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Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique

Alumina; Aluminum oxide; Carbon; Catalyst deactivation; Cobalt; Deposition; Forecasting; Greenhouse gases; Hydrogen production; Methane; Multilayer neural networks; Network architecture; Radial basis function networks; Statistical methods; Synthesis gas; Synthesis gas manufacture; Alumina-supported...

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Main Authors: Alsaffar M.A., Ayodele B.V., Mustapa S.I.
Other Authors: 57210601717
Format: Article
Published: Elsevier Ltd 2023
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spelling my.uniten.dspace-255712023-05-29T16:11:04Z Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique Alsaffar M.A. Ayodele B.V. Mustapa S.I. 57210601717 56862160400 36651549700 Alumina; Aluminum oxide; Carbon; Catalyst deactivation; Cobalt; Deposition; Forecasting; Greenhouse gases; Hydrogen production; Methane; Multilayer neural networks; Network architecture; Radial basis function networks; Statistical methods; Synthesis gas; Synthesis gas manufacture; Alumina-supported cobalt catalyst; Artificial neural network approach; Artificial neural network modeling; Methane dry reforming; Multi layer perceptron neural networks (MLPNN); Production of hydrogen; Radial basis functions; Syn-gas; Catalyst supports Methane dry reforming is a thermo-catalytical process that utilizes two principal components of greenhouse gases for the production of hydrogen-rich syngas. One major shortcoming of the methane dry reforming as a potential route for renewable hydrogen-rich syngas production is catalyst deactivation through carbon deposition. In this study, an artificial neural network approach was employed for predictive modeling of the deactivation of alumina supported cobalt catalyst used to catalyze methane dry reforming reaction. The effect of methane/carbon dioxide (CH4/CO2) ratio, reaction temperature and nitrogen (N2) flowrate on the carbon deposition were investigated using full factorial experimental design. Two artificial neural network modeling techniques namely multilayer perceptron neural network (MLPNN) and radial basis function (RBFNN) were employed for the prediction of carbon deposition per gram catalyst using data obtained from 170 experimental runs. The hidden neurons were optimized to obtain 16 and 20 units respectively for the MLPNN and the RBFNN resulting in the network architecture of 3, 16, 1 and 3, 20, 1, respectively. The statistical analysis of the network performance resulted in mean standard error (MSE) values of 0.048 and 0.00285 for training the MLPNN algorithm above and below stoichiometric conditions with corresponding R2 values of 0.945 and 0.965. While MSE values of 0.0073 and 0.00015 were obtained for the training of the RBFNN algorithm above and below stoichiometric conditions with R2 of 0.987 for both cases. Base on the statistical analysis the RBFNN model was adjudicated as a better predictor of the carbon deposition during the hydrogen-rich syngas production than the MLPNN model. The three input parameters were found to have varying levels of importance in the prediction of the carbon deposition. The reaction temperature was observed to be the most important parameters that influence the prediction of carbon deposition above stochiometric while CH4/CO2 was the most important parameters that influence the prediction of carbon deposition below stoichiometric conditions. � 2019 Elsevier Ltd Final 2023-05-29T08:11:04Z 2023-05-29T08:11:04Z 2020 Article 10.1016/j.jclepro.2019.119168 2-s2.0-85075445698 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075445698&doi=10.1016%2fj.jclepro.2019.119168&partnerID=40&md5=07e021327f5c65351e8cd9241b207790 https://irepository.uniten.edu.my/handle/123456789/25571 247 119168 Elsevier 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 Alumina; Aluminum oxide; Carbon; Catalyst deactivation; Cobalt; Deposition; Forecasting; Greenhouse gases; Hydrogen production; Methane; Multilayer neural networks; Network architecture; Radial basis function networks; Statistical methods; Synthesis gas; Synthesis gas manufacture; Alumina-supported cobalt catalyst; Artificial neural network approach; Artificial neural network modeling; Methane dry reforming; Multi layer perceptron neural networks (MLPNN); Production of hydrogen; Radial basis functions; Syn-gas; Catalyst supports
author2 57210601717
author_facet 57210601717
Alsaffar M.A.
Ayodele B.V.
Mustapa S.I.
format Article
author Alsaffar M.A.
Ayodele B.V.
Mustapa S.I.
spellingShingle Alsaffar M.A.
Ayodele B.V.
Mustapa S.I.
Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
author_sort Alsaffar M.A.
title Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
title_short Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
title_full Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
title_fullStr Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
title_full_unstemmed Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
title_sort scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique
publisher Elsevier Ltd
publishDate 2023
_version_ 1806423426816016384
score 13.211869

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