A probabilistic deep learning approach to enhance the prediction of wastewater treatment plant effluent quality under shocking load events.

Abstract

Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment of treated effluents to discharge quality standards. To aid in real-time decision-making for stable WWTP operations, this study developed a probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity of producing probability predictions, to enhance the robustness of real-time WWTP effluent quality prediction under such events. The developed probabilistic encoder-decoder LSTM (P-ED-LSTM) model was tested in an actual WWTP, where bihourly effluent quality prediction of total nitrogen was performed and compared with classical deep learning models, including LSTM, gated recurrent unit (GRU) and Transformer. It was found that under shocking load events, the P-ED-LSTM could achieve a 49.7% improvement in prediction accuracy for bihourly real-time predictions of effluent concentration compared to the LSTM, GRU, and Transformer. A higher quantile of the probability data from the P-ED-LSTM model output, indicated a prediction value more approximate to real effluent quality. The P-ED-LSTM model also exhibited higher predictive power for the next multiple time steps with shocking load scenarios. It captured approximately 90% of the actual over-limit discharges up to 6 hours ahead, significantly outperforming other deep learning models. Therefore, the P-ED-LSTM model, with its robust adaptability to significant fluctuations, has the potential for broader applications across WWTPs with different processes, as well as providing strategies for wastewater system regulation under emergency conditions.