Yin Hailong, Chen Yongqi, Zhou Jingshu, Xie Yifan, Wei Qing, Xu Zuxin
Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai, 200092, China.
Water Res X. 2024 Dec 3;26:100291. doi: 10.1016/j.wroa.2024.100291. eCollection 2025 Jan 1.
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.
污水处理厂（WWTPs）进水流量或浓度显著增加的突发冲击负荷事件，是实现达标排放的主要威胁。为了帮助进行污水处理厂稳定运行的实时决策，本研究开发了一种概率深度学习模型，该模型由编码器 - 解码器长短期记忆（LSTM）网络组成，并增加了生成概率预测的能力，以增强此类事件下污水处理厂实时出水水质预测的稳健性。所开发的概率编码器 - 解码器LSTM（P - ED - LSTM）模型在实际污水处理厂中进行了测试，对总氮进行了每两小时一次的出水水质预测，并与经典深度学习模型进行了比较，包括LSTM、门控循环单元（GRU）和Transformer。研究发现，在冲击负荷事件下，与LSTM、GRU和Transformer相比，P - ED - LSTM在每两小时一次的出水浓度实时预测中，预测准确率可提高49.7%。P - ED - LSTM模型输出的概率数据的较高分位数表明预测值更接近实际出水水质。P - ED - LSTM模型在冲击负荷场景下对未来多个时间步也表现出更高的预测能力。它能够提前6小时捕捉到约90%的实际超标排放情况，显著优于其他深度学习模型。因此，P - ED - LSTM模型对显著波动具有强大的适应性，有潜力在不同工艺的污水处理厂中得到更广泛的应用，并为紧急情况下的废水系统调控提供策略。
