Modeling and optimizing of an actual municipal sewage plant: A comparison of diverse multi-objective optimization methods

The activated sludge process of an actual municipal sewage treatment plant was systematically modeled, calibrated, and verified in this study. Identified multi-objective optimization (MOO) methods were employed to optimize the process parameters of the validated model, and the optimal MOO algorithm was obtained by comparing Pareto solution sets. The optimization model consisted of three key evaluation indicators (objective functions), which are the average effluent quality (AEQ), overall cost index (OCI), and total volume (TV) of the biochemical tank, along with 12 more process parameters (decision variables). Three optimization algorithms, i.e., adaptive non-dominated sorting genetic algorithm III (ANSGA-III), non-dominated sorting genetic algorithm II (NSGA-II), and particle swarm algorithm (PSO), were adopted using MATLAB. The comparison of these algorithms demonstrated that the ANSGA-III algorithm had better Pareto solution sets under the triple objective optimization, and the effluent quality of COD, TN, NH₄⁺-N, and TP after optimization decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. Additionally, the simulated AEQ was reduced by 13% compared to the original effluent, and the OCI and TV decreased from 21,023 kWh d^-1 and 17,065 m³ to 20,226 kWh d^-1 and 16,530 m³, respectively. The reported ANSGA-III algorithm and the proposed multi-objective method have a promising ability for energy conservation, emission reduction, and upgrading of municipal sewage treatment plants.

Modeling and performance improvement of an anaerobic-anoxic/nitrifying-induced crystallization process via the multi-objective optimization method

The trade-off between energy savings and emission reductions of an activated sludge process is a multi-objective problem relating to several potentially conflicting objectives. Therefore, the optimal modification of an anaerobic-anoxic/nitrifying/induced crystallization (A2N-IC) process by multi-objective optimization method was studied in this work. The multi-objective optimization model comprised three evaluative indices, (effluent quality (EQ), operation cost (OC), and total volume (TV) of structures), and 14 process parameters (decision variables) solving by non-dominated sorting genetic algorithm II (NSGA-II) in MATLAB. The trade-off relationships among EQ, OC, and TV were investigated under 30 days of dynamic influent with different constraint conditions. A series of Pareto solutions were obtained, and one Pareto solution was selected for further analysis. Results showed improved effluent concentrations of chemical oxygen demand (COD), total nitrogen (TN), ammonia-nitrogen (NH₄⁺-N), and total phosphorous (TP) under the optimized strategy compared to the original strategy, where the average effluent concentrations decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. The values of EQ and OC decreased from 0.015 kg/day and 0.15 ¥/m³ to 0.0023 kg/day and 0.12 ¥/m³, respectively, while the TV increased from 0.31 to 0.33 m³. These results indicated that the multi-objective optimization method is useful for modifying activated sludge processes.