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

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.

Characteristics of Gas–Solid Mixture Flows through a Packed Moving Bed of Coarse Particles

“…Applying more complicated methods, such as quadratic expressions or artificial neural networks (ANNs), can achieve a better prediction by considering nonlinear behavior. 24 The ANN is one machine learning method that can effectively predict systems without needing to use casual models and has been applied to various applications. 25−28 Whereas the parameters from the ANN do not have physical meaning, the quadratic expressions make it possible to interpret the interactions between independent variables.…”

“…Several studies have utilized dimensional analyses to develop empirical models assuming homogeneity and have shown that the models can accurately predict experimental data. ,, However, some models can contain some inaccuracies compared to experimental results. Applying more complicated methods, such as quadratic expressions or artificial neural networks (ANNs), can achieve a better prediction by considering nonlinear behavior . The ANN is one machine learning method that can effectively predict systems without needing to use casual models and has been applied to various applications. − Whereas the parameters from the ANN do not have physical meaning, the quadratic expressions make it possible to interpret the interactions between independent variables.…”

Parameter estimation combined with model reduction techniques for identifiability analysis of biological models

“…For this reason model reduction and identifiability methods can be used to reduce the complexity (Baker et al, 2015). In this study we compare two different approaches for model reduction combined with parameter estimation: LASSO based regularization (James et al, 2013;Binns and Ayub, 2021) and a sensitivity and identifiability analysis utilizing the Fischer information matrix and multi-objective optimisation to reduce correlations and improve identifiability.…”

“…In this work both types of methods are explored with examples. Cross-validation combined with a Least Absolute Shrinkage and Selection Operator (LASSO) regularisation method is used to reduce the complexity of linear empirical equations for predicting the performance of downdraft biomass gasification (Binns and Ayub, 2021). Sensitivity and identifiability methods utilizing the Fischer Information Matrix (FIM) are used to reduce the complexity of a nonlinear system of partial integral differential equations describing a population balance model for microalgae cultivation (Usai et al, 2022).…”

Prediction of the Behavior of Sunflower Husk Ash after Its Processing by Various Torrefaction Methods

“…These emissions can be significantly reduced by replacing coal with biomass, as biomass is considered a CO 2 -neutral fuel [2]. In addition, Energies 2022, 15, 7483 2 of 14 synthesis gas can be obtained from biomass, which can be used to produce environmentally friendly liquid fuels [3].…”