(503e) Dynamic Modeling and Explicit/Multi-Parametric Model Predictive Control Optimization of an Intensified Fluidized Bed Membrane Reactor for Oxidative Coupling of Methane

The catalytic oxidative coupling of methane (OCM) process has received intense interests from the reaction and process engineering community during the past decades. OCM provides the potential to directly convert natural gas to value-added chemicals – with reduced cost, energy consumption, and carbon emissions (Tiemersma et al., 2012; Onoja et al., 2019). However, major challenges such as low yield, fast catalyst deactivation, and reactor scaling up still obstruct the commercialization of this process. A promising solution to address these challenges is to develop innovative OCM reactor designs leveraging the recent advances in modular process intensification, e.g. micro-channel reactors and membrane reactors (Serres et al., 2012; Patcharavorachot et al., 2014). Moreover, the investigation of OCM reaction systems has mostly been focused on steady state conceptual design, while ignoring the process dynamics and control analysis to ensure actual operation under disturbances.

This work aims to develop an integrated design and control optimization approach for oxidative coupling of methane processes. An intensified fluidized bed membrane reactor (FBMR) is of interest, which has been demonstrated in our ongoing work to achieve higher C2+ yields, selectivity, and methane conversion than conventional packed or fluidized bed reactors – by keeping an low oxygen partial pressure enabled by the membrane (Ali et al., 2022). The optimal design and operational policy for the FBMR will be developed using the PAROC (PARametric Optimization and Control) framework (Pistikopoulos et al., 2015; Pappas et al., 2021). The PAROC framework consists of five steps: i) High fidelity process modeling, ii) Model approximation, iii) Formulation of the model predictive control (MPC) problem, iv) Solution of explicit control laws through multi-parametric programming, and v) Dynamic optimization for simultaneous design and control optimization. This framework has already been successfully applied to numerous conventional and intensified processes (Pistikopoulos & Tian, 2022). More specifically, the high fidelity dynamic FBMR model to describe the production of ethylene and ethane from methane is developed using gPROMS ModelBuilder®.The resulting partial differential algebraic equations take into account mass balances, hydrodynamics, catalyst solid distribution, etc. A detailed 10-step reaction kinetic model is adopted from Cruellas et al. (2020). Nevertheless, given the large-scale highly nonlinear nature of the high fidelity model, a linearized surrogate model is built to balance the computational complexity for model-based control execution and the model accuracy for predictive optimization. Then, the multi-parametric model predictive control (mp-MPC) problem is solved using the Matlab® POP toolbox (Pistikopoulos et al., 2020), allowing to analytically derive the optimal explicit control policy as an affine function of state variables, disturbances, design variables, etc. The design variables considered in this work includes reactor size, temperature, catalyst particle velocity, and membrane tube design, etc. Closed-loop validation is performed to test the mp-MPC controller against the original high fidelity FBMR model, aiming to regulate the desired ethylene product purity. Via a final mixed-integer dynamic optimization formulation, the optimal FBMR design configuration and control policies can be simultaneously obtained resulting in enhanced C2+ yield, selectivity, and CH4 conversion with guaranteed operational feasibility under disturbances.

References

Ali, M., Tian, Y., De, S., Van Bavel, A. P., Demirhan, C. D., & Pistikopoulos E. N. (2022). Process Modeling, Design, and Intensification of Oxidative Coupling of Methane Process: A Comparative Study on Membrane Reactor and Fluidized Bed Reactor. ACS Spring Meeting; 2022.

Cruellas, A., Melchiori, T., Gallucci, F., & van Sint Annaland, M. (2020). Oxidative Coupling of Methane: A Comparison of Different Reactor Configurations. Energy Technology, 8(8), 1–15. https://doi.org/10.1002/ente.201900148

Pappas, I., Kenefake, D., Burnak, B., Avraamidou, S., Ganesh, H. S., Katz, J., Diangelakis, N. A., & Pistikopoulos, E. N. (2021). Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward. Frontiers in Chemical Engineering, 2(January), 1–15. https://doi.org/10.3389/fceng.2020.620168

Patcharavorachot, Y., Tiraset, S., Wiyaratn, W., Assabumrungrat, S., & Arpornwichanop, A. (2014). Using a membrane reactor for the oxidative coupling of methane: Simulation and optimization. Clean Technologies and Environmental Policy, 16(7), 1295–1306. https://doi.org/10.1007/s10098- 014-0813-9

Pistikopoulos, E. N., Diangelakis, N. A., Oberdieck, R., Papathanasiou, M. M., Nascu, I., & Sun, M. (2015). PAROC – An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 136, 115–138. https://doi.org/10.1016/J.CES.2015.02.030

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