Model predictive control of an electrically-heated steam methane reformer

Abstract

Steam methane reforming (SMR) is one of the most widely used hydrogen (H2) production processes. In addition to its extensive utilization in industrial sectors, hydrogen is expanding it share as a clean energy carrier, and more sustainable and efficient H2 production methods are continuously being explored and developed. One method replaces conventional fossil fuel-based heating with electrical heating through the flow of electrons across the reformer. At UCLA, an experimental setup was built of an electrically heated steam methane reforming process. This paper describes the system components, explains the digitalization of the experimental setup and introduces methods for building a first-principles-based dynamic process model using parameters estimated via data-driven methods from process experimental data. The modeling approach uses a lumped parameter approximation and employs algebraic equations to solve for gas-phase variables. The reaction parameters are calculated from steady-state experimental data, and the temperature change is modeled with respect to change in electric current using a first-order dynamic model. The overall dynamic process model is then used in a computational model predictive control (MPC) scheme to drive the process to a new H2 production set-point under unperturbed and steam flowrate disturbance cases. The performance and robustness of the proposed MPC scheme are compared to the ones of a classical proportional–integral (PI) controller and are demonstrated to be superior in terms of closed-loop response, robustness, and constraint handling.

Keywords

• Steam methane reforming
• Experimental data modeling
• Digitalization
• Model predictive control (MPC)
• Real-time control
• Disturbance rejection