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Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach

In this study, response surface methodology (RSM) and artificial neural network-based genetic algorithm (ANN-GA) were utilized to predict two crucial output parameters of membrane performance, namely separation efficiency and oil flux, derived from experimental investigations. The central composite...

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Main Authors: Usman, Jamilu, Abba, Sani. I., Ishola, Niyi Babatunde, El-Badawy, Tijjani, Adamu, Haruna, Gbadamosi, Afeez, Salami, Babatunde Abiodun, Usman, A. G., Benaafi, Mohammed, Othman, Mohd. Hafiz Dzarfan, Aljundi, Isam H.
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Published: Institution of Chemical Engineers 2023
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TP Chemical technology
Online Access:http://eprints.utm.my/105925/
http://dx.doi.org/10.1016/j.cherd.2023.09.027
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spelling my.utm.1059252024-05-26T09:11:05Z http://eprints.utm.my/105925/ Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach Usman, Jamilu Abba, Sani. I. Ishola, Niyi Babatunde El-Badawy, Tijjani Adamu, Haruna Gbadamosi, Afeez Salami, Babatunde Abiodun Usman, A. G. Benaafi, Mohammed Othman, Mohd. Hafiz Dzarfan Aljundi, Isam H. TP Chemical technology In this study, response surface methodology (RSM) and artificial neural network-based genetic algorithm (ANN-GA) were utilized to predict two crucial output parameters of membrane performance, namely separation efficiency and oil flux, derived from experimental investigations. The central composite design (CCD) screening approach of RSM was employed to evaluate the influence of important process input parameters, such as oil concentration (ranging from 50 to 10,000 ppm), feed flow rate (ranging from 150 to 300 mL/min), and pH of the feed (ranging from 4 to 10), as well as their synergistic effects on the output variables. The constructed RSM model and ANN-GA were effectively employed to estimate the optimum conditions for maximizing the output variables. Statistical analysis using the determination coefficient (R2) and standard error of prediction (SEP), along with analysis of variance (ANOVA) and the t-test, demonstrated the accurate description of the membrane performance process by both models. For the oil flux, the RSM model showed an estimated R2 of 0.9916 and SEP of 3.54%, while the ANN model exhibited an R2 of 0.9933 and SEP of 3.31%. In terms of separation efficiency, the RSM model yielded R2 = 0.9929 and SEP = 1.31%, whereas the ANN model achieved R2 = 0.9961 and SEP = 0.99%. Remarkably, the ANN-GA approach revealed the best optimum conditions for both responses. Furthermore, the sensitivity analysis of the developed ANN model indicated the order of significance of the variables as follows: oil concentration > feed pH > feed flow rate. These findings substantiate the efficacy of the proposed approach, making it viable for implementation in diverse industries to facilitate sustainable monitoring and management practices. Institution of Chemical Engineers 2023 Article PeerReviewed Usman, Jamilu and Abba, Sani. I. and Ishola, Niyi Babatunde and El-Badawy, Tijjani and Adamu, Haruna and Gbadamosi, Afeez and Salami, Babatunde Abiodun and Usman, A. G. and Benaafi, Mohammed and Othman, Mohd. Hafiz Dzarfan and Aljundi, Isam H. (2023) Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach. Chemical Engineering Research and Design, 199 (NA). pp. 33-48. ISSN 0263-8762 http://dx.doi.org/10.1016/j.cherd.2023.09.027 DOI : 10.1016/j.cherd.2023.09.027
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TP Chemical technology
spellingShingle TP Chemical technology
Usman, Jamilu
Abba, Sani. I.
Ishola, Niyi Babatunde
El-Badawy, Tijjani
Adamu, Haruna
Gbadamosi, Afeez
Salami, Babatunde Abiodun
Usman, A. G.
Benaafi, Mohammed
Othman, Mohd. Hafiz Dzarfan
Aljundi, Isam H.
Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach
description In this study, response surface methodology (RSM) and artificial neural network-based genetic algorithm (ANN-GA) were utilized to predict two crucial output parameters of membrane performance, namely separation efficiency and oil flux, derived from experimental investigations. The central composite design (CCD) screening approach of RSM was employed to evaluate the influence of important process input parameters, such as oil concentration (ranging from 50 to 10,000 ppm), feed flow rate (ranging from 150 to 300 mL/min), and pH of the feed (ranging from 4 to 10), as well as their synergistic effects on the output variables. The constructed RSM model and ANN-GA were effectively employed to estimate the optimum conditions for maximizing the output variables. Statistical analysis using the determination coefficient (R2) and standard error of prediction (SEP), along with analysis of variance (ANOVA) and the t-test, demonstrated the accurate description of the membrane performance process by both models. For the oil flux, the RSM model showed an estimated R2 of 0.9916 and SEP of 3.54%, while the ANN model exhibited an R2 of 0.9933 and SEP of 3.31%. In terms of separation efficiency, the RSM model yielded R2 = 0.9929 and SEP = 1.31%, whereas the ANN model achieved R2 = 0.9961 and SEP = 0.99%. Remarkably, the ANN-GA approach revealed the best optimum conditions for both responses. Furthermore, the sensitivity analysis of the developed ANN model indicated the order of significance of the variables as follows: oil concentration > feed pH > feed flow rate. These findings substantiate the efficacy of the proposed approach, making it viable for implementation in diverse industries to facilitate sustainable monitoring and management practices.
format Article
author Usman, Jamilu
Abba, Sani. I.
Ishola, Niyi Babatunde
El-Badawy, Tijjani
Adamu, Haruna
Gbadamosi, Afeez
Salami, Babatunde Abiodun
Usman, A. G.
Benaafi, Mohammed
Othman, Mohd. Hafiz Dzarfan
Aljundi, Isam H.
author_facet Usman, Jamilu
Abba, Sani. I.
Ishola, Niyi Babatunde
El-Badawy, Tijjani
Adamu, Haruna
Gbadamosi, Afeez
Salami, Babatunde Abiodun
Usman, A. G.
Benaafi, Mohammed
Othman, Mohd. Hafiz Dzarfan
Aljundi, Isam H.
author_sort Usman, Jamilu
title Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach
title_short Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach
title_full Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach
title_fullStr Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach
title_full_unstemmed Genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: An integrated experimental approach
title_sort genetic neuro-computing model for insights on membrane performance in oily wastewater treatment: an integrated experimental approach
publisher Institution of Chemical Engineers
publishDate 2023
url http://eprints.utm.my/105925/
http://dx.doi.org/10.1016/j.cherd.2023.09.027
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score 13.211869

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