Research on temperature regulation of regenerative heating furnace based on GPC and improved double cross-limit control
In the heating process of a regenerative heating furnace, furnace temperature is a crucial factor affecting the quality of steel billets. Given the inherent nonlinearity, uncertainty, and hysteresis of the heating process itself, it is difficult to achieve precise control of furnace temperature. Accurate prediction and regulation of furnace temperature are conducive to controlling product quality. Therefore, this paper proposes an optimized combustion control strategy for heating furnaces, which combines the Generalized Predictive Control (GPC) algorithm with an improved double cross-limiting control system. Firstly, for the temperature-rising stage, a GPC controller is designed, and the prediction horizon and control weight coefficients are optimized through offline simulation. This ensures the furnace temperature rises stably and avoids overshoot. Secondly, for the temperature-holding stage, the improved double cross-limiting control is adopted. A furnace temperature deviation feedback adjustment mechanism is introduced to optimize the upper and lower threshold values of the air-fuel ratio, thereby suppressing furnace temperature fluctuations caused by changes in combustion air flow during the stabilization process. Finally, a comparative experiment is conducted between this segmented control strategy, double closed-loop control, and double cross-limiting control. The results confirm that the proposed "GPC-improved double crosslimiting" control strategy can achieve efficient temperature control of the regenerative heating furnace. The system exhibits small control overshoot, fast control speed, and good stability, which meets the process requirements for steel billet heating.