Prediction and Analysis of Hot Rolling Strip Tension Based on Back Propagation Neural Network

Abstract In modern hot strip mill control systems, strip tension control is the core control function, and its performance will be directly reflected in product quality. A strip tension prediction model based on Back Propagation (BP) neural network is proposed. To ensure that the true tension value is obtained, this paper proposes a four-dimensional judgment mode for the contact time between the looper and the strip steel and establishes a data set of tension parameters for hot rolled strip steel. The traditional BP neural network, genetic algorithm optimized BP neural network (GA-BP), and whale algorithm optimized BP neural network (WOA-BP) models were used to predict the strip tension, and their prediction performance was evaluated. The results show that the proposed WOA-BP model has the best prediction effect, with the highest model decision coefficient of 0.9330. At the same time, the contribution rate of each variable to the strip tension was studied, and the results showed that the looper angle and looper roller force had the greatest impact on it, consistent with physical laws. Propose improvement suggestions for the control performance of hot rolled strip tension based on the influence of looper angle on the prediction model.