Temperature Control of Industrial Coke Furnace Using Novel State Space Model Predictive Control

This paper proposes an enhanced model predictive control (MPC) using a new state space structure for temperature control of an industrial coke furnace. The advantage of the proposed controller lies in the fact that its implementation only requires a simple step-response process model, whereas controller design can be based on state space formulation to improve temperature regulation. To ensure control performance effectiveness under model/process mismatch and uncertainties, model predictions and the cost function optimization are done on the basis of a new improved state space model. The proposed MPC is applied to an industrial coke furnace, where the outlet temperature in the radiation room is regulated. Simulation comparisons with traditional state space MPC are illustrated first. Then experimental results are shown in comparison with the original proportional-integral differential (PID) controller, demonstrating the effectiveness of the proposed methodology.