摘要
本文提出了一种利用循环神经网络进行污水处理过程模拟的方法,旨在开发适用于序批式反应器(SBR)处理生活污水的软测量模型。结合易于获取的其他变量的实测值,该模型实现了污水处理重要水质指标的实时监测。通过收集生活污水处理单元的实测数据,并与模型预测结果进行比较研究,验证了该模型在实际运行环境中的有效性。本研究的软测量模型为SBR工艺过程水质预测提供了一种有效的低成本解决方案。通过重要指标的软测量,可以改善运行策略,提高处理效果,为污水处理提供可持续发展的路径。
This study proposes a method for simulating the wastewater treatment process using recurrent neural networks and aims to develop a soft measurement model applicable to the sequencing batch reactor for treating domestic wastewater.The model combines the measured values of other easily obtainable variables to achieve real-time monitoring of important water quality indicators in wastewater treatment.By collecting measured data from the domestic wastewater treatment unit and comparing it with the model′s predicted results,the effectiveness of the model in practical operating environments has been validated.The soft measurement model developed in this study provides an effective and cost-efficient solution for predicting water quality in the SBR process.Through soft measurement of important indicators,operational strategies can be improved,and treatment efficiency can be enhanced,thereby offering a sustainable pathway for wastewater treatment.
作者
代先锋
袁玥
曹强
Dai Xianfeng;Yuan Yue;Cao Qiang(Environmental Monitoring in Qianfeng District,Guang′an Sichuan 638019,China;Love-soil Engineering Environmental Technology Co.,Ltd.,Beijing 100020,China)
出处
《环境与发展》
2024年第6期56-61,共6页
Environment & Development
关键词
循环神经网络
SBR
软测量
Recurrent neural networks
SBR
Soft measurement
