Remote maintenance system of industrial ultra-pure water based on deep learning

Abstract

In order to meet the requirements of controlling the water quality of electric power, electronics and other manufacturing industries and reducing energy consumption through remote operation and maintenance system, an intelligent remote operation and maintenance system of ultra-pure water is constructed for ultra-pure water manufacturing in electronic industry. radial basis function neural network and generalized regression neural network are used to fit and predict the effluent quality of ultra-pure water. Through data analysis, the above algorithm is used to realize the accurate prediction of ultra-pure water system and intelligent adaptive control, which improves the accuracy and convergence speed of the algorithm. The results show that on the basis of the simulation of the model, the purpose of improving water production quality, saving energy and reducing consumption can be achieved through backwater utilization and frequency conversion speed regulation.