Self-learning method for improving forecasting precision of overall length coiling temperature of strip steel

Abstract

The invention discloses a self-learning method for improving a forecasting precision of an overall length coiling temperature of strip steel, belonging to the field of automatic control technologies of hot-rolled strip steels. The self-learning method is mainly characterized by comprising the following steps of: 1) collecting control parameters of each section of the strip steel in a coiling temperature control process; 2), determining the number of lag sections after the strip steel is rolled; 3) figuring up self-learning lag factors between the sections; and 4) comprehensively considering self-learning coefficients of the strip steel between the sections, self-learning coefficients of the rolled strip steel between the sections and the self-learning lag factors between the sections when the coiling temperature of each section of the sequent strip steel is forecasted. The self-learning method, related by the invention, can solve the lag problem of self-learning between the sections of the strip steel well, and the forecasting precision of the coiling temperature of each section of the overall length of the strip steel is remarkably improved.

Description

Technical field: [0001] The invention belongs to the technical field of automatic control of hot-rolled strip steel, in particular to a model self-learning method in the coiling temperature control process of hot-rolled strip steel. Background technique: [0002] The control level of the coiling temperature directly affects the stability of the microstructure and properties of the finished strip steel, and a high-precision coiling temperature prediction model is crucial to improving the control level of the coiling temperature. In actual production, the factors affecting the full-length coiling temperature of the strip are intricate and cannot be fully and accurately described in the control system. Among them, many factors change with the position in the strip length direction, such as inlet temperature, rolling speed, coiling tension, strip shape, etc., and the model must be continuously updated and corrected by using the self-learning method. The specific method is to di...

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Problems solved by technology

The starting point of this method is to use the rolled strip information to correct the model in time to adapt to the possible cooling effect changes at the feature points, although the inter-segment self-learning hysteresis at these feature points can be avoided in some working conditions , but due to the destruction of the continuity of self-learning between sections, if the cooling factors of the two strips before and after the difference are large, this method will worsen the control effect

In addition, due to the relatively complex change law of the cooling effect factors in the strip length direction, it is necessary to increase the feature points to reduce the error in the description of the change law. normal performance

In production, the above problems severely limit the practical application of this method

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