集成学习与深度学习融合的污水厂出水水质预测研究  

Study on Prediction of Effluent Quality of Wastewater Plant by Fusion of Integrated Learning and Deep Learning

作　　者：李翊君 刘亚珲 LI Yijun;LIU Yahui(Shanghai Municipal Engineering Design Institute(Group)Co.,Ltd.,Shanghai 200092,China)

机构地区：[1]上海市政工程设计研究总院(集团)有限公司,上海200092

出　　处：《自动化仪表》2025年第4期101-106,共6页Process Automation Instrumentation

摘　　要：针对污水处理厂出水水质波动大、影响因素复杂、传统机理模型仿真精度不足等问题,创新性地构建了集成学习与深度学习协同的预测框架。该框架通过融合极限梯度提升(XGBoost)算法和分类提升(CatBoost)算法在非线性特征映射方面的优势,结合长短期记忆(LSTM)网络与门控循环单元(GRU)双通道时序建模架构,实现了对水质多维度影响因素与动态时序特征的协同挖掘。此外,创新设计的自适应权重损失函数,有效平衡了模型对突变事件捕捉与长期趋势预测的需求。采用污水厂实际运行数据进行试验。试验结果表明,混合模型的预测误差显著低于单一模型。所提预测框架为污水处理厂出水水质的预测提供了一种新颖且高效的技术路径,在环境工程领域具有较高的应用潜力和价值。Aiming at the problems of large fluctuation of effluent quality of wastewater treatment plant,complex influencing factors,and insufficient simulation accuracy,etc.,of traditional mechanism model,a prediction framework that synergizes integrated learning and deep learning is innovatively constructed.The framework realizes the synergistic mining of multidimensional influencing factors and dynamic time series features of water quality by integrating the advantages of extreme gradient boosting(XGBoost)algorithm and categorical boosting(CatBoost)algorithm in nonlinear feature mapping,and combining the dual-channel time series modeling architectures of long short-term memory(LSTM)network and gated recurrent unit(GRU).In addition,the innovatively designed adaptive weight loss function effectively balances the model's need to capture sudden events and predict long-term trends.The actual operation data of the wastewater plant is used for the test.The experimental results show that the prediction error of the hybrid model is significantly lower than that of a single model.The proposed prediction framework provides a novel and efficient technical path for the prediction of wastewater treatment plant effluent quality,which has high potential and value for application in the field of environmental engineering.

关 键 词：污水处理 出水水质预测 集成学习 深度学习 自适应权重损失函数 模型融合 

分 类 号：TH-39[机械工程]