Mathematical modelling of gasification processes of bio-wastes (municipal solid waste)

Daya S Pandey, J. J. Leahy and W. Kwapinski

1

Funded by the European Union

Joint Scientific Workshop (FIRe) May 26, 2015

Erfurt, Germany

Introduction and Background

*Eurostat 2010. 2

Total primary energy consumption by energy source, EU-27*

Introduction and Background

*Eurostat 2010. 3

As per Renewable Energy Directive, the EU need to produce 20%

of its total energy from the renewables by 2020.

Biomass/wastes has the highest potential amongst renewable

energy resources, currently share 2/3 of the renewable energy in

the EU.

It is expected that biomass and wastes will contribute 13% the

total EU primary energy consumption by 2020 [Eurostat 2014].

The EU regulation 1069/2009 approves

unprocessed poultry litter as a fuel.

Processing Technologies

eXtension.org 4

Feedstocks (Biomass/wastes)

Processing Technologies

End use (Application)

Gasification

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Equivalence ratio (ER) 𝐸𝑅 =

𝐴𝑖𝑟 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒

𝑎𝑐𝑐𝑡𝑢𝑎𝑙𝐴𝑖𝑟 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒

𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 𝑠𝑡𝑜𝑖𝑐ℎ𝑖𝑚𝑒𝑡𝑟𝑖𝑐

Air required for complete combustion

𝑀𝑎𝑖𝑟 = 11.53 𝐶 + 34.34 𝐻 − O8 + 4.34𝑆 + 𝐴. 𝑆 kg/kg of biomass

“A thermochemical process in which partial oxidation of organic matter at

higher temperatures results in a mixture of products, mainly combustible

gases called synthesis gas (Syngas)”

Pyrolysis

ER = 0

Gasification

ER = 0.1-0.4

Combustion

ER >1 Equivalence Ratio (ER)

the ratio between the O2

content in the oxidant supply

and that required for

complete stoichiometric

combustion.

Increasing Oxygen: Fuel

Simulation of gasification process

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Gasification Models

Kinetic Based

Aspen Plus

Thermo. equilibrium

Neural networks

CFD

Simulation of gasification process

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Determine the optimal operating conditions.

Study wider range of conditions which can not be possible

experimentally.

To understand the complexity involved with the gasification

process.

Confirm results observed from the experiments.

Helps in designing & planning of the experiments.

Modelling of gasification process - Neural networks.

Statements of the following nature are commonplace:

‘‘Artificial neural networks (ANN) techniques have been used by several

contemporary researchers to predict the characteristics of the gasification

process’’(Guo et al., 2001, Brown et al., 2006, Puig-Arnavat et al., 2013 etc.)

‘‘The ANN model was used to predict the syngas yield and lower heating value

from municipal solid waste in a fluidized bed gasifier’’(Xiao et al., 2009)

• Guo et al. (2001) Bioresource Technology, 76, 77–83. • Brown et al. (2006) Computer Aided Chemical Engineering, 21, 1661–1666. • Puig-Arnavat et al. (2013) Biomass and Bioenergy, 49, 279–289. • Xiao et al. (2009) Waste Management, 29, 240–244.

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The prediction capability of ANN approach demonstrated that the artificial

intelligence technique can be used to exploit the complex thermochemical

processes.

Aim of Study

Proposed an evolutionary Genetic programming technique to predict the

performance of fluidized bed gasifier.

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Simulation Inputs:

Fuel characteristics and process parameters ( C, H, N, S, O, MC, Ash, ER, Temp.)

Initial analyses:

To determine the lower calorific value of the syngas produced (𝑦1).

To determine syngas yield production from the MSW (𝑦2).

5, 7,1, 2, 3, 4, 6, 8, 9[ ]ix x x x x x x x x x

An overview of Genetic Programming (GP)

Tree representation of a multi-gene genetic programming [Pandey et al. 2015] 10

Inputs: Output: y

1 2 3{x ,x ,x }

Results and Discussion

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The Pareto front the MGGP solutions for lower heating value calculation

Results and Discussion

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The convergence plot of the MGGP solutions for lower heating value calculation

R2 and RMSE of the lower heating value prediction

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9

8

2

51 4 4 9

1/4 2 2

5 4 4 8 4

4 2 8 2 658 6

2

0.4047 cos 3.937cos cos cos 0.2127 e

0.289 cos 0.1625 cos 2.7 7 1

x

x

y x x x x x x x x x

x x x x x x x xe

𝐏𝐚𝐫𝐞𝐭𝐨 𝐟𝐫𝐨𝐧𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐒𝐲𝐧𝐠𝐚𝐬 𝐲𝐢𝐞𝐥𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

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𝐓𝐡𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐩𝐥𝐨𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐒𝐲𝐧𝐠𝐚𝐬 𝐲𝐢𝐞𝐥𝐝

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R2and RMSE of the syngas yield prediction Solution

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Solution B (Syngas yield)

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Comparison of multi-gene GP and single-gene GP model

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Algorithm Mean (µ) Standard deviation (σ) Minimum

GP variants for LHV prediction

MGGP 0.050605 0.010224 0.031911

SGGP 0.116638 0.025553 0.076058

GP variants for Syngas yield production

MGGP 0.013192 0.00331 0.006521

SGGP 0.041831 0.015197 0.020957

Conclusions

Genetic programming is used to predict the performance of fluidized bed

gasifier.

The performance of the MGGP models is compared with the single-gene

GP model.

Comparisons of complexity and accuracy of GP prediction have been

reported.

The MGGP approach gives better results on both training and validation

data.

The data-driven GP modelling is useful for prediction with analytical

expressions.

Pandey, D.S., Pan, I., Das, S., Leahy, J.J., Kwapinski, W., 2015. Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. Bioresource Technology 179, 524-533. 19

Acknowledgement

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Funded by the

European Union

Dr. J. J. Leahy

Dr. Witold Kwapinski

Carbolea Research Group

Thank you

ReUseWaste, The EU FP7 Marie-Curie Initial Training Network (ITN)

Dr. S. Das and I. Pan