Application of Surrogate Modeling To Accelerate Design Space Exploration for Catalytic Reactor Systems

Digitalization has become a growing necessity within the process industry when challenged with increasing energy prices, demand and supply market volatility, and decarbonization goals. With increasing computational power and access to process data, companies are striving to push their assets to their limits while advancing sustainability and circularity initiatives. This study presents a methodology to accelerate catalytic reactor design by developing and deploying a surrogate model within an advanced process modeling framework. A case study for the CO2 methanation reaction system has been used to demonstrate the predictive accuracy and computational efficiency of this approach. The surrogate model-based approach provides 6 times speed up, 1/5th of memory usage, and improved robustness compared to the reactor model with the discretized catalyst pellet. This work demonstrates the potential of surrogate modeling to support scalable innovation in the context of sustainable chemical process development.