净水技术2024，Vol.43Issue(8)：160-166,7.DOI:10.15890/j.cnki.jsjs.2024.08.022
基于串级LSTM深度学习模型的二次供水余氯预测方法
Prediction Method of Residual Chlorine in Secondary Water Supply Based on Cascaded LSTM Deep Learning Model
摘要
Abstract
With the increase of high-rise residential buildings in urban areas,the number of secondary water supply pump rooms in residential areas is rapidly increasing.As the secondary water supply tank is located at the end of the urban water supply system,water quality safety has attracted widespread attention from society.To improve the water quality in tanks,some pump rooms have introduced automatic chlorination devices.However,traditional automatic control methods have limitations in dealing with the long time delay and non-linear characteristics of chlorination systems in secondary water supply systems,as they can only monitor the residual chlorine level in tanks.Excessive residual chlorine may be harmful to human health,making it imperative to ensure the safe operation of automatic chlorination systems.This study proposed a neural network model based on cascaded LSTM deep learning to analyze residual chlorine data in tanks,accurately predict the residual chlorine concentration in tank water,and formulate corresponding monitoring and control strategies.Experimental validation and practical application results demonstrated that this deep learning model could effectively intelligently predict residual chlorine levels in tanks,providing important intelligent control means for automatic chlorination systems and holding practical significance.关键词
二次供水/水箱补氯/LSTM深度学习/余氯预测/时间序列/串级网络模型Key words
secondary water supply/water tank chlorination/LSTM deep learning/residual chlorine prediction/time series/cascade network model分类
建筑与水利引用本文复制引用
肖磊,李中伟,刘书明,陈春芳,吴雪,伍丽燕..基于串级LSTM深度学习模型的二次供水余氯预测方法[J].净水技术,2024,43(8):160-166,7.基金项目
国家水体污染控制与治理科技重大专项(2017ZX07201002) （2017ZX07201002）
