(631h) Design of an Estimation-Based Model Predictive Control System for an Electrically-Heated Steam Methane Reforming Process

The demand for hydrogen (H₂) has experienced a significant upsurge in recent decades owing to its extensive utilization across various fields, including clean energy transportation and chemical synthesis [1]. Consequently, a thorough study of the dynamic behavior of H₂ production and the design of controllers to ensure optimal and stable production rates in an industrial setting becomes important. In this work, an electrically heated steam methane reforming (SMR) process is considered to produce H₂. In contrast to the conventional burner-based heating of SMR processes, the electrically heated SMR offers several advantages, including enhanced environmental sustainability, compactness, efficiency, and controllability [2]. As the entire system is heated using electricity, adjustments to the current can vary the reactor temperature. Consequently, changes in the hydrogen production rate correspond to shifts in reaction equilibrium. However, constructing a model capable of accurately capturing the dynamic behavior of hydrogen production poses a formidable challenge. Although certain complex models may exhibit precision (e.g., [3], [4], [5]), their computational demands render them unsuitable for integration into a real-time controller.

Considering these factors, an accurate and time-efficient first-principles-based lumped-parameter model is developed to provide a reliable approximation of hydrogen production. This model is experimentally validated and is used in a model predictive controller (MPC). To get the required state estimate information to be used in the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited and infrequent measurements gas-phase reactor outlet measurements and frequent reactor temperature measurements. The performance of this controller is compared in a simulation setting with that of a proportional-integral (PI) controller, revealing a six-fold faster response in achieving the desired H₂ production rate. Additionally, the controller demonstrates robustness when subjected to a disturbance such as a decrease in the activation energy of the catalyst, a scenario commonly encountered in the SMR process. This highlights the effectiveness of the controller in maintaining stable operation under varying process operating behavior. The ELO-based MPC will be further implemented in an experimental electrified SMR process at UCLA and compared with classical control to test its feasibility in an industrial setting.

References:

[1] Çıtmacı, B., Cui, X., Abdullah, F., Richard, D., Peters, D., Wang, Y., Hsu, E., Chheda, P., Morales-Guio, C.G., Christofides, P. D., 2024. Model predictive control of an electrically-heated steam methane reformer. Digital Chemical Engineering, 10, 100138.

[2] Wismann, S.T., et al, 2019. Electrified methane reforming: A compact approach to greener industrial hydrogen production. Science 364, 756–759.

[3] Lao, L., Aguirre, A., Tran, A., Wu, Z., Durand, H., Christofides, P. D., 2016. CFD modeling and control of a steam methane reforming reactor. Chemical Engineering Science, 148, 78-92.

[4] Tran, A., Aguirre, A., Durand, H., Crose, M., Christofides, P. D., 2017. CFD modeling of a industrial-scale steam methane reforming furnace. Chemical Engineering Science, 171, 576-598.

[5] Mokheimer, E. M., Ibrar Hussain, M., Ahmed, S., Habib, M. A., & Al-Qutub, A. A., 2015. On the modeling of steam methane reforming. Journal of Energy Resources Technology, 137, 012001.