Carbon Pollution Removal in Activated Sludge Process of Wastewater Treatment Systems Using Grey Wolf Optimization-Based Approach

Managing wastewater to effectively remove water pollution is inherently difficult. Ensuring that the treated water meets stringent standards is a main priority for several countries. Advances in control and optimization strategies can significantly improve the elimination of harmful substances, particularly in the case of carbon pollution. This paper presents a novel optimization-based approach for carbon removal in Activated Sludge Process (ASP) of Wastewater Treatment Plants (WWTPs). The developed pollution removal algorithm combined the concepts of Takagi-Sugeno (TS) fuzzy modeling, Model Predictive Control (MPC) and Grey Wolf Optimization (GWO), as a parameters-free metaheuristics algorithm, to boost the carbon elimination in terms of standard metrics, namely Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5) and Total Suspended Solids (TSS). To enhance such a pollution removal, the proposed fuzzy predictive control for all wastewater variables, i.e. effluent volume, concentrations of heterotrophic biomass, biodegradable substrate and dissolved oxygen, is formulated as a constrained optimization problem. The MPC parameters’ tuning process is therefore performed to select appropriate values for weighting coefficients, prediction and control horizons of local TS sub-models. To demonstrate the effectiveness of the proposed parameters-free GWO algorithm, comparisons with homologous state-of-the-art solvers such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as well as the standard commonly used Parallel Distributed Compensation (PDC) technique, are carried out in terms of key purification indices COD, BOD5, and TSS. Additionally, an ANOVA study is conducted to evaluate the reported competing metaheuristics using Friedman ranking and post-hoc tests. The main findings highlight the superiority of the proposed GWO-based carbon pollution removal in WWTPs with elimination efficiencies of 93.9% for COD, 93.4% for BOD5, and 94.1% for TSS, in comparison with lower percentages for PSO, GA and PDC techniques.

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Publication Details

Journal
International Journal of Advanced Computer Science and Applications
Published
2025-01-01
DOI
https://doi.org/10.14569/ijacsa.2025.0160339
Primary Topic
Wastewater Treatment and Nitrogen Removal
Type
article
Field-Weighted Citation Impact
0.00
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article

Carbon Pollution Removal in Activated Sludge Process of Wastewater Treatment Systems Using Grey Wolf Optimization-Based Approach

Saïda Dhouibi, Soufiene Bouallègue, Raja Jarray
International Journal of Advanced Computer Science and Applications
Wastewater Treatment and Nitrogen Removal
article

Carbon Pollution Removal in Activated Sludge Process of Wastewater Treatment Systems Using Grey Wolf Optimization-Based Approach

Saïda Dhouibi, Soufiene Bouallègue, Raja Jarray
article en

Abstract

Managing wastewater to effectively remove water pollution is inherently difficult. Ensuring that the treated water meets stringent standards is a main priority for several countries. Advances in control and optimization strategies can significantly improve the elimination of harmful substances, particularly in the case of carbon pollution. This paper presents a novel optimization-based approach for carbon removal in Activated Sludge Process (ASP) of Wastewater Treatment Plants (WWTPs). The developed pollution removal algorithm combined the concepts of Takagi-Sugeno (TS) fuzzy modeling, Model Predictive Control (MPC) and Grey Wolf Optimization (GWO), as a parameters-free metaheuristics algorithm, to boost the carbon elimination in terms of standard metrics, namely Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5) and Total Suspended Solids (TSS). To enhance such a pollution removal, the proposed fuzzy predictive control for all wastewater variables, i.e. effluent volume, concentrations of heterotrophic biomass, biodegradable substrate and dissolved oxygen, is formulated as a constrained optimization problem. The MPC parameters’ tuning process is therefore performed to select appropriate values for weighting coefficients, prediction and control horizons of local TS sub-models. To demonstrate the effectiveness of the proposed parameters-free GWO algorithm, comparisons with homologous state-of-the-art solvers such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as well as the standard commonly used Parallel Distributed Compensation (PDC) technique, are carried out in terms of key purification indices COD, BOD5, and TSS. Additionally, an ANOVA study is conducted to evaluate the reported competing metaheuristics using Friedman ranking and post-hoc tests. The main findings highlight the superiority of the proposed GWO-based carbon pollution removal in WWTPs with elimination efficiencies of 93.9% for COD, 93.4% for BOD5, and 94.1% for TSS, in comparison with lower percentages for PSO, GA and PDC techniques.

International Journal of Advanced Computer Science and ApplicationsVol. 16(3)
Clean water and sanitation
Openalex Percentile: Top 95%
Wastewater Treatment and Nitrogen Removal
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