Fuzzy inference optimization algorithms for enhancing the modelling accuracy of wastewater quality parameters

Abunama, Taher and Ansari, Mozafar and Awolusi, Oluyemi Olatunji and Gani, Khalid Muzamil and Kumari, Sheena and Bux, Faizal (2021) Fuzzy inference optimization algorithms for enhancing the modelling accuracy of wastewater quality parameters. Journal of Environmental Management, 293. ISSN 0301-4797, DOI https://doi.org/10.1016/j.jenvman.2021.112862.

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To ensure the safe discharge of treated wastewater to the environment, continuous efforts are vital to enhance the modelling accuracy of wastewater treatment plants (WWTPs) through utilizing state-of-art techniques and algorithms. The integration of metaheuristic modern optimization algorithms that are natlurally inspired with the Fussy Inference Systems (FIS) to improve the modelling performance is a promising and mathematically suitable approach. This study integrates four population-based algorithms, namely: Particle swarm optimization (PSO), Genetic algorithm (GA), Hybrid GA-PSO, and Mutating invasive weed optimization (M-IWO) with FIS system. A full-scale WWTP in South Africa (SA) was selected to assess the validity of the proposed algorithms, where six wastewater effluent parameters were modeled, i.e., Alkalinity (ALK), Sulphate (SLP), Phosphate (PHS), Total Kjeldahl Nitrogen (TKN), Total Suspended Solids (TSS), and Chemical Oxygen Demand (COD). The results from this study showed that the hybrid PSO-GA algorithm outperforms the PSO and GA algorithms when used individually, in modelling all wastewater effluent parameters. PSO performed better for SLP and TKN compared to GA, while the M-IWO algorithm failed to provide an acceptable modelling convergence for all the studied parameters. However, three out of four algorithms applied in this study proven beneficial to be optimized in enhancing the modelling accuracy of wastewater quality parameters.

Item Type: Article
Funders: National Research Foundation - South Africa [84166]
Uncontrolled Keywords: Fuzzy inference systems (FIS); Genetic algorithm (GA); Particle swarm optimization (PSO); Hybrid PSO-GA; Mutating invasive weed optimization (M-IWO); Wastewater treatment plant (WWTP) modelling
Subjects: Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 02 Aug 2022 01:16
Last Modified: 02 Aug 2022 01:16
URI: http://eprints.um.edu.my/id/eprint/28286

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