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Waste Quantification and Characterization – Sri Lanka (2009) ________________________________________________________________________________________________

________________________________________________________________________________________________ Extracted from “Project on Converting Waste Agricultural Biomass to Energy/Material Resource – Report

II: Waste Biomass Quantification and Characterisation”

Uva Province

The areas selected for the WAB project are the Monaragala and Buttala DS Divisions of

the Monaragala District of the Uva Province, as shown in Figure A.

Figure A: Sri Lanka Map - Uva Provincial Council

Introduction to Uva Province (Sri Lanka)

Monaragala District

Badulla District




Uva is Sri Lanka's largest province and second least populated province, with 1,187,335

people. It was created in 1896 and consists of two districts: Badulla (2,818 km²) and

Monaragala (5,545.6 km²). The provincial capital of the province is Badulla. Uva is

bordered by Eastern, Southern and Central provinces. It is a major tourist attraction with

several well known waterfalls situated within its boundaries. The Gal Oya hills and the

Central Mountains are the main uplands, while the Mahaweli and Menik rivers and the

huge Senanayake Samudraya and Maduru Oya Reservoirs are the major waterways.

The Monaragala administrative district (AD), which is situated in the Uva Province and

administered under that Uva Provincial Council, is considered the second largest AD in

the country with 8.6% of the total land area. It is made up of eleven (11) DS Divisions,

and situated around 288 km from the capital Colombo.

The total land area of the district is 5545.6 km2 and the total population is reported to be

about 400,000 (population census of 1991) with the majority being of Sinhalese origin.

The population density for the district is 58/km2.

Monaragala is located in a transitional zone between the central highlands and the

lowlands towards the south, East and Northeast. Situated in the Arid Zone of Sri Lanka,

Monaragala has an average annual temperature, ranging from 22.5 - 27.5 oC. The District

receives around 2,200 mm of rainfall in average annually. This is usually limited to 4-5

months of the year. However one sixth of the district receives less than 1750 mm of

rainfall per year. The variation in rainfall in the area has had adverse effects on its human

population. The south, south-eastern and eastern parts of the district are relatively drier

than the higher north-western parts.

The soil conditions in the district vary according to the topography and the climate.

However two distinguish soil groups can be identified from the area; the reddish brown

soil and the red yellow podzolic soil. Both soil varieties are suitable for cultivation.




Figure B: Mo naragala District Map




Monaragala has a complex agrarian society which has evolved around paddy cultivation.

More than 90% of the population in the region has agriculture based livelihoods.

Historically Monaragala has been a major paddy producer and agriculture has remained

the backbone of the economy. In addition to paddy the region is also famous for the

cultivation of vegetables, pulses and fruits mostly grown under “Chena” (Slash and Burn)

cultivation methods. Plantation crops such as tea, rubber, cocoa, sugar cane, tobacco and

coconut, were later introduced to this region and now exist and thrive side by side with

the long-established crops.

The cultivation in Monaragala can be separated into two seasons; the Maha (October-

March) season which is the major cultivation season and the Yala (April-September)

season. The total extent cultivated in the district is around 258.3 km2 during the Maha

season and 137.16km2 during the Yala season an annual harvest of (according to

Department of Census and Statistics – 2007/2008) 99,446 Tons and 56,987 Tons

respectively for Maha season and Yala season.

The district was an ideal candidate for such a project as it is essentially an

underdeveloped region and is considered the second poorest district in Sri Lanka. This is

further highlighted by the fact that around 70% of households receive “Samurdhi”

support, a scheme introduced as a monthly allowance system, similar to welfare,

provided by the government for low or no income generating households in the country.

This high level of rural poverty can be attributed to the inadequate economic activity. The

problem is further worsened as no investment is brought into the region due to lack of

infrastructure and basic necessities, electricity being one among them.

However Monaragala has many untapped natural resources, some of them being

agricultural crops and wastes. During surplus season much of the vegetable and fruit

harvest is wasted as the farmers are unable to secure a good price for the crops in the

market. In addition to this WABs generated in the area is currently not being utilized in a

proper manner and is therefore causing damage to the environment. Traditionally, the

agricultural waste is burned in open fields or dumped into abandoned lands and




waterways causing pollution to the air and water. In addition to this the burning and

decomposing of agricultural waste gives rise to GHGs.

The project is focussed on two of the DS divisions of the district; Monaragala and Buttala

(see Figure B). Monaragala DS is situated in the centre of the Monaragala District in an

area of 255 km2 and the main Administrative Centre for the District. The Buttala DS is

situated south of the Monaragala DS and is 685 km2 in area. The population for

Monaragala and Buttala DS divisions is 50,018 and 44,874 respectively. These DS

divisions were selected for the project as there are considerable quantities of waste

agriculture biomass being generated in the form of paddy straw, paddy husk, saw dust,

sugarcane Barbojo, corn (maize) cobs and stalks etc.

The Buttala D.S. Division is divided into 29 Gramaniladhari (G.N) Divisions, while the

Monaragala D.S. Division has 26 G.N. Divisions. G.N. Divisions are the smallest

administrative areas. Waste generation points and addresses of processing mills are based

on this G.N. Division and therefore they are required for the mapping of resource




1. Methodology

The “Roadmap” below lays out the procedure for data collection from setting up of the

boundaries and planning the data collection and analysis procedures before hand to the

actual collection and analysis of WAB related data. It can be categorized basically into

two main stages,

• Setting up of boundaries in terms of geographical and administrative coverage with

respect to various waste streams: Clearly defining and demarcating the geo-political

and administrative boundaries, types of crops and sources of WAB such as

agricultural farms and processing facilities.

• Setting procedures for data collection, analysis and presentation: Identifying methods

for data collection such as direct and indirect methods, selecting the number and

places from which samples should be collected. Furthermore procedures for

analyzing the samples and presenting these findings will be selected.

Figure 1.1: Flowchart for Data Collection & Analysis

1 Baseline Data (2009)




1.1 Defining Geo Administrative Boundaries

This includes the clear definition and demarcation of geo-political and administrative

boundaries, types of crops, various agricultural farms and processing facilities and types

of wastes generated. The information required is the following:

• Agricultural Farm Size and Distribution and Growth within the project area(s)

• Socio-Economic Patterns within the project area(s)

• Size and Number and Distribution of Processing facilities

• Administrative Boundaries and Responsibilities

• Sources of Waste Streams: Farms, Processing facilities, Commercial facilities

1.2 Geographical Size of the Area and Zoning

In order to facilitate the collection of required data for a particular region, appropriate

formats were selected, for example, as:

• Maps from local authorities identifying the geographical and administrative

boundaries.

• Maps for land-use/zoning plans.

• Farm population size and growth: Time-series data with future projections,

distribution of population among various zones, number of single-family owned

farms, cooperative farms and corporate farms.

• Size and number of processing facilities and commercial undertakings as per

national or local classification.

• Regulations concerning various waste streams.

• Primary data on waste, if already available.

1.3 Setting the procedures for data collection, analysis and presentation:

Once the boundaries of the project are established, procedures for data collection,

analysis and presentation are set as given below.

• Compilation of list of WAB generated by various sectors

• Quantification of total waste agricultural biomass generated

• Quantification of alternative uses of waste agricultural biomass




• Characterization of different types of waste agricultural biomass generated from

different sectors.

• Future projections incorporating possible changes in farming practices, socio-

economic growth, and technological development.

With relation to the quantification of waste agricultural biomass, two methods can be

used to gather information

• Direct measurements: Measurements and at the point of generation such as

sample material balances, onsite measurements etc.

• Indirect measurements: When direct measurement is not feasible, indirect

measurements such as examination of records at the point of generation and

disposal, vehicle surveys (waste transportation), interviews and surveys.

1.5 Waste Characterization

Another important element in collecting data on WAB is to characterize each of the waste

types. The characterization includes identifying the properties of the waste through visual

characterization, bulk density measurement, analysing moisture content, heating values

and other specific characterization parameters (e.g. composition of ash after combustion)

and distinguishing the composition (e.g. proximate analysis/ultimate analysis).

In addition to the above information cost data related to the WAB such as cost of waste

agricultural biomass, cost of pre-processing, cost of transportation, other costs (such as

disposal fees, taxes/levies etc.) must also be gathered.

1.6 Presentation of Data

The data collected through the above methodology are presented in the form tables and

graphs in the following report.




2. Paddy Processing Residues

2.1 Paddy Production Scenario

2.1.1 Production in Sri Lanka

Paddy is the most extensively cultivated crop in Sri Lanka. The crop is grown in two

seasons per year; the "Maha" season which is harvested in February and the "Yala"

season which is harvested in August. As shown in table below, the gross area of paddy

sown in 2008/09 was about 632,000 hectares in Maha season and 345,000 hectares in

Yala season. The corresponding total production is 2,384×106 kg in Maha season (65%)

and 1,268×106 kg in Yala season (35%)1

Table 2.1: Paddy Production Data in 2009 (Source 1)

Gross Area Sown Net area Harvested

Production

Average Yield

Season

hectare hectare (106 kg) (kg/net ha)

Maha 632,130 539,271 2,384 4,421 Yala 345,431 302,863 1,268 4,186 Total 977,561 842,134 3,652 4.337

In the past few decades, there has been a steady growth in paddy production in the Island.

Table 2.1 gives the annual paddy production from 1979 to 2008 for each season and the

entire year (note that the corresponding values for the year 2009 are presented). Figure

2.1 depicts the growth in annual paddy production since 1979. The increase in the

production during the period 1979 to 2009 is about 90% with the average annual

increment of about 1.5%. This increase of production is due to increase in both the 1 Paddy Statistics (2009). Extent, Sown, Harvested, Average Yield and Production By District - 2008/09 Maha Season.

Department of Census & Statistics, Ministry of Finance & Planning, Sri Lanka. Date of Issue – 25th June 2009.

Paddy Statistics (2009). Extent, Sown, Harvested, Average Yield and Production By District – 20089Yala Season.

Department of Census & Statistics, Ministry of Finance & Planning, Sri Lanka. Date of Issue – 24th December 2009.

2 Waste Generation: Quantification and Characterization




average yield per hectare as well as the total area of cultivation. The average yield has

increased from 2,748 kg/net ha in 1979 to 4,337 kg/net ha in 2009 (i.e. 57.8% increase)

and the net area harvested (in both seasons) is increased from 698,945 ha in 1979 to

842,134 ha in 2009 (i.e. 20.5% increase).

Table 2.2: Paddy Production by Season in Sri Lanka (1979 – 2008)

Maha Yala Total Maha Yala Total

Year '000 T '000 T '000 T Year '000 T '000 T '000 T

1979 1,395.60 525 1,920.60 1994 1,673.10 1,014.70 2,687.80

1980 1,455.70 681 2,136.70 1995 1,764.10 1,050.70 2,814.80

1981 1,524.90 708.2 2,233.10 1996 1,333.60 731.5 2,065.10

1982 1,365.00 794.2 2,159.20 1997 1,459.60 783.7 2,243.30

1983 1,788.90 698.7 2,487.60 1998 1,784.20 912.8 2,697.00

1984 1,354.50 1,062.00 2,416.50 1999 1,738.80 1,123.30 2,862.10

1985 1,750.90 908.6 2,659.50 2000 1,781.20 1,077.60 2,858.80

1986 1,687.10 897.5 2,584.60 2001 1,614.00 1,083.00 2,697.00

1987 1,392.70 736.6 2,129.30 2002 1,774.50 1,089.10 2,863.60

1988 1,527.30 953.6 2,480.90 2003 1,896.80 1,172.20 3,069.00

1989 1,344.80 722.6 2,067.40 2004 1,669.70 958.2 2,627.90

1990 1,650.10 890.4 2,540.50 2005 2,012.70 1,233.50 3,246.20

1991 1,554.00 836 2,390.00 2006 2,135.60 1,206.30 3,341.90

1992 1,634.10 711.2 2,345.30 2007 1,972.93 1,158.15 3,131.08

1993 1,695.50 879.7 2,575.20 2008 2,125.17 1,750.03 3,875.20




Figure 2.1: Growth in Paddy Production in Sri Lanka (1979 – 2009)

The residues generated through harvesting and processing of paddy includes paddy husk

and paddy straw. Most of the paddy straw generated is burnt in the field with the ash used

as organic fertilizer. Relatively small quantities are used as animal fodder, animal

bedding, packaging material, raw material for paper and board making or building

material. Paddy straw also plays a vital role as a candidate raw material for biogas

production systems which has been developed by various local institutions. Due to the

low density, bulkiness, and high combustion rate, paddy straw ranked somewhat low in

terms of being an energy source. The amount of residue generation is usually predicted

by Residue to Product Ratio (RPR).

Paddy husk is produced at rice mills at the time the paddy is milled. The husk production

per district cannot be taken directly from the district-wise paddy production figures, as a

considerable portion of the produced paddy is transferred between districts for processing.

There are approximately 7000 rice mills operating throughout the country from which

77% are custom mills which produce rice for the farmers who remain the owners of the

rice. The balance 23% are commercial scale mills which purchase the paddy and sell the

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

1975

1980

1985

1990

1995

2000

2005

2010

Pad

dy P

rodu

ctio

n ('0

00 M

T)

Year

Maha

Yala

Total

trend




rice thereafter. It is estimated that only 50% of the rice produced enters the commercial

market, and other 50% is consumed by farmers2.

In the Central and Eastern districts, paddy is usually parboiled before milling, while in

the Southern districts, mostly raw milling occurs. In parboiling mills, the husk is used for

combustion in the par-boiler or steam boiler. According to the detailed assessment

conducted in Anuradhapura and Polonnaruwa districts, approximately 50% of the paddy

husk produced is utilized as fuel for steam generation while 44% is left unutilized. In

most of the rice processing areas, the surplus paddy husk is considered as a waste

material creating environmental problems and often given free of charge. Some of these

husks are used as a domestic fuel for cooking, poultry industry and bricks manufacturing.

In some cases, larger mills even pay some money to contractors who are responsible for

taking the husk away regularly. It is difficult to obtain a complete picture of these present

uses. The situation varies strongly per district and region3.

In Sri Lanka, different paddy varieties are cultivated in different localities considering

factors such as climate, rainfall, soil type etc. Each variety has a different RP Ratio. A

study carried out in1999 to estimate generation of paddy husk shows that RPR for most

commonly grown Sri Lankan paddy varieties varies from 0.18 for BG 3-5 to 0.23 for LD

125 2. This data is presented in Table 2.3. Available literature shows a considerable

change in RPR of paddy straw 3. The amount of straw produced from paddy depends on

several factors such as paddy variety, fertilizer, season, and harvesting practices etc. For

the present study, field measurements were taken to estimate the RPR of paddy husk and

the results are presented section 2.1.1.1 of this report.

2 Source: Senanayake, D.P., Daranagama, U. & Fernando, M.D. (1999). Availability of paddy husk as a source of energy

in Sri Lanka. Research Seminar August 1999, NERD Center, Sri Lanka.

3 Source: Bhattacharya, S.C., Pham. H.L., Shrestha, R.M. & Vu, Q.V. (1993). CO2 emissions due to fossil and traditional

fuels, residues and wastes in Asia. AIT Worhshop on Global Warming Issues in Asia, 8-10 September 1992, AIT,

Bangkok, Thailand.




Paddy husk is a bulky material and consists of organic and mineral materials. Existing

literature on paddy husk shows considerable variations in the characteristics including

bulk density, moisture content and fuel characteristics4. Typical properties of paddy husk

and paddy straw including fuel analyses (ultimate and proximate) are presented in Tables

2.32 – 2.37 of this chapter. The key difference in the properties of paddy residues

compared with other biomass fuels is the considerably high ash contents. High generation

of ash during combustion creates difficulties in handling and disposal.

Table 2.3: Residue to Product Ratio for Paddy Varieties

Paddy Variety

RPR

H4 0.1972 BG 3 -5 0.1816 Podiwee A8 0.2166 Pachchaperumal 0.2065 BW 78 0.2274 BG 400 - 1 0.2012 LD 125 0.2300 BG 33 -2 0.1898 LD 66 0.2230 BW 170 0.2107 MI 329 0.2045

.

Figure 2.2: Paddy Husk Figure 2.3: Open Burning of Husk

4 Koopmans, A. & Koppejan, J. (1999). Agricultural and forest residues - Generation, Utilization and Availability. RWEDP

in Asia.




Figure 2.4: Paddy Husk Ash

While alternative uses of paddy husk ash have been investigated in greater detail5 the

husk ash has been found to have many applications due to its various properties. It is an

excellent insulator, and therefore is ideal for industrial processes such as steel foundries,

and in the manufacture of insulation for houses and refractory bricks. It is an active

pozzolan and has several applications in the cement and concrete industry. It is also

highly absorbent, and is used to absorb oil on hard surfaces and potentially to filter

arsenic from water. More recently, studies have been carried out to purify it and use it in

place of silica in a range of industrial uses, including silicon chip manufacture.

Paddy husk ash is a general term describing all types of ash produced from burning rice

husks. In practice, the type of ash varies considerably according to the burning technique.

Two forms predominate in combustion and gasification. The silica in the ash undergoes

structural transformations depending on the temperature regime it undergoes during

combustion. At 550°C – 800°C amorphous silica is formed and at greater temperatures,

crystalline silica is formed. These types of silica have different properties and it is

important to produce ash of the correct specification for the particular end use. Table 2.4

presents typical chemical composition of the paddy husk ash.

5 Source: Ltd. (2003). Rice Husk Ash Market Study. ETSU U/00/00061/REP.




Table 2.4: Chemical composition of rice husk ash

Parameter (dry basis)

Typical range (%)

SiO2 85-95 Al 2O3 0.5-1.5 Fe2O3 0.5-1.5 CaO 1.5-1.8 MgO 0.5-2.0 K2O 0.4-3.0 Na2O 0.5-2.0

Carbon 0.5-3.0 Others < 1

2.1.2 Production in Monaragala District

The main agricultural crop in Monaragala district is paddy and therefore the paddy straw

and husk are the key waste biomass generated within the region. In 2009, the total paddy

production within Monaragala district is 58.9×106 kg in Maha season and 26.2×106 kg in

Yala season. Table 2.5 gives the historical data of paddy production in the district while

figure 2.5 depicts the growth in paddy production during the period 1979-2009.

Table 2.5: Paddy Production in ‘000 MT in Monaragala District (1979 – 2008)

Maha Yala Total Maha Yala Total

Year ‘000 T ‘000 T ‘000 T Year ‘000 T ‘000 T ‘000 T

1979 27.70 3.1 30.80 1994 42.20 11.80 54.00

1980 28.10 4.6 32.70 1995 40.90 13.00 53.90

1981 29.90 6.1 36.00 1996 37.10 14 51.10

1982 27.90 12 39.90 1997 24.70 10.6 35.30

1983 29.90 7.5 37.40 1998 47.80 11.5 59.30

1984 30.10 16.40 46.50 1999 48.30 19.90 68.20

1985 30.20 11.4 41.60 2000 50.70 21.30 72.00

1986 42.20 14.4 56.60 2001 42.00 10.20 52.20

1987 29.60 8 37.60 2002 46.60 15.10 61.70




1988 32.40 9.6 42.00 2003 55.00 21.80 76.80

1989 28.70 5.5 34.20 2004 58.40 17.7 76.10

1990 33.40 13.5 46.90 2005 60.80 22.10 82.90

1991 48.40 14.9 63.30 2006 64.90 26.10 91.00

1992 28.80 7.1 35.90 2007 92.44 38.27 130.71

1993 47.70 5.8 53.50 2008 99.45 56.99 156.44

As in the case of the rest of the Island, there has been a steady growth in paddy

production within the district during past few decades. The increase in production during

this period considered is about 176%, which is much higher than that of the entire country

(90%). The average annual increment of is about 3.8% (Refer Annex IV for methodology

and the detailed calculations). It could be seen that the main contribution to this growth is

basically from the period 2000 – 2008 during which period the average yield has

increased from 2,953 kg/net ha in 1979 to about 4,200 kg/net ha in 2008 (i.e. 42%

increase). The trend in production could be directly attributed to the policies of the

present government, where paddy cultivation is given priority,

According to figure 2.5, a significant drop in paddy production is clearly noted in 2009

which is primarily due to unfavourable weather conditions. This also highlights the fact

that paddy production in Monaragala and the country is mostly dependant on a rain fed

agricultural system. This also highlights that the existing irrigation system needs to be

further developed.

It must also be noted that the general trend in paddy production should not be reflected by

the values in 2009.




Table 2.6: Reported Agricultural Holdings in Monaragala District

Operated Paddy Extent (Owned by Operator or Owned by Others)

Less

than

1/

2 A

cre

1/2

- <

1 A

cre

1 -

<2

Acr

es

2 -

<5

Acr

es

5 A

cres

and

ab

ove umber of

Holdings Reporting

Paddy

Paddy Extent

No.

Ext

ent

No.

Ext

ent

No.

Ext

ent

No.

Ext

ent

No.

Ext

ent

33,3

09

38,2

44

3,90

9

961

10,5

29

5,69

0

11,6

17

12,7

45

6,94

7

16,4

64

307

2,38

5

Figure 2.5: Paddy Production in Monaragala District (1979 – 2009)

0

20

40

60

80

100

120

140

160

180

1975

1980

1985

1990

1995

2000

2005

2010

Pad

dy P

rodu

ctio

n ('0

00 M

T)

Year

Maha

Yala

Total

Trend




More specific data on the net harvested area and the production of paddy within the

project areas (Buttala and Monaragala D.S. Divisions) during last three years are

presented in Tables 2.7 and 2.8.

Table 2.7: Total Area of Paddy Harvested (in hectares) Year

2006 2007 2008

Maha Yala Total Maha Yala Total Maha Yala Total DS Division

ha ha ha ha ha ha ha ha ha

Buttala 3,103 1,844 4,947 3,419 1,788 5,207 3,488 2,743 6,231

Monaragala 891 114 1,005 1,376 152 1,528 1,438 574 2,012

Table 2.8: Total Production of Paddy (in Tons) Year

2006 2007 2008

Maha Yala Total Maha Yala Total Maha Yala Total DS Division

Tons Tons Tons Tons Tons Tons Tons Tons Tons

Buttala 12,279 7,477 19,756 13,980 7,826 21,806 13,927 11,718 25,645

Monaragal

a 3,525 461 3,986 5,625 666 6,291 5,744 2,453 8,197

The data shows that the increase in net extent of area harvested during the last three years

is about 20% in Buttala D.S. Division and 100% in Monaragala D.S. Division.

Correspondingly, the total production of paddy has increased by 30% in Buttala D.S.

Division and 106% in Monaragala D.S. Division. The data also indicates that the average

yield has increased from 3,957 kg/ha to 4,089 kg/ha. The increase in production of paddy

can be considered as a contributory factor for the increase in generation of paddy husk

and straw, which is a positive aspect as far as the sustainability of this project, is

concerned. However a factor that could affect this in a negative manner is the trend of

sending paddy to other parts of the country for milling in which case the increase in husk




may not be as considerable. This factor should be kept in mind when developing the

project.

The present generation and utilization pattern of paddy wastes in the two D.S. divisions

considered above revealed that at least 85% of the paddy generated are processed at the

local mills. A survey was conducted to a sample group of paddy mills in both D.S.

Divisions covering a cross section of the sector. The total number of mills covered in the

data collection survey was 33. Capacities of these mills are ranged from less than 1 to 15

t/day. In the case of Monaragala D.S. Division, almost all the paddy husk generated was

found to be disposed off though dumping of burning. However around 5%-10% of the

husk was been taken by a few small scale industries for vegetable drying.

However, in the case of Buttala Division, around 90% of the husk is consumed by brick

manufacturing industries situated in the Wellewaya D.S. Division (transport distance of

about 9 km). As transport distance from Monaragala to Wellawaya is over 20 km, husk is

not collected from Monaragala. Therefore it is concluded that paddy husk generated in

Monaragala D.S. division is largely available for any useful application.

2.1.2 Paddy Based WAB Generation

2.1.1.1 RPR Values - Husk

The amount of WAB generated through paddy could be predicted by the production of

paddy together with Residue to Product Ratio (RPR). The data on RPR relevant to

different local paddy varieties are available in the literature (refer Table 2.4). The data

shows that the RPR values vary among different paddy varieties but the average value is

around 0.2, which is also reported in other literature. However, in the present study, field

surveys were conducted to verify the validity of this data and also to derive more relevant

RPR values for the most common types of paddy varieties used in the region.

The feedback from the mill owners / operators indicated that the production of rice is

about 65 – 70 % of the total paddy processed, which was proven though material balances

carried out at two rice mills. These two mills included one representing small-scale mills




and the other representing commercial mills. The waste at both mills included paddy husk

as well as bran.

At the small-scale mill, two trials were conducted to estimate the RPR value. In Trial 1,

61 kg of paddy was milled and the output contained 41 kg of rice and 5 kg of bran, and

15 kg of paddy husk. This sample contained about 95% of BG 352 paddy variety and the

remaining 5% is AT402 paddy variety. This data shows that RPR value in terms of paddy

husk to paddy is about 0.245. In the second Trial 45 kg of paddy was milled and the

output contained 30 kg of rice and 4 kg of bran, resulting 11 kg of paddy husk. This

sample totally contained BG 352 paddy variety. This data shows that RPR in terms of

paddy husk to paddy is about 0.18. Although there is a considerable variation within the

two estimations, the average value is about 0.21 which confirms the data presented in

Table 2.4.

The second test conducted at the commercial mill was a sample material balance for the

milling of 1500 kg of paddy (AT 362) and the output contained 1037 kg of rice, 320 kg of

paddy husk and 108 kg of bran. The data shows about 33 kg difference between the input

and the output, which may be attributed to the following reasons:

- Estimated 10 kg error due to spillage of milled rice, husk and bran during milling,

- Some portion of the weighed paddy was left spilt around the feeding silo and was

not milled,

- The weighing of the input paddy and the output husk and bran were conducted

using two different scales. Therefore the weight of the input paddy may not be

accurate.

Based on the above figures the RPR values could be estimated as 0.21, which is again

very close to the average values reported in the literature.

Considering the results achieved by the trail runs at the two mills the project team

decided to use a RPR value of 0.21 for calculation of husk.




Table 2.9: Total Estimated Production of Paddy Husk in Buttala D.S. Division

Year

2006 2007 2008

Maha Yala Total Maha Yala Total Maha Yala Total Product

(Tons) (Tons) (Tons) (Tons) (Tons) (Tons) (Tons) (Tons) (Tons)

Paddy 12,279 7,477 19,756 13,980 7,826 21,806 13,927 11,718 25,645

Husk 2,578.6 1,570.2 4,148.8 2,935.8 1,643.5 4,579.3 2,924.7 2,460.8 5,385.5

Table 2.10: Total Estimated Paddy Husk Production in Monaragala D.S. Division Year

2006 2007 2008



Paddy 3,525 461 3,986 5,625 666 6,291 5,744 2,453 8,197

Husk 740 97 837 1,181 140 1,321 1,206 515 1,721

Figure 2.6: Paddy Husk in Buttala Division (2005 – 2008)




Figure 2.7: Paddy Husk in Monaragala Division (2005 – 2008)

2.1.1.2 RPR Values - Straw

Since the actual measurement of straw generation is not possible practically within the

project areas the project team carried out a literature survey to ascertain the most suitable

value to be used for calculating Paddy Straw residues.

The UNEP - Guidelines for Waste Characterization and Quantification the RPR value for

straw is 1.5 (Refer Annex III).

According to literature the RPR values for paddy straw range from 0.416 to 3.96. These

values have been cited in research papers and reports from around the region. Actual

measurements carried out in Thailand have indicated the RPR value to around 1.7576.6

Therefore the project team opted to use the RPR value of 1.75, in calculating paddy straw

in the two DS divisions.

6 Source: http://wgbis.ces.iisc.ernet.in/energy/HC270799/RWEDP/acrobat/p_residues.pdf




Table 2.11: Total Estimated Paddy Straw Production in Buttala D.S. Division Year

2006 2007 2008



Paddy 3,525 461 3,986 5,625 666 6,291 5,744 2,453 8,197

Straw 6,168.8 806.75 6,975.55 9,843.8 1,165.5 11,009.3 10,052 4,292.8 14,344.8

Table 2.12: Total Estimated Paddy Straw Production in Monaragala D.S. Division

Year

2006 2007 2008



Paddy 12,279 7,477 19,756 13,980 7,826 21,806 13,927 11,718 25,645

Straw 21,488 13,085 34,573 24,465 13,696 38,161 24,372 20,507 44,879

In the case of paddy straw, feedbacks of the local farmers and other stakeholders

indicated that it will be very difficult to make available the straws generation in the field

for the proposed project as at present the farmers prefer to put back the straws to the

paddy field since it entitles them to a government subsidy on fertilizer. However, a recent

research study carried out by the University of Peradeniya7, Kandy has indicated that

around 15% of straw generated is sufficient to be returned to the field and that excessive

addition could affect the yield adversely and also resulting environment effects due to

GHG emissions. However in light of the situation in Sri Lanka straw may not be available

for the project.

7Source: Mitigation of emission of green house gases from submerged rice fields through bio- butanol production;

Wijewardhana, H.V.P., Bandara, J.M.R.S, De Silva, I.M.B.M, Silva, K.F.S.T.




Figure 2.8: Paddy Straw in Buttala Division (2005 – 2008)

Figure 2.9: Paddy Straw in Monaragala Division (2005 – 2008)

2.1.1.3 Characteristics of Paddy Husk

In addition to the quantification of the husk, four trials were conducted to identify

characteristics such as bulk density, moisture content and ash content for four different

samples of husk collected from the field. The results of the analysis of the 4 samples are

given below.




Table 2.13: Results of Sample Analysis – Paddy Husk

Test 1: Ash Content Test 2: Moisture Content Sample Weight

Dry Weight Ash Content

Wet Weight

Dry Weight

Moisture Content

Bulk Density

Residue Sample (Grams) (Grams) (Grams) (%) (Grams) (Grams) (%) kg/m3 Rice Husk New 15 13.58 2.34 17.2 25 22.63 0.095 130 Red Rice Husk 10 9.04 1.88 20.8 30 27.13 0.096 NA Rice husk 2 Seasons Old (Top Layer) 10 8.33 1.82 21.8 30 24.99 0.167 110

Pad

dy H

usk

Rice Husks 2 Seasons Old (Under Layer) 10 5.14 1.51 29.4 40 20.55 0.486 160

The bulk density of husk, for which samples were taken covered two varieties of new

husk and two samples of two seasons old husk, one from the surface of the pile (which

were observed to be relatively dry) and one from inside the pile (which were observed to

be relatively wet).

The results as indicated above show that bulk densities of the residues to vary from 110 –

160 kg/m3. The variations of these figures can be attributed to the moisture content of the

husk, higher the moisture content, higher the bulk density. Therefore moisture contents of

the samples taken from the three points were measured at the laboratory which revealed

the values to be 9.52 – 9.6% (New husk) and 16.70% (2 seasons old husk from surface)

and 48.63% (2 seasons old husk from inside the pile).

Generally, the moisture content value for new husk is in line with the data presented in

Table 2.32 (moisture content of new paddy husk is within the range of 8.5 – 12.5 %).

The moisture content of 2 season’s old husk (which was exposed to the environment) was

undeniably higher. This could be attributed to absorption of water during raining periods

and through absorption through the air. In the case of the sample taken from within the

pile, there is a little chance for evaporation of absorbed water, resulting in very high

moisture content. A moisture content of 16.7% is not an issue for combustion, though




there will be a reduction in net calorific value. However, over 20% moisture content

would result in difficulties in combustion. Such husk would require pre-drying.

The ash content was measured for four samples collected and the contents on dry basis

were found to be 17.2% 20.9% for the new husk samples and 21.8% (2 seasons old husk

from surface), and 29.4% (2 seasons old husk from inside the pile). This confirmed that

rice husk has considerably high ash content when compared with other biomass resources.

This data too is in agreement with the general data presented in Table 2.34 of this report.

The high ash content values for the old paddy husk could be attributed to the decaying of

biomass material due to higher moisture content. The difference in the ash content of the

two samples taken from newly generated paddy husks could be attributed to the

difference in paddy variety.

2.2 Wood Based WAB Generation

2.2.1 Quantification of Wood Based Residues

Another major source of WAB in the project areas is wood based residue such as sawdust,

shavings, wood chips and off cuts. Sawdust, though not falling under agro-residue

category, is considered a waste agricultural biomass as per UNEP Guidelines.

Around 12% of the input log is wasted as saw dust during milling operations while only

34% is converted into sawn wood/timbre planks. According to literature the RPR value of

saw dust is considered to be around 0.35 8. In Sri Lanka, there are more than 4000 saw

mills including pit-sawing units in operation. The number of major sawmills is about 380

and minor saw mills is about 500. In addition there are 680 mills owned by furniture

manufacturers. The rest of the producers use manual sawing techniques and operate at

micro-small level. Around 55% of the sawn wood is produced at major sawmills, with an

average annual output of 750 m3 per mill9 .

8 Assessment of sustainable energy potential of non-plantation biomass resources in Sri Lanka. Perera K.K.C.K.,

Rathnasiri P.G., Senarath S.A.S., Sugathapala A.G.T., Bhattacharya S.C., Abdul Salam P. 9 Forestry Sector Master Plan - FSMP (1995). Ministry of Forestry and Environment, Government of Sri Lanka.




A number of sawmills are in operation, in both Monaragala and Buttala Divisions. During

the data collection study, five sawmills were contacted (four in Buttala and one in

Monaragala), to carry out the survey. However many mill owners were not forthcoming

with information regarding quantities and types of wood processed as cutting and

processing wood is a sensitive issue in Sri Lanka. The information received was not

sufficient to estimate the total generation of sawdust within the regions.

The team did however manage to get information from, two mills (refer Annex VI) from

which the estimations of wood residues were made. Accordingly the first mill (Ref. No.:

WM 34) was estimated to generate 315 t/yr of sawdust while the second mill (Ref. No.:

WM37) generated 378 t/yr of sawdust. In addition to this the second mill was also

generated 617 m3/yr of wood off cuts.

The report on the country status of sawmill industry may be used to verify the above data

and roughly estimate the production of sawdust in the project areas considered.

According the data of the report, average output of the major sawmills is 750 m3/year and,

with a RPR of 0.35, the production of sawdust per sawmill is about 200 t/year. Although

this value is lower than that claimed by the sawmill owners given above, as a

conservative value, it is reasonable to assume, on average, the sawdust generation per

sawmill is 200 t/year.

In the present study, it is decided to consider the sawdust generation in the Buttala DS

division (which has higher number of sawmills compared with Monaragala) is available

for energy applications. Then, with the average of 200 t/year/sawmill, total generation of

sawdust in four mills is estimated to be around 800 t/year.

The estimation of future generation potential of sawdust is far more tedious a task in

comparison to paddy husk. An earlier study on similar estimations for the entire country

reveals that there is a slow growth of generation of sawdust (about 0.9% annual increase),

which is not very significant to have a major impact. Therefore in the present study it is




assumed that sawdust generations remain the same for the coming years at 800 t/year

within the Buttala DS division.

At present, around 60% of the sawdust generated in Monaragala Division used for

mushroom cultivation and as a domestic heat source (cooking purposes). The excess is

managed by open burning or dumping. In Buttala however reuse of saw dust is as little as

10% of the total generated. However it must be noted that these figures are estimations

based on the information received from the mills.

The project team did make observations that much of the sawdust generated is freely

available in large heaps around sawmills creating disposal and environmental problems.

Though the millers were not forthcoming with information regarding the quantity of

wood processed, many expressed willingness to provide sawdust for the project.

2.2.2 Characteristics of Wood Based Residues

Properties of sawdust and other wood based residues are given in tables 2.32 to 2.37 of

this chapter. In order to estimate the bulk density and moisture content of sawdust, two

sets of samples were tested, one from soft wood and one from hardwood. In addition to

the saw dust a sample of wood chips was also analysed. Given in table 2.14 are the

results of the analysis.

These data show that hardwood sawdust is better than softwood sawdust for energy

applications. In general sawdust is a bulky material with an average density of about 300

to 350 kg/m3.




Table 2.14: Results of Sample Analysis – Wood Residues

Test 1: Ash Content Test 2: Moisture Content

Wet Weight

Dry Weight Ash Content

Wet Weight

Dry Weight

Moisture

Content Bulk

Density

Residue Sample (Grams

) (Grams

) (Grams

) (%)

(Grams)

(Grams) (%) kg/m3

Woo

d C

hips

Wood Chips

10 9.42 0.63 6.7 30 28.27 0.058 NA

Sawdust-Mango

15 10.06 0.43 4.3 25 16.76 0.330 280

Saw

D

ust

Sawdust-Palu

15 10.71 0.31 2.9 40 28.56 0.286 344

2.3 Sugarcane Based WAB Generation

Table 2.15: Sugar Cane Extent & Production in Monaragala District (2000 – 2010)10

Monaragala District Extent Production Yield

Year (Hectares) (Tons) T/ha

2000/2001 17,523 975,126 55.65 2001/2002 17,097 945,069 55.28 2002/2003 16,251 983,928 60.55 2003/2004 16,256 982,774 60.46 2004/2005 16,620 980,300 58.98 2005/2006 18,429 1,126,341 61.12 2006/2007 13,775 772,414 56.07 2007/2008 12,454 795,695 63.89 Average 16,050.63 945,205.88 59.00

The Monaragala District is well known as a sugar cane growing region in the country.

According to the data available with the Department of Senses and Statistics the extent of

land used for Sugarcane cultivation in Monaragala District has gradually reduced over the

past several years. The average annual yield according to this data has been around 59

t/ha.

10Source: http://www.statistics.gov.lk/agriculture/hcrops/index.html




According to data collected from the Divisional Secretariat office of Buttala The total

land use for sugarcane cultivation in 2007/2008 has been 16,447 hectares. Out of this

total area the Pelwatta Sugar Plantation owned extent is around 9,000 hectares.

According to literature the total land use for sugarcane cultivation and production is as

follows.

1/4th of Total land – Virgin Crop

1/4th of Total land – Rattoon Crop

1/4th of Total land – 2nd Rattoon Crop

1/4th of Total land – Land under preparation

Therefore only ¾ of the total land area produces crops at any given time.

Sugarcane Production in Monaragal District

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

2000

/200

1

2001

/200

2

2002

/200

3

2003

/200

4

2004

/200

5

2005

/200

6

2006

/200

7

2007

/200

8

Year

Ext

ent/P

rodu

ctio

n

50.00

52.00

54.00

56.00

58.00

60.00

62.00

64.00

66.00

Yie

ld

MonaragalaDistrict Extent(Hectares)

MonaragalaDistrictProduction(Tons)MonaragalaDistrict YeildT/ha

Figure 2.10: Sugarcane Production in Monaragala District (2000 – 2008)

According to FAO11 the sugar cane yield for Sri Lanka is calculated to as 57 t/ha which is

within the yield figures given in table 2.15.

11 Source: http://www.fao.org/DOCREP/004/AD452E/ad452e2e.htm




According to data from Pelwatta Sugar 12 “Out growers” yield is around 42 t/ha,

“Settlers” yield is 38 t/ha while the “Nucleaus” yield is 57 t/ha. Therefore the average

yield for Pelwatta can be taken as 42 t/ha (based on weighted averaging).

The project team used the yield as per the data taken from the Department of Senses and

Statistics as it matches the figures from FAO. Accordingly the total yield of sugar cane is

estimated to be 946,950 Tons. The Sugar yield in Pelwatta is estimated to be 0.09 13 Tons

per ton of sugar cane processed.

Table 2.16: Sugar Cane Yield in Pelwatta Sugar Industries PLC 12

Sugar Cane Sugar Yield

Year Tons Tons Tons 2003 271,512 23,892 0.088 2004 498,222 42,442 0.085 2005 469,818 40,152 0.085 2006 473,298 39,141 0.083 2007 404,246 34,750 0.086 2008 238,214 18,982 0.080

Average 392,552 33,227 0.084

The data presented in table 2.16 is in line with the figure of 0.09 given in the above

paragraph. Therefore using the above figures a yield value of 0.085 is used to calculate

the total sugar production.

Total Sugar Production in the area is estimated to be 80,500 Tons. Taking these figures

into account the generation of Barbojo and Bagasse can be estimated using RPR values.

The RPR values taken for the calculation of Barbojo is 0.3 (@ 50% moisture content) and

Bagasse 0.29 (@ 50% moisture content)14. Therefore Barbojo generation is estimated to

be 24,150 Tons while Bagasse is estimated to be around 23,345 Tons.

12 Source: http://www.lankabusinessonline.com/fullstory.php?nid=378291245 13 Personal Communication – V. R. Sena Peiris Former Chief Engineers, Kantale Sugar Industry. 14 Source: Assessment of Sustainable Energy Potential of Non-plantation Biomass Resources in Sri Lanka, Journal of Biomass and Energy, Volume 29 (2005)




However the study revealed that none of the Barbojo or Bagasse generated by the

plantations and mills is available for the project as the Barbojo is left on the field as

fertilizer while the Baggase is used as an alternative fuel source by the Sugar mills and

the small scale jaggery mills.

2.4 Corn Based WAB Generation

Given in table 2.17 is the corn production data collected from the Monaragala Agrarian

Services Centre. Accordingly the yield in Monaragala has decreased from 1.52 t/ha in

2006/2007 to 0.74 t/ha in 2008/2009. Similarly Buttala too has seen a reduction in yield

from 2.11 t/ha in 2006/2007 to 1.97 t/ha in 2008/2009. Based on this data the yield of for

maize can be taken as 1.08 for Monaragala Division and 2.12 for Buttala Division.

However according to the data collected from the department of Statistics the average

yield is 1.72 for the entire district.

Based on the data and the RPR value (Maize Stalks) of 2 given in the UNEP guidelines

the total estimated waste stalks is calculated to be 884 Tons in Monaragala and 2,174

Tons in Buttala. Taking the RPR value for cob generation as 0.27 14 the total waste cob

generation for Monaragala and Buttala Divisions is calculated to be 119 Tons and 293

Tons respectively.

Table 2.17: Corn (Maize) Extent & Production in Monaragala and Buttala D.S. Divisions (2007 – 2009)

2006/2007 2007/2008 2008/2009 Extent

(Hectares) Production

(Tons) Extent


(Tons) Extent


(Tons)

D.S.

Division Yal

a

Mah

a

Tot

al

Yal

a

Mah

a

Tot

al

Yal

a

Mah

a

Tot

al

Yal

a

Mah

a

Tot

al

Yal

a

Mah

a

Tot

al

Yal

a

Mah

a

Tot

al

Monaragala 2

357.

3

359.

3

192.

4

353.

2

545.

6

8.09

441

449.

09

6 436

442

NA

596.

5

NA

NA

442.

2

NA

Buttala 36

468

504

315

750.

75

1,06

5.7

26.3

524

550.

3

52

1036

1087

NA

502

NA

NA

992.

8

NA




It must be noted however that since the farms are scattered and the quantities are

considerably small, the collection of this waste will not be viable for the project.

Table 2.18: Corn (Maize) Extent & Production in Monaragala District (2007 – 2009)15

Monaragala District Extent Production Yield

Year Yala Maha Total Yala Maha Total T/ha 2001 177 3,148 3,325 238 4,375 4,613 1.39 2002 161 2,889 3,050 219 4,075 4,294 1.41 2003 359 4,479 4,838 474 5,952 6,426 1.33 2004 190 4,576 4,766 169 6,194 6,363 1.34 2005 333 4,807 5,140 297 6,289 6,586 1.28 2006 487 5,027 5,514 435 6,593 7,028 1.27 2007 539 6,169 6,708 796 10,199 10,995 1.64 2008 578 8,704 9,282 1,573 17,999 19,572 2.11 2009 NA 9,888 9,888 NA 24,452 24,452 2.47

Average 353 5,521 5,835 525.1 9,570 10,037 1.72

2.5 Banana Based WAB Generation

The other main biomass source included in the study is Banana waste from a large scale

farm producing export quality Cavendish bananas. The farm and its processing facility

are situated in the Buttala D.S. Division. While the project team did not get necessary

permission to carry out a data collection exercise at the farm the following data was

provided to the team.

• Maximum daily capacity:

13 kg ×1300 packs + 7 kg ×800 packs + 13 kg ×500 packs (For local market)

• Average daily capacity:

3 kg ×1000 packs + 7 kg ×500 packs + 13 kg ×400 packs (For local market)

• Average waste generation:

15,000 kg/day

15 Source: http://www.statistics.gov.lk/agriculture/hcrops/index.html




According to the information provided the company a large quantity of fresh banana with

slight damages are rejected and as such are disposed off. The amount of waste banana is

estimated to be about15 t/day (information provided by the company and not verified).

The management of the company is looking for possibility of using banana waste for

generation of biogas as a mean of waste management. However, more details are needed

to predict the biogas production potential and its viability.

2.6 Market Waste

The local authorities in the D.S. Divisions considered under the present study showed

keen interest on using market waste for any useful application (e.g. biogas generation), as

at present the waste is managed through open dumping. More details are needed in terms

of quantity of collection, composition, and generation pattern, in order to estimate the

potential. According to figures provided by the local authorities around 544 Tons of

market waste is generated from both D.S. Divisions each year.

2.7 Panicum maximum

Panicum maximum is a tufted perennial grass found in abundance in both D.S. Divisions.

This grass variety is often considered a nuisance in the areas due to its growth and spread

patterns. Locally it is known as Ginihirassa.

During data collection the project team interviewed people from the areas who have

stated that controlling its growth has been a major issue for them. The plant propagates

profusely through seeds which are dispersed by wind, birds, flowing water or as a

contaminant. The seeds can survive long periods of drought. Fire will sweep through

stands of this grass but it regenerates rapidly from underground rhizomes.

As Guinea grass is reasonably palatable, spread is minimal or slow under grazed

conditions. It is a very effective colonizer in un-grazed areas, particularly where some

form of soil disturbance has occurred. The plant will grow well even under trees because

it is shade-tolerant. The grass grows in most soil types providing they are well-drained,

moist and fertile although some varieties are tolerant of lower fertility and poorer




drainage. The species is generally intolerant of water logging or salinity. The grass

spreads in areas receiving 1,000 to 1,700 mm of rainfall and having a dry season not

exceeding 4 months. Varieties are mostly grown in areas with annual rainfall above

1,000 mm. Drought tolerances vary among cultivars, although generally they do not

tolerate dry periods longer than 4 or 5 months. It is tolerant of short term flooding by

moving water. Some strains prefer wet situations. An annual temperature of 12.2 to 30°C

is suitable for growing.

2.7.1 Quantification of Panicum maximum

The Project appointed an external consultant to provide the necessary Quantification and

Characterization data on the grass. Following a study, the consultant submitted a Report

to the project team. The following data information is based on this report. (Refer Annex

V for Report).

According to the table 2.18 the grass grows in Open Grasslands, Open Forests and is

grown through Silviculture16 in Monaragala and Buttala D.S. Divisions. Though the

grass is distributed in large areas in both regions the actual extent is less. This is because

in grassland and open forest areas the grass grows in combination with other plants and

tree varieties. However lands in which the grass is grown under silviculture can be taken

as the actual extent. The estimated actual land extent with Panicum maximum in both D.S.

Divisions is around 5,095 ha.

Figures 2.11 and 2.12 clearly indicates that land with properly managed growth of the

grass (silviculture) is quite small when compared to the open forest areas.

Seed production is estimated at approximately 0.5 million seeds per hectare. There is a

great potential for the survival of this grass in Monaragala district because its ecology is

suited very much for its growing. The number of livestock farmers is also increasing and

the use of the grass as a fodder is becoming more popular in the area. On the other hand

the low density of population per unit area as compared to Sri Lanka in general and other

16 Silviculture is the art and science of controlling the establishment, growth, composition, health, and quality of forests to

meet diverse needs and values of the many landowners, societies and cultures.




districts are also a key factor for the survival of Guinea grass in the area. The forest cover

in Monaragala district has not change significantly during past years when compared with

other districts. Therefore, it is safe to assume that the area covered by this grass will also

not change during coming years.

Table 2.19: Extent of Land with Panicum maximum

Grasslands Open Forests Silviculture Total Land with

Grass

Actual Extent

Land with

Grass

Actual Extent

Land with

Grass

Actual Extent

Land with

Grass

Actual Extent

Land Use

ha ha ha ha ha ha ha ha Thanamalwila 4,462 753 6,988 2,445 3012.8 3,013 14,463 6,211 Siyambalanduwa 446 111 8,429 2,528 - - 8,875 2,639 Bibila 1,593 398 5,679 1,703 - - 7,272 2,101 Monaragala 90 23 6,928 2,078 - - 7,018 2,101 Badalkumbura 720 180 2,162 649 30.2 30 2,912 859 Buttala 353 88 8,193 2,457 449.1 449 8,995 2,994 Madulla 534 136 13,150 3,495 - - 13,684 3,631 Medagama 347 87 7,516 2,254 - - 7,863 2,341 Katharagama 13 3 8,828 2,648 3378.7 3,379 12,220 6,030 Wellawaya 3,733 933 18,199 5,459 1331.7 1,332 23,264 7,724 Sevanagala 4,780 1,195 828 248 432.1 432 6,040 1,875

Based on information collected from livestock farmers, it was revealed that the grass is

the sole feed for cattle and buffaloes in the area. It matures very fast and therefore,

farmers feed their animals mainly at mid / post bloom stage. Animals prefer to graze

leafy portion and normally reject the stem portions.

Figure 2.11: Extent of Panicum maximum Growth in Monaragala D.S. Division




Figure 2.12: Extent of Panicum maximum Growth in Buttala D.S. Division

The total dry matter yield from the harvesting land area can be calculated using the data

given in Table 2.20. This yield is given for each D.S. Division in the district in Table 2.21.

Table 2.20: Production Potential of Panicum maximum in Sri Lanka

Dry Matter Yield Growth and Harvesting Method

(kg/ha/yr)

Good Management: At 45d cutting interval and 0.60 x 0.75 m spacing under

12,000 to 15,000

Normal Management: At 45d cutting interval and 0.60 x 0.75 m spacing under

10,000 to 12,000

No Management: At 45d cutting interval under roadside and natural grassland conditions

8,000 to 10,000




Table 2.21: Expected Dry Matter Yield of Panicum maximum According to D.S. Division

Dry Matter Yield

Gra

ssla

nds

Ope

n F

ores

ts

Sub

tota

l

Silv

icul

tur

e

Tot

al

No

M

anag

eme

nt (

9 t/h

a/yr

)

With

M

anag

eme

nt (

11

t/ha/

yr)

Tot

al

Land Use

(Hectares) t/yr t/yr t/yr

Thanamalwila 753 2,445 3,198 3,013 6,211 28,782 33,141 61,923 Siyambalanduwa 111 2,528 2,639 - 2,639 23,751 0 23,751 Bibila 398 1,703 2,101 - 2,101 18,909 0 18,909 Monaragala 23 2,078 2,101 - 2,101 18,909 0 18,909 Badalkumbura 180 649 829 30 859 7,461 332 7,793 Buttala 88 2,457 2,545 449 2,994 22,905 4,940 27,845 Madulla 136 3,495 3,631 - 3,631 32,679 0 32,679 Medagama 87 2,254 2,341 - 2,341 21,069 0 21,069 Katharagama 3 2,648 2,651 3,379 6,030 23,861 37,165 61,026 Wellawaya 933 5,459 6,392 1,332 7,724 57,528 14,649 72,177 Sevanagala 1,195 248 1,443 432 1,875 12,987 4,754 17,741

Accordingly the dry matter from Silviculture lands is calculated as average 11 Tons per

hectare per year while dry matter from grassland and open forests was calculated on 9

Tons per hectare per year. Using these two figures dry matter available from each D.S.

Division was calculated and is presented in table 2.20. The total dry matter yield expected

per annum is 46,754 Tons.

Around 90% of this quantity comes from unmanaged lands where the grass grows “wild”.

The stem and the leaves of the grass are covered in bristly hair which makes it difficult to

handle. Many people that the project team spoke to said that the grass was difficult to cut

as it was considered to cause skin irritations. Therefore it is safe to assume that it will not

be possible for the project to get as much as the estimated dry matter. In addition to this

some parts of the grasslands in question will have to be left untouched to conserve the

natural habitat. Therefore it is safe to estimate that out of the total dry yield of 46,754




Tons at 30% collection efficiency (50% remains in the field, 20% for other uses) only

14,026 Tons of dry matter will be available for the project.

If however a reasonable price can be allocated to harvest the grass this problem can be

overcome. In addition to this introduction of a technology which makes harvesting easier

will also increase access to grass.

2.7.2 Characteristics of Panicum maximum

Table 2.22 Proximate & Chemical Composition of Panicum maximum (% / DM) Pre-bloom Stage Late Bloom Stage

Characteristics 2-3 Weeks (After 4 Weeks)

Moisture 79.00±3.4 70.00±3.5

Dry Matter 21.00±1.2 30.00±1.3

Crude Protein 9.0±0.6 3.0±0.2

Lignin 4.00±0.14 7.00±0.11

Cellulose 24.00±1.2 31.00±2.1

Hemi-cellulose 18.00±0.97 22.00±1.1

Total Digestible

Nutrients

55.2±2.2 34.9±2.4

Neutral Detergent Fibre 52.00±2.3 60.00±3.4

Acid Detergent Fibre 30.00±1.8 38.00±1.8

Ether Extract 7.00±0.24 7.2±0.31

Ash 12.00±0.78 13.00±0.67




Per 100 g, the forage is reported to contain 2,090 mg of Calcium and 590 mg Phosphorus.

The grass is rich in carotene (24–39 mg/100 g) and contains vitamin B1 and C. It also

contains around 23.0–34.6 mg/100 g of tocopherol.

Table 2.22 indicates that the quality of the grass changes considerably with maturity. In

animal production point of view, pre bloom stage is desirable. However, grass is mainly

used by farmers after flowering.

Table 2.23: Analysis of Panicum maximum Ash (per 100 grams of ash)

Nutrients Content

(% from Ach Content)

CaO, 0.71%

P2O5, 0.56%

K2O 2.92%

Na2O 0.41%

MgO 0.45%

Table 2.24: Combustion Characteristics of Panicum maximum Calorific

Value Calorific

Value Calorific

Value Calorific

Value

(J/Kg) (MJ/Kg) (Cal/Kg) (KCal/g)

Pre bloom stage 14,826,859.99 14.82686 3,541,334.668 3.541335

Late bloom stage 14,096,977.34 14.09698 3,367,005.192 3.367005




2.8 Summery of Data Collected - Quantification

Table 2.25: Sources of WAB - Monaragala D.S. Division (Data for 2007/2008)

Type of Source

Number/

Population

Extent

Remarks

Farmlands (Parcels17) (Hectares)

Paddy Yala Maha Yala Maha

(a) Major Schemes 211 211 76 218

(b) Minor Schemes 1,015 1,015 608.6 961.5

(c) Rain Fed 2,376 2,376 1,004.8 2,492.3

Corn N/A N/A 8.9 441

Banana N/A N/A

Other Number (Hectares) Remarks

Markets 3 -

Guinea Grass (Total) N/A 2,101

(a) Silviculture N/A -

(b) Grasslands N/A 23

(c) Open Forests N/A 2,078

Processing

Facilities

Number Remarks

Rice Mills

Small and Medium 20

Large 1

Sugarcane Mills

Small and Medium 2 Treacle and Jaggery

Large 0

Saw Mills

Small and Medium 22

Large 1

17 Parcels = paddy fields




Table 2.26: Sources of WAB - Buttala D.S. Division (Data for 2007/2008)

Type of Source

Number/

Population

Extent

Remarks

Farmlands (Parcels17) (Hectares)

Paddy Yala Maha Yala Maha

(a) Major Schemes 3,839 3,839 4,457 1,843

(b) Minor Schemes 2,134 2,134 2,201 76.5

(c) Rain Fed 302 302 254 105.5

Corn N/A N/A 26.3 524

Sugarcane NA 16,447 Based on data collected from

Buttala D.S. Division

Banana 1 312 Only 1 large scale farm in

area

Other Number (Hectares) Remarks

Markets 2 -

Guinea Grass (Total) N/A 3,082.09

(a) Silviculture N/A 449.09

(b) Grasslands N/A 88

(c) Open Forests N/A 2,457

Processing Facilities Number Remarks

Rice Mills

Small and Medium 105

Large 5

Sugarcane Mills

Small and Medium 5 Treacle and Jaggery

Large 1 Sugar Factory

Saw Mills

Small and Medium 10

Large 4




Table 2.27: Waste Agricultural Biomass Generation in Project Areas (2007 - 2008)

Acreage Harveste

d Generate

d

Source

Crops (Hectares

) (Tons) (Tons)

Method of Determination

Paddy - Husk 1,721 Straw

2,012 8,197

14,345

WAB Calculated based on average RPR given in UNEP guidelines

Corn Stalks 884 Cobs

449.9 442

119

Estimations based on data from Agrarian Services Bureau and WAB calculated based on RPR figures taken from Journal of Biomass and Bio-energy (Volume 29) (2005) Page 199-213

Wood - - 162 Estimated based on average sawdust generated from saw mill. Will not be considered for the present study, as the majority of the saw mills are in Buttala DS division, but the technology may be used. 90% collection efficiency, 10%-other uses

Panicum maximum

2101 - 18,909 Based on report prepared for Panicum maximum (Refer Annex V)

M

onar

agal

a D

.S. D

ivis

ion

Market Waste

- - 364 Estimated based on number of tractor loads collected and 80% of total taken as Biodegradable Market Waste.

Paddy - Husk 5,385 Straw

6,231 25,645

44,879

WAB Calculated based on average RPR given in UNEP guidelines

Corn Stalks 2,174 Cobs

550.3 1,087

293

Estimations based on data from Agrarian Services Bureau and WAB calculated based on RPR figures taken from Journal of Biomass and Bio-energy (Volume 29) (2005) Page 199-213

Banana 312 11,010 4,500 Estimations based on survey

Sugarcane -

Barbojo 24,150

Bagasse

16,050

946,950

23,345

Harvest calculated at 59 t/ha and WAB calculated at RPR figures Barbojo: 0.3 and Bagasse 0.29

Wood - - 648 Considered as a potential source for the present study, 90% collection efficiency, 10%-other uses

Panicum maximum

2,994.1 - 27,845

B

utta

la D

.S. D

ivis

ion

Market Waste

- - 180 Estimated based on number of tractor loads collected and 80% of total taken as Biodegradable Market Waste.




Table 2.28: Waste Agricultural Biomass from Processing Facilities

WAB Gross

Generation

WAB Reused

WAB Disposed

Husk Bran Husk Bran Husk Bran

S

ourc

e

Type

(Tons/Yr) (Tons/Yr) (Tons/Yr)

Method of

Determination of WAB

RM 1 140.4 36 0 36 140.4 0

RM 2 159.7 11.2 0 11.2 159.7 0

RM 3 48 13.5 0 13.5 48 0

RM 4 225 3.6 0 3.6 225 0

RM 5 9 5.7 0 5.7 9 0

RM 6

Pad

dy

225 11.2 0 11.2 225 0

Estimated based on data collected through survey. The husk is not reused and is disposed off through dumping or open burning. The Bran is sold as animal feed.

Saw Dust Saw Dust Saw Dust

Sou

rce

Type


Method of Determination of

WAB

Mon

arag

ala

D.S

. Div

isio

n

WM 34

Woo

d

315 189 126

WAB calculated based on data collected through survey. Reuse of saw dust estimated to be 60% of total generation as waste is taken by mushroom growers as well as for domestic cooking purposes. This was an assumption based on the information given by the sawmill.

WAB Gross

Generation

WAB Reused

WAB Disposed

Husk Bran Husk Bran Husk Bran Sou

rce

Type



WAB

RB 7 1404 378 1123.2 378 280.8 0 RB 8 86.4 29 69.12 29 17.3 0 RB 9 162 54 129.6 54 32.4 0 RB 10 1458 486 1166.4 486 291.6 0 RB 11 130 43.2 104 43.2 26 0

But

tala

D.S

. Div

isio

n

RB 12

Pad

dy

81 27 64.8 27 16.2 0

Estimated based on data collected through survey. The team found that around 80% of the husk taken by brick kilns as a source of fuel and therefore the calculation of WAB Reused and WAB




RB 13 1053 351 842.4 351 210.6 0 RB 14 36.45 5.4 29.2 5.4 7.3 0 RB 15 24.3 3.6 19.4 3.6 4.9 0 RB 16 72.9 10.8 58.3 10.8 14.6 0 RB 17 91.1 13.5 72.9 13.5 18.2 0 RB 18 72.9 10.8 58.3 10.8 14.6 0 RB 19 437.4 64.8 349.9 64.8 87.5 0 RB 20 60.7 9 48.6 9 12.1 0 RB 21 97.2 14.4 77.8 14.4 19.4 0 RB 22 48.6 7.2 38.9 7.2 9.7 0 RB 23 60.7 9 48.6 9 12.1 0 RB 24 24.3 3.6 19.4 3.6 4.9 0 RB 25 182.2 27 145.8 27 36.4 0 RB 26 21.9 3.2 17.5 3.2 4.4 0 RB 27 12.1 1.8 9.7 1.8 2.4 0 RB 28 91.1 13.5 72.9 13.5 18.2 0 RB 29 NA NA NA NA NA 0 RB 30 24.3 3.6 19.4 3.6 4.8 0 RB 31 12.1 1.8 9.7 1.8 2.4 0 RB 32 36.4 5.4 29.1 5.4 7.3

Disposed were based on these figures.

Saw Dust

Off Cuts

Saw Dust

Off Cuts

Saw Dust

Off Cuts

Sou

rce

Type

(Tons/Yr)

(m3/ Yr)

(Tons/Yr)

(m3/ Yr)

(Tons/Yr)

(m3/ Yr)


WAB

WB 37

Woo

d

378 617 38 617 340 -

WAB calculated based on data collected through survey. Reuse of saw dust estimated to be 10% of total generation as waste is taken by mushroom growers only. This was an assumption based on the information given by the Sawmill.

Barb. Bag. Barb. Bag. Barb. Bag.

Sou

rce

Type



WAB

Sugar Factory S

ugar

C

ane

90,270 79,650 90,270 79,650 - -

WAB calculated based on RPR figures given in the guidelines.




Fruits Plant Parts

Fruits Plant Parts

Fruits Plant Parts

Sou

rce

Type



WAB

Banana Packing Facility B

anan

a

4500 NA - NA 4500 NA

WAB Calculated based on figures given by facility – 15 T/day for 300 working days a year)

Note:

RM: Rice Mill Monaragala WM: Saw Mill Monaragala RB: Rice Mill Buttala WB: Saw Mill Buttala

Table 2.29: Waste Agricultural Biomass from Commercial Facilities

WAB

Generation

(gross)

WAB

Reused/

Recycled

WAB

Disposed

Source Type of

WAB


Method of

Determination of

WAB

Mon

arag

ala

D.S

. Div

isio

n)

Markets

1, 2, 3

Fruit and

Vegetable

Waste

364 0 364

Estimated based on

number of tractor loads

collected and 80% of total

taken as Biodegradable

Market Waste.

(But

tala

D.S

.

Div

isio

n)

Markets

1, 2

Fruit and

Vegetable

Waste

180 0 180

Estimated based on

number of tractor loads

collected and 80% of total

taken as Biodegradable

Market Waste.

Note: The National Solid Waste Survey conducted in 2002 by the Ministry of

Environment and Natural Resources in Local Authorities around the country

indicates biodegradable market waste constitutes 80% of the total MSW.




Table 2.30: Overall WAB Quantification

WAB

Generation

(Gross)

WAB

Consumed /

Reused

WAB

Disposed/

Available

Type of

WAB

Waste

Stream (Tons/Yr) (Tons/Yr) (Tons/Yr)

Period of

Availability Remarks

Paddy

Husk 7,106 4,308 2,798 6 to 12 months

In most mills milling happens during

March – May and Sept. – Nov. In

Others the milling occurs throughout

the year.

Paddy

Straw 59,224 17,769 41,455 2 -3 months

Only during the harvesting periods

after the two main seasons of Maha

and Yala. Calculated based on 70%

collection efficiency (15% - return to

field as fertilizer and further 15%- losses)

Corn Cobs 3,058 3,058

Corn

Stalks 412

0 412

2 -3 months

Only during the harvesting periods

after the two main seasons of Maha

and Yala

Banana 4,500 0 4,500 12 months

Barbojo 24,150 24,150 0 12 months All waste generated is reused back in the

field as fertilizer

Bagasse 23,345 23,345 0 Seasonal All Bagasse produced is used as an

energy source by the generators. Nothing

is left over.

Wood

(Buttala) 900 90 810 12 months

Based on the estimation that 90%

collection efficiency, 10%-other uses

Market

Waste 544 0 544 12 months

Panicum

maximum 46,754 - 14,026 12 months

This total quantity may not be

available to the project as certain

areas will have to be untouched to

maintain the natural habitat and

because accessibility will also be

difficult to some areas. Therefore it

is estimated that only 30% of the

total quantity will be available.




2.9 Summary of Data Collected – Characterization

Table 2.31: Visual Characterization

Source Fresh

Waste Stream Visual Observations

Paddy Farms Paddy Straw Long fibres, Loose, dry, moderately rough to touch, Golden brown in colour

Banana Farms Banana Waste Moist, soft to touch, heavy, light green in colour, fresh

Sugarcane Farms Barbojo Dry, moderately rough to touch, brown in colour

Paddy Mills

• Small and Medium

• Large

Paddy Husk

Dry, rough and prickly to touch, small and loose, dusty

Saw Dust Dry, Powder like, soft to touch, colour varies with type of timber, easily airborne

Saw Mills

Wood Chips and Shavings Dry, Hard, colour varies with type of timber, chips are solid, shavings are curved and sliver like

Sugar Mill Bagasse Moist, short fibres, dark brown in colour

Jaggery Mills Bagasse Moist, short fibres, dark brown in colour

Panicum maximum Plant parts Green, Bristly and irritating to skin, bendy




Table 2.32: Composition of Biomass

Approximate Composition (% by weight) Type of Biomass

Cellulose Hemi-cellulose Lignin

Rice Husk18 35 25 20

Straw stalks 40 45 15

42 38 20 Wood • Hardwood • Softwood 45 25 30

Bagasse19 42.96 16.71 11.36

Barbojo

24.00±1.2 18.00±0.97 4.00±0.14

Panicum maximum20

• Pre-bloom Stage (2-3 weeks) • Late bloom Stage (After 4

weeks) 31.00±2.1 22.00±1.1 7.00±0.11

In the attempt to identify the potential use of agricultural residues, it is essential to

recognize their chemical, thermal as well as physical characteristics. The chemical

composition of plant biomass varies among species. Yet, in general terms, most land

biomass is composed primarily of cellulose, hemi-cellulose and lignin (see above table).

Celluloses consist of many sugar molecules linked together in long chains or polymers.

The lignin fraction consists of non-sugar type molecules that act as a glue holding

together the cellulose fibres, and contributes to structural rigidity of plant tissues. In the

case of non-energy applications, mechanical properties such as strength, stiffness as well

as fibre content and related properties too become important.

18 Source: http://www.unu.edu/unupress/unupbooks/80362e/80362E05.htm 19 Source: Compositional Changes in Sugarcane Bagasse on Low Temperature, Long-term Diluted Ammonia Treatment; Misook Kim, Giovanna Aita and Donal F. Day 20 Source: PROJECT ON CONVERTING WASTE BIOMASS INTO ENERGY/MATERIAL RESOURCE Panicum maximum –WILD GUINEA GRASS : Prof. (Mrs.) Thakshala Seresinhe




Table 2.33: Moisture Content

One of the most important characteristics of an agro-residue is its moisture content,

which is usually regarded as an unavoidable nuisance. It increases the weight (or density)

of residues making transportation cumbersome and costly, reduces the amount of net

utilization of heat from combustion (heating value) and enhances putrefaction during

storage. There are two ways of reporting moisture content (MC) of biomass materials:

MC on wet basis (wb) and MC on dry basis (db). MC on wet basis is the amount of

moisture in the biomass expressed as a percentage of total weight of the wet biomass. MC

on dry basis is the amount of moisture expressed as a percentage of weight of the

moisture free biomass. Table 2.33 shows possible ranges in moisture content for selected

biomass resources.

Biomass resource

Percentage Wet Basis

Dry paddy straw 12 – 15

Paddy husk 8.5 – 12.5

Industrial fresh wood chips and sawdust 40 - 60

Industrial dry wood chips and sawdust 10 - 20

Fresh forest wood chips 40 - 60

Chips from wood stored and air-dried several months 30 - 40

Waste wood 10 - 30

Bagasse 50

Barbojo 45

Panicum maximum 19 Percentage Dry Basis Pre-bloom Stage (2-3 weeks) 79.00±3.4

Late bloom Stage (After 4 weeks) 70.00±3.5




Table 2.34: Proximate Analysis of Selected WAB Types (Dry Basis)

Percentage Dry Basis Component

Volatile Matter

Fixed Carbon

Ash Paddy Straw 54.1 24.8 21.1 Paddy Husk 68.8 14.5 16.7 Wood / Sawdust 77 – 87 13 – 21 0.1 – 2.0 Bagasse 74.0 19.3 6.7 Panicum maximum NA NA 12-13 Corn Cob 75.6 15.5 8.9 Peanut Shell 72.8 20.1 7.2

Proximate analysis is the standard test method for evaluating solid fuels, which classifies

the raw material in terms of moisture, volatile matter, ash and fixed carbon content. The

moisture content of biomass is the weight loss observed when it is dried under standard

conditions. Volatile matter is the weight of biomass lost in the form of vapours and gases

when heated in the absence of air under prescribed conditions. Ash is the inorganic

residue left after biomass is burnt under standard condition. Fixed carbon content in terms

of percentage is estimated by subtracting the total percentage of the other three

parameters from 100. Proximate analysis can also be presented on dry basis, i.e. in terms

of volatile matter, fixed carbon and ash.

Proximate analysis thus indicates the percentage of fuel burned in the gaseous and solid

states, and also shows the quantity of non-combustible ash remaining on the fire grates or

ash pit, or entrained with flue gases. Table 2.34 shows the proximate analysis of some

selected biomass materials. Such information provides the furnace designer with

important information for the sizing and location of primary and secondary air supplies,

refractory equipment, ash removal and exhaust handling equipment.

The proximate analysis given in table 2.34 indicates that the main contribution to the total

calorific content of the biomass fuel is from the volatile constituents. This shows the

importance of efficiency in burning the volatile constituents and in extracting heat from

the flames, as the volatile constituents are usually burned as flames.




Table 2.35: Ultimate Analysis of selected fuels (Dry Basis)

Percentage by Weight (Dry Basis) Component

C

H

O

N

S

Ash Paddy Straw 38.3 5.0 35.0 0.6 0.1 21.1 Paddy Husk 38.0 5.6 39.2 0.5 0.0 16.7 Bagasse 46.4 5.4 42.6 0.7 - 6.7 Hard Wood 50.8 6.4 41.8 0.4 - 0.9 Soft Wood 52.9 6.3 39.7 0.1 - 1.0 Panicum maximum NA NA NA NA NA 12-13 Corn Cob 46.2 7.6 42.3 1.2 0.3 2.4 Cotton Stalk 45.3 5.6 45.3 0.5 - 3.4

Ultimate analysis of biomass shows its composition in terms of ash and chemical

elements such as carbon, hydrogen, nitrogen, sulphur and oxygen. Table 2.35 shows the

ultimate analysis of some selected biomass materials. Since biomass materials contain

moisture, the amount which varies depending on storage conditions and the absorbed

moisture is reflected in the forms of additional hydrogen and oxygen, ultimate analysis is

better represented on moisture free basis.

Biomass materials have lower carbon, sulphur and nitrogen but higher oxygen and

hydrogen content than coal. Carbon, hydrogen and sulphur contribute positively towards

the calorific value, whereas oxygen and nitrogen tend to lower it. Presence of oxygen and

hydrogen also tends to lower the yield of charcoal when biomass is pyrolysed. Note that

the nitrogen content of organic matter is a measure of its protein content.

The ultimate analysis is useful in calculating the quantity of oxygen (and thus combustion

air) required to sustain the combustion reactions. It also permits the estimation of the

amount of water formed by burning hydrogen in the fuel. During combustion, heat is

absorbed to vaporise and exhaust this moisture in addition to the inherent fuel moisture.

Consequently the recoverable heat energy of the fuel is reduced. As biomass fuels have

very low sulphur and nitrogen, they produce minimal SOx and NOx pollutants, but

particulate emissions of unburned carbon in the flue gases can present pollution control

problems.




Table 2.35: Energy Content

Biomass Component

Carbon Content (% by weight)

HHV (MJ/kg)

Cellulose 40 – 44 17 Hemi-cellulose 40 – 44 17 Lignin 63 25 Ash 0 0

Direct combustion is the most commonly used and energy efficient means of deriving

useful energy from biomass. Moisture content and particle size are the most important

critical factors affecting combustibility of biomass fuels. Moisture reduces the heat

available from fuel combustion in two ways. Firstly, the initial gross calorific value of

wood is lowered by the presence of water, which does not contribute to the heating value.

Secondly, combustion efficiency is reduced because (i) heat is absorbed in the

evaporation of water in the first stage of combustion and (ii) flame temperature, and

consequently radiant heat transfer, is lowered.

Particle size directly affects the rate of combustion and heat content per unit bulk volume

of the fuel. As biomass fuels burn principally in the gaseous state, the rate of combustion

is proportional to the time it takes for the required heat to reach and ignite volatile

constituents, this in turn is dependent on the exposed surface area per unit volume of fuel.

Theoretically the minimum particle size should be chosen since the total surface area of a

given quantity of fuel is inversely proportional to the square of the average particle

diameter. However the size of voids in the fire-bed decreases as the particle size is

reduced and a point is reached where individual voids become so small that the resistance

to passage of combustion air is unacceptable. Consequently, the volume and velocity of

excess air through the furnace must be increased; which results in the loss of a

considerable amount of heat energy to raise ambient air to exhaust temperature and the

high velocity may cause entrainment of light fuel particles in the flue gases.

Heating value of biomass depends on its composition. Dry woody biomass consists of

cellulose, hemi-cellulose, lignin and ash. Its calorific value can therefore be estimated




from the calorific value and weight fraction of each constituent. Table 2.35 provides

carbon content and calorific value of each component basic component in biomass. Three

different conventions are commonly used in deriving the value of biomass fuels:

(1) Gross Calorific Value (or Gross Heating Value-GHV or Higher Heating Value-HHV),

(2) Net Calorific Value (or Net Heating Value-NHV or Lower Heating Value-LHV) and

(3) Usable Heat Content. Each of these measures is useful to the engineers or scientists

when applied in its correct context.

Table 2.36: Comparison with Other Fuels

Typical Characteristics Fuel HHV

(MJ/kg) TOE kg/m³ GJ/m³ Volume oil

equivalent (m³) Fossil Fuels

Fuel oil 41.9 1.00 950 39.81 1.00 Coal 25.0 0.60 1000 25.00 1.59 Biomass Fuels

Paddy straw chopped 15% moist. 14.5 0.35 60 0.87 45.75 Paddy straw big bales 15% moist. 14.5 0.35 140 2.03 19.61 Paddy husk 9% moist. 15.1 0.36 130 1.96 20.28 Sawdust 30% moist. 13.3 0.32 350 4.66 8.55 Pellets 8% moist. 17.5 0.42 650 11.38 3.50 Pile wood (stacked, 50%) 9.5 0.23 600 5.70 6.98 Industrial softwood chips 50% moist. 9.5 0.23 320 3.04 13.09 Industrial softwood chips 20% moist. 15.2 0.36 210 3.19 12.47 Forest softwood chips 30% moist. 13.3 0.32 250 3.33 11.97 Forest hardwood chips 30% moist. 13.3 0.32 320 4.26 9.35 Bagasse 50% moist. 8.8 0.21 200 1.76 22.62 Barbojo 45% moist. 9.7 0.23 175 1.70 23.45 Panicum maximum

Pre bloom stage 14.8 NA NA NA NA Late bloom stage 14.1 NA NA NA NA




Biomass resources include a wide variety of materials diverse in both physical and

chemical properties. Depending on the application, these variations may be critical for the

final performance of the system. In particular, some advanced applications require fairly

narrow specifications for moisture, ash content, physical size, etc. Both the physical and

chemical characteristics vary significantly within and between the different biomass raw

materials. However, biomass feed-stocks are more uniform for some of their properties

compared with competing feed-stocks such as coal or petroleum. For example, coals

show HHV ranges from 20 to 30 MJ/kg. However, nearly all kinds of biomass feed-

stocks destined for combustion fall in the range 16-19 MJ/kg. The values for most woody

materials are 18-19 MJ/kg, while for most agricultural residues, the heating values are in

the region of 16-18 MJ/kg.

Some typical characteristics of biomass fuels compared to oil and coal are shown in the

above table. The volume (in m³) required to substitute one cubic meter of oil by each of

the other fuels is also given in the table. Typically bulk density of biomass residues is

relatively lower and therefore the volume oil equivalent is quite high, which is a main

disadvantage of them.