Intelligent Compensation Control Method for Billet Heating Process Tracking

In order to track the heating process of the billet controlled intelligently by the ring heating furnace in a steel plant, the LSTM (Long Short-term Memory) neural network model is used to predict the furnace temperature, and a digital simulation platform is built, to simulate the field working conditions of the ring heating furnace. Aiming at the oscillation of billet core temperature during the billet heating up, the intelligent control rules based on fuzzy control are designed, and a compensation control method is proposed based on coordinated feedback between billet surface temperature deviation and billet core temperature deviation. The simulation results show that through compensation control, based on the preliminary fitting of the furnace temperature heating process curve and the setting curve, the billet temperature rise process is basically fitted with the set temperature rise process, and the accuracy of billet core temperature is improved from 57.34% to 79.82%. The method proposed in this paper improves the control rules of the billet temperature rise process of the ring heating furnace, and improves the accuracy of the billet temperature control.