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, part...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 16; no. 3
Main Authors: Dhouibi, Saïda, Jarray, Raja, Bouallègue, Soufiene
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
Subjects:
Activated carbon
Activated sludge process
Algorithms
Artificial intelligence
Biochemical oxygen demand
Carbon
Chemical oxygen demand
Computer science
Design
Dissolved oxygen
Effluents
Energy consumption
Genetic algorithms
Heuristic methods
Neural networks
Nitrogen
Optimization
Parameters
Particle swarm optimization
Pollutants
Predictive control
Process controls
Quality standards
Sludge
Solid suspensions
Variance analysis
Wastewater treatment
Water pollution
Water quality
Water treatment
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary: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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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160339