Hot continuous rolling electromagnetic induction heating temperature prediction method based on BP (back-propagation) neural network

Abstract

The invention discloses a hot continuous rolling electromagnetic induction heating temperature prediction method based on BP (back-propagation) neural network. The existing prediction method is dependent on manual work, and thus, has the defects of low efficiency and poor reliability. The method disclosed by the invention comprises the following steps: selecting prediction model variables: reasonably selecting input / output variables of a prediction model by utilizing mechanism analysis and prior information; normalizing data to be input; establishing a BP neural network, and training and testing the neural network; and finally, denormalizing the data obtained by the neural network, thereby obtaining the predicted heating temperature. In the invention, the steel billet temperature is predicted according to the operation history data of the electromagnetic induction heater, and has higher prediction precision than the traditional engineering computation method.

Description

technical field [0001] The invention belongs to the technical field of automation, and in particular relates to a neural network-based method for predicting the temperature of a billet heated by electromagnetic induction for hot continuous rolling. Background technique [0002] In the iron and steel industry, the traditional steelmaking, continuous casting, and steel rolling processes are independent production links. However, the modern production mode is gradually transforming into an integrated hot rolling process of "steelmaking-continuous casting-steel rolling". This is the highly intensive automatic hot rolling production line model widely praised at home and abroad. The heating equipment (heating, soaking, holding furnace) used between continuous casting and rolling is the key equipment connecting the continuous casting and rolling production line. From a technical point of view, the intermediate heating equipment needs to supplement the heat during the process of co...

AI Technical Summary

Problems solved by technology

[0004] Since electromagnetic induction heating is a complex nonlinear large-delay process, it is difficult to establish an accurate mechanism model, and it is difficult to obtain satisfactory results with conventional control methods (such as PID adjustment). Generally, manual experience is used for debugging and control.

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