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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach

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dc.citedby12
dc.contributor.authorBahadar A.en_US
dc.contributor.authorKanthasamy R.en_US
dc.contributor.authorSait H.H.en_US
dc.contributor.authorZwawi M.en_US
dc.contributor.authorAlgarni M.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorCheng C.K.en_US
dc.contributor.authorWei L.J.en_US
dc.contributor.authorid35182494100en_US
dc.contributor.authorid56070146400en_US
dc.contributor.authorid24554608200en_US
dc.contributor.authorid56584631800en_US
dc.contributor.authorid55363754600en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid57204938666en_US
dc.contributor.authorid57240757900en_US
dc.date.accessioned2023-05-29T09:42:29Z
dc.date.available2023-05-29T09:42:29Z
dc.date.issued2022
dc.descriptionBiomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperatureen_US
dc.description.abstractThe thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857�0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output. � 2021 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo132052
dc.identifier.doi10.1016/j.chemosphere.2021.132052
dc.identifier.scopus2-s2.0-85114033888
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85114033888&doi=10.1016%2fj.chemosphere.2021.132052&partnerID=40&md5=429906f8114934513610e2a11243d671
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27308
dc.identifier.volume287
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleChemosphere
dc.titleElucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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