Intelligent Control Based on BP Artificial Neural Network for Electrochemical Nitrate Removal
Achieving effective control of parameters in the process of nitrate wastewater treatment is significant to the effect of electrochemical treatment. The powerful nonlinear mapping ability, self-adaptation and self-learning ability of neural network technology can optimize the electrochemical processing process. However, there are few researches in this direction. Hence, based on the test data of the electrochemical reduction of nitrate, an electrochemical prediction model was established by using the BP neural network algorithm. Considering the correlation of various parameters in the electrochemical process, the reaction time, initial nitrate nitrogen concentration, pH and current density were determined as the input layer of the BP neural network for model establishment. Results showed that optimal network configuration of 4-7-1 was achieved by optimizing the hyperparameters of number of hidden layers, the number of neurons and epoch. Then, the predicted value of nitrate nitrogen concentration was consistent with the measured value, and an R2 of 0.9095 was obtained. Meanwhile, the model predicts the effect of initial concentration, pH and current density on the removal efficiency of nitrate nitrogen. In the weak alkaline environment, the stability and reliability of nitrate electroreduction are higher than those in acidic and alkaline environments, and the predicted value of nitrate nitrogen is highly correlated with the true value (R2=0.9908). The initial concentration is negatively correlated with the removal rate, and the current density is positively correlated. Finally, the neural network model was used to control the electrochemical nitrate reduction process. Energy consumption tests were designed by optimizing current density and an 15% reduction energy consumption was obtained within same processing time and processing efficiency. Also, through the prediction model, the effluent quality can be guaranteed by timely adjusting the parameter in the case of sudden water quality changes. The research results can provide a reference for the intelligent control of the electrochemical removal of nitrate. At the same time, combining the understanding of the electrochemical treatment system and artificial intelligence technology, several ideas were proposed for the application of artificial intelligence technology in the direction of electrochemical water treatment.