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SummaryIn 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%.
Each slab entering to the reheating furnace has an unique and optimal reheating curve. The process of obtaining the optimal reheating curve is to solve the typical Partial differential equations (PDE) constrained optimization problem. Obviously, the solution of optimization problem is determined by both the precision of the mathematical PDE model and the numerical method. Firstly, the more accurate mathematical PDE model, in which some key parameters are reconsidered as temperature-dependent, is built for the reheating furnace. Secondly, the first-optimize-then-discretize approach is introduced to solve this PDE-constrained optimization problem. The analysis of the Fréchet gradient of the cost functional is given and we can prove the gradient is Lipschitz continuous. Then, an improved conjugate gradient method is proposed to solve this problem. Finally, numerical simulations and experiment examples are given and analyzed. The results can prove the effectiveness of the proposed strategy. INDEX TERMSReheating furnace; PDE-constrained optimization problem; first-optimize-thendiscretize; Lipschitz continuous; improved conjugate gradient algorithm;
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