Fuzzy Gain Scheduled Smith Predictor for Temperature Control in an Industrial Steel Slab Reheating Furnace

The steel industry is among the companies with highest energy consumption due to the heating processes in their production. Furnaces used for slabs reheating in this industry require a good regulator to control temperature in each zone optimizing combustible consumption. Considering the complex and variable dynamic behavior of furnace temperature, this paper proposes a modification of the Smith predictor scheme, combined with a gain scheduled fuzzy block idea, for controlling the soaking zone temperature in a steel slab-reheating furnace. For this purpose, a soaking zone temperature dynamic model is obtained from an identification procedure resulting in a second order plus time delay transfer function, where the dominant time delay varies with respect to the slab thickness. The performance of the proposed method is compared against the filtered Smith predictor and the classic one. The best results were obtained using the fuzzy gain-scheduled Smith predictor achieving a smooth transition in each operation point.