Toward Digital Twins for Power-to-X Processes: Comparing Surrogate Models for a Catalytic CO 2 Methanation Reactor
Year: 2025
Type: article
Abstract: Power-to-X processes convert excess renewable energy into storable fuels such as methane, addressing critical energy storage and transportation challenges. However, implementing these processes presents operational difficulties, particularly in managing fluctuating energy inputs. Digital Twins, or virtual replicas of physical systems, offer a promising solution by enabling model-based strategies for optimization and control. However, traditional mechanistic models are computationally intensive, limiting their application in real-time scenarios. This highlights the need for surrogate models that are computationally efficient and capable of accurately capturing the essential dynamics of complex systems. In this study, we evaluate and compare advanced techniques for modeling spatio-temporal processes, including a graph attention network (GAT), operator inference (OpInf) with stability guarantees, and sparse identification with greedy sampling (GN-SINDy). These methods are evaluated for their ability to model the dynamic operation and control of a catalytic CO2 methanation reactor. By evaluating their accuracy and computational efficiency, we aim to identify surrogate models tailored to spatio-temporal systems, advance the development of Digital Twins, and improve renewable energy conversion.
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409986157