山东科学2024，Vol.37Issue(6)：116-124,9.DOI:10.3976/j.issn.1002-4026.20240010
基于LSTM模型的污水处理厂出水总氮预测研究
Prediction of effluent total nitrogen in wastewater treatment using LSTM neural network
余铭铨 1师浩铭2
作者信息
- 1. 中国电建集团华东勘测设计研究院有限公司,浙江 杭州 311122
- 2. 浙江大学 建筑工程学院,浙江 杭州 310058
- 折叠
摘要
Abstract
The effluent total nitrogen(TN)is one of the key indicators for assessing the biological denitrification performance of wastewater treatment plants(WWTPs).To mitigate the prevalent issue of excessive TN discharges from WTTPs,we proposed a real-time prediction model based on long short-term memory(LSTM)networks.We performed Pearson correlation analysis to determine model inputs and used grid search algorithm to optimize model hyperparameters.Then,we used the proposed model to predict the actual effluent TN in a WWTP in Chongqing and compared its predictive performance with that of traditional time-series models.Results indicate that the proposed model can effectively predict effluent TN with an average absolute error of 0.911 mg/L,an average root mean square error of 1.074 mg/L,and an average absolute percentage error of 11.28%.All of these performance indicators surpass those of the recurrent neural network and ARIMA models.The proposed model can serve as the foundation for effective monitoring of effluent TN.关键词
LSTM模型/皮尔逊相关性/网格搜索算法/出水总氮Key words
long short-term memory model/Pearson correlation/grid search algorithm/effluent total nitrogen分类
建筑与水利引用本文复制引用
余铭铨,师浩铭..基于LSTM模型的污水处理厂出水总氮预测研究[J].山东科学,2024,37(6):116-124,9.
