Dynamic setting optimization method and device for thickness of cold continuous rolling raw materials

Abstract

The embodiment of the invention provides a dynamic setting optimization method and device for the thickness of a cold continuous rolling raw material. The method comprises the steps of receiving raw material parameters input by a current rolling batch; according to the raw material parameters, querying in a preset long-term adaptive data table to obtain a first set optimization coefficient; basedon the first set optimization coefficient, rolling each roll of strip steel of the current batch, and obtaining a thickness optimization coefficient corresponding to each roll of strip steel; performing exponential smoothing on the thickness optimization coefficient of each roll of strip steel of the current batch to obtain a second set optimization coefficient; judging whether the second set optimization coefficient is credible or not according to the first set optimization coefficient and the second set optimization coefficient; and when the second set optimization coefficient is credible, updating the first set optimization coefficient in the long-term adaptive data table into the second set optimization coefficient. According to the method, the optimization efficiency and the optimization timeliness of the cold continuous rolling process control model are improved.

Description

technical field [0001] The invention relates to the technical field of continuous cold rolling, in particular to a method and device for dynamically setting and optimizing the thickness of raw material for continuous cold rolling. Background technique [0002] It is one of the main tasks of the cold tandem rolling process control model to realize a reasonable load distribution given the inlet and outlet thicknesses of each stand. At present, due to the production characteristics of the hot rolling process, the thickness of the raw material at the head, tail and middle is not uniform. In the process of continuous cold rolling, only a single thickness value can be used for setting. At present, most of the thickness optimization methods are based on the mean value of the nominal thickness of hot rolling. Based on the characteristics of steel grades, and combined with the thickness reduction of cold rolling, tension leveling and pickling processes, optimization is carried out. ...

AI Technical Summary

Problems solved by technology

At the same time, the original steel type follows the improvement of the process and composition, and the setting thickness will also be thinner or thicker. It is necessary to manually track the thickness setting data and maintain the optimization program for a long time.

However, this kind of manual maintenance and setting has the problem that the correction is not accurate enough and not timely, and the efficiency is low

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