kg)* 46.62 15.15 56.45 28.21

Construction materials available locally & costs Stone, bricks, cement, lime, gravel (1x2), coarse sand, fine sand, waterproof cement, chicken wire, steel rod/round bar 8 mm, steel rod 6 mm, binding wire, gas piping & fittings, stoves (single), biogas lamp,

pressure gauge, concrete feeding mixer Economics

Monthly disposable income (FRw)* 22,783.21 2,857.08 5,714.17 5,714.17

Savings available for capital expenditure (FRw)* 50,000.00 100,000.00 160,000.00 50,000.00

Type of subsidy available Amount Amount Amount Amount

387


Value of subsidy (FRw)* 300,000.00 300,000.00 300,000.00 300,000.00


Reliability 3 3 3 3

Robustness 3 3 3 3

Simple operation 4 4 4 4

Low-cost 5 5 5 5

Technical efficiency 4 4 4 4

Environmentally benign 4 4 4 4

Local materials & labour 3 3 3 3

Save time 3 3 3 3


388

Comparison of recommended biogas system design details from the OBSDM and installed biogas systems

Table C-4: Comparison of recommended biogas systems from OBSDM, where all priority criteria ratings are equal, with installed systems from the Rwandan Comparative Biodigester Study

HH No. District Status Biodigester type Digester size (m³)

Daily biogas production (m³/d)

Daily hours of cooking (h/d)

HRT (d) OLR (kg

oDM/m³/d) Installation

costs (FRw)*

1 Kayonza

Recomm. Fiberglass (Prefab.) 3.07 0.83 1.81 58.31 2.42 555,800.00

Installed Fiberglass 4.60 0.49 1.01 80.58 0.97 750,000.00

%difference (recomm. - installed) -40.00% 52.87% 56.66% -32.07% 85.96% -29.74%

2 Kicukiro

Recomm. Modified CAMARTEC

stabilised blocks 4.00 1.09 2.36 45.15 1.88 397,681.36

Comparison Fiberglass (Prefab.) 3.07 1.00 2.18 36.04 2.45 555,800.00

Installed Fiberglass 4.60 0.96 2.47 48.42 1.69 1,020,000.00

%difference (recomm. - installed) -40.00% 4.39% -12.70% -29.31% 37.00% -58.92%

3 Kirehe


Installed Fiberglass 4.60 0.14 0.54 123.94 0.75 650,000.00

%difference (recomm. - installed) -40.00% 126.17% 84.48% -26.46% 108.76% -15.62%

4 Kicukiro


Installed Fiberglass 4.60 1.04 2.59 55.65 1.46 1,090,000.00

%difference (recomm. - installed) -40.00% -11.12% -25.19% -23.56% 55.74% -64.92%

5 Kayonza


Comparison RW III (based on GGC

2047) 3.04 0.72 1.56 50.67 2.34 534,593.08

Installed Fixed dome 4.51 0.82 2.44 71.29 0.84 600,000.00


6 Kicukiro



2047) 3.04 0.61 1.31 62.17 2.41 534,593.08



7 Gasabo Recomm.


4.00 1.18 2.56 40.53 2.05 397,681.36


2047) 3.04 1.15 2.49 32.39 2.68 534,593.08

389




HRT (d) OLR (kg


costs (FRw)*



8 Rwamagana

Recomm. Kentainer BlueFlame

BioSluriGaz 1.80 0.67 1.46 26.78 3.25 437,054.68


2047) 3.04 0.88 1.91 39.16 3.46 534,593.08


%difference (recomm. - installed) -38.97% 41.54% 23.81% -47.98% 101.57% 41.74%

9 Rwamagana



2047) 3.04 0.93 2.02 40.42 3.37 534,593.08



10 Kicukiro




2047) 4.51 1.55 3.37 34.17 2.76 624,461.54


%difference (recomm. - installed) 0.00% -52.24% -52.51% 7.89% 13.53% -24.65%

11 Ngoma




2047) 3.04 1.05 2.28 32.98 2.99 534,593.08


%difference (recomm. - installed) -38.97% -25.10% -33.78% -32.60% 58.66% 24.01%

12 Ngoma



2047) 3.04 0.92 1.99 38.95 3.34 534,593.08



13 Ngoma



2047) 3.04 0.68 1.47 55.32 2.81 534,593.08


390




HRT (d) OLR (kg


costs (FRw)*


14 Ngoma



2047) 3.04 0.81 1.76 44.36 2.81 534,593.08



15 Kirehe



2047) 3.04 0.74 1.60 49.80 2.34 534,593.08



16 Gasabo



Comparison Flexi biogas digester 3.50 0.74 1.61 89.05 1.16 414,546.34

Installed Flexi-bag 8.00 0.47 1.18 165.91 0.39 350,000.00


17 Rwamagana





18 Rwamagana

Recomm. Kentainer BlueFlame

BioSluriGaz 1.80 0.42 0.91 44.12 2.72 437,054.68



%difference (recomm. - installed) -78.26% 170.87% -42.74% -56.60% 142.31% -10.39%

19 Kirehe





*Costs based on 1 USD = 811.40 FRw as of 25 November 2016

391

Detailed output from the OBSDM when an equal priority rating is used for the sustainability criteria

Table C-5: Detailed output from the OBSDM for Households 1 to 10 when equal priority criteria rating and updated EROI figures are used

Household No. 1 2 3 4 5 6 7 8 9 10

District Kayonza Kicukiro Kirehe Kicukiro Kayonza Kicukiro Gasabo Rwamagana Rwamagana Kicukiro

Installed biodigester type

Fiberglass Fiberglass Fiberglass Fiberglass Fixed dome Fixed dome Fixed dome Fixed dome Fixed dome Fixed dome

Size (m3) 6 6 6 6 6 6 6 6 6 6

Digester size (m3) 4.60 4.60 4.60 4.60 4.51 4.51 4.51 4.51 4.51 4.51

Recommended Biodigester - name


Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks

Size specifications -size name

4 4 4 4 4 4 4 4 4 6

Recommended digester size (m3)

2.00 4.06 1.45 3.01 2.32 1.69 4.15 1.98 1.70 5.16

Recommended available total digester size (m3)

3.07 4.00 4.00 4.00 4.00 4.00 4.00 4.00 3.07 6.00

Number of digesters 1 1 1 1 1 1 1 1 1 1

Total gasholder size (m3)

3.07 0.90 0.90 0.90 0.90 0.90 0.90 0.90 3.07 1.60

Additional recommended gas storage (m3)

0 0 0 0 0 0 0 0 0 0

Gas and energy production

Estimated daily biogas production (m3)

0.83 1.09 0.55 0.93 0.71 0.59 1.18 0.87 1.01 1.59

Estimated hours of energy production per day

1.81 2.36 1.19 2.01 1.54 1.28 2.56 1.89 2.19 3.45

Specific gas production per dig. vol. (m3 biogas/ m3 installed)

0.14 0.22 0.11 0.19 0.14 0.12 0.24 0.18 0.16 0.21

392

Household No. 1 2 3 4 5 6 7 8 9 10

Estimated daily energy production (kWh)

5.24 6.85 3.45 5.81 4.46 3.70 7.43 5.48 6.33 10.00


179% 96% 221% 77% 63% 40% 54% 126% 42% 60%

Operational specifications

Minimum amount of water required to mix with feedstock (L/d)

15.00 31.00 12.00 24.00 17.00 13.00 35.00 22.00 18.00 45.00

Maximum amount of water required to mix with feedstock (L/d)

23.33 41.96 17.55 41.23 34.83 24.33 48.12 34.26 20.00 67.10

Average hydraulic retention time (HRT) (d)

58.31 45.15 82.83 48.78 62.35 76.28 40.53 48.25 52.01 42.90

Organic loading rate (OLR) (kg oDM/m3/d)

2.42 1.88 1.94 1.97 1.76 1.83 2.05 2.64 3.37 2.11

Economics

Estimated capital cost (considering subsidy if avail.) (FRw)*

0.00 0.00 0.00 97681.36 97681.36 97681.36 97681.36 97681.36 255800.00 193334.37

Estimated capital cost (excl. subsidy) (FRw)*

555800.00 397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 43334.37

Additional funds required to meet capital cost based on intended user's current savings (FRw)*

0.00 0.00 0.00 0.00 0.00 0.00 0.00 47681.36 0.00 0.00

Months of saving req to meet capital cost (based on current savings & disposable income) (FRw)*

0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.69 0.00 0.00

393

Household No. 1 2 3 4 5 6 7 8 9 10

Estimated monthly running costs (FRw)*

1982.10 1657.01 1657.01 1657.01 1657.01 1657.01 1657.01 1657.01 1982.10 2055.56

Additional monthly income required to meet running costs (FRw)*

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Annual savings (from fuel and fertiliser replacement) (FRw)*

44753.76 87766.60 27786.85 68025.20 49163.81 53190.31 79265.89 76850.07 62691.92 136293.14

Estimated simple payback period (y)

0.00 0.00 0.00 0.00 1.99 1.84 1.23 1.27 4.08 1.42

Estimated NPV (FRw)*

159488.49 577922.26 67280.86 409852.64 151593.60 185873.45 407869.58 387302.36 40127.56 757004.27

Cost per kWh (FRw/kWh)*

12.44 7.96 15.78 9.37 15.21 18.35 9.14 12.39 17.66 9.41

Emissions reduction, energy economics & time

Estimated greenhouse gas emissions reduced (t CO2-e/y)

93.86 11.48 50.00 116.18 140.92 93.37 235.02 137.89 112.14 268.02


36.74 36.35 18.33 30.86 23.68 19.63 39.43 29.08 44.41 46.14

Estimated time saved per day (negative number indicates additional time rather than a time saving) (h/d)

-1.24 -1.23 -0.87 -0.54 -1.72 -1.94 -1.96 -1.15 -1.32 -0.74


394

Table C-6: Detailed output from the OBSDM for Households 11 to 19 when equal priority criteria rating and updated EROI figures are used

Household No. 11 12 13 14 15 16 17 18 19

District Ngoma Ngoma Ngoma Ngoma Kirehe Gasabo Rwamagana Rwamagana Kirehe

Installed biodigester type Fixed dome Fixed dome Fixed dome Fixed dome Fixed dome Flexi-bag Flexi-bag Flexi-bag Flexi-bag

Size (m3) 6 6 6 6 6 8 8 8 8

Digester size (m3) 4.51 4.51 4.51 4.51 4.51 8.00 8.00 8.00 8.00

Recommended Biodigester -name Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Kentainer BlueFlame

BioSluriGaz

Kentainer BlueFlame

BioSluriGaz

Size specifications -size name 4 4 4 4 4 4 4 1.8 1.8

Recommended digester size (m3) 3.18 2.21 1.67 2.23 2.41 1.61 2.27 0.87 1.66


4.00 4.00 4.00 4.00 4.00 4.00 3.07 1.80 1.80

Number of digesters 1 1 1 1 1 1 1 1 1

Total gasholder size (m3) 0.90 0.90 0.90 0.90 0.90 0.90 3.07 1.50 1.50

Additional recommended gas storage (m3) 0 0 0 0 0 0 0 0 0


Estimated daily biogas production (m3) 1.07 0.92 0.66 0.80 0.73 0.65 1.05 0.42 0.63


2.31 1.99 1.43 1.74 1.59 1.40 2.27 0.91 1.36


0.22 0.19 0.13 0.16 0.15 0.13 0.17 0.13 0.19

Estimated daily energy production (kWh) 6.70 5.77 4.13 5.04 4.60 4.06 6.57 2.64 3.94

Proportion of energy requirements met 66% 53% 48% 21% 119% 119% 78% 46% 39%



30.00 23.00 15.00 20.00 18.00 14.00 22.00 10.00 19.00


47.97 28.61 24.68 33 30.72 24.22 56.27 26.64 27.83

Average hydraulic retention time (HRT) (d) 40.98 48.29 67.19 54.44 60.64 69.34 36.29 44.12 31.19

395

Household No. 11 12 13 14 15 16 17 18 19

Organic loading rate (OLR) (kg oDM/m3/d) 2.28 2.56 2.13 2.14 1.78 2.14 3.08 2.72 2.82

Economics


97681.36 97681.36 97681.36 97681.36 97681.36 97681.36 255800.00 137054.68 137054.68


397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 437054.68 437054.68

Additional funds required to meet capital cost based on intended user's current savings (FRw)

0.00 0.00 0.00 0 47681.36 47681.36 155800.00 0.00 87054.68


0.00 0.00 0.00 0 8.34 2.09 54.53 0.00 15.23

Estimated monthly running costs (FRw)* 1657.01 1657.01 1657.01 1657.01 1657.01 1657.01 1982.10 1558.63 1558.63


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


79126.53 64723.65 35773.85 44810.82 58795.99 96855.85 93361.41 44752.42 30576.04

Estimated simple payback period (y) 1.23 1.51 2.73 2.18 1.66 1.01 2.74 3.06 4.48

Estimated NPV (FRw)* 406683.10 284063.28 37597.32 114564.11 233597.79 557622.85 273402.13 108505.77 -25133.78

Cost per kWh (FRw/kWh)* 10.13 11.77 16.43 13.47 14.77 16.71 17.02 24.15 16.17



208.94 187.35 100.45 162.75 122.03 148.44 208.39 83.63 143.55

Energy returned on energy invested (EROI) 35.56 30.62 21.93 26.76 24.40 21.56 46.09 21.91 32.72


-0.73 -0.78 -0.77 -1.72 -1.04 -0.94 -1.29 -1.13 -2.19


396

Detailed output from the OBSDM when a priority rating favourable to the installed biodigester types is used

for the sustainability criteria

Table C-7: Detailed output from the OBSDM for Households 1 to 10 when priority criteria rating favourable to the installed biodigester types and updated EROI figures are used

Household No. 1 2 3 4 5 6 7 8 9 10


Installed biodigester type Fiberglass Fiberglass Fiberglass Fiberglass Fixed dome Fixed dome Fixed dome Fixed dome Fixed dome Fixed dome

Size (m3) 6 6 6 6 6 6 6 6 6 6

Digester size (m3) 4.60 4.60 4.60 4.60 4.51 4.51 4.51 4.51 4.51 4.51

Recommended Biodigester -name Fiberglass

(Prefab.)

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks


4 4 4 4 4 4 4 4 4 6


2.00 4.06 1.10 3.01 2.32 1.69 4.15 1.98 1.70 5.16


3.07 4.00 3.07 4.00 4.00 4.00 4.00 4.00 3.07 6.00

Number of digesters 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Total gasholder size (m3) 3.07 0.90 3.07 0.90 0.90 0.90 0.90 0.90 3.07 1.60


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00



0.83 1.09 0.61 0.93 0.71 0.59 1.18 0.87 1.01 1.59


1.81 2.36 1.33 2.01 1.54 1.28 2.56 1.89 2.19 3.45


0.14 0.22 0.10 0.19 0.14 0.12 0.24 0.18 0.16 0.21


5.24 6.85 3.85 5.81 4.46 3.70 7.43 5.48 6.33 10.00

397

Household No. 1 2 3 4 5 6 7 8 9 10


179% 96% 246% 77% 63% 40% 54% 126% 42% 60%



15.00 31.00 9.00 24.00 17.00 13.00 35.00 22.00 18.00 45.00


23.33 41.96 17.55 41.23 34.83 24.33 48.12 34.26 20.00 67.10


58.31 45.15 94.98 48.78 62.35 76.28 40.53 48.25 52.01 42.90


2.42 1.88 2.55 1.97 1.76 1.83 2.05 2.64 3.37 2.11

Economics


0.00 0.00 0.00 97681.36 97681.36 97681.36 97681.36 97681.36 255800.00 193334.37


555800.00 397681.36 555800.00 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 43334.37


0.00 0.00 0.00 0.00 0.00 0.00 0.00 47681.36 0.00 0.00


0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.69 0.00 0.00


1982.10 1657.01 1982.10 1657.01 1657.01 1657.01 1657.01 1657.01 1982.10 2055.56


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


44753.76 87766.60 27786.85 68025.20 49163.81 53190.31 79265.89 76850.07 62691.92 136293.14


0.00 0.00 0.00 0.00 1.99 1.84 1.23 1.27 4.08 1.42

Estimated NPV (FRw)* 159488.49 577922.26 30436.85 409852.64 151593.60 185873.45 407869.58 387302.36 40127.56 757004.27

Cost per kWh (FRw/kWh)* 12.44 7.96 16.91 9.37 15.21 18.35 9.14 12.39 17.66 9.41

398

Household No. 1 2 3 4 5 6 7 8 9 10



93.86 11.48 50.64 116.18 140.92 93.37 235.02 137.89 112.14 268.02


36.74 36.35 27.02 30.86 23.68 19.63 39.43 29.08 44.41 46.14


-1.24 -1.23 -0.87 -0.54 -1.72 -1.94 -1.96 -1.15 -1.32 -0.74


399

Table C-8: Detailed output from the OBSDM for Households 11 to 19 when priority criteria rating favourable to the installed biodigester types and updated EROI figures are used

Household No. 11 12 13 14 15 16 17 18 19



Size (m3) 6 6 6 6 6 8 8 8 8

Digester size (m3) 4.51 4.51 4.51 4.51 4.51 8.00 8.00 8.00 8.00


stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Kentainer BlueFlame

BioSluriGaz

Kentainer BlueFlame

BioSluriGaz

Size specifications -size name 4 4 4 4 4 4 4 1.8 1.8



4.00 4.00 4.00 4.00 4.00 4.00 3.07 1.80 1.80

Number of digesters 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00


Additional recommended gas storage (m3) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00




2.31 1.99 1.43 1.74 1.59 1.40 2.27 0.91 1.36


0.22 0.19 0.13 0.16 0.15 0.13 0.17 0.13 0.19





30.00 23.00 15.00 20.00 18.00 14.00 22.00 10.00 19.00

400

Household No. 11 12 13 14 15 16 17 18 19


47.97 28.61 24.68 33.00 30.72 24.22 56.27 26.64 27.83



Economics


97681.36 97681.36 97681.36 97681.36 97681.36 97681.36 255800.00 137054.68 137054.68

Estimated capital cost (excl. subsidy) (FRw)* 397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 437054.68 437054.68


0.00 0.00 0.00 0.00 47681.36 47681.36 155800.00 0.00 87054.68


0.00 0.00 0.00 0.00 8.34 2.09 54.53 0.00 15.23



0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


79126.53 64723.65 35773.85 44810.82 58795.99 96855.85 93361.41 44752.42 30576.04






208.94 187.35 100.45 162.75 122.03 148.44 208.39 83.63 143.55



-0.73 -0.78 -0.77 -1.72 -1.04 -0.94 -1.29 -1.13 -2.19


401

Comparison of months of savings required to meet installation costs from the OBSDM with and without

considering available subsidies

Table C-9: Comparison of months of savings required to meet installation costs of biogas systems recommended by the OBSDM when no subsidies are available and with subsidies

Household No.

District Installed biodigester type

Status Biodigester & size name Months of saving req. to meet capital cost (based on current savings & disposable income)


1 Kayonza Fiberglass Recommended (incl. subsidy) Fiberglass (Prefabricated) 0 0

Recommended (excl. subsidy) Fiberglass (Prefabricated) 71.02 12.42

% change (subsidised - unsubsidised) -100% -100%

2 Kicukiro Fiberglass Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 0.00

Recommended (excl. subsidy) Modified CAMARTEC stabilised blocks

0.00 4.53

% change (subsidised - unsubsidised) 0% -100%

3 Kirehe Fiberglass Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 0.00


60.85 14.31


4 Kicukiro Fiberglass Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 0.00

Recommended (excl. subsidy) Modified CAMARTEC stabilised 0.00 5.85


5 Kayonza Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 1.99


17.09 8.09


6 Kicukiro Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 1.84


0.00 7.48


402

Household No.




7 Gasabo Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 1.23


3.24 5.02


8 Rwamagana Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

16.69 1.27


121.69 5.17


9 Rwamagana Fixed dome Recommended (incl. subsidy) Fiberglass (Prefabricated) 0.00 4.08



10 Kicukiro Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 1.42


0.00 3.62


11 Ngoma Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

0.00 1.23




0.00 1.51


0.00 6.14


13 Ngoma Fixed dome Recommended (incl. subsidy) Fiberglass (Prefabricated) 0.00 2.73




0.00 2.18

403

Household No.





86.69 8.87


15 Kirehe Fixed dome Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

8.34 1.66


60.85 6.76


16 Gasabo Flexi-bag Recommended (incl. subsidy) Modified CAMARTEC stabilised blocks

2.09 1.01


15.26 4.11


17 Rwamagana Flexi-bag Recommended (incl. subsidy) Fiberglass (Prefabricated) 54.53 2.74



18 Rwamagana Flexi-bag Recommended (incl. subsidy) Kentainer BlueFlame BioSuriGaz 0.00 3.06

Recommended (excl. subsidy) Kentainer BlueFlame BioSuriGaz 48.49 9.77


19 Kirehe Flexi-bag Recommended (incl. subsidy) Kentainer BlueFlame BioSuriGaz 15.23 4.48


% change (subsidised - unsubsidised) N/A N/A

404

Comparison of recommended digester size and estimated biogas production from the OBSDM when using

default and local climate data

Table C-10: Comparison of digester size and estimated biogas production for biogas systems recommended by the OBSDM when using default and local climate data

Household No.


Installed digester size (m³)

Status Biodigester type Recommended digester size

(m³)

Recommended avail. total

digester size (m³)


production (m³/d)

1 Kayonza Fiberglass 4.60 Recommended (measured climate data)


2.00 3.07 0.83

4.60 Recommended (default climate data)


2.40 3.07 0.78

% change (default - measured climate data) 20% 0% -6%

2 Kicukiro Fiberglass 4.60 Recommended (measured climate data)


4.06 4.00 1.09



3.91 4.60 1.18

% change (default - measured climate data) N/A N/A N/A

3 Kirehe Fiberglass 4.60 Recommended (measured climate data)


1.45 4.00 0.55



1.97 4.00 0.52


4 Kicukiro Fiberglass 4.60 Recommended (measured climate data)


3.01 4.00 0.93



4.27 4.00 0.89


405

Household No.




(m³)


digester size (m³)


production (m³/d)

5 Kayonza Fixed dome 4.51 Recommended (measured climate data)


2.32 4.00 0.71



3.17 4.00 0.68


6 Kicukiro Fixed dome 4.51 Recommended (measured climate data)


1.69 4.00 0.59



2.33 4.00 0.56


7 Gasabo Fixed dome 4.51 Recommended (measured climate data)


4.15 4.00 1.18



5.76 6.00 1.30

% change (default - measured climate data) 39% 50.0% 10%

8 Rwamagana Fixed dome 4.51 Recommended (measured climate data)


1.98 4.00 0.87


Modified CAMARTEC stabilised blocks)

3.71 4.00 0.81


9 Rwamagana Fixed dome 4.51 Recommended (measured climate data)


1.70 3.07 1.01



2.66 3.07 0.87


406

Household No.




(m³)


digester size (m³)


production (m³/d)

10 Kicukiro Fixed dome 4.51 Recommended (measured climate data)


5.16 6.00 1.59



7.56 9.00 1.73

% change (default - measured climate data) 46% 50% 9%

11 Ngoma Fixed dome 4.51 Recommended (measured climate data)


3.18 4.00 1.07



5.15 6.00 1.15




2.21 4.00 0.92



3.71 4.00 0.87




1.67 4.00 0.66



2.57 4.00 0.62




2.23 4.00 0.80


Modified CAMARTEC stabilised block

3.44 4.00 0.76


407

Household No.




(m³)


digester size (m³)


production (m³/d)

15 Kirehe Fixed dome 4.51 Recommended (measured climate data)


2.41 4.00 0.73



3.13 4.00 0.70


16 Gasabo Flexi-bag 8.00 Recommended (measured climate data)


1.61 4.00 0.65



2.49 4.00 0.61


17 Rwamagana Flexi-bag 8.00 Recommended (measured climate data)


2.27 3.07 1.05



4.03 4.60 1.08


18 Rwamagana Flexi-bag 8.00 Recommended (measured climate data)


0.87 1.80 0.42



1.40 3.07 0.50


19 Kirehe Flexi-bag 8.00 Recommended (measured climate data)


1.66 1.80 0.63



2.46 3.07 0.75


408

Selected details from output of the OBSDM when maximum biodigester lifespan values are used

Table C-11: Selected details from the OBSDM output for Households 1 to 10 when equal priority criteria rating and maximum biodigester lifespan values are used

Household No. 1 2 3 4 5 6 7 8 9 10



Size (m3) 6 6 6 6 6 6 6 6 6 6

Digester size (m3) 4.60 4.60 4.60 4.60 4.51 4.51 4.51 4.51 4.51 4.51


(Prefab.)

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks


4 4 4 4 4 4 4 4 4 6


2.00 4.06 1.45 3.01 2.32 1.69 4.15 1.98 1.70 5.16


3.07 4.00 4.00 4.00 4.00 4.00 4.00 4.00 3.07 6.00





0.83 1.09 0.55 0.93 0.71 0.59 1.18 0.87 1.01 1.59


5.24 6.85 3.45 5.81 4.46 3.70 7.43 5.48 6.33 10.00


179% 96% 221% 77% 63% 40% 54% 126% 42% 60%



58.31 45.15 82.83 48.78 62.35 76.28 40.53 48.25 52.01 42.90


2.42 1.88 1.94 1.97 1.76 1.83 2.05 2.64 3.37 2.11

Economics

409

Household No. 1 2 3 4 5 6 7 8 9 10


555800.00 397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 493334.37

Estimated monthly running costs (FRW)*

1982.10 1657.01 1657.01 1657.01 1657.01 1657.01 1657.01 1657.01 1982.10 2055.56


44753.76 87766.60 27786.85 68025.20 49163.81 53190.31 79265.89 76850.07 62691.92 136293.14


0.00 0.00 0.00 0.00 1.99 1.84 1.23 1.27 4.08 1.42




93.86 11.48 50.00 116.18 140.92 93.37 235.02 137.89 112.14 268.02


61.24 90.87 45.84 77.16 59.21 49.08 98.59 72.70 74.02 115.36


410

Table C-12: Selected details from the OBSDM output for Households 11 to 19 when equal priority criteria rating and maximum biodigester lifespan values are used

Household No. 11 12 13 14 15 16 17 18 19



Size (m3) 6 6 6 6 6 8 8 8 8

Digester size (m3) 4.51 4.51 4.51 4.51 4.51 8.00 8.00 8.00 8.00


stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks




Size specifications -size name 4 4 4 4 4 4 4 4 4



4.00 4.00 4.00 4.00 4.00 4.00 3.07 3.07 3.07








Average hydraulic retention time (HRT) 40.98 48.29 67.19 54.45 61.64 69.34 36.29 104.85 57.60


Economics




79126.53 64723.65 35773.85 44810.82 58795.99 96855.85 93361.41 56195.16 38859.68

411

Household No. 11 12 13 14 15 16 17 18 19





208.94 187.35 100.45 162.75 122.03 148.44 208.39 111.73 203.00



Table C-13: Selected details from the OBSDM output for Households 1 to 10 when equal priority criteria rating and the second highest biodigester lifespan values are used

Household No. 1 2 3 4 5 6 7 8 9 10



Size (m3) 6 6 6 6 6 6 6 6 6 6

Digester size (m3) 4.60 4.60 4.60 4.60 4.51 4.51 4.51 4.51 4.51 4.51


(Prefab.)

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks


4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 6.00


2.00 4.06 1.10 3.01 2.32 1.69 4.15 1.98 1.70 5.16


3.07 4.00 3.07 4.00 4.00 4.00 4.00 4.00 3.07 6.00




412

Household No. 1 2 3 4 5 6 7 8 9 10


0.83 1.09 0.61 0.93 0.71 0.59 1.18 0.87 1.01 1.59


5.24 6.85 3.85 5.81 4.46 3.70 7.43 5.48 6.33 10.00


179% 96% 246% 77% 63% 40% 54% 126% 42% 60%



58.31 45.15 94.98 48.78 62.35 76.28 40.53 48.25 52.01 42.90


2.42 1.88 2.55 1.97 1.76 1.83 2.05 2.64 3.37 2.11

Economics


555800.00 397681.36 555800.00 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 493334.37


1982.10 1657.01 1982.10 1657.01 1657.01 1657.01 1657.01 1657.01 1982.10 2055.56


44753.76 87766.60 27786.85 68025.20 49163.81 53190.31 79265.89 76850.07 62691.92 136293.14


0.00 0.00 0.00 0.00 1.99 1.84 1.23 1.27 4.08 1.42




93.86 11.48 50.64 116.18 140.92 93.37 235.02 137.89 112.14 268.02


48.99 54.52 36.02 46.30 35.52 29.45 59.15 43.62 59.22 69.22


413

Table C-14: Selected details from the OBSDM output for Households 11 to 19 when equal priority criteria rating and the second highest biodigester lifespan values are used

Household No. 11 12 13 14 15 16 17 18 19



Size (m3) 6 6 6 6 6 8 8 8 8

Digester size (m3) 4.51 4.51 4.51 4.51 4.51 8.00 8.00 8.00 8.00

Recommended Biodigester -name

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks




Size specifications -size name 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00



4.00 4.00 4.00 4.00 4.00 4.00 3.07 3.07 3.07





1.07 0.92 0.66 0.80 0.73 0.65 1.05 0.56 0.89


6.70 5.77 4.13 5.04 4.60 4.06 6.57 3.53 5.58


66% 53% 48% 21% 119% 119% 78% 62% 55%



40.98 49.28 73.95 55.03 61.64 69.34 36.29 104.85 57.60


2.28 3.34 2.81 2.81 1.78 2.14 3.08 3.23 3.34

Economics


397681.36 397681.36 397681.36 397681.36 397681.36 397681.36 555800.00 555800.00 555800.00


1657.01 1657.01 1657.01 1657.01 1657.01 1657.01 1982.10 1982.10 1982.10

414

Household No. 11 12 13 14 15 16 17 18 19


79126.53 64723.65 35773.85 44810.82 58795.99 96855.85 93361.41 56195.16 38859.68


1.23 1.51 2.73 2.18 1.66 1.01 2.74 4.55 6.58




208.94 187.35 100.45 162.75 122.03 148.44 208.39 111.73 203.00


53.34 45.93 32.90 40.13 36.60 32.34 61.45 32.97 52.13


Table C-15: Selected details from the OBSDM output for Households 1 to 10 when equal priority criteria rating and the third highest biodigester lifespan values are used

Household No. 1 2 3 4 5 6 7 8 9 10


Installed biodigester type Fiberglass Fiberglass Fiberglass Fiberglass

Fixed dome

Fixed dome

Fixed dome

Fixed dome Fixed dome Fixed dome

Size (m3) 6 6 6 6 6 6 6 6 6 6

Digester size (m3) 4.60 4.60 4.60 4.60 4.51 4.51 4.51 4.51 4.51 4.51

Recommended Biodigester -name Fiberglass (Prefab.)









Modified CAMARTEC

stabilised blocks

Size specifications -size name 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 6.00

Recommended digester size (m3) 2.00 3.11 1.10 2.31 1.75 1.29 3.18 1.51 1.70 5.16


3.07 3.07 3.07 3.07 3.07 3.07 3.07 3.07 3.07 6.00



415

Household No. 1 2 3 4 5 6 7 8 9 10


Estimated daily biogas production (m3) 0.83 1.00 0.61 0.93 0.75 0.64 1.08 0.95 1.01 1.59


5.24 6.30 3.85 5.83 4.71 4.04 6.76 5.94 6.33 1.00

Proportion of energy requirements met 179% 88% 246% 78% 67% 44% 49% 137% 42% 60%



58.31 36.04 94.98 43.92 61.54 81.98 32.04 50.44 52.01 42.90


2.42 2.45 2.55 2.58 2.34 2.41 2.68 3.46 3.37 2.11

Economics


555800.00 555800.00 555800.00 555800.00 555800.00 555800.00 555800.00 555800.00 555800.00 493334.37


1982.10 1982.10 1982.10 1982.10 1982.10 1982.10 1982.10 1982.10 1982.10 2055.56


44753.76 82251.36 27786.85 68154.92 51920.59 57469.32 73395.04 76850.07 62691.92 136293.14

Estimated simple payback period (y) 0.00 0.00 0.00 0.00 4.93 4.45 3.49 3.33 4.08 1.42

Estimated NPV (FRw)* 178517.13 497755.35 34068.28 377744.41 -16267.58 30971.84 166556.47 195971.14 75434.79 757004.27

Emissions reduction, energy economics and time


93.86 10.96 50.64 116.89 149.29 102.51 214.33 138.56 112.14 268.02


48.99 58.92 36.02 54.47 44.03 37.77 63.21 55.57 59.22 46.14


416

Table C-16: Selected details from the OBSDM output for Households 11 to 19 when equal priority criteria rating and the third highest biodigester lifespan values are used

Household No. 11 12 13 14 15 16 17 18 19


Installed biodigester type Fixed dome

Fixed dome

Fixed dome Fixed dome

Fixed dome

Flexi-bag Flexi-bag Flexi-bag Flexi-bag

Size (m3) 6 6 6 6 6 8 8 8 8

Digester size (m3) 4.51 4.51 4.51 4.51 4.51 8.00 8.00 8.00 8.00

Recommended Biodigester -name Fiberglass (Prefab.)









Size specifications -size name 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00


Recommended available total digester size (m3) 3.07 3.07 3.07 3.07 3.07 3.07 3.07 3.07 3.07










Economics




78552.25 68539.90 38736.42 47125.34 58795.99 96855.85 93361.41 56195.16 38859.68


Estimated NPV (FRw)* 210463.02 125221.99 -128511.87 -57092.30 42266.57 366291.62 336541.43 20124.17 -127462.49

417

Household No. 11 12 13 14 15 16 17 18 19

Emissions reduction, energy economics and time


207.46 201.59 111.43 174.28 122.59 149.09 208.39 111.73 203.00


* Costs based on 1 USD = 811.40 FRw as of 25 November 2016

Table C-17: Comparison of recommended biogas systems from OBSDM using default feedstock TS and VS values and location specific measured TS and VS values for cattle dung from the Rwandan Comparative Biodigester Study

HH No.

District Installed digester

type

Dig. size

Status Recommended digester type

Recomm. digester size

(m³)

Recomm. avail. total

dig. size (m³)

Biogas production

(m³/d)

Min. water req.

(L/d)

Avg. HRT (d)

OLR (kg VS /m³.d)

1 Kayonza Fiberglass 4.60 Recommended (measured VS & TS)

Fiberglass (Prefab.) 1.90 3.07 1.00 9.00 62.72 2.34

Recommended (default VS & TS)


% difference (measured – default VS & TS) -5.2% 0.0% 18.4% -50.0% -7.3% -3.4%

3 Kirehe Fiberglass 4.60 Recommended (measured VS & TS)


1.40 7.87 0.35 9.00 156.51 2.48



1.45 4.00 0.55 12.00 82.83 1.94

% difference (measured – default VS & TS) -3.5% N/A -43.9% -28.6% 61.6% 24.5%

4 Kicukiro Fiberglass 4.60 Recommended (measured VS & TS)


3.19 4.00 0.52 35.00 45.44 2.10



3.01 4.00 0.93 24.00 48.78 1.97

% difference (measured – default VS & TS) 5.6% 0.0% -55.4% -37.3% -7.1% 6.2%

5 Kayonza Fixed dome 4.51 Recommended (measured VS & TS)


2.23 4.00 0.83 11.00 68.25 1.71



2.32 4.00 0.71 17.00 62.35 1.76

% difference (measured – default VS & TS) -4.4% 0.0% 15.0% -42.9% 9.0% -2.7%

418

HH No.


type

Dig. size



(m³)


dig. size (m³)

Biogas production

(m³/d)

Min. water req.

(L/d)

Avg. HRT (d)

OLR (kg VS /m³.d)

6 Kicukiro Fixed dome 4.51 Recommended (measured VS & TS)




1.69 4.00 0.59 13.00 76.28 1.83

% difference (measured – default VS & TS) N/A N/A N/A N/A N/A N/A

7 Gasabo Fixed dome 4.51 Recommended (measured VS & TS)

Senegal GGC 2047 7.22 8.00 0.83 33.00 76.95 1.42



4.15 4.00 1.18 35.00 40.53 2.05


8 Rwamagana Fixed dome 4.51 Recommended (measured VS & TS)


1.95 4.00 0.93 20.00 49.29 2.61



1.98 4.00 0.87 22.00 48.25 2.64


9 Rwamagana Fixed dome 4.51 Recommended (measured VS & TS)





10 Kicukiro Fixed dome 4.51 Recommended (measured VS & TS)


5.04 6.00 1.62 37.00 44.23 2.16



5.16 6.00 1.59 45.00 42.90 2.11

% difference (measured – default VS & TS) -2.4% 0.0% 1.9% -19.5% 3.1% 2.4%

13 Ngoma Fixed dome 4.51 Recommended (measured VS & TS)




1.67 4.00 0.66 15.00 67.19 2.13


15 Kirehe Fixed dome 4.51 Recommended (measured VS & TS)


2.32 4.00 0.72 13.00 65.80 1.88



2.41 4.00 0.73 18.00 61.64 1.78

419

HH No.


type

Dig. size



(m³)


dig. size (m³)

Biogas production

(m³/d)

Min. water req.

(L/d)

Avg. HRT (d)

OLR (kg VS /m³.d)

% difference (measured – default VS & TS) -3.7% 0.0% -1.8% -32.3% 6.5% 5.7%

16 Gasabo Flexi-bag 8.00 Recommended (measured VS & TS)


1.55 4.00 0.74 10.00 74.92 2.01



1.61 4.00 0.65 14.00 69.34 2.14


17 Rwamagana Flexi-bag 8.00 Recommended (measured VS & TS)





19 Kirehe Flexi-bag 8.00 Recommended (measured VS & TS)


1.72 1.80 0.82 24.00 30.06 2.45



1.66 1.80 0.63 19.00 31.19 2.82

% difference (measured – default VS & TS) 3.0% 0.0% 27.0% 23.3% -3.7% -13.9%

420

Comparison of recommended biogas system sizes and biogas production from the OBSDM output for the

feedstock sensitivity analysis (feedstock amount based on number of cattle)

Table C-18: Comparison of recommended biogas systems from OBSDM using number of cattle to estimate the amount of feedstock and location specific (measured) daily supply of cattle dung from the Rwandan Comparative Biodigester Study

HH No.


type

Digester size (m³)

No. of cattle (excl.

calves)

Status Amount (kg/d)

Recommended biodigester type

Recomm. digester size (m³)


dig. size (m³)

Biogas prod.

(m³/d)

1 Kayonza Fiberglass 4.60 2 Recommended 33.77 Fiberglass (Prefab.) 2.00 3.07 0.83

Recommended (cattle dung estimated on no. of cattle)

24.50 Fiberglass (Prefab.) 1.59 3.07 0.68

% difference* -31.8%

-23.2% 0.0% -20.1%

2 Kicukiro Fiberglass 4.60 4 Recommended 53.07 Modified CAMARTEC stabilised blocks

4.06 4.00 1.09


49.00 Modified CAMARTEC stabilised blocks

3.84 4.00 1.03

% difference* -8.0%

-5.5% 0.0% -5.9%

3 Kirehe Fiberglass 4.60 1 Recommended 19.58 Modified CAMARTEC stabilised blocks

1.45 4.00 0.55



1.06 4.00 0.39


-30.8% 0.0% -34.0%

4 Kicukiro Fiberglass 4.60 6 Recommended 41.45 Modified CAMARTEC stabilised blocks

3.01 4.00 0.93



% difference* 55.8%

16.1% N/A 26.4%

5 Kayonza Fixed dome 4.51 2 Recommended 28.47 Modified CAMARTEC stabilised blocks

2.32 4.00 0.71



2.11 4.00 0.64


-9.6% 0.0% -10.0%

6 Kicukiro Fixed dome 4.51 2 Recommended 21.58 Modified CAMARTEC stabilised blocks

1.69 4.00 0.59



1.83 4.00 0.65

421

HH No.


type

Digester size (m³)


calves)





dig. size (m³)

Biogas prod.

(m³/d)

% difference* 12.7%

7.7% 0.0% 9.6%

7 Gasabo Fixed dome 4.51 3 Recommended 59.32 Modified CAMARTEC stabilised blocks

4.15 4.00 1.18



3.02 4.00 0.84


-31.4% 0.0% -33.6%

8 Rwamagana Fixed dome 4.51 2 Recommended 36.39 Modified CAMARTEC stabilised blocks

1.98 4.00 0.87



1.52 4.00 0.67


-25.9% 0.0% -26.3%

9 Rwamagana Fixed dome 4.51 3 Recommended 40.00 Fiberglass (Prefab.) 1.70 3.07 1.01



% difference* -8.5%

-6.5% 0.0% -5.3% 10 Kicukiro Fixed dome 4.51 3 Recommended 75.81 Modified CAMARTEC

stabilised blocks 5.16 6.00 1.59



3.30 4.00 0.84


-43.9% -40.0% -61.9%

11 Ngoma Fixed dome 4.51 1 Recommended 50.50 Modified CAMARTEC stabilised blocks

3.18 4.00 1.07



1.00 4.00 0.40


-103.9% 0.0% -91.4%


2.21 4.00 0.92



2.11 4.00 0.87

% difference* -6.9%

-4.7% 0.0% -5.0% 13 Ngoma Fixed dome 4.51 2 Recommended 24.74 Modified CAMARTEC

stabilised blocks 1.67 4.00 0.66



1.64 4.00 0.66

% difference* -1.0%

-1.4% 0.0% -0.1%

422

HH No.


type

Digester size (m³)


calves)





dig. size (m³)

Biogas prod.

(m³/d)


2.23 4.00 0.80



1.05 4.00 0.40


-71.5% 0.0% -68.0%

15 Kirehe Fixed dome 4.51 1 Recommended 29.88 Modified CAMARTEC stabilised blocks

2.41 4.00 0.73



1.25 4.00 0.39


-63.2% 0.0% -61.6%

16 Gasabo Flexi-bag 8.00 5 Recommended 24.00 Modified CAMARTEC stabilised blocks

1.61 4.00 0.65


61.25 Flexi biogas digester 5.82 5.50 1.71

% difference* 87.4%

113.3% N/A 90.1%

17 Rwamagana Flexi-bag 8.00 1 Recommended 48.73 Fiberglass (Prefab.) 2.27 3.07 1.05


12.25 Kentainer BlueFlame BioSluriGaz

0.80 1.80 0.35


-95.6% N/A -100.8% 18 Rwamagana Flexi-bag 8.00 2 Recommended 16.44 Kentainer BlueFlame

BioSluriGaz 0.87 1.80 0.42



% difference* 39.4%

17.7% N/A 56.0%

19 Kirehe Flexi-bag 8.00 2 Recommended 32.60 Kentainer BlueFlame BioSluriGaz

1.66 1.80 0.63


24.50 Kentainer BlueFlame BioSluriGaz

1.38 1.80 0.52


-18.8% 0.0% -17.9%

*Using cattle dung amount based on number of cattle – using measured cattle dung amount

423

Detailed output from the OBSDM for the feedstock sensitivity analysis (feedstock amount based on number

of cattle)

Table C-19: Details from the OBSDM output for Households 1 to 10 when equal priority criteria rating and the estimated cattle dung supply based on the number of cattle are used

Household No. 1 2 3 4 5 6 7 8 9 10



Size (m3) 6 6 6 6 6 6 6 6 6 6

Digester size (m3) 4.60 4.60 4.60 4.60 4.51 4.51 4.51 4.51 4.51 4.51

Recommended Biodigester - name


Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks


Modified CAMARTEC

stabilised blocks


4 4 4 4 4 4 4 4 4 4


1.59 3.84 1.06 3.54 2.11 1.83 3.02 1.52 1.60 3.30


3.07 4.00 4.00 3.07 4.00 4.00 4.00 4.00 3.07 4.00

Number of digesters 1 1 1 1 1 1 1 1 1 1



0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00



0.68 1.03 0.39 1.21 0.64 0.65 0.84 0.67 0.96 0.84


1.48 2.23 0.85 2.62 1.39 1.40 1.82 1.45 2.07 1.82


0.11 0.21 0.08 0.20 0.13 0.13 0.17 0.14 0.16 0.17


4.28 6.45 2.45 7.58 4.03 4.07 5.29 4.20 6.01 5.27

424

Household No. 1 2 3 4 5 6 7 8 9 10


146% 90% 157% 101% 57% 44% 38% 97% 39% 32%



11.00 29.00 7.00 34.00 14.00 14.00 22.00 14.00 17.00 22.00


23.33 41.96 17.55 41.23 34.83 24.33 48.12 34.26 20.00 67.10


75.27 46.76 121.70 27.64 69.74 70.68 51.86 64.91 55.56 49.95


2.22 1.83 1.66 2.98 2.98 1.92 1.74 2.31 3.30 1.60

Economics


0.00 0.00 0.00 0.00 97681.36 97681.36 97681.36 97681.36 255800.00 97681.36


555800.00 397681.36 397681.36 555800.00 397681.36 397681.36 397681.36 397681.36 555800.00 397681.36


0.00 0.00 0.00 0.00 0.00 0.00 0.00 47681.36 0.00 0.00


0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.69 0.00 0.00


1982.10 1657.01 1657.01 1982.10 1657.01 1657.01 1657.01 1657.01 1982.10 1657.01


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


44753.76 82362.55 24849.22 104834.06 44466.45 58774.86 55163.22 62342.16 58725.18 70796.56


0.00 0.00 0.00 0.00 2.20 1.66 1.77 1.57 4.36 1.38


425

Household No. 1 2 3 4 5 6 7 8 9 10

Cost per kWh (FRw/kWh)* 15.21 8.44 22.23 8.60 16.82 16.68 12.83 16.14 18.62 12.88



93.38 10.79 49.49 150.65 127.40 102.80 167.16 132.80 106.37 141.04


30.04 34.26 13.01 53.15 21.42 21.60 28.08 22.32 42.13 27.98


-1.24 -1.24 -0.87 -0.41 -1.75 -1.92 -2.06 -1.17 -1.34 -0.91


426

Table C-20: Details from the OBSDM output for Households 11 to 19 when equal priority criteria rating and the estimated cattle dung supply based on the number of cattle are used

Household No. 11 12 13 14 15 16 17 18 19



Size (m3) 6 6 6 6 6 8 8 8 8

Digester size (m3) 4.51 4.51 4.51 4.51 4.51 8.00 8.00 8.00 8.00

Recommended Biodigester - name Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Modified CAMARTEC

stabilised blocks

Flexi biogas

digester

Kentainer BlueFlame

BioSluriGaz


Kentainer BlueFlame

BioSluriGaz

Size specifications -size name 4 4 4 4 4 6 1.8 4 1.8



4.00 4.00 4.00 4.00 4.00 5.50 1.80 3.07 1.80

Number of digesters 1 1 1 1 1 1 1 1 1


Additional recommended gas storage (m3) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00




0.86 1.89 1.42 0.86 0.84 3.70 0.75 1.62 1.14


0.08 0.18 0.13 0.08 0.08 0.25 0.10 0.12 0.16





7.00 22.00 14.00 7.00 7.00 15.00 7.00 11.00 14.00


23.00 28.61 24.68 23.00 23.00 24.22 23.00 26.64 27.83

427

Household No. 11 12 13 14 15 16 17 18 19



Economics


97681.36 97681.36 97681.36 97681.36 97681.36 267939.33 137054.68 255800.00 137054.68


397681.36 397681.36 397681.36 397681.36 397681.36 567939.33 437054.68 555800.00 437054.68


0.00 0.00 0.00 0.00 47681.36 217939.33 37054.68 95800.00 87054.68


0.00 0.00 0.00 0.00 8.34 9.57 12.97 16.77 15.23



0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


27365.07 61314.21 35678.89 20755.50 30770.34 116809.21 30133.91 76469.16 24664.77


Estimated NPV (FRw) -33991.38 255036.78 36788.86 -90262.33 -5000.39 435657.32 -29301.71 144918.33 -80858.78




77.51 178.11 100.36 79.89 76.53 152.13 68.67 148.70 119.95



-0.99 -0.79 -0.77 -1.79 -1.27 -0.94 -1.85 -0.67 -2.23


428

Comparison of economic parameters from the OBSDM output for the cost sensitivity analysis (considering

import costs)

Table C-21: Comparison of economic parameters of recommended biodigester types using equal priority criteria rating with and without consideration of import costs in the OBSDM

HH No.


Status Recommended biodigester type





Cost per kWh (FRw/ kWh)*

1 Kayonza Fiberglass Recommended (considering max import costs)

Fiberglass (Prefab.) 0.00 599000.00 0.00 145426.95 13.40

Recommended Fiberglass (Prefab.) 0.00 555800.00 0.00 159488.49 12.44

% difference (max import costs – no import costs) 0.0% 7.5% 0.0% -9.2% 7.5%

2 Kicukiro Fiberglass

Recommended (considering max import costs)


0.00 407879.11 0.00 573581.30 8.16

Recommended Modified CAMARTEC stabilised blocks

0.00 397681.36 0.00 577922.26 7.96

% difference (max import costs – no import costs) 0.0% 2.5% 0.0% -0.8% 2.5% 3 Kirehe

Fiberglass



0.00 407879.11 0.00 62939.90 16.18


0.00 397681.36 0.00 67280.86 15.78


4 Kicukiro

Fiberglass



0.00 407879.11 0.00 405511.68 9.61


0.00 397681.36 0.00 409852.64 9.37


429

HH No.








5

Kayonza

Fixed dome Recommended (considering max import costs)


107879.11 407879.11 2.19 137054.88 15.84

Fixed dome Recommended Modified CAMARTEC stabilised blocks

97681.36 397681.36 1.99 151593.60 15.21

Fixed dome % difference (max import costs – no import costs) 9.9% 2.5% 9.9% -10.1% 4.0%

6

Kicukiro

Fixed dome Recommended (considering max import costs)


107879.11 407879.11 2.03 171334.74 19.11

Fixed dome Recommended Modified CAMARTEC stabilised blocks

97681.36 397681.36 1.84 185873.45 18.35

Fixed dome % difference (max import costs – no import costs) 9.9% 2.5% 9.9% -8.1% 4.0%

7

Gasabo

Fixed dome



107879.11 407879.11 1.36 393330.86 9.51


97681.36 397681.36 1.23 407869.58 9.14


8

Rwamagana

Fixed dome



107879.11 407879.11 1.40 372763.65 12.90


97681.36 397681.36 1.27 387302.36 12.39


9

Rwamagana

Fixed dome


Fiberglass (Prefab.) 299000.00 599000.00 4.77 -17133.98 19.71



430

HH No.








10

Kicukiro

Fixed dome



208080.66 508080.66 1.53 735980.80 9.81


193334.37 493334.37 1.42 757004.27 9.41


11

Ngoma

Fixed dome



107879.11 407879.11 1.36 392144.39 10.55


97681.36 397681.36 1.23 406683.10 10.13


12

Ngoma

Fixed dome



107879.11 407879.11 1.67 269524.56 12.25


97681.36 397681.36 1.51 284063.28 11.77


13

Ngoma

Fixed dome



107879.11 407879.11 3.02 23058.61 17.11


97681.36 397681.36 2.73 37597.32 16.43


14 Ngoma Fixed dome Recommended (considering max import costs)


107879.11 407879.11 2.41 99995.40 14.02


97681.36 397681.36 2.18 114534.11 13.47

431

HH No.









15

Kirehe

Fixed dome



107879.11 407879.11 1.83 219059.08 15.37


97681.36 397681.36 1.66 233597.79 14.77


16

Gasabo

Flexi-bag



107879.11 407879.11 1.11 543084.13 17.40


97681.36 397681.36 1.01 557622.85 16.71


17

Rwamagana

Flexi-bag


Fiberglass (Prefab.) 299000.00 599000.00 3.20 216140.58 18.99



18

Rwamagana

Flexi-bag



182474.59 482474.59 4.08 44762.55 27.74

Recommended Kentainer BlueFlame BioSluriGaz

137054.68 437054.68 3.06 108505.77 24.15


19

Kirehe

Flexi-bag


Fiberglass (Prefab.) 299000.00 599000.00 7.69 -198403.88 22.39

Recommended Kentainer BlueFlame BioSluriGaz

137054.68 437054.68 4.48 -25133.78 16.17

% difference (max import costs – no import costs) 74.3% 31.3% 52.8% 155.0% 32.3%

*1 USD = 811.40 FRw as of 25 November 2016

432

Comparison of recommended and highest scoring biogas system designs in OBSDM for priority criteria

Table C-22: Comparison of highest scoring biogas system designs for reliability and the systems recommended by the OBSDM when reliability is the top priority

HH No. Highest scoring biogas system Recommended biogas system

1 Kentainer BlueFlame BioSluriGaz Kentainer BlueFlame BioSluriGaz








9 PUXIN (Bioeco Sarl), PUXIN (Biogas Burundi) PUXIN (Bioeco Sarl)











433

Table C-23: Comparison of highest scoring biogas system designs for robustness and the systems recommended by the OBSDM when robustness is the top priority


1 Fiberglass (Prefabricated) Fiberglass (Prefabricated)



4 Fiberglass (Prefabricated), RW III (based on GC 2047) Fiberglass (Prefabricated)


6 Fiberglass (Prefabricated), RW III (based on GC 2047), Sinidu model (mod. GGC-2047)







10 Fiberglass (Prefabricated), RW III (based on GC 2047) RW II (based on GC 2047)












434

Table C-24: Comparison of highest scoring biogas system designs for simple operation and construction and the systems recommended by the OBSDM when simple operation and construction is the top priority




3 Flexi biogas digester Kentainer BlueFlame BioSluriGaz

4 Kentainer BlueFlame BioSluriGaz Flexi biogas digester

5 Flexi biogas digester Fiberglass (Prefabricated)


7 Kentainer BlueFlame BioSluriGaz Fiberglass (Prefabricated)













435

Table C-25: Comparison of highest scoring biogas system designs for low-cost and the systems recommended by the OBSDM when low-cost is the top priority



2 Modified CAMARTEC stabilised blocks Modified CAMARTEC stabilised blocks

3 Flexi biogas digester Modified CAMARTEC stabilised blocks

4 KENBIM Modified CAMARTEC stabilised blocks





9 RWIII (based on GGC 2047) Fiberglass (Prefabricated)








17 Flexi biogas digester Flexi biogas digester


19 Flexi biogas digester Flexi biogas digester

436

Table C-26: Comparison of highest scoring biogas system designs for technical efficiency and the systems recommended by the OBSDM when technical efficiency is the top priority


1 AGAMA BiogasPro Fiberglass (Prefabricated)

2 AGAMA BiogasPro Modified CAMARTEC stabilised blocks

3 AGAMA BiogasPro Fiberglass (Prefabricated)

4 AGAMA BiogasPro KENBIM



7 Senegal GGC 2047 Senegal GGC 2047



10 Flexi biogas digester KENBIM






16 AGAMA BiogasPro Flexi biogas digester


18 AGAMA BiogasPro Kentainer BlueFlame BioSluriGaz


437

Table C-27: Comparison of highest scoring biogas system designs for environmentally benign and the systems recommended by the OBSDM when environmentally benign is the top priority





















438

Table C-28: Comparison of highest scoring biogas system designs for local material and labour and the systems recommended by the OBSDM when local material and labour is the top priority




3 Modified CAMARTEC design (MCD) Modified CAMARTEC stabilised blocks


5 RWIII (based on GGC 2047), Modified CAMARTEC design (MCD)








10 RWIII (based on GGC 2047) Modified CAMARTEC solid state digester (SSD))

11 RWIII (based on GGC 2047) Modified CAMARTEC stabilised blocks








16 Modified CAMARTEC design (MCD) Modified CAMARTEC stabilised blocks




439

Table C-29: Comparison of highest scoring biogas system designs for save time and the systems recommended by the OBSDM when save time is the top priority


1 Fiberglass (Prefabricated), AGAMA BiogasPro, Flexi biogas digester Fiberglass (Prefabricated)


3 Modified CAMARTEC stabilised blocks, AGAMA BiogasPro, Fiberglass (Prefabricated), Flexi biogas digester,


4 Flexi biogas digester PUXIN (Bioeco Sarl)

5 PUXIN (Bioeco Sarl) Modified CAMARTEC stabilised blocks

6 Modified CAMARTEC solid state digester (SSD) Modified CAMARTEC stabilised blocks

7 Senegal GGC 2047 Modified CAMARTEC stabilised blocks

8 Modified CAMARTEC stabilised blocks, AGAMA BiogasPro, Fiberglass (Prefabricated), Flexi biogas digester, KENBIM, Modified CAMARTEC solid state digester (SSD), RWIII (based on GC 2047)


9 Modified CAMARTEC solid state digester (SSD) Fiberglass (Prefabricated)

10 Flexi biogas digester Modified CAMARTEC solid state digester (SSD)

11 PUXIN (Bioeco Sarl) PUXIN (Bioeco Sarl)


13 PUXIN (Bioeco Sarl) Fiberglass (Prefabricated)


15 Modified CAMARTEC stabilised blocks, AGAMA BiogasPro, Fiberglass (Prefabricated), Flexi biogas digester, KENBIM, Modified CAMARTEC solid state digester (SSD), RWIII (based on GC 2047)


16 Modified CAMARTEC stabilised blocks, AGAMA BiogasPro, Fiberglass (Prefabricated), Flexi biogas digester




19 PUXIN (Bioeco Sarl) Fiberglass (Prefabricated)

Documents

Increasing the Potential of Biogas in Sub-Saharan Africa · 2018. 9. 18. · assisting biogas installers, program implementers, and other stakeholders in the biogas industry to carry