Publication:
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst

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dc.citedby7
dc.contributor.authorHaiqi O.A.en_US
dc.contributor.authorNour A.H.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorBargaa R.en_US
dc.contributor.authorid57216178924en_US
dc.contributor.authorid14719696000en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid57216186977en_US
dc.date.accessioned2023-05-29T08:09:12Z
dc.date.available2023-05-29T08:09:12Z
dc.date.issued2020
dc.descriptionBatch reactors; Biodegradation; Crude oil; II-VI semiconductors; Irradiation; Network architecture; Network layers; Organic pollutants; Oxide minerals; Phenols; Photodegradation; Sol-gel process; Sol-gels; Sports; Water pollution; Water treatment; Zinc oxide; Bayesian regularization; Coefficient of determination; Effective performance; Multi layer perceptron neural networks (MLPNN); Multi-layer perceptron neural networks; Non-linear relationships; Phenol concentration; Photo catalytic degradation; Multilayer neural networksen_US
dc.description.abstractThe processing of crude oil in the onshore platform often results in the generation of produce water containing harmful organic pollutants such as phenol. If the produce water is not properly treated to get rid of the organic pollutants, human exposure when discharged could be detrimental to health. Photocatalytic degradation of the organic pollutant has been a proven, non-expensive techniques of removing these harmful organic compounds from the produce water. However, the detail experimentation is often tedious and costly. One way to investigate the non-linear relationship between the parameters for effective performance of the photodegradation is by artificial neural network modelling. This study investigates the predictive modelling of photocatalytic phenol degradation from crude oil wastewater using Bayesian regularization-trained multilayer perceptron neural network (MLPNN). The ZnO/Fe2O3 photocatalyst used for the photodegradation was prepared using sol-gel method and employed for the phenol degradation study in a batch reactor under solar irradiation. Twenty-six datasets generated by Box-Behken experimental design was used for the training of the MLPNN with input variables as irradiation time, initial phenol concentration, photocatalyst dosage and the pH of the solution while the output layer consist of phenol degradation. Several MLPNN architecture was tested to obtain an optimized 4 5 1 configuration with the least mean standard error (MSE) of 1.27. The MLPNN with the 4 5 1 architecture resulted in robust prediction of phenol degradation from the wastewater with coefficient of determination (R) of 0.999. � 2020 IOP Publishing Ltd. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo52058
dc.identifier.doi10.1088/1742-6596/1529/5/052058
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85087438513
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087438513&doi=10.1088%2f1742-6596%2f1529%2f5%2f052058&partnerID=40&md5=e4860e1fda0bdf4c4bb5304e6af0b1b3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25421
dc.identifier.volume1529
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleJournal of Physics: Conference Series
dc.titleBayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalysten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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