Method for improving rolling mill thickness control accuracy using data redundance

Abstract

This present invention relates to a method of using data redundancy to improve the rolling mill thickness' controlled precision. It considers computer and thickness testing equipment as the thickness controlling equipment. This invention comprises the steps as follow. Intercalate multifunctional testing equipment which is set at the exit of rolling mill as the as thickness testing equipment together to collect data about thickness. Send the data to PLC and process computer, then judge the two group data. If the thickness testing equipment is abnormal, compare the data from multifunctional testing equipment and thickness controlling equipment to get dispersion from which we can acquire the optimal study coefficient. Modify the designed model with the coefficient. This invention enhances model's auto-adapting ability and advances initialization.

Description

technical field [0001] The invention relates to the control technology of rolling mills in the metallurgical industry, and belongs to the field of control technology. Background technique [0002] In the metallurgical industry, the hot-rolled sheet production line is very complex, the computer application level is very high, and the process control technology occupies a pivotal position. The self-learning function of the rolling mill setting model is the core part of the computer control of the hot rolling production process, and it is an important means to improve the setting accuracy. The self-learning of the rolling mill setting model is to compare the actual data generated during the rolling process with the data generated by the pre-calculation of the rolling mill setting model, and obtain the optimal learning coefficient according to the learning strategy. Use this coefficient to modify the setting model, so as to improve the control accuracy and self-adaptive ability...

AI Technical Summary

Problems solved by technology

However, since the current self-learning function uses the actual data collected by a single thickness tester, the failure of the thickness tester and the occurrence of large deviations often occur, which often cause the failure of the self-learning function and lead to the failure of the model setting. Decreased control precision and loss of self-adaptive ability

Images