UNEP Characterization of Waste Agricultural Biomass for Energy Applications Training on Technologies for Converting Waste Agricultural Biomass into Energy.

UNEP

Characterization of Waste Agricultural Biomass for

Energy Applications

Training onTechnologies for Converting Waste Agricultural Biomass into Energy

Organized by

United Nations Environment Programme (UNEP DTIE IETC)23-25 September, 2013

San Jose, Costa Rica

Surya Prakash ChandakSenior Programme Officer

International environmental Technology CentreDivision of Technology, Industry and Economics

Osaka, Japan

UNEPInternational Environmental Technology Centre2

Why Characterisation of WAB

Characterization of WAB provides essential information for:

• Selection of WAB2E technology• System design• Assessment of operational

performance• Provides data for tendering


Characterization of waste agricultural biomass

Parameters of characterization• Visual characterization• Moisture content• Chemical Composition• Calorific value• Specific characterization parameters



Visual characterization

Source Waste Stream Visual Observations

Commercial Facilities Fruit and vegetable waste

High moisture (estimated to be 60-80%), sometimes putrified, mixed with packing hay

Corporate Farms Rice husk Clean, stacked in heaps, approximate volume …m3

Jaggery Plants Bagasse Moist waste (estimated moisture 50%), scattered around, some spread on ground for sun-drying, mixed with barbojo

Private farms -- --

-- -- --



Moisture contentTwo ways of reporting

Moisture content on wet basis (MCwb)Moisture content on dry basis (MCdb)

Relationship between MCwb and MCdb

.1 wb

wbdb MC

MCMC

0.0

0.5

1.0

1.5

2.0

2.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Moi

stu

re C

onte

nt

on D

ry B

asis

Moisture Content on Wet Basis



Chemical composition – Ultimate Analysis

Component

Percent by weight (dry basis)

Carbon Hydrogen Oxygen Nitrogen Sulphur Ash

Wheat Straw 48.5 5.5 39.9 0.3 0.1 5.7

Rice Straw 39.2 5.1 35.8 0.6 0.1 19.2

Rice Husk 38.5 5.7 39.8 0.5 <0.01 15.5

Bagasse 46.4 5.4 42.6 0.7 <0.01 4.9

Hard Wood 50.8 6.4 41.5 0.4 <0.01 0.9

Soft Wood 52.9 6.3 39.7 0.1 <0.01 1.0

Corn Cob 46.2 7.67 42.3 1.2 0.3 2.4

Cotton stalk 45.3 5.6 45.3 0.5 <0.01 3.3

Anthracite coal 78.8 2.3 2.5 0.9 0.5 15



Chemical composition – Proximate Analysis

Component

Percent by weight (dry basis)

Volatile Matter(%dry ash free basis)

Fixed Carbon(%dry ash free

basis)Ash

(% dry basis)

Wheat Straw 83.9 16.1 11.2

Rice Straw 80.2 19.8 19.8

Rice Husk 81.6 18.4 23.5

Bagasse 84.2 15.8 2.9

Wood 77-87 13-21 0.1-2.0

Peanut shell 78.4 21.6 7.2

Corn Cob 85.4 14.6 2.8

Cotton stalk 80.0 20.0 5.3

Anthracite coal 5.9 94.1 15.0



Energy Content

Three expressions:

Higher Heating Value (HHV) or Gross Calorific Value (GCV)

Lower Heating Value (LHV) or Net Calorific Value (NCV)

Usable Heat Content

HHV – Total energy generated from combustion including the heat of condensation of water vapor – represents maximum theoretical potential energy

LHV -- Total energy generated from combustion less the heat of condensation of water vapor – represents maximum realizable energy

UHC – LHV less the sensible heat of the combustion products – represents actual usable energy

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UNEPInternational Environmental Technology Centre


Relationships between heating values– HHV of wet biomass = (1-m)HHVD

– LHV = (1-m)HHVD - (latent heat)(moisture content

in product gas per kg fuel)

= (1-m)HHVD – 2.447[m + 9.0(1-m)H]

– Utilizable heat content =

LHV - [(mass fraction)

(CP)]all products(Texht - Tamb)

9

where m is the fractional moisture content in biomass

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Characterization of waste agricultural biomass Estimation of Higher Heating Value of WAB

– Usually, heating values of biomass materials are determined

through direct experimental measurement by means of a

device called bomb calorimeter

– Alternative to the practical measurements, approximate

estimations for HHVD could be made through analytical

equations that are derived based on fuel composition

– Based on ultimate analysis

Three models:

– Model – X: HHV=0.352xC + 1.162xH – 0.111xO + 0.063xN + 0.105xS

– Model – Y: HHV=0.349xC + 1.178xH – 0.103xO + 0.015xN + 0.101xS – 0.021A

– Model – Z: HHV=0.341xC + 1.323xH – 0.120xO + 0.120xN + 0.680xS – 0.015A

– HHV – Higher Heating Value in MJ/Kg

– C,H,O,N,S,A are the % mass fractions of Carbon, Hydrogen, Oxygen, Nitrogen, Sulfur and Ash respectively in dry biomass.

– Try matching with the formula !!• Q = 337C + 1442(H - O/8) + 93S

10

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Based on ultimate analysis

11

Biomass Fuel

Composition (% by weight) HHVD (MJ/kg)

C H O N S Ash ModelX

ModelY

ModelZ

Paddy Straw 39.2 5.1 35.8 0.6 0.1 19.2 15.8 15.6 15.5

Paddy Husk 38.5 5.7 39.8 0.5 0 15.5 15.8 15.7 15.6

Corn Cob 46.2 7.6 42.3 1.2 0.3 2.4 20.5 20.7 20.6

Bagasse 46.4 5.4 42.6 0.7 0 4.9 17.9 18.1 17.7

Cotton Stalk 45.3 5.6 45.3 0.5 0 3.3 17.4 17.7 17.3

Hard Wood 50.8 6.4 41.5 0.4 0 0.9 20.7 21.0 20.7

Soft Wood 52.9 6.3 39.7 0.1 0 1.0 21.5 21.8 21.6

Miscanthus 48.1 5.4 42.2 0.5 0.1 3.7 18.5 18.7 18.4

Barley Straw

45.7 6.1 38.3 0.4 0.1 9.4 18.9 19.0 18.9

Wheat Straw 48.5 5.5 39.9 0.3 0.1 5.7 19.0 19.2 18.9

Lignite 64.0 4.2 19.2 0.9 1.3 10.4 25.4 25.2 24.9

Anthracite Coal

78.8 2.3 2.5 0.9 0.5 15 30.2 29.7 29.3

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Estimation of Higher Heating Value of WAB Based on ultimate analysis

12

Biomass constituent /Chemical equation

Ultimate Analysis (%) HHVD (MJ/kg)

C H OModel

-XModel

-YModel-

Z

Cellulose / (C6H10O5)x

44.4 6.2 49.4 17.3 17.7 17.4

Hemicelluloses / (C5H8O4)y

45.5 6.1 48.5 17.6 18.0 17.7

Lignin / (C9H10O3(CH3O)0.9 – 1.7)z

58.7 – 61.3

6.5 – 6.9

32.2 – 34.4

24.9 – 25.6

25.1 – 25.8

25.0 – 25.7

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Estimation of Higher Heating Value of WAB Based on proximate analysis

Three Models

Model A: HHV = 0.1559xVM + 0.3536xFC – 0.0078xA

Model B: HHV = 0.1708xVM + 0.3543xFC

Model C: HHV = 0.3133x(VM+FC) – 10.8141

HHV – Higher Heating Value in MJ/Kg

VM, FC,A are the % mass fractions of Volatile Matter, Fixed Carbon and

Ash respectively in dry biomass.

13

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Based on proximate analysis

14

Biomass Fuel Composition (% by weight) HHVD (MJ/kg)VM

(ash free)FC

(ash free)Ash Model-

AModel-

BModel-

C

Bagasse 84.2 15.8 2.9 18.1 19.4 19.6

Coconut coir 82.8 17.2 0.9 18.8 20.1 20.2

Coconut shell 80.2 19.8 0.7 19.4 20.6 20.3

Coir pith 73.3 26.7 7.1 19.3 20.4 18.3

Corn cob 85.4 14.6 2.8 17.9 19.2 19.6

Corn stalks 80.1 19.9 6.8 18.1 19.3 18.4

Groundnut shell 83.0 17 5.9 17.8 19.0 18.7

Paddy Husk 81.6 18.4 23.5 14.5 15.6 13.2

Paddy Straw 80.2 19.8 19.8 15.5 16.6 14.3

Wheat Straw 83.9 16.1 11.2 16.6 17.8 17.0

Peanut Shell 78.4 21.6 7.2 18.4 19.5 18.3

Cotton Stalk 80.0 20.0 5.3 18.5 19.7 18.9

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Effects of Moisture on Heating Value


15

.-1biomass wet of DHHVmHHV

,,10.92.447--1

fuel) kgper gasproduct in content (moistureheat)(latent --1

HD

D

mmHHVm

HHVmLHV

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Hea

ting

Val

ue (M

J/kg

)

Moisture Content on Wet Basis

LHV

HHV

Wood Pellet (8% moisture)

Air Dried Wood (20% moisture)

Green Wood (50% moisture)


HAPPY WORKING ON CHARACTERIZATION OF WASTE AGRICULTURAL

BIOMASS


THANK YOU

For further information:http://www.unep.or.jp

UNEP Characterization of Waste Agricultural Biomass for Energy Applications Training on Technologies for Converting Waste Agricultural Biomass into Energy.

Documents

vegetable waste high

mc db slide

tendering slide

dry basis mc db relationship

rice straw39

rice husk38

energy applications

essential information