Comparative analysis and application of soft sensor models in domestic wastewater treatment for advancing sustainability.

This study focuses on the development and evaluation of soft sensor models for predicting NH₃-N values in a wastewater treatment process. The study compares the performance of linear regression (LR), neural networks (NN) and random forest regression (RFR) models. The proposed methodology involves optimizing the sequencing batch reactor process using artificial intelligence and an automatic control system. Real-time NH₃-N values are obtained by inputting data from electronic conductivity and temperature sensors into the prediction models. Once the predicted NH₃-N value falls below the effluent standard, the cycle ends, improving energy efficiency and sustainability by cutting down the agitator and aerator. The research results demonstrate that the RNN-based NH₃-N soft sensor built in this study exhibits the best performance, which is promising for wastewater treatment process optimization and evaluation. The results show that sensor model NNR_[0.5Y]H exhibits exceptional performance, utilizing recurrent neural network with 5-step input delays. Sensor NN_R[0.5Y]H exhibits an R² of 0.921, an RMSE of 6.110, and an MAE of 4.558. Based on the findings, recurrent neural network (RNN) variants emerge as the most effective modeling technique due to their ability to capture temporal dependencies and handle variable-length sequences. This study provides satisfied performance results for the NNR_[0.5Y]H soft sensor model in NH₃-N monitoring and process optimization in wastewater treatment, highlighting the effectiveness of recurrent neural networks and their contribution to improving interpretability, accuracy, and adaptability of soft sensor models.