A Method for Predicting Head Width of Hot Strip Rolling by Combining Rolling Mechanism and Deep Learning

Abstract

The present invention provides a method for predicting the head width of hot continuous rolling strip combined with rolling mechanism and deep learning. Firstly, the production data of the hot continuous rolling site is obtained, and the outlier data is eliminated by using the Pauta criterion to obtain sample data; according to the rolling width Screen the data of influencing factors based on the influencing factors of the development, and then build a rolling mechanism prediction model for each stand, calculate the prediction benchmark value of the hot strip head width according to the influencing factor data, and construct a deep confidence neural network model to predict the strip head width. The correction value of the head width, and finally add the predicted reference value and the predicted correction value of the head width of the strip to obtain the final predicted value of the width at the exit of the measurement position of the head of the strip. The invention integrates the rolling mechanism and the deep confidence neural network to predict the width of the head of the strip steel, improves the prediction model based on the traditional single hidden layer neural network. The optimization of the model provides a good basis.

Description

technical field [0001] The invention relates to the technical field of automatic control of steel rolling, in particular to a method for predicting the head width of a hot continuous rolling strip that combines rolling mechanism and deep learning. Background technique [0002] Width accuracy is one of the most important dimensional indicators in strip production. Although most of the width control methods of the hot continuous rolling production line are concentrated in the rough rolling area, the setting of the width model of the rough rolling area is affected by the change of the width of the finishing rolling area. When adjusting the setting model parameters of the process automation level, if the head width after finishing rolling can be accurately predicted, it can guide the adjustment of model parameters and provide a basis for correcting the width setting model. [0003] The prediction of the width parameter is very complicated due to the non-linear interaction and d...

AI Technical Summary

Problems solved by technology

[0003] Prediction of the width parameter is very complicated due to the nonlinear interaction, dynamic coupling process in the steel production process

Although the width prediction model established according to the rolling mechanism conforms to the general rolling law, it will inevitably be simplified and approximated in the derivation process, and the theory-guided modeling idea will ignore many field factors, which is different from the actual production conditions. Therefore, the error in predicting the width by the rolling mechanism prediction model alone is relatively large, which cannot meet the increasingly accurate rolling requirements

[0004] With the development of intelligent technology, some width prediction methods based on rolling data and neural network have appeared in recent years. Although the accuracy of these methods has been improved, due to the black box characteristics of neural network, the prediction model based on neural network The width is poorly interpretable and reliable, and the prediction accuracy of the ordinary single hidden layer neural network structure is still lacking, the generalization ability is not strong and it is easy to fall into local extremum

Images