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Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers

Author

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  • Michael Binns

    (Department of Chemical and Biochemical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Hafiz Muhammad Uzair Ayub

    (Department of Chemical and Biochemical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

Abstract

Various modeling approaches have been suggested for the modeling and simulation of gasification processes. These models allow for the prediction of gasifier performance at different conditions and using different feedstocks from which the system parameters can be optimized to design efficient gasifiers. Complex models require significant time and effort to develop, and they might only be accurate for use with a specific catalyst. Hence, various simpler models have also been developed, including thermodynamic equilibrium models and empirical models, which can be developed and solved more quickly, allowing such models to be used for optimization. In this study, linear and quadratic expressions in terms of the gasifier input value parameters are developed based on linear regression. To identify significant parameters and reduce the complexity of these expressions, a LASSO (least absolute shrinkage and selection operator) shrinkage method is applied together with cross validation. In this way, the significant parameters are revealed and simple models with reasonable accuracy are obtained.

Suggested Citation

  • Michael Binns & Hafiz Muhammad Uzair Ayub, 2021. "Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers," Sustainability, MDPI, vol. 13(21), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12191-:d:672330
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    References listed on IDEAS

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    1. Michela Costa & Maurizio La Villetta & Daniele Piazzullo & Domenico Cirillo, 2021. "A Phenomenological Model of a Downdraft Biomass Gasifier Flexible to the Feedstock Composition and the Reactor Design," Energies, MDPI, vol. 14(14), pages 1-29, July.
    2. Hafiz Muhammad Uzair Ayub & Sang Jin Park & Michael Binns, 2020. "Biomass to Syngas: Modified Non-Stoichiometric Thermodynamic Models for the Downdraft Biomass Gasification," Energies, MDPI, vol. 13(21), pages 1-17, October.
    3. Hafiz Muhammad Uzair Ayub & Sang Jin Park & Michael Binns, 2020. "Biomass to Syngas: Modified Stoichiometric Thermodynamic Models for Downdraft Biomass Gasification," Energies, MDPI, vol. 13(20), pages 1-14, October.
    4. Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
    5. Mutlu, Ali Yener & Yucel, Ozgun, 2018. "An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification," Energy, Elsevier, vol. 165(PA), pages 895-901.
    6. Sérgio Ferreira & Eliseu Monteiro & Paulo Brito & Cândida Vilarinho, 2019. "A Holistic Review on Biomass Gasification Modified Equilibrium Models," Energies, MDPI, vol. 12(1), pages 1-31, January.
    7. Pio, D.T. & Tarelho, L.A.C., 2020. "Empirical and chemical equilibrium modelling for prediction of biomass gasification products in bubbling fluidized beds," Energy, Elsevier, vol. 202(C).
    8. Patra, Tapas Kumar & Sheth, Pratik N., 2015. "Biomass gasification models for downdraft gasifier: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 583-593.
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    Cited by:

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