Abstract
The thermal physical properties of water will be changing in the process of heat transfer. A mathematical model of flow water cooling is established by the heat transfer theory and the similarity criterion. The heat transfer medium parameters are predicted by the neural network and the system temperature difference will be considered. It implemented network training and testing through the programming by the artificial neural network tool box of Mat lab. The man-machine conversation interface is developed based on the Lab view. A data-acquisition card is used as input and output devices for the temperature signals and control signals. It also drives control unit to complete flow speed control. The cooling water flow rate control system has high cooling efficiency. The consumption of the cooling water and energy is much lower. Finally, in the CVD coating system of float glass manufacturing process, we carried through a few simulation tests for the cooling water temperature and flow rate control. The simulation result shows the effectiveness and practicability of the proposed approach.
Published Version
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