Intelligent control of the electrochemical nitrate removal basing on artificial neural network (ANN)

Artificial intelligence technologies were confirmed as a useful tool for wastewater treatment, but its application in the electrochemical nitrate removal had less been reported. In this work, the artificial neural network in machine learning was used to construct the model, which combines the methods of electrochemistry and arti-ficial intelligence to achieve the prediction and intelligent control of nitrate removal. The control system consists of a prediction module with an artificial neural network (ANN) algorithm model and a control module. First, initial nitrate concentration, pH, time and current density were considered as input. An ANN algorithm using 7 hidden layers and a negative feedback regulation mechanism was developed to optimize the model and predict the nitrate removal rate. Results indicates that the proposed prediction model (4-7-1) yields a better coefficient of determination and lower root mean square error. The optimal set-points of the current density in the elec-trochemical process can be obtained according to the water quality change and qualified effluent quality using the ANN model. Also, the proposed intelligent control strategy can eliminate the influence of water quality change on nitrate removal and reduce energy consumption by 15.0 % compared to the post strategy. This work demonstrated the potential of artificial intelligence in the electrochemical process of nitrate removal.

Automated summary

This study utilized artificial intelligence technology to enhance the electrochemical process of nitrate removal by constructing a prediction and control model using artificial neural network in machine learning. It demonstrated the potential of artificial intelligence in achieving intelligent control and reducing energy consumption.