Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst
Batch 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...
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2023
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my.uniten.dspace-254212023-05-29T16:09:12Z Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst Haiqi O.A. Nour A.H. Ayodele B.V. Bargaa R. 57216178924 14719696000 56862160400 57216186977 Batch 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 networks The 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. Final 2023-05-29T08:09:12Z 2023-05-29T08:09:12Z 2020 Conference Paper 10.1088/1742-6596/1529/5/052058 2-s2.0-85087438513 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087438513&doi=10.1088%2f1742-6596%2f1529%2f5%2f052058&partnerID=40&md5=e4860e1fda0bdf4c4bb5304e6af0b1b3 https://irepository.uniten.edu.my/handle/123456789/25421 1529 5 52058 All Open Access, Bronze Institute of Physics Publishing Scopus |
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Batch 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 networks |
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57216178924 |
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57216178924 Haiqi O.A. Nour A.H. Ayodele B.V. Bargaa R. |
| format |
Conference Paper |
| author |
Haiqi O.A. Nour A.H. Ayodele B.V. Bargaa R. |
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Haiqi O.A. Nour A.H. Ayodele B.V. Bargaa R. Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst |
| author_sort |
Haiqi O.A. |
| title |
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst |
| title_short |
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst |
| title_full |
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst |
| title_fullStr |
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst |
| title_full_unstemmed |
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst |
| title_sort |
bayesian regularization-trained multi-layer perceptron neural network predictive modelling of phenol degradation using zno/fe2o3 photocatalyst |
| publisher |
Institute of Physics Publishing |
| publishDate |
2023 |
| _version_ |
1806428231387054080 |
| score |
13.214268 |