Prediction of wastewater COD and total nitrogen based on Informer model improved by Long Short-Term Memory

Abstract

[Objective] Monitoring and managing effluent quality indicators of wastewater treatment plants is critical for environmental protection. However, wastewater treatment involves highly variable and unpredictable complex biochemical processes. [Methods] A prediction model for effluent COD and total nitrogen in wastewater treatment plants was established based on a Long Short-Term Memory(LSTM)-Informer. The LSTM module was first used to extract temporal dependencies from the influent quality indicators. Subsequently, the extracted temporal features were integrated into the Informer module to address the issue of long-term dependency. Additionally, through the analysis of performance evaluation indicators of several typical models(Convolutional Neural Network, CNN, LSTM, CNN-LSTM, and Informer), the fitting performance and prediction performance of each model were compared. [Results] The experimental result showed that the LSTM-Informer model could accurately predict the effluent COD and TN concentrations. On the experimental dataset, the mean squared errors between the predicted and actual values were 0. 630 mg · L^-1 and 0. 105 mg · L^-1, respectively. The root mean square errors were 0. 79 mg·L^-1 and 0. 32 mg · L^-1, the mean absolute errors were 0. 60 mg · L^-1 and 0. 24 mg · L^-1, the mean absolute percentage errors were 2. 16% and 2. 26%, and the coefficients of determination(R²) were 0. 97 and 0. 90, respectively. All these indicators outperformed those of several typical neural network models. In the statistical significance test, it was verified that the p-values of the LSTM-Informer model were all greater than those of the other comparative models at a significance level of 0. 05. [Conclusion] The result demonstrate that the LSTM-Informer model, which captures short-term temporal dependencies via the LSTM network and extracts long-term temporal features through the multi-head sparse self-attention mechanism of the Informer, provides a reliable basis for the long-term monitoring and prediction of wastewater effluent quality. Its accuracy demonstrates advantages in both evaluation indicators and statistical tests. Overall, this model can provide early warning of water quality changes for wastewater treatment plants, which is crucial for achieving intelligent control and optimizing treatment processes.

Keywords

wastewater treatment / neural network / LSTM-Informer model / COD prediction / total nitrogen prediction / attention mechanism / long-term time series prediction / water quality early warning