Model predictive control of an experimental protonic membrane steam methane reforming system

Abstract The automation and control of protonic membrane reformers can facilitate the commercialization of this emerging hydrogen‐producing technology. To this end, a decentralized and offset‐free model predictive control (MPC) approach is developed to explore potential automation pathways for a protonic membrane reforming system that achieves 84% hydrogen recovery at 0.69 Acm (15.2 cm active surface area). Three physics‐based and data‐driven models estimate thermal hydrogen generation on the anode, electrochemical hydrogen recovery on the cathode, and steam generation. During setpoint tracking, the experimental results of the MPC architecture show faster hydrogen purification rate settling times and implementable control action profiles between sampling times, protecting the actuators and the thermo‐electrochemical performance of the protonic membrane. Ultimately, this work points to control strategies for protonic membrane systems that incorporate a combination of classical and predictive controllers with disturbance‐observer‐based error quantification for optimal hydrogen production.