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Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts

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dc.citedby23
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorVo D.-V.N.en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid36651549700en_US
dc.contributor.authorid35957358000en_US
dc.date.accessioned2023-05-29T08:07:26Z
dc.date.available2023-05-29T08:07:26Z
dc.date.issued2020
dc.descriptionBackpropagation; Catalysts; Chemical industry; Degradation; Ketones; Network architecture; Neural networks; Photocatalysts; Polycyclic aromatic hydrocarbons; Silver compounds; Titanium dioxide; Titanium oxides; Advanced Oxidation Processes; Back propagation artificial neural network (BPANN); Back propagation neural networks; Methyl blue degradation; Non-linear relationships; Optimized architectures; Photo catalytic degradation; TiO2-based photocatalysts; Organic pollutants; anthraquinone; dye; ferrous gluconate; indole; silver; titanium dioxide; Article; back propagation neural network; chemical oxygen demand; flow rate; leaching; photocatalysis; photodegradation; pollutant; reaction duration (chemistry); ultraviolet radiation; waste water managementen_US
dc.description.abstractBACKGROUND: The advanced oxidation process using photocatalysts has been proven to be an efficient technique used for the degradation of organic pollutants in wastewater. However, there exists a nonlinear relationship between the process parameters of the photodegradation reaction, which needs to be well understood for the design of an efficient photoreactor. This study employed a backpropagation artificial neural network (BPANN) for the modelling of photocatalytic degradation of indole, anthraquinone dye and methyl blue using undoped and Ag+-doped TiO2 catalysts. RESULTS: A Levenberg�Marquardt algorithm was employed to train the BPANN by varying the hidden neurons to obtained an optimized architecture. Optimized architectures with 3-14-1, 4-12-1 and 3-16-1 consist of the input layers, hidden layer and the output layer, were obtained using the datasets from photodegradation of indole, anthraquinone dye and methyl blue, respectively. The optimized BPANN accurately predicts the indole, anthraquinone dye and methyl blue degradation as a function of colour removal from the wastewater. High coefficients of determination (R2) of 0.999, 0.961 and 0.993 were obtained for the prediction of the photodegradation of indole, anthraquinone dye and methyl blue, respectively, with over 95% confidence level. The study revealed that dye concentration, catalyst dosage and reaction time have the highest level of importance for the photodegradation of indole, anthraquinone dye and methyl blue, respectively. CONCLUSION: This study has demonstrated the robustness of BPANN for predictive modelling of photodegradation of organic pollutants such as indole, anthraquinone dye and methyl blue. � 2020 Society of Chemical Industry. � 2020 Society of Chemical Industryen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1002/jctb.6407
dc.identifier.epage2749
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85082598961
dc.identifier.spage2739
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082598961&doi=10.1002%2fjctb.6407&partnerID=40&md5=85043c1e11d010f433c073893da7a042
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25226
dc.identifier.volume95
dc.publisherJohn Wiley and Sons Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Chemical Technology and Biotechnology
dc.titleBackpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalystsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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