Application Evaluation of Particle Swarm Optimization Algorithm in Organic Pollution Control Prediction System

Pollution control is a complex system engineering, and in order to truly achieve comprehensive environmental management and protection, it is necessary to do a good job in controlling pollution at the source. However, due to historical reasons and economic development levels, many places still have serious pollution phenomena. This paper mainly analyzes and discusses the problem of organic pollution, uses particle swarm optimization (PSO) algorithm to screen pollutants, and combines BP (back propagation) neural network to establish a pollution source prediction system. According to the selected optimal solution, a new mathematical model is constructed, which can better serve environmental protection, improve work efficiency, and reduce operating costs, It has high practical value. Comparing the PSO algorithm model with the BP neural network model in the treatment of organic pollutants, experimental data shows that the application effect of the PSO algorithm is more ideal, the prediction results are more accurate, and the pollutant removal rate has also increased by about 3.53%, providing new ideas and methods for environmental science and engineering exploration. The research results show that applying PSO algorithm to organic pollution control prediction systems can achieve certain results. By optimizing model parameters or control strategy parameters, the accuracy and control effectiveness of organic pollutant prediction can be improved. The PSO algorithm can also be used to optimize risk assessment models and improve the accuracy of organic pollutant risk assessment.