b i o m a s s a n d b i o e n e r g y 3 4 ( 2 0 1 0 ) 4 8 3 – 4 8 8

Avai lab le a t www.sc iencedi rec t .com

ht tp : / /www.e lsev i er . com/ loca te /b iombioe

A mathematical model for transporting the biomass tobiomass based power plant

Jagtar Singh a,*, B.S. Panesar b, S.K. Sharma c

a Mechanical Engineering Department, SLIET Longowal, District Sangrur, Punjab, Indiab Project Professional, SCS Engineers, 11260 Roger Bacon Drive, #300, Virginia 20190, USAc Mechanical Engineering Department, NIT Kurukshetra, Haryana, India

a r t i c l e i n f o

Article history:

Received 29 March 2007

Received in revised form

8 August 2009

Accepted 14 December 2009

Available online 20 January 2010

Keywords:

Baled biomass

Briquetted biomass

Spatially scattered

Energy

Transport systems

Collection cost and mathematical

modeling etc

List of abbreviations: hr, Hour; Km, KilomeRupees)}; r, Spatial density (ton km�2).

* Corresponding author. Tel.: þ91 98726 6810E-mail address: j_s_ranu@rediffmail.com

0961-9534/$ – see front matter ª 2009 Elsevidoi:10.1016/j.biombioe.2009.12.012

a b s t r a c t

In Punjab, million of tons of agricultural biomass are being generated every year, but it is

spatially scattered. The spatial distribution of this resource and the associated costs on

collection and transportation are the major bottleneck in the success of biomass energy-

conversion facilities. This paper deals with the mathematical model for collection and

transporting the biomass from fields to biomass based power plant. The unit transport cost

was calculated by using this model. Four systems of transport were conceptualized for two

transport modes (tractor with wagon and truck). Three types of agricultural biomass (loose,

baled and briquetted) were considered for transport analysis. For all modes of transport, it

was observed that unit cost of transport decreases with increase in distance. The transport

cost was least for briquetted biomass as compared to loose and baled biomass.

ª 2009 Elsevier Ltd. All rights reserved.

1. Introduction Agricultural biomass residues have the potential for sustain-

Availability of energy is critical for all round economic, social,

cultural and political developments in developing countries

like India. In India, the energy shortages are increasing day by

day thereby threatening the sustainability of all round devel-

opment of the country. The energy self-reliance has depleted as

our domestic production of petroleum products is below 30%

resulting into increased import bill. A need is now being felt by

scientists, planners and policy makers to find alternate and

dependable energy resource not only to sustain the present

level of development but also to accelerate its growth. Biomass

is one such energy resource that meets all these requirements.

ter; L, Liter; MJ, Mega Jou

1; fax: þ91 1672 280057.(J. Singh).er Ltd. All rights reserved

able production of biofuels and to offset greenhouse gas

emissions [1–3]. The straw and agricultural residues existing in

the waste streams from commercial crop processing plant have

little inherent value and have traditionally constituted

a disposal problem. In fact, these residues represent an abun-

dant, inexpensive and readily available source of renewable

energy resource [4–6]. Due to technological developments and

cost reductions, renewable especially solar, hydro, wind and

biomass energy are gaining momentum. Further, the renew-

able sources, particularly biomass, are less environmentally

destructive than the current fossil fuel sources [7]. Of all the

renewable energy sources, agricultural biomass is the largest,

le (1 MJ ¼ 106 J); m, Meter; US$, US Dollar {1US$ ¼ 47.49 Rs.(Indian

.

dr

b i o m a s s a n d b i o e n e r g y 3 4 ( 2 0 1 0 ) 4 8 3 – 4 8 8484

most diverse and most readily exploitable resource. Bioenergy

technologies provide opportunities for conversion of biomass

into liquid and gaseous fuels as well as electricity [8].

Department of Energy, US is going to replace 30% of current

petroleum consumption by biomass and its products by the

year 2030, various systems capable of harvesting, storing and

transporting biomass efficiently, at a low cost, need to be

designed. A biomass transportation system of cotton gin has

been simulated using discrete event simulation procedure

[9].There are a number of system- related issue associated

with the harvest, storage and transport from on farm storage

locations to centrally located plant. A linear programming (LP)

models for designing an herbaceous biomass delivery system

and for solving the day to day tactical planning problem were

also developed [10]. These efforts were directed towards the

design of a biomass delivery system that considers storage,

scheduling and transportation issues.

In order to reduce industry’s operational costs, as well as to

meet the requirement of raw material for biofuel production,

biomass must be processed and handled in an efficient manner.

Because of its high moisture content, irregular shape and sizes,

low bulk density and spatially scattered biomass is very difficult

to handle, transport, store and utilize in its original form [11].

Densification of biomass into durable compacts is an effective

solution to these problems and it can reduce material waste.

Densification can increase the bulk density of biomass from an

initial bulk density of 40–200 kg m�3 to a final compact density of

600–1200 kg m�3 [12–16]. The availability of agricultural biomass

in Punjab is spatially scattered. The spatial distribution of this

resource and the associate costs of collection and transportation

are major bottlenecks for the success of biomass energy-

conversion facilities. Biomass, being scattered and loose, has

huge collection and transportation costs, which can be reduced

by properly planning and developing the proper methodology. A

study was conducted in 2007–2008 to evaluate the spatial

potential of biomass with geographical information system (GIS)

and a mathematical model for collection of biomass in the field

has been developed. The total amount of unused agricultural

biomass is about 13.73 Mt year�1. The total power generation

capacity from unused biomass is approximately 900 MW. The

collection cost in the field up to the carrier unit is US$3.90 t�1 [17].

This amount of biomass can support to generate electricity 15–

20% of its present installed capacity, but the spatial distribution

of this resource and the associated costs on collection and

transportation are the major bottleneck in the success of

biomass based power plant. To simplify such type of problems,

in thispaper anattempthasbeenmadetodevelop mathematical

model for transporting the biomass from fields to power plant.

O

ro

r

'O' Position of Transport Unit

Fig. 1 – Collection cost analysis.

2. Mathematical models

Mathematical models of costs of biomass collection and

transporting the biomass have been developed. These are

discussed below:

2.1. Model for collection cost

Collection cost is the cost to collect the biomass from the field

in scattered form near the transport unit for its loading. For

manually and reaper harvested field, the collection costs are

to be assumed zero, because biomass already collected at one

location in the field. So collection costs are considered for

combine-harvested field only. Collection costs depend on the

spatial density, unit costs of recovery and capacity of the

transportation units. The collection costs are the sum of total

recovery costs for harvesting biomass and transport costs for

moving the biomass from a loosely spread form to the trans-

port unit. The recovery costs depend on technology used for

biomass recovery. It is assumed that recovery costs are

proportional to the area from where the biomass is recovered.

A mathematical model has been developed for unit collection

cost in the field and presented below:

Let the transport unit be placed at ‘O’ in Fig. 1 and biomass

be recovered from a circular area of radius ‘ro’ surrounding the

transport unit. If qc is load capacity of transport unit, r is

spatial density of biomass availability, Cr is the biomass

recovery costs, US$ km�2; and Ct is the unit costs of biomass

transport (manual or machine transport) from place where it

is lying to the transportation unit, US$ km�1 t�1.

qc ¼Z ro

0

r2prdr ¼ prr2o0ro ¼

ffiffiffiffiffiffiqc

pr

r(2.1)

Total Collection costs of biomass in the field

¼R ro

0 ðCr2pr drþ Ctrr2pr drÞ ¼ prr2o

hCr

rþ 2

3Ctro

�

¼ qc

hCr

rþ 2

3Ctro

� (2.2)

Unit collection cost (Cc) is defined as the ratio of total collec-

tion cost to the carrying capacity of transport unit (qc).

rCc ¼ Cr1rþ 2

3Ctro (2.3)

From Equation (2.3) it is clear that unit cost of biomass

collection (Cc) is a function of r, Cr and Ct. The value of Cr

considered in the present model is 1862.43 US$ km�2 [17].

2.2. Model for transportation

Transport analysis is to find out the best modes and systems

of transport for different distances of transport. Two modes of

transport are considered for analysis. These are tractor with

b i o m a s s a n d b i o e n e r g y 3 4 ( 2 0 1 0 ) 4 8 3 – 4 8 8 485

wagon and truck. The four systems have been considered for

transport analysis, which are discussed as below.

2.2.1. Transport systemThe complete transport operation can be divided into smaller

elements such as loading, travelling, unloading, stacking and

return journey. During operation we come across idle time,

may be of labour (in case travelling time is large) or of the

mode of transport (in case loading or unloading/stacking time

is more and the mode is waiting for the completion of the

activity). The capacity of the system can be increased and cost

per ton can be decreased if this idle time is reduced. Keeping

these points in mind, the following four systems are

conceptualized.

2.2.1.1. System I. In this system multiple complete unit of

mode of transport is used. It carries labour with it. As such the

same labour is used for loading, unloading and stacking. In

this case the labour is always idle during travelling.

2.2.1.2. System II. This system is the same as System I but

labour is divided for loading and unloading sites in the ratio of

loading time and unloading plus stacking time. No labourer is

carried during travelling. It will reduce idle time of labour.

2.2.1.3. System III. It consists of single power unit (Tractor)

with multiple carriers (Trailers, wagons). Labour is divided for

loading and unloading sites in the ratio of loading time and

unloading plus stacking time. It will reduce idle time of power

unit as well as the labour.

2.2.1.4. System IV. It has multiple power units with two extra

carriers. Labour is divided in the same manner as in System II.

System III and IV are not feasible in case of trucks and hence

are not used for them.

System I and II are considered in case of truck transport

whereas all four systems are used in tractor with wagon. The

assumptions used for computing fixed and operational costs

of the equipment are given in Appendix ‘‘A’’ (Table A.1, A.2)

[21]. The analysis will be carried out for the transport distance

upto 40 km (in steps of 2 km) and for different number of

labourers available. The division of labour for loading and

unloading sites is made in the ratio of loading time and

unloading plus stacking labour requirements. The analysis

was conducted [18,21] with laboureres varying from 1 to 50 for

system I and from 2 to 50 for systems II, III, IV the same is used

in the present study.

2.3. Formulation of model for transport costs

Transport cost is the major contribution in the total cost for

storage and handling of biomass for power generation. It is

directly proportional to the total cost. It can be reduced by

optimum location of collection centres and power plants. It

has two main components, one is unit cost of transport and

second is the transportation cost from collection centres to

power plant. It also depends upon the type of biomass to be

transported. It can be determined with the mathematical

models. The following mathematical relationships are devel-

oped for determining unit cost of transport (Ct):

FCPL ¼ FCPH� TTPLþ FCCH� TTCL (2.4)

OCPL ¼ ðOCPHþOCCHÞ �OTPL (2.5)

LCPL ¼ LWPH� LUPL� TL (2.6)

VCPL ¼ ðT5� FCULþ T4� FCLÞ �DPR� ð1þ LUBC=100Þor D� ðFCULþ FCLÞ �DPR� ð1þ LUBC=100Þ ð2:7Þ

Ct ¼ ðFCPL� CCPLþ LCPLþ VCPLÞ=ðQcDÞ (2.8)

LUPL ¼ T1þ T2þ T3þ T4 for System I

MaxððT1þ T2þ T4þ T5Þ; ðT2þ T3ÞÞ for System II

MaxððT1þ T4þ T5Þ; ðT2þ T4þ T5Þ; ðT2þ T3ÞÞfor System III

MaxðT1; ðT4þ T5Þ; ðT2þ T3ÞÞ for System IV (2.9)

T1 ¼ QcLl=TLl T2 ¼ QcLul=TLul T3 ¼ QcLst=TLst T4

¼ D=VL T5 ¼ D=VUL (2.10)

TLl ¼ TLul ¼ TLst ¼ TL for System ITLl ¼ TL� Ll=ðLl þ Lul þ LstÞ;TLul ¼ TL� Lul=ðLl þ Lul þ LstÞ for Systems II; III & IVTLst ¼ TL� Lst=ðLl þ Lul þ LstÞ

(2.11)

where, FCPL is the total fixed costs per load, US$; FCPH is the

fixed costs of power unit, US$ hr�1; FCCH is the fixed costs of

carrier, US$ hr�1 (for truck it is zero); TTPL is the total time of

power unit per load, hr; TTCL is the total time of carrier per

load, hr; OCPL is the total operational costs per load, US$;

OCPH is the operational cost of power unit, US$ hr�1; OCCH is

the operational cost of carrier, US$ hr�1 (for truck it is zero);

OTPL is the travelling time per load, hr; LCPL is the labour cost

per load, US$; LWPH is the labour wages per man-hour, US$.;

LUPL is the labour use time per load, hr; TL is the total number

of labourers employed; TLl, TLul, TLst is labourers used for

loading; unloading and stacking respectively; Ll, Lul, Lst is

labour requirements for loading, unloading and for stacking,

man hr ton�1; VCPL is the variable costs per load, US$; D is the

transport distance, km; FCUL is the diesel consumption of

unloaded units, L hr�1 for tractor and L km�1 for truck; FCL is

the diesel consumption of loaded units, L hr�1 for tractor and

L km�1 for truck; VUL is the travel velocity of unloaded units,

km hr�1; VL is the travel velocity of loaded units, km hr�1; DPR

is the price of diesel, US$ L�1; LUBC is the lubricant costs, % of

fuel costs; Ct is the units cost of biomass transport,

US$ ton�1 km�1; T1, T2, T3 is the time for loading, unloading

and stacking, hrs; T4 is the travel time from loading site to

unloading site, hrs; and T5 is the travel time from unloading

site to loading site; hrs. The first form of Equation (2.7) was

used for tractor while the second form was used for truck.

3. Methodology

The primary data has been collected from the School of Energy

Studies for Agriculture, Punjab Agricultural University

a Tractor with Wagon

0.40

0.60

0.80

1.00

1.20

1.40]1-^mk1-^not

$SU[tsoctrops

System ISystem IISystem IIISystem IV

b i o m a s s a n d b i o e n e r g y 3 4 ( 2 0 1 0 ) 4 8 3 – 4 8 8486

Ludhiana, Punjab (India) and secondary data reported in the

literature have been used for the analysis. Models on cost of

transportation and cost of biomass recovery are implemented

on computer. The primary data on biomass recovery, mois-

ture content, storage and utilization were collected under

various projects in the School of Energy Studies for Agriculture

[18]. The secondary data on spatial variations in area and

production of various crops has been collected from various

statistical abstracts of Punjab [19,20].

0.00

0.20

0 5 10 15 20 25 30 35 40

Distance, km

narttinU

b Truck

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0 5 10 15 20 25 30 35 40

Distance, km

]1-^mk1-^not

$SU[tsoctropsnarttin

U

System ISystem II

Fig. 2 – Unit Transport Cost for Transporting Loose Biomass

by different Transport Modes.

a Tractor with Wagon

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 10 20 30 40Distance, km

]1-^mk1-^not

$SU[tsoctropsnarttin

U

System ISystem IISystem IIISystem IV

b Truck

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 10 20 30 40Distance, km

]1-^mk1-^not

$SU[tsoctropsnarttin

U

System ISystem II

Fig. 3 – Unit Transport Cost for Transporting Baled Biomass

by Different Transport Modes.

4. Results and discussion

Models on costs of biomass recovery and transport were

implemented on computer. The results are discussed below:

4.1. Biomass recovery cost

The effects of spatial density of biomass and carrying capacity

of the transport unit have been presented in the previous

study [17] and same was implemented in the present study.

The collection cost has been calculated with equation no (2.3)

with a variation of carrying capacity of the transport unit from

1 to 5 t and biomass density of the collection area vary from

100 t km�2 to 1 Kt km�2. It is shows that unit collection costs

decreases with increase in spatial density of biomass. It is also

observed that marginally increase in unit cost of biomass

collection when carrying capacity was increased from 1 ton to

5 ton. The main reason of unit collection cost increases while

carrying capacity increases from 1 to 5 t, because larger

carrying capacity requires larger quantity of biomass, which is

to be collected from larger radius. When radius increases then

definitely unit collection cost increases in the field. Further, it

is observed that decrease in unit collection 50%, 65%, 75%,

80%, 83%, 85%, 87%, 89% and 90% when spatial density

increased to 2, 3, 4, 5, 6, 7, 8, 9, and 10 – folds.

4.2. Transportation analysis

The unit transport cost of biomass straw (Ct) is calculated by

using the mathematical equation mentioned earlier (Equation

(2.8)). Four systems of transport are conceptualized for two

transport modes. The results of transport analysis are pre-

sented in Figs. 2–4. For all modes of transport, it is observed that

the unit cost of transport decreases with increase in distance. It

is observed that the cost is least for briquetted biomass and

maximum for the loose, for obvious reasons of higher carrying

capacity due to higher bulk density of the briquetted biomass.

The unit transport costs with tractor with wagon is the highest

followed by truck amongst all systems of transport. The least

unit cost of transport is for truck in case of briquetted biomass

amongst all systems of transport. The detailed analysis of

transportation for loose, baled and briquetted biomass has

been conducted and results of the same are as follows:

� Loose biomass:

The unit cost of transport for transporting the loose

biomass has been determined by considering four systems

and two transportation modes (Tractor –Wagon & Truck). It is

a Tractor with Wagon

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40Distance, km

-^mk1-^not

$SU[tsoctropsnarttin

U1]

System ISystem IISystem IIISystem IV

b Truck

0.00

0.05

0.10

0.15

0.20

0.25

0 5 10 15 20 25 30 35 40Distance, km

Uni

t tra

nspo

rt co

st [U

S$ to

n^-1

km^-

1]

System ISystem II

Fig. 4 – Unit Transport Cost for Transporting Briquetted

Biomass by Different Transport Modes.

Table A.1 – Assumptions for transport analysis (PurchasePrice, Life, Capacity etc.).

Sr.No.

Name of theItem

Truck Tractor(35 HP)

Trailer Wagon

1. Purchase Price US$

10 528.50/-

US$.

6317.10/-

US$.

1052.85/-

US$

1579.28/-

2. Life 10 15 15 15

3. Straw Load Capacity (Tons):

a) Loose straw 2.5 – 1.5 2.5

b) Baled straw 5.0 – 3.0 4.5

c). Briquetted

Straw

8.0 – 4.5 6.0

4. Working Speed km h�1:

a) Unloaded 60–70 16.7 16.7 16.7

b) Loaded 45–55 12.5 12.5 12.5

5. Fuel Consumption:

a) Unloaded 0.18 L km�1 2.5 L h�1 – –

b) Loaded 0.22 L km�1 3.5 L h�1 – –

6. Annual

Operators

Wages in US$.

US$ 0.1137/- US$ 885/- – –

Table A.2 – Other assumptions for transport analysis.

Sr. No. Name of the Item US$

1. Price of Diesel fuel, US$L�1 0.64

b i o m a s s a n d b i o e n e r g y 3 4 ( 2 0 1 0 ) 4 8 3 – 4 8 8 487

observed that the highest unit cost of transport in System-I,

while lowest in System-IV with tractor-wagon transport

mode, for all distances. System-III and System-II are sand-

wiched between System-I and System-IV. In case of truck, the

highest unit cost is observed in System-I, while System–II had

lowest for all distances (Fig. 2).

� Baled biomass:

The trend of unit cost and number of units required for

optimum value of unit transport costs for baled biomass, with

transport distance are similar to those for loose biomass

(Fig. 3). However, the unit cost of transporting the baled

biomass is lower than the loose biomass (Figs. 2 and 3).

� Briquetted Biomass:

The trends of unit cost and number of units required for

optimum value of transport costs for briquetted biomass, with

transport distance are similar to those for loose or baled

biomass (Fig. 4). But, the unit cost of transporting the bri-

quetted biomass is lower than the baled biomass and further

lowers than the loose biomass.

2. Lubricant Cost, L�1 2.11

3. Labour wages, US$ hr�1 10.65

4. Salvage value, % age of purchase price 0.23

5. Annual interest on Investment, %age 11

6. Annual Repair & Maintenance cost, %age

of average purchase price

10

(continued on next page)

5. Conclusion

Unit collection cost of biomass depends upon spatial density,

biomass recovery cost and unit transport cost of biomass. The

results of transportation analysis indicate that unit cost of

transport decreases with increasing distances of various

modes and systems of transport. The cheapest mode of

transport of loose biomass up to 30 km distance was tractor-

wagon (System–IV) and beyond 30 km it was truck (System-II).

Similar were the results for transportation of bales and

biomass briquettes except that the distance for which tractor

is the cheapest up to 10 km and the truck (System–II) is suit-

able beyond 10 km. However, truck (System-II) was the

cheapest mode of transport of briquetted biomass for all

distances. The unit collection cost of biomass decreases with

increase in spatial density of biomass.

Acknowledgement

The authors are grateful to Director, School of Energy Studies

for Agriculture, Punjab Agricultural University Ludhiana,

Punjab (India) for their valuable suggestions and discussions

from time to time during the research.

Appendix A

Table A.2 (continued )

Sr. No. Name of the Item US$

7. Annual Housing Charge %age of average

purchase price.

1

8. Annual charges for tax, Insurance etc,

5age of average purchase price

1

9. Annual Availability of machinery, days 300

10. Daily Availability of Machinery, hour 10

11. Depreciation at constant price C�SL

12. Fixed Costs are distributed uniformly

over annual availability

13. Interest on investment is calculated on

average purchase price

Where C¼Average Purchase price (US$.); S¼ Salvage value; L¼ Life

(Years).

b i o m a s s a n d b i o e n e r g y 3 4 ( 2 0 1 0 ) 4 8 3 – 4 8 8488

r e f e r e n c e s

[1] Campbell CA, Zentner RP, Gameda S, Blomert B, Wall DD.Production of annual crops on the Canadian prairies: trendsduring 1976–1998. Canadian Journal of Soil Science 2002;82:45–50.

[2] Sokhansanj S, Mani S, Stumborg M, Samson R, Fenton J.Production and distribution of cereal straw on the Canadianprairies. Canadian Biosystems Engineering 2006;48:3.39–46.

[3] Qingyu J, Yuan-bin H, Bhattacharya SC, Sharma M, Amur GQ.A study of biomass as a source of energy in China. RERICInternational Energy Journal 1999;21(1):1–10.

[4] Amur GQ, Bhattacharya SC. A study of biomass as a source ofenergy in Pakistan. RERIC International Energy Journal 1999;21(1):25–36.

[5] Liu Y, Liu H. High-pressure densification of wood residues toform an upgraded fuel. Biomass and Bioenergy 2000;19:177–86.

[6] Liu R, Huang Y. Structure and morphology of cellulose inwheat straw. Cellulose 2005;12:25–34.

[7] Goldemberg J, Johannson TB, Reddy AKN, Williams RH.Energy for a sustainable world. New Delhi: Wiley EasternLimited; 1988.

[8] World Bank. Development in practice, rural energy anddevelopment, improving energy supplies for two billionpeople; 1996.

[9] Ravula Poorna P, Grisso Robert D, Cundiff John S. Cottonlogistics as a model for a biomass transportation system.Biomass and Bioenergy 2008;32(4):314–25.

[10] Cundiff JohnS,DiasNeil,Sherali Hanif D.A linearprogrammingapproach for designing a herbaceous biomass delivery system.Bioresource Technology 1997;59:47–55.

[11] Sokhansanj S, Mani S, Zaini P, Tabil LG. Binderlesspelletization of biomass. ASAE Annual InternationalMeeting, Tampa Convention Centre, Tampa, FL, July, 2005,17–20, Paper Number 056061, 2950 Niles Road, St. Joseph, MI49085-9659, USA.

[12] Mani S, Tabil LG, Sokhansanj S. An overview of compactionof biomass grinds. Powder Handling & Process 2003;15(3):160–8.

[13] Obernberger, Thek G. Physical characterization and chemicalcomposition of densified biomass fuels with regard to theircombustion behavior. Biomass and Bioenergy 2004;27:653–69.

[14] McMullen J, Fasina CW, Feng Y. Storage and handlingcharacteristics of pellets from poultry litter. AppliedEngineering in Agriculture 2005;21(4):645–51.

[15] Adapa PK, Schoenau GJ, Tabil LG, Arinze EA, Singh A,Dalai AK. Customized and value-added high quality alfalfaproducts – a new concept. Agricultural EngineeringInternational: the CIGR E Journal 2007;IX:1–28 [Manuscript FP07 003].

[16] Adapa Phani, Tabil Lope, Schoenau Greg. Compactioncharacteristics of barley, canola, oat and wheat straw.Biosystems Engineering 2009;. doi:10.1016/j.biosystemseng.2009.06.022.

[17] Singh Jagtar, Panesar BS, Sharma SK. Energy potentialthrough agricultural biomass using geographicalinformation system -a case study of Punjab. Biomass andBioenergy 2008;32(4):301–7.

[18] Panesar BS, Gupta PK, Jain AK, Singh Jagtar. Location ofbiomass collection and conversion facilities: theoreticalaspects. In: Proceedings of workshop on prevention andcontrol due to harvest residue, was held at NIT Kurukshetra,on April 24; 2004. p. 15–37.

[19] Anonymous. Statistical abstract of Punjab, economic advisorto government of Punjab, Chandigarh; 2000.

[20] Jain AK. Correlation models for predicting heating valuethrough biomass characteristics. Journal of AgriculturalEngineering 1997;34(3):12–25.

[21] Jagtar Singh, Optimization of collection and utilization ofagricultural biomass for commercial power generation, Ph.D.Thesis, mechanical engineering department, NITKurukshetra; 2007.