A prediction method of electrocoagulation reactor removal rate based on Long Term and Short Term Memory–Autoregressive Integrated Moving Average Model

Abstract

In the process of electrochemical wastewater treatment, the removal rate of electrocoagulation reactor will be affected by various factors such as the pH value of wastewater solution, the current density, the wastewater flow rate and the initial concentration of heavy metal ions. Therefore, this study proposes a prediction method of the removal rate of the electrocoagulation reactor based on deep learning Long and Short-Term Memory (LSTM) network combined with the Autoregressive Integrated Moving Average Model (ARIMA) commonly used in engineering. Firstly, according to the concentration of heavy metal ions in the outlet and inlet solution of the reactor, the calculation formula for the removal rate of the reactor is defined. Secondly, in order to deepen the LSTM network model to analyze and learn the change trend of the historical removal rate data, the gradient value of the historical removal rate of the reactor before and after the change is extracted as its change feature value, and this feature value is taken as one of the input variables of the LSTM network model. Comprehensive analysis considered important factors such as the historical removal rate value of the reactor, the initial pH value of the wastewater solution, the voltage and current value, and the wastewater flow rate as the input variables of the LSTM deep learning network. The predicted value of the removal rate of electrocoagulation reactor is concluded by testing the combination of activation function and the number of fully connected layers, and the error compensation of the predicted value is carried out by using the ARIMA model. The effectiveness of the proposed method is verified by the industrial data collected from a wastewater treatment plant.