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

Abstract

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%.

Enhancing the efficiency of a gas-fueled reheating furnace of the steelmaking industry: assessment and improvement

“…Another influencing factor is the throughput of material: as production levels increase, there is a corresponding rise in furnace efficiency (Maliarenko et al, 2020). Efficient material flows can be achieved with adaptive, predictive control strategies (Zanoli et al, 2023) that ensure stability and optimal performance (Yang et al, 2023. The same is true regarding temperature uniformity across the billet sections, which also depends on the burner placement inside the furnace (Liu et al, 2020). A waste recovery system also plays a key role in enhancing efficiency in reheating furnaces (Inayat, 2023).…”