Nonlinear multivariable predictive control of an autothermal reforming reactor for fuel cell applications

In this work, we present a computationally efficient nonlinear multivariable predictive controller (NMPC) for an autothermal reforming (ATR) reactor. The proposed NMPC scheme is based on a fast reduced order nonlinear model and consists of three parts. The first is a steady state optimizer, which aims to minimize fuel flow for a given hydrogen demand while simultaneously observing the lower bound on the reactor temperature to maintain ignition of the reactor. The second part of the controller is to find a desired input/output trajectory via an offline nonlinear dynamic optimization problem subject to process constraints. The third portion of the controller employs a linearized model around the trajectory and minimizes an unconstrained trajectory tracking problem of which analytic solution is obtained. A linear Kalman Filter (KF) is employed to estimate process states. The proposed NMPC is compared with the classic feed-forward controller to illustrate improved performance.