Operation Optimization of Wastewater Treatment Process Based on Robust Subgroup Information Synergy Strategy

In order to address the issues of system fluctuation and low efficiency brought on by various disturbance factors in wastewater treatment plants (WWTPs), this study builds a robust multi-objective optimization model and determines the ideal parameters for enhancing the robustness and stability of energy consumption (EC) and effluent quality (EQ). Firstly, a novel robust index is proposed, merging the signal-to-noise ratio and standard deviation., comprehensively considering data volatility and anti-interference capability to guide the establishment of a robust optimization model. Secondly, considering the energy consumption errors and water quality data measurement caused by noise factors, which increases the difficulty and time complexity of the solution. Therefore, this paper uses a multi-objective mantis search algorithm (MOMSA) based on a robust subgroup, and an information synergy strategy is used to solve the problem. The strategy first divides the population into strong and weak robust subgroups according to their robustness. Then, the co-evolutionary strategy and the information enhancement strategy will be used to guide the evolutionary direction of the individual from the whole and the local perspectives, thereby comprehensively improving the convergence speed of the algorithm. Based on the test function, this shows that the convergence, diversity, and comprehensive performance of the improved algorithm are better than other algorithms. Experimental results based on the BSM1 platform show that the improved algorithm is effective in finding robust solutions with strong stability despite changes in external factors (e.g., precipitation). According to the experimental results based on real industrial problems, the new robustness metrics can effectively evaluate the robustness of the optimization model, and compared with other algorithms, the algorithm proposed in this paper yields solutions of more reliable quality and faster convergence performance, which can successfully solve the robust optimization problem in wastewater treatment plants.