Neural Network Modeling based XAI of Activated Sludge Process in Wastewater Treatment System for Dissolved Oxygen Control

In this paper, we proposed Dissolved Oxygen(DO) neural network model of activated sludge process using XAI(eXplainable AI) in wastewater treatment system. To improve the model performance, input water qualities are to be reliable and have a much influences in DO biological operation. In regulations, COD, T-N, T-P, pH, SS of effluent are hourly to transmitted in Korea Environment Corporation. If these values are exceed the legal standards, the penalty is given. Therefore these data are very reliable and is monitored by operators critically. So these data is to be inputs of DO neural network model. And XAI(eXplainable AI) is utilized to decide which input water qualities have much influences in the process. LRP(Layer-wise Relevance Propagation) is used among various XAI(eXplainable AI) methods. NH4, MLSS, pH in aeration tank and COD, TN, TP, SS in secondary clarifier are input candidates of model for Do neural network modeling. Using LRP, COD, NH4, MLSS, SS are decided to be inputs of Do neural network model. The validity of the proposed method was proved by applying to the DO neural network model of activated sludge process which was developed in previous research. 3 years hourly data was used for modeling and estimation. The result show that the performance of the proposed model was improved in comparison of conventional neural network models. In the future, absolute values of weight in LRP will be more considered because we considered only the inputs orders of influencing on DO biological operation. © 2022 Korean Institute of Electrical Engineers. All rights reserved.