基于深度卷积注意力时序网络的污水处理厂进水水质预测

Prediction of influent water quality in wastewater treatment plants based on deep convolutional attention temporal networks

摘要

【目的】在我国“双碳”背景下，污水处理厂进水水质的准确预测对于节能减排和降低能耗具有重要意义。【方法】针对传统污水进水水质预测方法(如人工神经网络、循环神经网络及长短期记忆网络)在处理污水水质特征的随机性和非线性时精度不足的问题，提出了一种基于卷积注意力时序网络(CAT-NN)的预测模型。该模型结合了多尺度信息融合与混合注意力机制，并采用时序解码模块，有效捕捉污水水质指标的长期趋势与短期突变特性。【结果】通过对陕西省延安市某污水处理厂进水COD、NH₃-N、TN和TP四项典型水质指标数据的预测分析，CAT-NN模型展现了优越的预测性能，其均方根误差(RMSE)为4.50%,平均绝对误差(MAE)为5.00%。与传统模型(如ANN、LSTM和门控循环单元GRU)相比，RMSE和MAE分别提升了16.13%和20.00%以上。【结论】结果表明：CAT-NN模型在污水处理厂进水水质预测中具有更高的精度和更强的鲁棒性。该模型不仅为污水处理厂的精确控制与高效运维提供了有力支撑，也为实现节能减排目标提供了重要的技术保障。

Abstract

[Objective] Under the “dual carbon” goals in China, the accurate prediction of influent water quality in wastewater treatment plants is crucial for energy conservation, emission reduction, and energy consumption reduction. [Methods] To address the insufficient accuracy of traditional influent water quality prediction method(such as artificial neural networks, recurrent neural networks, and long short-term memory networks) in handling the randomness and nonlinearity of wastewater water quality characteristics, a prediction model based on convolutional attention temporal neural network(CAT-NN) was proposed. The model integrated multi-scale information fusion and a hybrid attention mechanism, along with a temporal decoding module, to effectively capture the long-term trends and short-term abrupt changes in wastewater water quality indicators. [Results] Through the predictive analysis of four typical water quality indicators—COD, NH₃-N, TN, and TP—of influent water data from a wastewater treatment plant in Yan'an City, Shaanxi Province, the CAT-NN model demonstrated excellent prediction perfor-mance, with a root mean square error(RMSE) of 4.50% and a mean absolute error(MAE) of 5.00%. Compared to traditional models(such as ANN, LSTM, and gated recurrent units(GRU)), the RMSE and MAE improved by over 16.13% and 20.00%, respectively. [Conclusion] The result indicate that the CAT-NN model achieves higher accuracy and stronger robustness in predicting influent water quality in wastewater treatment plants. The model not only provides strong support for the precise control and efficient operation of wastewater treatment plants, but also serves as a key technological solution for achieving energy conservation and emission reduction goals.

关键词

污水处理厂 / 进水水质预测 / 卷积注意力时序网络 / 深度学习 / 碳中和 / 模型性能

Key words

wastewater treatment plants / prediction of influent water quality / convolutional attention temporal network / deep learning / carbon neutrality / model performance

基金资助

国家重点研发计划(2018YFE0103800)

陕西省住建节能能力建设课题项目(2022-03514)