Nonlinear model predictive control “MPC” for slab reheating furnace based on transient numerical model Authors Names and Affiliations

In steel industry, without consideration of blast furnaces, reheating furnaces are classified the biggest energy consumers. The research of efficient control algorithms 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 to predict the furnace thermal behaviour and to optimize zone temperature set points. The MPC control strategy uses this transient model to solve at each time step a dynamic optimization problem under constraints, in order to obtain the best zone temperature set points. This optimization problem is solved using “Nelder-Mead simplex” method that allows having fast minimization of objective function. The controller is able to deal with transient operating situations of the furnace. Simulation on industrial data shows an energy reduction of 5%, and a significant improvement of heating performances.