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

SUBMITTER: Yin H 

PROVIDER: S-EPMC11667701 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature