## 1 Introduction
[PERSON] used an adaptive particle swarm algorithm to optimize a mass spectrometer in his laboratory, and found that the algorithm significantly improved the performance of the mass spectrometer [13]. [PERSON] proposed a threshold method combined with the MOPSO algorithm, and compared it with the existing population-based threshold method, and finally concluded that the new method had a better processing effect [14]. These research works on particle swarm optimization are relatively detailed, but they did not involve sewage treatment. In view of the current situation of the sewage treatment system, this paper first introduces the concept and principle of the MOPSO algorithm, then constructs an intelligent control system for sewage treatment based on the MOPSO algorithm, then optimizes the adaptive energy consumption and effluent quality model of sewage treatment using the MOPSO algorithm, and uses a PID (proportional integral differential) controller to track and control the optimal set values in each key model. Finally, the multi-objective optimization control of the sewage treatment process is realized. The research shows that the new intelligent control optimization method of sewage treatment has a good effect for improving the efficiency of sewage treatment. Correlation between Sewage Treatment and Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm  
The purpose of the optimal control of wastewater treatment is to achieve energy conservation and consumption reduction under the condition of effluent constraints. The total cost of its control operation mainly includes two aspects: system energy consumption and fines caused by excessive effluent quality. These two evaluation indicators contradict each other in the process of wastewater treatment control, and many of them are limited by indicators. Therefore, it is of great significance to develop a method that can deal with the multi-objective optimization problems of the wastewater treatment control process. The particle swarm optimization (PSO) algorithm simulates the foraging behavior of birds, and compares the search space of the problem with the flight space of birds, which can realize the search of the optimal solution in the complex space. However, just like the effluent quality and system energy consumption in the sewage treatment process,they are a pair of contradictory target indicators. Many optimization problems need to be optimized simultaneously for multiple target problems. Compared with the single objective optimization problem, the multi-objective optimization problem cannot find an excellent solution to the multi-objective problem in a simple search process. In this paper, combined with the multi-objective particle swarm optimization (MOPSO) algorithm, the sewage treatment system was studied. Figure 1 is a schematic diagram of the MOPSO algorithm, which is formed on the basis of single objective PSO algorithm and has a good performance when solving multi-objective optimization problems. When selecting the individual optimal solution, the individual optimal solution can be replaced with the set optimal solution, or the selection of the global optimal solution can be the focus. At present, most MOPSO algorithms use the Pareto optimality theorem to solve problems. The calculation mode formed by this method has become a stable program flow [15]. Intelligent Control Method of Sewage Treatment Combined with the Multi-Objective Particle Swarm Optimization Algorithm