(734h) Application of Paroc in the Optimization and Control of PEM Water Electrolysis Process | AIChE

(734h) Application of Paroc in the Optimization and Control of PEM Water Electrolysis Process


Ogumerem, G. S. - Presenter, Texas A&M University
Pistikopoulos, S., Texas A&M University
Proton Exchange Membrane Water Electrolyzer (PEMWE) has gained attention within the last decade due to its relative advantage over other water electrolysis technologies. However, its high operational energy intensity (losses) has been the major setback (1). Energy losses are categorized as faradaic losses, and overpotentials and they are caused by complex irreversible interactions within the PEMWE process. The bubble effect, which involves the accumulation of oxygen gas bubble on the surface of the Membrane Electrode Assembly (MEA), is one of the various phenomena that has a significant impact on the overpotential of the PEMWE process (2–4). Furthermore, the flow of the electrolyte has a substantial effect on the accumulation of bubbles on the surface of the MEA(3). Few researchers have considered the bubble effect in their models (5) and none has attempted to systematically optimize their model to obtain an optimal operating point.

In this work, we present an experimentally validated dynamic mathematical model and control optimization of a PEMWE system using the Parametric Optimization and Control (PAROC) framework (6). The main objectives of this work are to (i) formulate and validate a comprehensive model; (ii) to obtain optimal operating points that minimizes the energy losses in the PEMWE process; (iii) and to design advanced controller to maintain the optimal operating points obtained. The PAROC framework enables the representation and solution of demanding model-based operational optimization and control problems. The application of PAROC on PEMWE features: (i) a high-fidelity dynamic mathematical model of the PEMWE that captures the detailed electrochemical interaction, transport phenomenon, and other interaction associated with energy losses in the system; (ii) a model reduction step that develops a more tractable state-space model from the original complex model without losing its sense of the original model; (iii) a Model Predictive Control (MPC) design and its reformulation to a multi-parametric (mp) optimization problem (which becomes mp-MPC) (7). The solution of the mp-MPC provides a map of solution that spans the feasible operating region of the PEMWE. Unlike the MPC the mp-MPC avoids the online optimization procedure at every time step (7). The optimization is done once and offline to determine the control action at every realization of the parameters while simultaneously accounting for physical and operational constraints. For a system with fast dynamics (like the PEMWE), mp-MPC is ideal.


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