A T-S fuzzy model-based intelligent temperature prediction model of laminar cooling system

In order to improve the accuracy of temperature prediction model of laminar flow cooling system, TS fuzzy model is introduced to control the temperature of steel plate cooled down by laminar flow. Based on the analysis of the cooling process of laminar flow, it is found that the process of water cooling heat dissipation is not only controllable, but also plays a predominating role in the whole cooling process. Therefore, we establish a TS fuzzy model of water cooling heat transfer coefficient to improve the mechanism of cooling system. By collecting data from production line and inputting them into database, the parameters of TS model are continuously updated, which endows TS model with self-study ability to adjust to the product line. The novelty method enables the intelligent model to monitor the process of a laminar flow cooling process rapidly, and provide it self-study ability and highly accurate results. After making a comparison between the result output from TS model and the data collected from the product line, the conclusion can be drawn that the proposed model can reduce error between the production and simulated data by 50%, which means the accuracy of temperature predicting model of laminar flow cooling system is largely improved.