Multi-objective Optimal Control of Wastewater Treatment Process Based on Neural Network

The essential to reduce the loss in this stage and produce high-quality final products. First, to this end, a centralized model is developed using multi-objective optimization methods. The results show that these two objectives can be achieved simultaneously and satisfactorily by considering all the parameters and calculating the overall objective function value and each parameter affecting the final result respectively. The activated sludge wastewater treatment process mainly uses biological degradation and other methods to remove pollutants. It has the characteristics of multivariable, nonlinear, strong coupling, large lag and uncertainty. The process operates in an unstable state, making it difficult to measure process parameters in real time and control them very difficult. Therefore, the study of intelligent optimization control method and its application in sewage treatment process to achieve effective control of the treatment process can not only improve the effluent quality, but also have important significance for energy conservation and consumption reduction.