Neural network MPC for heating section of annealing furnace.

Annealing is a process that modifies the properties of steel by heating. An annealing furnace has large thermal inertia due to its size; therefore, research using a model predictive controller (MPC) has been conducted for precise control. An traditional physical model-based MPC requires a dynamic model of the annealing furnace, which necessitates several states and many accurate physical parameters. In addition, predicting the future output of such a high-order dynamic model requires too much time for real-time control. To resolve such issues, the neural network MPC has been proposed on other fields, but the algorithms were not applicable to the large-scale thermal system. Therefore, we propose a novel data-driven neural network MPC and predictive model that uses LSTM layer, which does not require knowledge of the physical parameters, and is applicable to the annealing furnace. Additionally, the proposed neural network MPC is fast enough for real-time control. Simulation scenarios show transient and steady-state performance respectively. The proposed neural network MPC showed high control accuracy and fast speed compared to the previous controllers. The proposed method resolves problems that the conventional MPC have such as the requirement of many physical parameters and computational complexity, enabling real-time predictive control of annealing furnaces to be achieved.