A combined adaptive entropy-TOPSIS and model predictive control strategy for mixed loading and delay operations in the reheating furnace

In this paper, a combined adaptive entropy-TOPSIS and model predictive control strategy is proposed to deal with the mixed loading and delay operations in the reheating furnace. Firstly, the mathematical models consistent with the behaviour of the real reheating furnace are built to describe the complicated heat exchange process. Secondly, a dynamical optimization problem for the mixed loading operation and delay operation in the walking beam reheating furnace is obtained. To adjust the weighting factors of the optimization problem in real time, the adaptive entropy-TOPSIS method is proposed. Then, the rolling horizon approach is applied to solve the proposed optimization problem. Finally, numerical experiments and simulation analysis are undertaken to verify the reliability and accuracy of the proposed strategy. The simulation results demonstrate that the proposed strategy can deal with three typical cases of delays effectively and the control accuracy is successfully improved from 74.79% to 99.17%.