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

Chemical 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...

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Main Authors: Ayodele B.V., Mustapa S.I., Kanthasamy R., Zwawi M., Cheng C.K.
Other Authors: 56862160400
Format: Article
Published: John Wiley and Sons Ltd 2023
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spelling my.uniten.dspace-262322023-05-29T17:08:03Z Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms Ayodele B.V. Mustapa S.I. Kanthasamy R. Zwawi M. Cheng C.K. 56862160400 36651549700 56070146400 56584631800 57204938666 Chemical 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 production This 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 Ltd Final 2023-05-29T09:08:03Z 2023-05-29T09:08:03Z 2021 Article 10.1002/er.6483 2-s2.0-85100098854 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100098854&doi=10.1002%2fer.6483&partnerID=40&md5=f43ef6221e18dfe69f3057bec8f916b5 https://irepository.uniten.edu.my/handle/123456789/26232 45 6 9580 9594 John Wiley and Sons Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Chemical 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 production
author2 56862160400
author_facet 56862160400
Ayodele B.V.
Mustapa S.I.
Kanthasamy R.
Zwawi M.
Cheng C.K.
format Article
author Ayodele B.V.
Mustapa S.I.
Kanthasamy R.
Zwawi M.
Cheng C.K.
spellingShingle Ayodele B.V.
Mustapa S.I.
Kanthasamy R.
Zwawi M.
Cheng C.K.
Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
author_sort Ayodele B.V.
title Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
title_short Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
title_full Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
title_fullStr Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
title_full_unstemmed Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
title_sort modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
publisher John Wiley and Sons Ltd
publishDate 2023
_version_ 1806427810711994368
score 13.211869

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