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Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms

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dc.citedby14
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorKanthasamy R.en_US
dc.contributor.authorZwawi M.en_US
dc.contributor.authorCheng C.K.en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36651549700en_US
dc.contributor.authorid56070146400en_US
dc.contributor.authorid56584631800en_US
dc.contributor.authorid57204938666en_US
dc.date.accessioned2023-05-29T09:08:03Z
dc.date.available2023-05-29T09:08:03Z
dc.date.issued2021
dc.descriptionChemical activation; Gasification; Learning algorithms; Machine learning; Multilayer neural networks; Neurons; Plastics industry; Predictive analytics; Rubber; Rubber industry; Activation functions; MLP neural networks; Model architecture; Multi layer perceptron; Neural network algorithm; Optimized performance; Process operation; Radial Basis Function(RBF); Hydrogen productionen_US
dc.description.abstractThis study aimed to investigate the application of radial basis function (RBF) and multilayer perceptron (MLP) artificial neural networks for modeling hydrogen production by co-gasification of rubber and plastic wastes. Both the RBF and MLP neural networks were configured by determining the best-hidden neurons that could offer optimized performance. Based on the best-hidden neurons, a model architecture of 4-16-1, 4-20-1, 4-17-1, and 4-3-1 was obtained for RBF (with standard activation function), RBF (with ordinary activation function), one-layer MLP, and two-layer MLP, respectively, indicating the number of input nodes, the hidden neurons, and the output nodes. The predicted hydrogen production from the co-gasification closely agrees with the observed values. The 1-layer MLP with R2 of.990 displayed the best performance with all the input parameters having a significant influence on 9 the model output. The neural network algorithm obtained in this study could be implemented in the eventuality of making a vital decision in the process operation of the co-gasification process for hydrogen production. � 2021 John Wiley & Sons Ltden_US
dc.description.natureFinalen_US
dc.identifier.doi10.1002/er.6483
dc.identifier.epage9594
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85100098854
dc.identifier.spage9580
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100098854&doi=10.1002%2fer.6483&partnerID=40&md5=f43ef6221e18dfe69f3057bec8f916b5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26232
dc.identifier.volume45
dc.publisherJohn Wiley and Sons Ltden_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Energy Research
dc.titleModeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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