(503e) Dynamic Modeling and Explicit/Multi-Parametric Model Predictive Control Optimization of an Intensified Fluidized Bed Membrane Reactor for Oxidative Coupling of Methane
AIChE Annual Meeting
2022
2022 Annual Meeting
Innovations in Process Engineering
Process Intensification and Modular Manufacturing: Modeling and Simulation
Wednesday, November 16, 2022 - 1:50pm to 2:10pm
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 â 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
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