Operation Optimization of Slab Reheating Process Based on Differential Evolution

Abstract

In the slab reheating process, the temperature distribution of walking beam furnace is critical for the temperature of slab, as well as the subsequent hot rolling. Besides, this process is dynamic, nonlinear, and time varying due to coupling with complex physical and chemical reactions. In this paper, we focus on an operation optimization problem of tuning furnace temperature to optimize the slab reheating process, which considers the reheating quality and production cost. For this purpose, we develop a novel operation optimization method. Specifically, first, from the view of mechanism, a heat transmit model based on the heat-exchange of slab and furnace, an energy consumption function, and especially, an oxidation loss function are established. Therefore, incorporating the mechanism models, we build an optimization model to minimize temperature deviation of slab, energy consumption, and oxidation loss to describe our problem. Then, based on the features of the problem, we propose a modified differential evolution algorithm, which includes a space contraction scheme, a new self-adaptive parameter strategy, and a new mutation operator. Finally, to evaluate the performance of our method, extensive numerical experiments are implemented by comparing it with other well-known evolutionary algorithms based on practical data. The experimental results demonstrate the effectiveness of proposed operation optimization method on solving our problem.