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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

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dc.citedby24
dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36651549700en_US
dc.date.accessioned2023-05-29T08:11:04Z
dc.date.available2023-05-29T08:11:04Z
dc.date.issued2020
dc.descriptionAlumina; 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 supportsen_US
dc.description.abstractMethane 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 Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo119168
dc.identifier.doi10.1016/j.jclepro.2019.119168
dc.identifier.scopus2-s2.0-85075445698
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075445698&doi=10.1016%2fj.jclepro.2019.119168&partnerID=40&md5=07e021327f5c65351e8cd9241b207790
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25571
dc.identifier.volume247
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.titleScavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling techniqueen_US
dc.typeArticleen_US
dspace.entity.typePublication
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