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Elucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: An artificial intelligence approach

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dc.citedby2
dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorMageed A.K.en_US
dc.contributor.authorAbdel Ghany M.A.R.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid57202098304en_US
dc.contributor.authorid57215843327en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36651549700en_US
dc.date.accessioned2023-05-29T08:06:37Z
dc.date.available2023-05-29T08:06:37Z
dc.date.issued2020
dc.description.abstractThis study focuses on the non-linear effect of gas hourly space velocity (GHSV), oxygen (O2) concentration in the feed, the reaction temperature, and the CH4/CO2 ratio on hydrogen production by catalytic methane dry reforming using artificial neural networks (ANN). Ten different ANN models were configured by varying the hidden neurons from 1 to 10. The various ANN model architecture was tested using 30 datasets. The ANN model with the topology of 4-9-2 resulted in the best performance with the sum of square error (SSE) of 0.076 and coefficient of determination (R2) greater than 0.9. The predicted hydrogen yield and the CH4 conversions by the optimized ANN model were in close agreement with the observed values obtained from the experimental runs. The level of importance analysis revealed that all the parameters significantly influenced the hydrogen yield and the CH4 conversion. However, the reaction temperature with the highest level of importance was adjudged the parameter with the highest level of influence on the methane dry reforming. The study demonstrated that ANN is a robust tool that can be employed to investigate predictive modeling and determine the level of importance of parameters on methane dry reforming. � 2020 Institute of Physics Publishing. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12078
dc.identifier.doi10.1088/1757-899X/991/1/012078
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85099149615
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85099149615&doi=10.1088%2f1757-899X%2f991%2f1%2f012078&partnerID=40&md5=55875b8ab7d6bb1a45c65fad013d9aa2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25062
dc.identifier.volume991
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Materials Science and Engineering
dc.titleElucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: An artificial intelligence approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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