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

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 gasifi...

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Autores principales:	Michael Binns, Hafiz Muhammad Uzair Ayub
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	biomass gasification machine learning computer modeling computer simulation regression model reduction Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/98762160e7284934881e66b215326b43
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spelling	oai:doaj.org-article:98762160e7284934881e66b215326b432021-11-11T19:47:07ZModel Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers10.3390/su1321121912071-1050https://doaj.org/article/98762160e7284934881e66b215326b432021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12191https://doaj.org/toc/2071-1050Various 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.Michael BinnsHafiz Muhammad Uzair AyubMDPI AGarticlebiomass gasificationmachine learningcomputer modelingcomputer simulationregressionmodel reductionEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12191, p 12191 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	biomass gasification machine learning computer modeling computer simulation regression model reduction Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	biomass gasification machine learning computer modeling computer simulation regression model reduction Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Michael Binns Hafiz Muhammad Uzair Ayub Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
description	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.
format	article
author	Michael Binns Hafiz Muhammad Uzair Ayub
author_facet	Michael Binns Hafiz Muhammad Uzair Ayub
author_sort	Michael Binns
title	Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
title_short	Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
title_full	Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
title_fullStr	Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
title_full_unstemmed	Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
title_sort	model reduction applied to empirical models for biomass gasification in downdraft gasifiers
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/98762160e7284934881e66b215326b43
work_keys_str_mv	AT michaelbinns modelreductionappliedtoempiricalmodelsforbiomassgasificationindowndraftgasifiers AT hafizmuhammaduzairayub modelreductionappliedtoempiricalmodelsforbiomassgasificationindowndraftgasifiers
_version_	1718431423956779008

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

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