1

Techno-Economics and Sensitivity Analysis of Microalgae as 1

Commercial Feedstock for Bioethanol Production 2

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Nazia Hossaina,b*, Teuku Meurah Indra Mahliaa, Juliana Zainib, R. Saidurc,d 4

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aSchool of lnformation, Systems and Modelling, 7

Faculty of Engineering and Information Technology, University of Technology Sydney, 8

NSW 2007, Australia 9

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bFaculty of Integrated Technologies, Universiti Brunei Darussalam, 11

Jalan Tungku Link, Gadong BE1410, Brunei Darussalam 12

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cResearch Centre for Nanomaterials and Energy Technology (RCNMET), School of Science and 14

Technology, Sunway University, No. 5, Jalan University, Bandar Sunway, 47500 Petaling Jaya, Malaysia 15

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dDepartment of Engineering, Lancaster University, Lancaster, LA1 4YW, UK. 17

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Abstract 25

The foremost purpose of this techno-economic analysis (TEA) modelling was to predict 26

a harmonized figure of comprehensive cost analysis for commercial bioethanol 27

generation from microalgae species in Brunei Darussalam based on the conventional 28

market scenario. This model was simulated to set out the economic feasibility and 29

probabilistic assumption for large scale implementations of a tropical microalgae 30

species, Chlorella vulgaris for a bioethanol plant located in the coastal area of Brunei 31

Darussalam. Two types of cultivation system: closed system (photobioreactor) and open 32

pond approach were anticipated for total approximate biomass 220 tonnes y-1on 6 33

hectare coastal areas. The biomass productivity was 56tonnes hectare-1 for 34

photobioreactor and 28tonnes hectare-1 for pond annually. Plant output was 58.90m3 35

hectare-1 for photobioreactor and 24.9m3 hectare-1 for pond annually. Total bioethanol 36

output of the plant was 57,087.58gallony-1 along with value added by-products (crude 37

bio-liquid and slurry cake). Total production cost of this project was 2.22 million US$ 38

for bioethanol from microalgae and total bioethanol selling price was 2.87 million US$ 39

along with by-product sale price 1.6 million US$. A sensitivity analysis was conducted 40

to forecast the uncertainty of this conclusive modelling. Different data sets through 41

sensitivity analysis also presented positive impact for economical and environmental 42

view. This TEA model is expected to be initialized to determine an alternative energy 43

as well and minimize environmental pollution. With this current modelling, microalgal-44

bioethanol utilization mandated with gasoline as well as microalgae cultivation, biofuel 45

production integrated with existing complementary industries are strongly 46

recommended for future applications. 47

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Keywords: Bioethanol; Life Cycle Cost; Microalgae; Payback Period; Sensitivity 49

Analysis; Techno-Economic Assessment 50

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Nomenclatures 51

Symbol Description Unit

DE Delivered Equipment $

FCI Fixed capital investment $

i Project year year (y)

LCC Life Cycle Cost $

MC Maintenance Cost $

n Project life time year (y)

OC Operating Cost $

OLC Operating Labour Costs $

PP Payback Period Year (y)

RMC Raw Material Cost $

SV Salvage Value $

TAX Total Tax $

TBS Total Bioethanol Sale $

TBPS

TCAC

Total By-Product Sale

Total Cultivation Area Cost

$

$

TCI Total Capital Investment $

TEC Total Equipment Cost $

TPC Total Production Cost $

TPP Total Plant Profit $

TUC Total Utility Cost $

WC Working Capital $

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Introduction 53

In the recent world, energy turned into a key driving force to be researched for 54

enhancing the optimized usages and generating renewable sources due to tremendous 55

depletion of fossil fuel and threatening greenhouse effect[1, 2]. In this regard, 56

alternative source of energy generation became a crucial concept to be considered. 57

Renewable energy production such as biofuel is the best choice to be applied for 58

generating alternative energy source[3]. Among various biofuels, bioethanol has been 59

considere das one of the leading and popular source of bio-energy, especially for 60

transportation fuel blended with gasoline and diesel now-a-days[4-7]. Bioethanol 61

contains very high relative octane number (RON), self-ignition capability by low cetane 62

number (LCN), notable heating value for evaporation and low carbon mono-oxide (CO) 63

emissions to the environment[8]. Several countries worldwide already initiated 64

producing bioethanol for fuel purpose since 1980s’ such as United States, Brazil, China, 65

Canada, India and others and production in the US was the most. Fig.1 and Fig.2 66

showed the latest scenario of bioethanol production worldwide and the bioethanol 67

production rise curve in the US, respectively[9]. 68

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Fig. 1. Worldwide Bioethanol Production in 2015 [9] 70

Fig. 2. Bioethanol Production Rise Curve in U.S. (2000-2015) [10] 71

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Currently, many feedstocks are being experimented and utilized for bioethanol 73

mercantile production. First generation biofuels (extracted from palm oil, soybean oil, 74

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sugarcane and others) caused escalation of food prices and diminished food sources for 75

human and animals. Second generation biofuels (extracted from non-food biomass e.g. 76

sugarcane bagasse, agricultural residue, grass and others) are not feasible due to the 77

high cost of pre-treatment[11]. To resolve this issue, bioenergy experts were searching 78

for3rdgenerationbioethanol sources and identified microalgae for bioethanol production 79

since several types of them are enriched with carbohydrate to generate an immense 80

amount of bioethanol than other energy crops. The bioethanol yield comparison among 81

various energy crops and microalgae was presented in Fig.3. Besides bioethanol 82

production, microalgae used to treat wastewater by using CO2 and waste components as 83

nutrients and released O2 (Rc. 1) to the environment that turns down environmental 84

pollution[11-13]. Apart from this, the amount of CO2 produced during fermentation of 85

algal sugars to bioethanol, can be fed to the microalgae culture as a microalgal growth 86

component[14]. 87

6𝐶𝑂2 + 12𝐻2𝑂 + 𝐿𝑖𝑔ℎ𝑡 → 𝐶6𝐻12𝑂6(𝐶𝑎𝑟𝑏𝑜ℎ𝑦𝑑𝑟𝑎𝑡𝑒) + 6𝑂2 + 6𝐻2 (1) 88

Techno-economic analysis (TEA) is one of the most significant issues for any 89

industrial application of research output as economic feasibility is the major concern of 90

commercial execution of any product[15]. This study constructed a TEA modelling of 91

bioethanol production from microalgae by reviewing energy and cost scenario of 92

similar types of bioethanol project worldwide. This modelling has been emerged to 93

strike highly on the current biofuel scenario in South-East Asia. The application of 94

microalgae biomass on bioethanol in industrial level has not been practiced much in 95

South-East Asia, especially not in Brunei Darussalam. In this region, the climate is 96

exquisitely suitable for microalgae cultivation[16, 17]. 97

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The TEA modelling was projected for Brunei Darussalam on the island of 98

Borneo in Southeast Asia. Brunei Darussalam was in outlook for the bioethanol plant 99

modelling from microalgae for several aspects such as tropical climate. That is perfectly 100

favourable for high rate of microalgae growth. The country also have coastal territory 101

which is commendatory for marine algae cultivation, plenty of barren inexpensive 102

coastal area to establish bioethanol plant with minimum cost, handiness of marine 103

water, direct sunlight through the year and cheaper labour cost[18-21]. A survey in 104

Brunei reefs clarified that Brunei currently is experiencing high rates of microalgae 105

growth in coastal area as well as escalating CO2 emission in environment by highly 106

fossil fuel usages[22-24]. Consequently, microalgae cultivation for green energy 107

(bioethanol) production at industrial level is highly expected to mitigate free CO2 in the 108

air and utilize the suitability of the microalgae growth environment. The specific 109

predominant tropical species of microalgae Chlorella vulgaris was preferred for this 110

TEA due to the availability of this species in the selected region and high content of 111

carbohydrate amount[25, 26]. The overall economic conditions and costs associated 112

with microalgae cultivation to the bioethanol production and purification were 113

illustrated exhaustively in this study. This TEA model also illustrated economic 114

practicability for large extent. Fig.4 showed the technical treads to generate bioethanol 115

from microalgae chronologically and economical assessment based on these technical 116

procedures[27]. 117

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Fig. 3. Bioethanol yield comparison among various sources[28] 119

Fig. 4. Technical steps for bioethanol production from microalgae[29] 120

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This TEA modelling emphasized on environmental and economical prospects. 122

To illustrate the environmental factor, microalgae is cultivated for wastewater treatment 123

in many industries since it is capable to utilize waste components, inhale CO2 as food 124

sources for growth and exhale O2 to the environment[30]. Thus, no carbon payback 125

period is required and that is the most significant knock for cleaner and greener 126

environment. The economic factor is coupled with the superficial richness of 127

carbohydrate content to produce plenty of bioethanol from it. Several species and 128

strains of microalgae are capable to produce high amount of carbohydrates which is the 129

main driving factor for bioethanol production. For instance, Chlorella vulgaris is one of 130

these microalgae species[12, 28, 31, 32]. 131

The main objective of this research was to cultivate microalgae efficiently 132

through both techniques that are pond and photobioreactors. The commercial 133

microalgae cultivation system is far different than other usual energy crops. The 134

techniques involved are quite new in most of regions in the world and the industries 135

might endure some risk factors due to this point[33]. The aim of this study is to draw a 136

detailed design of techno-economic assessment of a scale-up bioethanol generation 137

plant from microalgae in a Brunei costal area. That accounted every single cost of fixed 138

and variable components for a whole project lifetime through 20 year period. The 139

analysis includes the sensitivity analysis; determine the life cycle cost assessment, cash-140

flow, break-even analysis as well as payback period to retrieve the total capital 141

investment. The start-up period and total plant profit amount were determined to 142

illustrate whether the project is desirable economically for future establishment or 143

not[34]. 144

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To establish a detailed techno-economic assessment model was very crucial due 145

to several rationales[35]: 146

i. Techno-economic analysis is the initial phase to transform lab scale 147

invention to industrial application. 148

ii. To verify the bioethanol output from microalgal biomass through 149

commercial scale is economically viable and realistic or not. 150

iii. To estimate the total plant profit as the key point to attract industrial market. 151

iv. To develop a mixed process combined with traditional (ponds) and advanced 152

technological (photobioreactors) approaches as a form of the optimization 153

process of bioethanol plant design from microalgae. 154

v. To inspect an ideal bioethanol generation plant from microalgae where every 155

step (from microalgal cultivation to bioethanol purification) of biomass 156

production to pure product manufacturing is included to integrate with by-157

product generation. 158

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Materials and Methods 160

Materials 161

In this study, Chlorella vulgaris was utilized for bioethanol production due to the 162

high cellulosic carbohydrate content as well as availability and growth capability in this 163

tropical region. Chlorella vulgaris is spherical shaped, single cells (with nucleus) 164

microalgae, contains cellulose and hemicelluloses (carbohydrate components) in cell 165

wall and starch is the main carbohydrate storage product[12]. Chlorella vulgaris dry 166

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biomass contains 52% carbohydrate during hydrolysis period producing glucose yield 167

90.4% by the fermentation process and produced almost 88% bioethanol yield[28]. A 168

comparison table of Chlorella vulgaris with other tropical microalgae in terms of 169

carbohydrate content has been tabulated in Table 1. 170

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Table 1 172

Comparison between studies species and other microalgae species in the 173

projected location in terms of carbohydrate accumulation [28, 36] 174

Thus, the finding stipulated the economic feasibility and efficiency of 175

microalgae for bioethanol generation in commercial level[31]. Among various 176

microalgae species and strains, C. vulgaris was manifested the best fitting to produce 177

carbohydrate. It is easy to sequence the genome and recombination for the yield 178

improvement of this species in future. Hence, this type of microalgae species was 179

considered to cultivate for a TEA model[37]. 180

Methods 181

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Data Collection 183

Process design and data collection is one of the most crucial factors for TEA. In 184

this project, the process design, planning and input data were assembled from diverse 185

types of sources. The sources were bioethanol production experts, bioethanol 186

production companies’ database and reports, researcher-experts in bioethanol and 187

microalgae fields, related journal articles, technical datasheets, suppliers and 188

manufacturers, up-to-date websites for market price for items included in the project. 189

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Techno-economic model of large-scale bioethanol production plant from microalgae 190

was simulated with integrated process design. The simulation model was plotted based 191

on the universal economic analysis of several chronological phases such as microalgae 192

cultivation, biomass pre-treatment, extraction and fermentation, bioethanol separation 193

and purification diagrammed by Fig.5[38-40]. 194

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Fig. 5. Technical process flow diagram of input, output and internal flows of the project 196

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The operations and technologies in current process modelling was adopted by 198

microalgae biomass cultivation in Tuscany, Italy and bioethanol production in Italy[38, 199

41]. The coastal area of Brunei Darussalam was preferred as plant location since the 200

cultivation water will be submerged from sea, suitable climatic condition and cheaper 201

land and these conditions carried similarity with model plant type. The comprehensive 202

process flow system incorporated few varied sectors such as 1. Microalgae cultivation 203

in different approaches: pond system and photobioreactor, 2. Biomass pre-treatment, 3. 204

Biomass extraction by extractor and fermentation by fermenter, 4. Bioethanol 205

separation through the beer column and 5. Bioethanol purification through the rectifier. 206

Several specific modifications for this modelling were mentioned here[38, 41]. 207

1. Two submersible pumps were planned to be used, one pump was for seawater 208

withdrawal and another for water supply to ponds and PBR. 209

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2. The single circulation pump will be used for each reactor and pond and feed 210

pumps for feeding nutrients to the cultivation systems. Heat exchangers will be 211

used for cooling water and re-using it in order to save energy. 212

3. For piping and instrumentation design, PVC material will be used. Higher 213

quality materials will be applied for photobioreactors for long lasting life-span. 214

Sensors for pH, temperature, nutrient addition and contamination identifier will 215

be used in order to control the microalgae growth rate. 216

However, all types of cost ventures, including direct cost (e.g. equipment cost), 217

indirect cost (e.g. engineering and supervision cost, contingency, legal expenses and 218

others), operation cost, raw material cost, utility cost, maintenance cost and others, total 219

sale of produced bioethanol and by-products from the plants were carefully counted. 220

Life cycle cost (LCC), total production cost (TPC), payback period (PP), total plant 221

profit (TPP) were calculated. Cash flow diagram and break-even analysis were 222

simulated based on the plant ventures and earnings using certain economical 223

formulae[42]. The conclusive simulation and graphical presentations were constructed 224

by using Microsoft Excel Software. 225

Techno-economic Simulations 226

Life Cycle Cost (LCC) 227

Life cycle cost (LCC) illustrated the costing calculation process of a plant, 228

project equipments that include all the detailed cost information of the project lifetime. 229

That includes all fixed capital cost and variable costs for manufacturing desired 230

product[43]. In this TEA, LCC included total capital investment (TCI) and total 231

production cost (TPC) where salvage value (SV) and total by-product sale (TBPS) were 232

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deducted. Salvage value (SV) defined the re-selling price of plant equipment after the 233

usual project lifespan[40]. This project lifetime was drafted for 20 years and LLC was 234

determined for the whole 20 years using the Eq.1 and Eq.2. LLC was plumbed based on 235

the initial cost info and calculation for future projection. It may vary in real life in term 236

of dynamic market of the costing[44]. 237

𝐿𝐶𝐶 = 𝑇𝐶𝐼 + 𝑇𝑃𝐶 − 𝑆𝑉 − 𝑇𝐵𝑃𝑆 (1) 238

𝐿𝐶𝐶 = 𝑇𝐶𝐼 + ∑ 𝑇𝑃𝐶𝑖𝑛𝑖=1 − ∑ 𝑆𝑉𝑖 + ∑ 𝑇𝐵𝑃𝑆𝑖

𝑛𝑖=1

𝑛𝑖=1 (2) 239

For total capital investment (TCI), salvage value (SV) and tax, the simulation formula is 240

at Eq.3, Eq.4 and Eq.5, respectively: 241

𝑇𝐶𝐼 = 𝐹𝐶𝐼 + 𝑊𝐶 + 𝑇𝐶𝐴𝐶 (3) 242

𝑆𝑉 = 0.05 𝑜𝑓 𝐹𝐶𝐼 (4) 243

𝑇𝑎𝑥 = 0.02 𝑜𝑓 𝐹𝐶𝐼 (5) 244

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Total Production Cost (TPC) 246

Total production cost (TPC) was predictedon the basis of simultaneous costing 247

analysis to producethe desired product, bioethanol. TPC for this project covered the 248

sum of operation cost (OC), maintenance cost (MC) and raw material cost for 20 years 249

of project lifetime (Eq.6). OC determined the total addition operating labor cost (OLC) 250

and total utility cost (TUC) by (Eq.7)[45]. TPC assessed a fluid assumption for the 251

project what may remain approximate simulated calculation or may change anytime 252

based on the material and labor market demand and price[46]. 253

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𝑇𝑃𝐶 = ∑ (𝑂𝐶𝑖 + 𝑀𝐶𝑖𝑛𝑖=1 + 𝑅𝑀𝐶𝑖) (6) 254

𝑂𝐶 = ∑ (𝑂𝐿𝐶𝑖𝑛𝑖=1 + 𝑇𝑈𝐶𝑖) (7) 255

Payback Period(PP) 256

Payback period (PP) elucidated the estimation of projected years that is usually 257

needed to recover the total cost total capital investment. Therefore, the profit of the 258

plant was contingent on the years after the payback period. In this modelling, the PP 259

was calculated as the ratio of TCI over yearly earnings from the bioethanol plant (Eq.8). 260

Yearly earnings were the income from the total bioethanol sale and total by-product 261

sales (crude bio-liquid and slurry cake) per annum where yearly production cost and tax 262

were eliminated. PP also strongly depended on the variability of TPC in term of market 263

fluidity. Tax is usually measured on an area basis since it varies from region to region 264

[40]. 265

𝑃𝑃 =𝑇𝐶𝐼

𝑇𝐵𝑆−𝑇𝑃𝐶−𝑇𝐴𝑋 (8) 266

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Total Plant Profit (TPP) 268

Total plant profit delineates the net project income from the plant within whole 269

plant life. For this TEA, TPP was clarified by the total bioethanol sale (TBS) 270

throughout the whole plant lifetime (20 years) where LCC was subtracted from it 271

(Eq.9). TPP is considered as one of the first-rate strands to design a profit-oriented ideal 272

plant. Usually the expected profit amount for a project relies on TPP simulations[47]. 273

𝑇𝑃𝑃 = 𝑇𝐵𝑆𝑛 − 𝐿𝐶𝐶 (9) 274

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Cash flow and Break-even analysis 276

To deal with the series of cash flow of 20 years for the project, cash amount was 277

calculated for each year. Cash flow for this TEA was conducted for the profit facet and 278

cash flow diagram rendered a brief view of cash incoming. Aside of that, cash flow also 279

measures how favourable it would be for the project effectively. Cash flow of this 280

project was calculated according to Eq.10[48]. 281

𝐶𝑎𝑠ℎ 𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛(𝑝𝑒𝑟 𝑦𝑒𝑎𝑟) = 𝐶𝑎𝑠ℎ 𝐸𝑎𝑟𝑛𝑖𝑛𝑔 − 𝐶𝑎𝑠ℎ 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 (10) 282

Break-even point defined the point where a total sale (TBS and TBPS) amount 283

and the total invested amount of fixed and variable cost are uniform. Amounts before 284

and after meeting break-even point have interpreted the loss and profit for the project, 285

respectively. Break-even analysis amounts were calculated based on Eq.11 for each 286

year. 287

𝐵𝑟𝑒𝑎𝑘 − 𝑒𝑣𝑒𝑛 𝑝𝑜𝑖𝑛𝑡 = (𝑇𝐵𝑆 + 𝑇𝐵𝑃𝑆) − (𝑇𝐶𝐼 + 𝑇𝑃𝐶) (11) 288

Cash flow diagrams and break-even analysis were simulated based on yearly cost 289

investment and sales[48]. 290

Sensitivity Analysis 291

Sensitivity analysis is an appraisal to analyze the uncertainty of the process with 292

different scenarios in term of few major factors of the whole process from microalgae 293

cultivation to bioethanol production from it[49]. Sensitivity analysis was performed for 294

this project to investigate the projected alternations based on major factors regarding 295

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cost involvement of the plant set up and system-run. Bioethanol production cost from 296

microalgae was the prime key vehicle for this techno-economic analysis study. 297

Sensitivity analysis was conducted based on TPC for both PBR and pond cultivation 298

methods of microalgae where chemical agents, nutrients, water, CO2 prices were 299

variedin different ranges. Furthermore, another sensitivity analysis was run for the 300

alternative variations of combined TPC, Tax, SV, TBS, TBPS that influenced LLC and 301

TPP[40, 49]. 302

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Results and Discussion 304

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Techno-Economics Analysis 306

Most of TEAs and plant design are carried out to impart data collection and 307

simulations regarding estimation of capital and operating costs. TEA estimation is a 308

specific sector of engineering economics and management where usually engineers plan 309

and simulate an approximate economic projection with the proper technological 310

applications and optimized designs. This chapter introduced of capital and operating 311

costs and the techniques used for estimation. The main methods used for economic 312

evaluation of projects are introduced, together with an overview of factors that 313

influence project selection[16, 50]. In addition, the process economics restrains three 314

different fundamental attributions in system design that are design alternatives, 315

optimizing the project in term of economic feasibility and overall plant benefit. For this 316

project, two types of cultivation process were applied: PBRs and ponds and the desired 317

dry biomass production amount were110 tonnes y-1 (100,000 kg y-1) for each cultivation 318

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system and total bioethanol production annually was esteemed 220 tonnes y-1[51]. Key 319

assumptions for annual biomass production, required cultivation area, system geometry, 320

bioethanol yield and production were presented in Table 2 [41, 51]. 321

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Table 2 323

Key Estimations for Microalgae Cultivation and Bioethanol Production [41, 51] 324

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Microalgae biomass productivity was 56 tonnes ha-1y-1 in PBR while ponds 326

yielded 28 tonnes ha-1y-1 as PBR is closed system with very low possibility of 327

contamination and controlled factors albeit pond cultivation is a cheaper and more land-328

consuming than PBR. Total productivity of both ways was lessened due to stress 329

condition of carbohydrate content. Ponds occupied almost 4 hectares land to plough 330

microalgae where PBR required only 2 hectares. Moreover, bioethanolic yield for PBR 331

and the pond was 58.90m3 ha-1y-1 and 24.94m3 ha-1y-1, respectively. Although both of 332

species contains more than 50wt% carbohydrates, in most cases of reality, it is usually 333

expected 30%-40% (w/w). At the end, the total bioethanol output was 57087.58gallons 334

y-1 from the projected plant (Table 2). 335

The total equipment cost (TEC) was designed to construct the plant and conduct 336

the process. This cost comprised of the components: construction of ponds and PBRs, 337

cost of water mixers, dose pump (supplementation, CO2 supply), sensors (to control pH, 338

water level, temperature, light amount), extractor (to extract biomass after pre-339

treatment), hydrolysis tank, fermenters (to hydrolysis and ferment the extracted 340

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biomass), scrubber, beer column (to separate bioethanol from crude bio-liquid and 341

slurry cake), rectifier (to produce and purify bioethanol), evaporator and others. The 342

construction cost of single PBR is more than 5 times higher than the traditional pond 343

system due to technological advancement and high quality construction material (Table 344

3). The total cost of equipment was presented in Table 3[51] and Fig.6 clarified the 345

distribution of total equipment cost. 346

347

Table 3 348

Total Equipment Cost (TEC) [51] 349

350

Fig. 6. Distribution of Total Equipment Cost (TEC) estimation (%) 351

352

According to Fig.6, the dominant equipment expenditure was for PBR 353

construction, beer column and others; for ponds construction and pumps purchase price 354

was average and reasonable. The lowest budget in total equipment cost was for mixers 355

and sensors. Total capital investment (TCI) was calculated to accumulate of newly 356

produced physical entities, such as plant set up area, machinery, equipment, goods and 357

inventories (Table 4). Fixed capital investment (FCI) demonstrated fundamental 358

amount invested for installed equipment for the technical steps to operate the whole 359

process. FCI incorporated direct costs (e.g. equipment delivery, installation, 360

instrumentation controls, piping, electrical system, building, yard improvement, service 361

facilities) and indirect costs (e.g. engineering and supervision, construction expenditure, 362

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legal expenditure, contractor’s fees, contingency)[52]. Total cultivation area cost 363

(TCAC) and working capital (WC) were covered under TCI (Table 4)[46]. Fig. 7 364

showed the distribution of TCI. For this project, delivered equipment method was 365

applied to estimate the capital investment. The fraction of delivering equipment method 366

applied for this project was a fluid processing plant. 367

368

Table 4 369

Total Capital Investment (TCI) Calculation [46] 370

371

Fig. 7. Distribution of Total Capital Investment (TCI) 372

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In this project, bioethanol was the main product, crude bio-liquid and slurry 374

cake were the by-products. Both of by-products would be sold to other companies and 375

retailers in the market. Crude bio-liquid maintains high market price due to medicinal, 376

nutritional and other biofuel production values. Slurry cake usually is pressed into 377

organic fertilizer. Total utility cost (TUC) was the expenses for electricity to run the 378

plant process and produce UV lights for PBRs supply, gas and other heating fuels[46]. 379

In this project, electricity was the dominating parameter for utility cost calculation. 380

Operation cost (OC) was the sum up of operating labour cost (OLC) and TUC (Table 381

5). Operators were assumed to work on two shifts with 7h-1US$ every day of the year 382

based on the local labour market in Brunei. The project was expected to run 383

continuously and should be supervised daily basis (Table 5). Maintenance cost (MC) 384

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was the expenses for the equipment and plant maintenance on a yearly basis. It was 385

counted based on a small fraction of TCI amount presented in Table 5)[35, 53-55]. The 386

raw materials included water, nutrients, CO2 and all chemicals for pre-treatment process 387

(Table 6) of microalgae biomass. 388

389

Table 5 390

Cost calculation of OLC, TUC, OC and MC [35, 53-55] 391

392

Table 6 393

Raw Material Cost (RMC) [35] 394

395

Total production cost (TPC) combined of all the expenditure on operation cost, 396

maintenance cost and raw material cost. This was considered one of the most crucial 397

parts of the cost measurement for operating the plant and selling price for bioethanol 398

and by-products[52]. Fig.8 presented the distribution of bioethanol production cost for 399

this project. The market price of the product (bioethanol) and by-products were 400

demonstrated in Table 7[56, 57]. In this study, TPC was US$ 111066 y-1 to produce 401

200000 kg dry biomass annually where OC carried the most expenses US$89800 y-1, 402

RMC was US$13000 y-1 and the least expenses was on MC, US$8265.74 y-1 (Table 8). 403

404

Fig. 8. Distribution of bioethanol production cost from microalgae 405

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406

Table 7 407

The market price of product and by-products [56, 57] 408

409

Since the design was upgraded, more information was gathered. The most 410

favourable approach to analyse the profitability of the plant are based on life cycle cost 411

(LCC) and total plant profit (TPP) estimation during this project life. The projected 412

LCC and TPP for this study throughout its lifespan, usually form the basis for more 413

elaborate estimation and prediction for establishment[34]. For this study, project 414

lifetime was presumed as 20 years. It was expected that the whole project would 415

perform efficiently with whole lifespan. Another prediction was that the whole project 416

would be built up on individual funding and no loan was expected. LCC and TPP were 417

set up based on these assumptions. The all production cost, tax, salvage value (SV), 418

total product sale, LCC, TPP were presented in Table 8 on annual and project lifetime 419

basis[58]. 420

421

Table 8 422

Key Simulations of Project Techno-Economical Assessment[58] 423

424

In Table 8, LLC for the 20 year project lifetime was US$2,274,463 where the 425

total bioethanol sale, by-products sale and salvage value of the plant equipment were 426

22

US$286, 5797, US$1,600,000 and US$65,186.2 per project lifespan, respectively. Fig.9 427

displayed the comparison between TPC and TBS. For most of plants, usually SV is 428

estimated as zero, but for this project, it was predicted 5% of FCI (Eq.4) since 429

photobioreactors are high-tech equipments and they last long period of time with 430

efficiency[13]. TPP for this project was calculated as LCC was deducted from total 431

sales of the products for 20 years and the TPP resulted well amount for the whole 432

project lifetime US$591,333 with positive impact on existing environment. That also 433

stipulated the project design and calculation assumptions profitable economically and 434

environmentally with innovative findings. 435

436

Fig. 9. Bioethanol production cost vs. selling price 437

438

Payback period (PP) clarified the gross period, which elapses from the initiation 439

of the project to the break-even point. The shorter the payback period is, the more 440

attractive the project will be commercially[52]. Mostly PP is counted as the time to 441

regain the TCI in terms of total annual sale (product and by-product sale), total 442

production cost and tax[59]. In this study, payback time was calculated as the time to 443

recoup the retrofit TCI from the annual improvement in operating costs[60] and it was 444

only 0.74 years(Table 8). 445

Cash flow for this project was taken into account for every year from plant set 446

up to end drawn by Fig.10. In the first year, for TCI, cash flow was down and then from 447

next year, earning amount from TBS and TBPS started to add up and cash flow went 448

23

up. The cash flow was constant after year 1 till before the year 20 since this TEA model 449

expected similar profit in each year. The profits might vary after execution due to the 450

market price variation in term of bioethanol production and selling cost, growth 451

productivity of different microalgae batches and any other reasons. However, for the 452

year 20, the earning amount was higher than previous years since SV was counted for 453

the last year of the project. Fig.10 presented the 20 years cash incoming and outgoing 454

flow for the whole project. 455

456

Fig.10. Yearly based process cash flow diagram in terms of total investment and 457

income 458

459

Fig.11 demonstrated the break-even analysis for this techno-economic project. 460

In Fig.11, the graph denoted that the break-even point was at the year 11 what meant 461

project needed 11 years to recover the TCI and TPC and after 11 years.The project 462

started to get net profit until the last year. 463

464

Fig.11. Break-even analysis of the bioethanol production process from 465

microalgae 466

467

Furthermore, for this project, the inflation rate was assumed unchanged or 468

changed co-currently with input and output ratio. Generally, inflation causes the rise in 469

24

the price of raw material, services, products and co-products over time. Inflation draws 470

impact on the amount of money needed for purchasing raw material and services. 471

Inflation was estimated by the percentage of the fractional manipulation in the cost with 472

time-frame and calculated as a certain added percentage per annum, what impacts on 473

annual price rates. The effect of inflation rate for this TEA can best be explained 474

through examining such effects before and after project time zero[61]. 475

476

Sensitivity Analyses 477

The sensitivity analysis was conducted for TPC to generate bioethanol from 478

microalgae per annum for both photobioreactors are given in Fig.12 and pond 479

cultivation method is given in Fig.13. For PBR method, four specific raw material 480

factors e.g. chemical agents, nutrients, water and CO2 had liquidity based on different 481

ranges of RMC on current market where chemical agent price influenced the most and 482

nutrients and CO2 did the least. Chemical agents’ price can be varied from US$5,500 483

kg-1 annually to US$10,500kg-1 annually (Fig.12). As the plant was planned to set up 484

nearby coastal area, water source was freely accessible. Consequently, no extra cost 485

was required for water source[32]. Nutrient and CO2 costs were totally varied by the 486

market based on availability and demand[62]. For the case of pond plough approach, 487

only nutrients and chemical agent costs mattered and the cost variations were totally 488

current market and demand based (Fig.13). 489

490

Fig.12. Sensitivity analysis for TPC market price by photobioreactor 491

25

Fig.13. Sensitivity analysis for TPC market price by pond approach 492

493

Fig.14 presented the sensitivity analysis for LCC and TPP of the whole plant 494

life span. According to Fig.14, while TPC, Tax, SV, TBS, TBPS, all were varied with 495

different ranges of estimations, LCC and TPP were influenced but not too much. The 496

LCC was more than US$2,000,000 and TPP was around US$600,000 for project 497

lifetime. Thus, by this sensitivity analysis, it was projected that the bioethanol 498

production plant from microalgae would be feasible if microalgae growth would go as 499

expected. Moreover, the TPP could be increased if the TEC is reduced since TEC might 500

vary from region to region. As microalgae cultivation is environment friendly, eliminate 501

CO2, produce O2 to the environment and purifies wastewater, so microalgae cultivation 502

for bioethanol is highly recommended to integrate with heavy metal, chemical 503

industries to reduce the environmental pollution and more economical[30]. 504

505

Fig.14. Sensitivity analyses for bioethanol production from microalgae on 506

different market price 507

508

Advantages, Limitations, Challenges and Recommendations to Microalgal-509

Bioethanol Commercialization 510

• The microalgae-bioethanol plant in Brunei Darussalam is capable to produce 511

year-round microalgae biomass with no weather disruption since Brunei 512

Darussalam does not contain winter season due to geographical location. 513

26

Because of being surrounded by sea and having adequate rainfall throughout the 514

year, this region does not have a water supply problem to microalgae culture. 515

Other study mentioned that freely available sunlight, abundant water, CO2, 516

nutrients, essential inorganic elements (e.g. Zn, Cu, Fe, Mn, Co, Mo and others) 517

can reduce production cost[26]. With this view, current project is more feasible 518

than the other previous TEA studies performed in winter based countries like 519

European countries, Canada, USA and others. Winter based countries required 520

extra heat and electricity cost in winter season to maintain the cultivation 521

temperature and water temperature (prevention to transform into ice) as well as 522

artificial UV light (alternative to sunlight)[63]. Furthermore, compared to other 523

biofuels from microalgae, bioethanol is comparatively cheaper to produce, 524

which is economical for the plant set up. The previous case studies of TEA from 525

microalgae biofuel presented that biodiesel from biomass was approximately 526

20% higher expensive to generate than the wholesale diesel price while 527

bioethanol was roughly 5% more expensive to produce than the wholesale 528

gasoline price[64]. 529

• The current TEA project presented the total production cost 2.22 million US$ 530

for bioethanol from microalgae while the total bioethanol selling price was 2.87 531

million US$ with by-product selling price 1.6 million US$. Apart from by-532

product selling price, total production cost for microalgal-bioethanol and co-533

products was 11, 10,666 US$/y where total bioethanol production was 57087.62 534

gallon/y bioethanol with co-products: crude bio-liquid and bio-solid. This result 535

summarized 19.45 US$/gallon bioethanol for this project, which is very high 536

compared to other industrial TEA. Different case studies from different 537

27

industries and projects presented that the production cost of microalgae-538

bioethanol (Algenol) can vary with different prices such as 1.27US$/gallon, 539

2.20 US$/gallon, 6.27 US$/gallon, 8.34 US$/gallon, 31.36 US$/gallon[63]. 540

Therefore, the studied TEA project did not demonstrate very large profit to 541

commercialize by private sector albeit government sector may initialize this 542

project to address alternative biofuel production in the country as well as 543

minimize the tremendous GHGs from the environment. But the fuel policy 544

support through blending mandates and tax credit policies like Brazil 545

(bioethanol from sugarcane) can be very effective to allow some variants to 546

consumer fuel market entry. In addition, subsidies associated with biofuel 547

accounted for the addition benefits of lower net environmental effect compared 548

to fossil fuels and advantages from improved fuel access as well 549

regional/national fuel independence as economic freedom for fuel purpose. 550

Brazil, USA and some regions in Africa reduced dependence on fuel imports 551

and increased fuel security as well as impacted on socio-economic development 552

by opening lower-skill level job opportunities (biomass cultivation) as well as 553

higher-skill level such as engineers, human resources for research and 554

development. Thus, the current TEA model was encouraged to be established in 555

Brunei[64]. Moreover, to make the microalgae-bioethanol commercialization 556

attractive to the private sector, R &D should focus on the other microalgae 557

species with higher yield of bioethanol and potential nano-catalyst applications 558

on microalgae cultivation and conversion to bioethanol during fermentation. 559

Overall, microalgal-bioethanol utilization mandated with gasoline as well as 560

28

microalgae cultivation and biofuel production integrated with existing 561

complementary industries can be a superior alternative for future applications. 562

• Compared to the other studies, studied TEA model has presented higher capital 563

and production cost of bioethanol from microalgae due to the higher price of 564

equipment and production materials in the current location. To note, all 565

production materials and equipment in Brunei are usually imported from 566

developing countries with high expense. Since the TEA was projected for 567

microalgae-bioethanol production at offshore in Brunei, all costs were 568

calculated based on this specific location. In this case, to reduce the capital and 569

production cost, lower-cost machineries might be imported from the cheaper 570

market in India, China, Indonesia, Malaysia and others. However, the 571

microalgae growth yield was higher and the land cost and operating cost in this 572

TEA project is less than other countries like USA, Australia and Canada[63, 573

64]. According to the case study of microalgae-biofuel commercialization, 574

indirect cost of the current project such as engineering and supervision, 575

construction expenses, contractors’ fees were lower than the case study, legal 576

expense was similar, working capital of FCI was higher than the case study. In 577

the case of direct cost, cost for installation, instrumentation and controls were 578

higher than the case studies, building cost was lower and other costs: piping and 579

insulations, electrical facilities and yard improvements was almost similar like 580

case studies[64]. The FCI of this project was 78% of TCI which is lower than 581

the FCI (89%) of other algae-biofuel commercial plant albeit the working 582

capital of this current project was, 0.09 of TCI which was higher than algae-583

biofuel commercial plant[65]. The variations of the current study with other 584

29

studies have been occurred due to the expense difference of key components 585

based on different regions. 586

587

Conclusions 588

The demand of bioethanol utilization is rising day by day as both fossil fuel blend 589

and substitute of relic fuel due to environmental issues and quick fossil fuel depletion. 590

Many candidates are being experimented to generate bioethanol, but most of them 591

usually clash with human and animal food chain where microalgae turns to disturb no 592

food chain, carry higher amounts of oil than other energy crops, clean wastewater, 593

gasps CO2 and emanate O2 to the environment. Thus, it is being considered an ideal 594

source of bioethanol production. To assess the techno-economic aspect of this 595

application, LCC model, TPP, PP, cash-flow diagram and break-even analysis were 596

built up and project life spanwas predicted for 20 years. It has been determined that by 597

considering continuous O2 supply to the environment, the TPP was US$591,333 what 598

identified the project environment-friendly and beneficial. Even with sensitivity 599

analysis comprising variable ranges of all influencing factors, the study is still 600

providedto feasible indication economically. As bioethanol production from microalgae 601

still contemporary application with modern technology, the required steps for this 602

project should be taken care by considering all the risks related to the success of 603

massive microalgae cultivation, machines especially PBR operation and bioethanol 604

separation. 605

606

Acknowledgment 607

30

The award of Brunei Darussalam Govt. Scholarship to Nazia Hossain and the 608

Faculty of Integrated Technologies, Universiti Brunei Darussalam are highly 609

acknowledged. The authors would also like to acknowledge the support of the AAIBE 610

Chair of Renewable grant no: 201801 KETTHA, and Universiti Tenaga Nasional 611

Internal Grant (UNIIG 2017) (J510050691) for supporting this research. 612

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798

39

Table 1 799

Comparison between studied species and other microalgae species in the projected 800

location in terms of carbohydrate accumulation 801

Microalgae Carbohydrate Accumulations (%)

Chlorella vulgaris 52 [28, 36]

Chlorella sokoniana 40.3 [36]

Scenedesmus obliquus 26 [36]

Tribonema sp. 31.2 [36]

Chlorococcum humicola 32 [36]

802

803

804

805

806

807

808

809

40

Table 2 810

Key Estimations for Microalgae Cultivation and Bioethanol Production[41, 51] 811

Key Items Photobioreactors (PBR) Ponds

Microalgal Biomass Productivity 56 tonnes ha-1y-1 28 tonnes ha-1y-1

Total Biomass Production 110 tonnes y-1 or 100000kgy-1 110tonnes y-1 or100000kgy-1

Cultivation Area (ha) 2 ha 3.94 ha

Cultivation system geometry

(Single Unit)

130 aligned tube per unit, 75

tubes, tube diameter 0.05 m

975 m2 per ponds, width 10m,

length 85, depth 0.30 m

Bioethanol yield 58.90m3ha-1 y-1 24.94m3ha-1 y-1

Total Bioethanol Production 31119.49 gallons y-1 25968.13 gallons y-1

812

41

Table 3 813

Total Equipment Cost (TEC) [39, 51] 814

Equipment Total Cost (US$)

Ponds 20,000

Photobioreactors (PBR) 102,000

Mixers 2,800

Pumps 27,400

Sensors 7,400

Extractor 13,000

Fermentor 15,000

Rectifier 20,000

Beer Column 43,000

Evaporator 14,000

Hydrolysis Tank 15,000

Scrubber 10,000

Others 50,000

Total Equipment Cost (TEC) 339,600

815

816

817

42

Table 4 818

Total Capital Investment (TCI) Calculation[39, 46] 819

Descriptions Fraction of delivered equipment:

Bioethanol Production from

Microalgae

Calculated

Values(US$)

Direct Costs

Purchased equipment, TEC 339,600

Delivery, fraction of TEC 0.10 of TEC 33,960

Subtotal: Delivered Equipment (DE) 373,560

Purchased equipment installation 0.47 of DE 175,573

Instrumentation Controls (installed) 0.36 of DE 134,482

Piping (installed) 0.10 of DE 37,356

Electrical systems (installed) 0.11 of DE 41,091.6

Buildings (including services) 0.18 of DE 67,240.8

Yard improvements 0.10 of DE 37,356

Service facilities (installed) 0.70 of DE 261,492

Total direct costs 2.02 of DE 1,128,151

Indirect Cost

Engineering and supervision 0.10 of DE 37,356

Construction expenses 0.20 of DE 74,712

Legal expenses 0.04 of DE 14,942.4

Contractor's fee 0.05 of DE 18678

Contingency 0.08 of DE 29884.8

Total indirect costs 0.47 of DE 175573

Fixed capital investment (FCI) 1303724

Total Cultivation Area Cost (TCAC) 200000.00

Working capital (WC) 0.40 of DE 149424

Total capital investment (TCI) 1653148

820

43

Table 5 821

Cost calculation of OLC, TUC, OC and MC [35, 53-55] 822

Cost Type Value Calculated Value,

US$year-1

Calculated Value, US$

per project lifetime

Operating Labour Costs

(OLC)

2 shifts /day, 2

operators/shift, operator

rate US$7/hour

61320 1226400

Total Utility Cost

(TUC)

Electricity cost

US$0.08/kWh,

1000kWh/day, 365 days

28480 569600

Operation Cost (OC) Sum of OLC & TUC 89800 1796000

Maintenance Cost (MC) 0.5% of TCI 8265.74 165315

823

824

825

44

Table 6 826

Raw Material Cost (RMC) [35] 827

Microalgae

Cultivation Type

Raw Material

Items

Item cost

for

treatment

kg-1,

US$

Total cost

for

treatment

kg-1, US$

Total dry

biomass y-

1, kg

RMC y-1,

US$

RMC for

project

life time

Photobioreactors

(PBR)

CO2 0.01 1 100,000 100,000 200,000

Water 0.025

Nutrients

(Medium)

0.01

Chemical Agents

(Pre-treatments)

0.055

Ponds Nutrients

(Medium)

0.01 0.3 100000 3000 80000

Chemical Agents

(Pre-treatments)

0.02

Total Raw Material Cost (RMC)/y US$13,000

Total Raw Material Cost (RMC)/project lifetime US$ 260,000

828

829

45

Table 7 830

Market price of product and by-products[57, 66] 831

Items Current Market Price (US$)

Bioethanol 2.51 gallon-1

Crude Bio-liquid 5.00 gallon-1

Slurry Cake (Bio-fertilizer) 3.75 kg-1

832

833

46

Table 8 834

Key Simulations of Project Techno-Economical Assessment[58] 835

Cost Calculations Calculated Values y-1, $ Calculated Value of Project Life

time, $

Total Capital Investment (TCI) - 1,653,148

Operation Cost (OC) 89,800 1,796,000

Maintenance Cost (MC) 8,265.74 165,315

Raw Material Cost (RMC) 13,000 260,000

Total Production Cost (TPC) 111,066 2,221,315

TAX 26,074.5 521,490

Salvage Value (SV) 651,86.2

Total Bioethanol Sale (TBS) 143,290 2,865,797

Total By-Product Sale (TBPS) 80,000 1,600,000

Payback Period (PP) 0.74 year

Life Cycle Cost (LCC) $2,274,463

Total Plant Profit (TPP) $ 591,333

836

837

47

838

839

840

Fig.1.Worldwide Bioethanol Production in 2015[9] 841

842

14806

7093

1387813 436 334 211 211 391

United

Sta

tes

Brazil

Euro

pea

n U

nio

n

Chin

a

Canad

a

Thailan

d

Arg

entin

a

India

Oth

ers

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Bio

eth

an

ol

Pro

du

ctio

n(M

illi

on

Gall

on

s)

48

843

844 Fig.2.Bioethanol Production Rise Curve in U.S. (2000-2015)[10] 845

846

0

5000

10000

15000

20000

25000

30000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Bio

eth

an

ol(

Mil

lion

Gall

on

s)

Years

49

847

848

Fig.3.Bioethanol yield comparison among various sources[28] 849

850

150

277

354

435

430

714

1150

15000

0 5000 10000 15000 20000

Corn Stover

Wheat

Cassava

Sweet sorghum

Corn

Sugar Beet

Swiss grass

Microalgae

Bioethanol yield (gal/acre)

50

851

852 Fig.4.Technical steps for bioethanol production from microalgae[29] 853

854

855

856

51

857 Fig.5.Technical process flow diagram of input, output and internal flows of the project 858

52

859

Fig.6.Distribution of Total Equipment Cost (TEC) estimation (%) 860

861

Ponds,

5.9%

Photobioreactors

(PBR), 30.0%

Mixers, 0.8%Pumps,

8.1%

Sensors, 2.2%

Extractor, 3.8%

Fermentor, 4.4%

Rectifier, 5.9%

Beer Column,

12.7%

Evaporator, 4.1%

Hydrolysis Tank,

4.4%

Scrubber, 2.9% Others, 14.7%

53

862

863

Fig.7.Distribution of Total Capital Investment (TCI) 864

865

866

FCI, 78.86%

TCAC, 12.10%

WC, 9.04%

Distribution of TCI

54

867

Fig.8.Distribution of bioethanol production cost from microalgae 868

869

$1.65$1.80

$0.17 $0.26$0.52

-$0.07

-$1.60-$2.00

-$1.50

-$1.00

-$0.50

$0.00

$0.50

$1.00

$1.50

$2.00

Tota

l Capita

l Investm

ent

Operatio

n C

ost

Main

tenan

ce Cost

Raw

Materia

l Cost

TA

X

Salv

age V

alue

Tota

l By-P

roduct S

aleP

rod

uct

ion

Cost

(U

S $

mil

lion

)

55

870

871

Fig.9.Bioethanol production cost vs. selling price 872

873

$2.22

$2.87

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

Total Production Cost Total Bioethanol Sale

Cost

Dis

trib

uti

on

(U

S $

mil

lion

)

56

874

Fig.10.Yearly based process cash flow diagram in terms of total investment and income 875

876

$0.00

$0.02

$0.04

$0.06

$0.08

$0.10

$0.12

$0.14

$0.16

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

US

$(M

illi

on

s)

Years

57

877

878

Fig.11.Break-even analysis of the bioethanol production process from microalgae 879

880

881

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

$4.00

$4.50

$5.00

Year

1

Year

2

Year

3

Year

4

Year

5

Year

6

Year

7

Year

8

Year

9

Year

10

Year

11

Year

12

Year

13

Year

14

Year

15

Year

16

Year

17

Year

18

Year

19

Year

20

US

$(M

illio

ns)

Total Sale(Product & By-products) Fixed Capital Investment Total Cost

Profit

Production Cost (Variable)

Loss

Break-even point

58

882

Fig.12.Sensitivity analysis for TPC market price by Photobioreactor 883

884

-$1.20-$1.00-$0.80-$0.60-$0.40-$0.20 $0.00 $0.20 $0.40 $0.60

CO2($0.01,$0.02,$0.04)

Water($0.025,$0.05,$0.08)

Nutrients(Medium)($0.01,$0.02,$0.04)

Chemical agents (Pre-

treatment)($0.055,$0.085,$0.105)

Change to Production Cost per year, US$ kg-1 dry biomass of microalgae

59

885

886

Fig.13.Sensitivity analysis for TPC market price by pond approach 887

888

-$1.20-$1.00-$0.80-$0.60-$0.40-$0.20 $0.00 $0.20 $0.40 $0.60 $0.80

Nutrients(Medium)($0.01,$0.02,$0.04)

Chemical agents (Pre-

treatment)($0.02,$0.05,$0.08)

Change to Production Cost per year, US$ kg-1 dry biomass of microalgae

60

889

890

Fig.14.Sensitivity analyses for bioethanol production from microalgae on different 891

market price 892

893

-$0.60 -$0.40 -$0.20 $0.00 $0.20 $0.40 $0.60

TPC(2.02mil,2.22mil,2.42mil)

Tax(2%FCI,4%FCI,6%FCI)

SV(3%FCI,5%FCI,7%FCI)

TBS(2.5mil,2.8mil,3.1mil)

TBPS(1.4mil,1.6mil,1.8mil)

LCC(2.23mil,2.20mil,2.18mil)

TPP(0.34mil,0.65mil,0.96mil)

US $ (Million)