## 5 Experimental Simulation Results of Intelligent Control Method for Sewage Treatment  
This paper optimizes the sewage treatment process using the MOPSO algorithm and data analysis technology. In this paper, variable constraint conditions and performance indexes are input according to the state equation of the sewage treatment-controlled system, and a group of optimal control variables are provided for sewage treatment through calculation. The overall sewage treatment can be realized according to the optimal control, which can maximize the treatment effort, treatment efficiency, and treatment cost. In order to verify the optimization effect, the following experiments were designed: using MATLAB software to simulate the sewage treatment environment. The sample data came from the domestic sewage treatment data of City A in 2020. In this paper, the domestic sewage treatment data were taken as the optimization objective function, and the multi-objective particle swarm optimization algorithm was used to optimize the objective function so as to obtain the optimal set value of the control variable, and then the optimal set value of the control variable was transmitted to the controller for the tracking control. Then, the optimization methods proposed in this paper were tested from the sewage treatment cost, sewage treatment power consumption, sewage treatment capacity, and sewage treatment efficiency. Table 1 shows the sewage treatment cost of City A for two months, which is divided into pre-optimization and post optimization. The treatment cost per ton of sewage before optimization is set at 1200 RMB/ton. It can be seen from the above table that under the same total amount of sewage, the sewage treatment cost in the first month after optimization is 0.723 million RMB less than that before optimization, and the sewage treatment cost per 100 tons is 10.14 million RMB, 0.86 million yuan less than that before optimization. Similarly, although the total amount of sewage increased in February, with the help of the optimization method, both the total cost and the cost per 100 tons decreased a lot compared with that before optimization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1 st Month** & \multicolumn{3}{c}{**2 nd Month**} \\ \hline  & **before Optimization** & **Optimized** & **before Optimization** & **Optimized** \\ \hline Sewage treatment volume & 38.9 tons & 38.9 tons & 45.7 tons & 45.7 tons \\ \hline Total cost & 4.668 million & 3.945 million & 5.484 million & 4.413 million \\ \hline
100 tons of water cost & 12 million & 10.14 million & 12 million & 9.67 million \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sewage treatment cost of factory A for two months. The change in sewage treatment can could reflect that the new method is effective at capital consumption. In the method part, the intelligent control of the sewage treatment process is realized combined with the MOPSO algorithm. According to the above content, the simulation experiment is designed as follows: the MOPSO algorithm is used to set and control the oxygen demand concentration in the sewage treatment process. The water inflow is a constant value, and the expected oxygen demand concentration is 6 mg/L. The simulation results are shown in Figure 4.  
It can be concluded from the graph that with the support of the MOPSO algorithm, the control system becomes more accurate and stable, and the response speed is also improved compared with that before optimization. The most important energy consumption in the sewage treatment process is electricity. In order to verify whether the optimized sewage treatment method can reduce the electricity consumption, this paper investigates the electricity consumption of sewage treatment in City A within 5 weeks. The power consumption is divided into before optimization and after optimization. The weekly sewage treatment amount before and after optimization is the same.