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

Backpropagation; 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 pr...

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Main Authors: Ayodele B.V., Alsaffar M.A., Mustapa S.I., Vo D.-V.N.
Other Authors: 56862160400
Format: Article
Published: John Wiley and Sons Ltd 2023
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spelling my.uniten.dspace-252262023-05-29T16:07:26Z Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts Ayodele B.V. Alsaffar M.A. Mustapa S.I. Vo D.-V.N. 56862160400 57210601717 36651549700 35957358000 Backpropagation; 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 management BACKGROUND: 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 Industry Final 2023-05-29T08:07:26Z 2023-05-29T08:07:26Z 2020 Article 10.1002/jctb.6407 2-s2.0-85082598961 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082598961&doi=10.1002%2fjctb.6407&partnerID=40&md5=85043c1e11d010f433c073893da7a042 https://irepository.uniten.edu.my/handle/123456789/25226 95 10 2739 2749 John Wiley and Sons Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Backpropagation; 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 management
author2 56862160400
author_facet 56862160400
Ayodele B.V.
Alsaffar M.A.
Mustapa S.I.
Vo D.-V.N.
format Article
author Ayodele B.V.
Alsaffar M.A.
Mustapa S.I.
Vo D.-V.N.
spellingShingle Ayodele B.V.
Alsaffar M.A.
Mustapa S.I.
Vo D.-V.N.
Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts
author_sort Ayodele B.V.
title Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts
title_short Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts
title_full Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts
title_fullStr Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts
title_full_unstemmed Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2-based photocatalysts
title_sort backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using tio2-based photocatalysts
publisher John Wiley and Sons Ltd
publishDate 2023
_version_ 1806428172190744576
score 13.214268

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