The operation state prediction of industrial circulating cooling water system is an important guarantee to maintain normal operation of industrial equipment. Therefore

an intelligent operation state prediction method for industrial circulating cooling water system based on deep convolutional neural network is proposed. According to the characteristics of water quality of industrial circulating cooling water

a deep convolutional neural network framework is designed using online instrument sampling data of industrial circulating water(including real-time data of pH value

alkalinity

hardness

chloride ion

etc.). A Dropout layer is added to each layer of the network to avoid over-fitting by neural network training. Moreover

the validity of this method is verified by the actual water quality measurement data of a power plant. The results show that

this method has a prediction accuracy of 94%

and has good generalization ability

which is better than other existing methods.