Nonlinear model predictive control of steel slab walking-beam reheating furnace based on a numerical model

In steel industry, without consideration of blast furnaces, reheating furnaces are classified the biggest energy consumers. The research of efficient control algorithm permitting less energy consumption has become an important issue for those furnaces. In this paper, a nonlinear Model Predictive Controller (MPC) is designed for a steel slab walking-beam reheating furnace. A numerical nonlinear model is utilized in predictions of furnace thermal behavior and in optimization of furnace zone temperature set points. The MPC control strategy uses this numerical model to solve at each sampling instant a constrained dynamic optimization problem in order to obtain the best zone temperature set points. This optimization problem is solved using Nelder-Mead simplex method that allows fast decline of objective function. The controller is able to deal with non-steady-state operating situations of the furnace. Simulation based on industrial data shows an energy reduction of 5%, and a significant improvement of heating performances.