Development and application of filtering algorithm for low coiling temperature of thick gauge high strength steel
Aiming at the problem that the measured value of coiling temperature(CT)fluctuates greatly and the CT feedback con-trol and model self-learning function cannot be put into normal operation during the low temperature coiling of thick gauge high strength steel in a 2 150 mm hot rolling production line,a new combined filtering algorithm based on limiting filtering method and Kalman filtering method was developed.Three typical combination algorithms were compared and analyzed.Compared to the Limiting Weighted Smooth Filtering and Limiting Recursive Average Filtering algorithms,it was found that the Limiting Kalman Filtering algorithm improved the authenticity and reliability of CT measurements while ensuring good robustness in noisy envi-ronments.The Limiting Kalman Filtering algorithm was embedded into the existing laminar cooling control system,which real-ized the accurate measurement of the CT values under the low temperature coiling condition and ensured the normal input of the CT control function.When the optimized control system was used to produce X80 pipeline steel,the actual CT values can be controlled within the target temperature of±30 ℃,and good application results were achieved.
thick gauge high strength steellow temperature coilingcoiling temperature(CT)Limiting Kalman FilteringLim-iting Weighted Smooth FilteringLimiting Recursive Average Filtering
万方数据
维普
