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Mathematical modelling of gasification processes of bio-wastes (municipal solid waste)
Daya S Pandey, J. J. Leahy and W. Kwapinski
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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