Construction and Optimization of Pollution Control Cost Model Based on Neural Network Technology

In practice, there is still a great deal of uncertainty about the choice of specific water pollution prevention and control technologies, and it is difficult to effectively support the current significant demand for water pollution prevention and control. This paper takes the copper mining industry as an example, and through the data analysis of neural network, it is concluded that the operating cost of wastewater treatment has individual differences among different enterprises. The relationship between the effectiveness of mining water pollution control and cost control is analyzed by combining an analogous approach with a market value approach. Based on the pollutant source survey data, the copper mining water pollution control (WPC) cost model is used to estimate the sewage treatment operation cost function, and on this basis, the study on the influence factors of industrial sewage treatment operation cost is carried out. In the comparison of average daily water consumption and annual water cost of different mining and selection plants, before adopting the method of this paper, the average daily water consumption of mining and selection plant A is 10,000 tons, and the annual water cost is 5 million yuan. After adopting the method of this paper, the average daily water consumption of the mining and selection plant A is 5000 tons, and the annual water cost is 2.2 million yuan. By constructing a cost model for controlling water pollution in mineral processing, the generation of pollutants can be more accurately predicted and their emissions are controlled. The optimal process combination under different working conditions can be realized through the continuous improvement and optimization of the model, thus effectively reducing the energy consumption and cost of the sewage treatment process and realizing the goal of sustainable development and environmental protection.