Automation and control of an experimental protonic membrane steam methane reforming system

Abstract

Nickel dispersion on doped barium-zirconate ceramics is a state-of-the-art material formulation used to fabricate proton conducting membranes that can reform methane at lower operational temperatures (600 to 800 °$C$). Although steady-state operational data have been reported for these ion-conducting ceramic reformers, transient datasets are uncommon and not readily available. Moreover, the automation of protonic membrane reformers is a major technical challenge for the commercialization of modular thermo-electrochemical hydrogen generators with highly nonlinear process dynamics. Here, a multi-input multi-output feedback control scheme has been designed from a relative gain array analysis of three process variables for an experimental 500 W (thermal and electrochemical power consumption) protonic membrane reforming system. Specifically, the proposed control architecture automatically calculates hydrogen separation rate setpoints while safely and effectively reaching hydrogen production rate setpoints and desired steam-to-carbon ratios. The control architecture also drives the system to 99.6% methane conversion at a current density of 0.564 ± 0.0125 A$\cdot$cm⁻² at 788 °$C$. Internal temperature fluctuations are mostly constrained to $\pm$ 6.00 °$C$ $\cdot$min⁻¹, which improves catalyst longevity when operating at hydrogen recovery rates exceeding 50%. Chief among these findings is an experimental demonstration of a control scenario that alters the hydrogen production rate setpoint every 150 min without sacrificing system-wide controllability. Integrator windup scenarios and counterproductive control actions are also avoided through rational controller design and proper controller tuning exercises. Industrial-scale applications of protonic membrane reformers may therefore be automated to control up to three process variables and have up to three additional control degrees of freedom for process intensification and optimization, making for well-governed, autonomous hydrogen generation units.