Nonlinear model predictive control of the strip temperature in an annealing furnace

A nonlinear model predictive controller is designed for the strip temperature in a combined directand indirect-fired strip annealing furnace. Based on a tailored first-principles dynamical model and the estimated current system state, the receding horizon controller selects optimal trajectories for both the fuel supply and the strip velocity so that the strip temperature is controlled to its desired target temperature. The controller additionally maximizes the throughput and minimizes the energy consumption. In the control algorithm, the dynamic optimization problem with equality constraints is numerically solved by using the Gauss-Newton method. The gradient and the approximated Hessian matrix of the objective function are analytically computed using an adjoint-based method. The capabilities of the proposed controller are demonstrated for a validated high-fidelity simulation model of an industrial annealing furnace.