Fuzzy inference optimization algorithms for enhancing the modelling accuracy of wastewater quality parameters
Document Type
Article
Publication Date
9-1-2021
Abstract
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.
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
Divisions
sch_civ
Funders
National Research Foundation - South Africa [84166]
Publication Title
Journal of Environmental Management
Volume
293
Publisher
Elsevier
Publisher Location
24-28 OVAL RD, LONDON NW1 7DX, ENGLAND