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

journal contribution
posted on 2023-10-09, 15:45 authored by Jamilu Usman, Sani.I. Abba, Niyi Babatunde Ishola, Tijjani El-Badawy, Haruna Adamu, Afeez Gbadamosi, Babatunde SalamiBabatunde Salami, A.G. Usman, Mohammed Benaafi, Mohd Hafiz Dzarfan Othman, Isam H. Aljundi

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.

History

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Citation

Usman, J., Abba, S.I., Ishola, N.B., El-Badawy, T., Adamu, H., Gbadamosi, A., Salami, B.A., Usman, A.G., Benaafi, M., Othman, M.H.D. and Aljundi, I.H. (2023) 'Genetic neuro-computing model for insights on membrane perforFmance in oily wastewater treatment: an integrated experimental approach', Chemical Engineering Research and Design. https://doi.org/10.1016/j.cherd.2023.09.027

Print ISSN

0263-8762

Cardiff Met Affiliation

  • Cardiff School of Management

Cardiff Met Authors

Babatunde Salami

Copyright Holder

  • © The Publisher

Language

  • en