Feedback control of an experimental electrically-heated steam methane reformer

Abstract

Steam methane reforming (SMR) is the most common industrial process to produce hydrogen (H2) from methane and water vapor. The SMR reactions are overall highly endothermic and traditionally, fossil fuels are burned to provide the necessary heat of reaction. However, it was found that electrifying the tubular SMR reactors using renewable electrons instead of conventional heating gives the opportunity of producing H2 with lower carbon emissions, lower reactor volumes, and higher carbon conversion. While current studies in this new technology focus on improving the catalysts or heating sources, a more efficient process control scheme can also significantly contribute to the understanding of potential challenges and opportunities of combining renewables and natural gas in the production of H2 at industrial scales. In order to further develop the electrically-heated SMR process and the associated control strategies, we have constructed an experimental Joule-heated SMR system at UCLA. The system contains a tubular reactor with a washcoated Ni/ZrO2 catalyst, two thermocouples connected to the top and bottom of the reactor, a power supply for providing electrical heating, and an on-line gas chromatograph (GC) for measuring outlet gas concentrations. The synthesis procedure of the catalyst, SMR data collection, and thermal considerations for catalyst degradation are described in this paper and are all accounted for experimentally when using a proportional integral (PI) controller to gradually increase the reactor temperature without harming the catalyst. Advanced control strategies, such as model predictive control (MPC), require a process model that uses measurement feedback to make predictions of the process time-evolution in order to optimize the control actions in real-time. We have previously developed a lumped-parameter modeling strategy for the SMR process. The MPC objective is to control the H2 production rate by manipulating the current flowing through the outer reactor shell. However, to use this model in an MPC, feedback values for all state variables should be provided by the sensors. In our experimental system, the on-line GC only gives discrete measurements with a long sampling period. To this end, the process model is incorporated into an extended Luenberger observer (ELO) that uses the reactor temperature and GC measurements to provide estimates of all the variables needed by the MPC. The ELO-based MPC system is then experimentally implemented on the process and it is demonstrated to be more efficient in terms of speed of the closed-loop response than the PI controller using delayed, measurement feedback by the GC.