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 Tart Pans/Molds 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 Dog Treats nitrogen (NH4+-N) 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.