Optimization of a real municipal sewage treatment plant using CRFSMA algorithm and a mathematical model

This study presents the development, calibration, and validation of a mathematical model tailored for biological wastewater treatment at an actual urban sanitation facility. Utilizing multi-criteria optimization techniques, the research identified the most effective MCO algorithm by assessing Pareto optimal solutions. The model incorporated three primary performance measures energy consumption, overall volume, mean quality of effluent, and optimized 12 process parameters. Three algorithms, CRFSMA, particle swarm algorithm, and adaptive non-dominated sorting genetic algorithm III, were rigorously tested using MATLAB. The CRFSMA method emerged as superior, achieving enhanced Pareto optimal solutions for three-dimensional optimization. Quantitative improvements were observed with a 14.8 % increase in wastewater quality and reductions in total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and ammonium nitrogen ( $$ - $$ ) concentrations by 0.95, 2.38, 0.04, and 0.14 mg/L, respectively. Additionally, the total cost index and overall volume were decreased, contributing to an 18.27 % reduction in overall volume and an 18.83 % decrease in energy utilization. The adapted anaerobic-anoxic-Oxic (A²O) framework, based on real-world wastewater treatment plants, demonstrated compatibility with observed effluent variables, signifying the potential for energy savings, emission reductions, and urban sanitation enhancements.