Optimization method of hot strip rolling production process control system

Abstract

The invention relates to an optimization method of a hot strip rolling production process control system, which belongs to the technical field of hot strip rolling control. The method comprises the following steps of: data collection, de-noising processing, coefficient optimization, verification test, on-line application and the like. According to the coefficient optimization step, through correlation analysis of influence between model coefficients and parameters, the model coefficients are fit, an algorithm error precision is selected, and when a calculation error is less than the algorithm error precision, the coefficient optimization step is stopped to obtain new model coefficients and corresponding offsets. According to the optimization method, through sampling and carrying out fitting analysis on a large number of steel rolling historical data, and optimizing hot continuous rolling force model coefficients, the optimization of the hot strip rolling production process control system is realized, the hot strip rolling production process control accuracy is effectively improved, and considerable economic value and practical value are achieved.

Description

technical field [0001] The invention belongs to the technical field of strip steel hot continuous rolling control, in particular to an optimization method applied to a strip steel hot continuous rolling production process control system. Background technique [0002] At present, the hot strip rolling production process control system has been widely used in the strip hot rolling production. The strip steel hot rolling production process control system is based on the establishment of the strip hot rolling control model, including the rolling force model, temperature model, power model, and plate shape model, and controls the continuous rolling production line through a computer. Among them, the rolling force model includes the geometric coefficients of rough rolling and finishing rolling force control, and its accuracy ultimately reflects the accuracy of the roll gap setting, which has a great influence on whether the strip can be threaded smoothly and the thickness accuracy...

AI Technical Summary

Problems solved by technology

The traditional method of improving the accuracy of the rolling force model of hot continuous rolling focuses on proposing various load distribution methods and self-learning in a certain part of the model, such as the setting of finishing rolling. Although these methods have achieved good control effects in the actual production site, due to Restricted by the level of computer control and programming at that time, these methods are still methods such as the distribution coefficient method based on experience in essence, without much consideration from the actual production

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