The Application of a Modified Distributed Activation Energy based

Model to the Combustion and Gasification of Biomass and Coal Char

BlendsPatience Mavhengere

School of Chemical and Metallurgical Engineering, University of the Witwatersrand

Co-Authors: Prof Nicola Wagner, Dr Shehzaad Kauchali

Ø Combustion is a major energy conversion process. Ø The determination of combustion kinetics of coal

and biomass is a crucial area of study for the design and optimization of energy systems.

Ø 230.3 million tones CO2 emitted from Eskom’s coal fired units per annum. 1

Ø Co-firing 15% biomass with coal reduces GHGs emitted by 18%.

Ø Some biomass types are believed to have catalytic effects.

Ø Existing coal units may be used for co-firing with very few modifications. 2

Ø Combustion is a major energy conversion process. Ø The determination of combustion kinetics of coal

and biomass is a crucial area of study for the design and optimization of energy systems.

Ø 230.3 million tones CO2 emitted from Eskom’s coal fired units per annum. 1

Ø Co-firing 15% biomass with coal reduces GHGs emitted by 18%.

Ø Some biomass types are believed to have catalytic effects.

Ø Existing coal units may be used for co-firing with very few modifications. 2

[1]Biagini, E., Lippi, F., Petarca, L., Tognotti, L., 2002. Devolatilization rate of biomasses and coal biomass blends : An experimental investigation. Fuel, 81(8),pp.1041-1050.

[2]Koko, M., 2012. A 1-D Simulation Tool for Biomass Co-Firing Development and Application, Co-firing Biomass with coal. Copenhagen. March 27-28.pp.1-29.

The reaction model function The pre-

exponential factor

The activation energy

700 750 800 850 900 950 1000 1050 11000

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0.2

0.3

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0.5

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0.9

1

Temperature

Mas

s fra

ctio

n re

mai

ning

Reactions assumed at

of E

Reactions assumed at various conversions, each with a unique set of E and A.

n=2

n=3

n=4

n=5

n=6

n=7

n=8

n=9

n=1

3

Model independent technique!!!

Figure 1: Discretisation of non-isothermal data. The DAE based model.

[3] Scott, S. A., Dennis, J. S., Davidson, J. F., Hayhurst, A. N., 2006. Thermogravimetric measurements of the kinetics of pyrolysis of dried sewage sludge. Fuel, 85(9), pp.1248-1253.

• Discretization of the curves by the reactions– Model the system using the matrix notation

,

, , ,

Total mass fraction remaining obtained from thermo-gravimetric

analysis

Initial mass fraction of component decomposing in

reaction i.

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0.4

0.6

0.8

1

1.2

1.4

First order Ψ ( , ) = ln(1 − )

RPM Ψ(T, ) = 1 − ln (1 − ) −1 1-xmax

Rate

of r

eact

ion

Temperature

Conv

ersio

n

Model dependent technique!

Figure 2: Determination of the pre-exponential factor: The DAE based model.

• Once E and A are known, use Matrix inversion to determine fo.– Automatically evaluates the active reactions from

your initial set.

, , ,

AIM: To determine and analyse the reaction kinetics of the combustion of coal and biomass char blends under non isothermal conditions. OBJECTIVES:Ø To collect thermo-analytical combustion data on

coal and biomass char blends.Ø To apply the DAE based model onto the collected

combustion data and determine the combustion kinetics.

Ø To investigate the effect of the presence of biomass char on coal char combustion using the determined kinetics.

600 700 800 900 1000 1100 1200 13000

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0.9

1

Temperature (K)

Mas

s Fr

actio

n R

emai

ning

(1-

x)

600 700 800 900 1000 1100 1200 13000

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1

Temperature (K)

Mas

s Fr

actio

n R

emai

ning

(1-

x)C

onve

rsio

n (1

-x)

Temperature (K)

100K/min simulated exp data30K/min simulated exp data10K/min simulated exp dataDAE model prediction

Root Mean Square error=0.00034R2 coefficient=1

Reaction 1 2 3 4 5 6 7 (kJ/mol) 150 175 200 225 235 250 275 1/7 1/7 1/7 1/7 1/7 1/7 1/7

Seven parallel random pore model (RPM) reactions

Figure 3: Model validation by simulation.

Table 1: Specified kinetics for the seven simulated RPM models

ØSamples:ØVitrinite rich South African coal (VC)ØHighveld grass (HG)ØPine wood chips (PW)

ØPulverized to -53µm ØBlend ratio of 10% and 50% by thermal input

was used. 4-5

[4]Fernando, R., 2005. Fuels for biomass co-firing. (October).IEA Clean Coal Centre[5]Livingston, W. R., 2012. Recent developments in biomass co-firing in large coal-fired utility. Copenhagen. March, Babcock Energy.

SAMPLE CHARACTERIZATION

Calorific value determination

Ultimate analysis

ICP-OES(inductively coupled plasma optical

emission spectrometry)

THERMO-GRAVIMETRIC ANALYSIS

Proximate analysis

TA instruments

Petrographic analysis

Char formation in N215K/min to 1523K

Combustion in air8K/min,12K/min,15K/min

Max T=923K

Figure 4: TA instruments thermo-gravimetric analyser

Proximate analysis (wt.%, dry basis)

Ultimate analysis(wt.%, dry ash free basis)

Gross

calorific

value (MJ/kg)Ash FC VM C H N S O

VC 12.55 50.67 36.8 79.5 5.6 1.70 0.87 12.30 28.12

HG 7.45 15.15 77.4 50.1 6.5 0.40 0.14 42.87 17.12

PW 0.23 15.2 84.6 51.4 6.5 0.1 0.00 41.95 18.52

Ash content (Oxide wt. %)

SiO2 Fe2O3 CaO MgO K2O Total catalytic species (Fe2O3,K2O,MgO,CaO) %

Ratio K / Si (wt.%/wt.%)

VC 68.00 3.53 1.69 0.56 1.28 7.06 0.03

HG 73.80 0.86 5.95 2.67 9.06 18.58 0.21

PW 8.36 8.22 40.10 15.10 3.39 66.81 0.72

VC Petrography

Vitrinite % Liptinite % Inertinite % Mineral matter % Rank

78 7.3 10.4 4.8 Medium rank C

Table 2: Characterization results.

All materials successfully modelled by the RPM, modelling errors ranged from RMS errors of 0.0021 to 0.0081 and R2

coefficients of 0.9996 to 1.

8 K/min experiment

RMS error =0.0045R2 coefficient =0.9999

Figure 5: Model Application to thermo-gravimetric data.

CombustionChar Sample E (kJ/mol) A (s-1m-1) Total weighted %

difference in E

VC 137.4 2.82E+5 1.00 18.5 137.4 0%PW 255.5

222.3193.7183.3

2.80E+152.23E+127.88E+95.34E+8

0.050.040.160.74

8.3 299.7249.3209.4190.3

-6.0%

HG 127.8130.5

1.60E+52.17E+5

0.190.80

10.5 133.3135.5`

-4.0%

VC-HG 90:10 125.7 5.38E+4 1.00 18.98 125.3 0.3%

VC-PW 90:10 125.6

135.0

6.2E+4

1.93E+5

0.42

0.57

18.98 126.4

134.6

-0.1%

VC-HG 50:50 135.9 3.09E+5 1.00 15.94 138.1 -2.0%

VC-PW 50:50 138.3 3.94E+5 1.00 17.88 139.8 -1.1%

[6] Vyazovkin, S., 2008. Handbook of Thermal Analysis and Calorimetry, Isoconversional kinetics. Chapter 13, pp. 503-537.

Table 3: Comparison of kinetic parameters obtained during the combustion of the char blends.

The compensation effect

Blend Name R2 Coefficient RMS Error

VC-HG 90:10 0.9997 0.0076

VC-PW 90:10 0.9999 0.0040

VC-HG 50:50 0.9987 0.0154

VC-PW 50:50 0.9968 0.0239

[8]Gil, M. V., Casal, D.,Pevida, C., Pis, J. J., Rubiera, F., 2010a. Thermal behaviour and kinetics of coal/biomass blends during co-combustion.Bioresource technology, 101, pp.5601–8..[9]Moghtaderi, B., Meesri, C. & Wall, T. F., 2004. Pyrolytic characteristics of blended coal and woody biomass. Fuel, 83(6), pp.745–750.[10]Sadhukhan, A. K., Gupta, P., Goyal, T., Saha, R. K., 2008. Modelling of pyrolysis of coal-biomass blends using thermogravimetric analysis.Bioresource technology, 99(17), pp.8022–6.[11]Vuthaluru, H., 2004. Thermal behaviour of coal/biomass blends during co-pyrolysis. Fuel Processing Technology, 85(2-3), pp.141–155.

Additive Method

Table 4: Application of the Additive method

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1.8Coal-grass 50-50 combustion

DTG Additive methodDTG Experimental

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1.8Coal-pine 50-50 combustion plot

DTG Additive methodDTG Experimental

Calculated curveExperimental curve

Calculated curveExperimental curve

VC-HG 50-50 blend

VC-PW 50-50 blend

Frac

tiona

l rat

e of

mas

s los

s (1/

min

)Fr

actio

nal r

ate

of m

ass l

oss (

1/m

in)

Temperature (K)

Temperature (K)

Figure 2: Application of the Additive method for the synergy analysis.

Ø The modified DAE model is a robust and accurate method of kinetics determination.Ø The RPM is suitable for modelling the combustion of

the coal and biomass char blends.Ø PW char is the most reactive material, followed by HG

and VC chars respectively.ØNegligible changes were observed in the activation

energy for the 90:10 coal-biomass char blends. ØA decrease in activation energy was observed during

the combustion of the 50:50 blends.Ø Synergetic behaviour between the coal and the biomass

was observed for the 50:50 coal-biomass blends

[7] Babinski, P., Łabojko, G., Kotyczka-Moran´ska, M., Plis, A., 2013. Kinetics of coal and char oxycombustion studied by TG–FTIR. Journal of Thermal Analysis and Calorimetry. DOI 10.1007/s10973-013-3002-x.