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Modeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networks

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dc.citedby30
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorCheng C.K.en_US
dc.contributor.authorWitoon T.en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid36651549700en_US
dc.contributor.authorid57204938666en_US
dc.contributor.authorid23487511100en_US
dc.date.accessioned2023-05-29T09:12:51Z
dc.date.available2023-05-29T09:12:51Z
dc.date.issued2021
dc.descriptionAntibiotics; Azo dyes; Biodegradation; Network architecture; Neural networks; Phenols; Sensitivity analysis; Styrene; Hydrothermal temperature; Initial concentration; Levenberg-Marquardt; Mean absolute error; Modelling techniques; Phenol concentration; Photo catalytic degradation; Photocatalyst concentration; Organic pollutantsen_US
dc.description.abstractThe need for pollutant-free wastewater has necessitated a huge volume of research on the photocatalytic degradation of organic pollutants. The data obtained from various photocatalytic degradation experimental runs can be employed in data-driven machine learning modelling techniques such as artificial neural networks. In this study, the use of Levenberg-Marquardt-trained artificial neural network for modelling the photocatalytic degradation of chloramphenicol, phenol, azo dye, gaseous styrene, and methylene blue is presented. For each of the photocatalytic degradation processes, 20 neural network architectures were investigated by optimizing their hidden neurons. Optimized ANN configurations of 3?20-1, 3?5-1, 3?2-1, 4?17-1, 4?6-1, and 3?10-1 were obtained for modelling the photodegradation of chloramphenicol, phenol, phenol, azo dye, gaseous styrene, and methylene blue, respectively. The optimized ANN architectures were robust in predicting the degradation of the organic pollutants with R2 > 0.9 at a 95 % confidence level with very low mean absolute errors. The sensitivity analysis using the modified Garson algorithm revealed that all the process parameters significantly influenced the photodegradation of the organic pollutants. The photocatalyst concentration, phenol concentration, pH of the solution, hydrothermal temperature, and methylene blue initial concentration were however found to have the most significant influence on the photodegradation processes. The ANN algorithm can be implemented in a photocatalytic degradation process for making vital decisions regarding the operation of the process. � 2020 Institution of Chemical Engineersen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.psep.2020.07.053
dc.identifier.epage132
dc.identifier.scopus2-s2.0-85089415421
dc.identifier.spage120
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089415421&doi=10.1016%2fj.psep.2020.07.053&partnerID=40&md5=3712a77def16d1241bf948de586ba17d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26617
dc.identifier.volume145
dc.publisherInstitution of Chemical Engineersen_US
dc.sourceScopus
dc.sourcetitleProcess Safety and Environmental Protection
dc.titleModeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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