Prediction Method of Residual Chlorine in Secondary Water Supply Based on Cascaded LSTM Deep Learning Model
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.
secondary water supplywater tank chlorinationLSTM deep learningresidual chlorine predictiontime seriescascade network model
国家科技期刊平台
NSTL
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