Early-Stage Evaluation of Catalyst Using Machine Learning Based Modeling and Simulation of Catalytic Systems: Hydrogen Production via Water-Gas Shift over Pt Catalysts

The goal of this research is to create a new machine learning (ML)-centric methodology for process modeling, designing, and evaluating hydrogen production based on the water-gas shift reaction (WGSR). The approach evaluates the one-pass conversion of catalyst and process overall conversion, as well as the economics of the catalytic conversion process, without the use of kinetics and a process model that requires much trial and error. To accomplish this, an ML model was developed to predict the catalyst performance based on critical catalysis descriptors like catalyst composition, operating conditions, and feed composition. We developed a surrogate model for the hydrogen production process based on the predicted results to determine the mass and energy information on the process, which includes multiple unit operations and recycling. Finally, we assessed the hydrogen production process using different technical and economic metrics such as hydrogen amount, energy consumption, and unit energy cost. The approach can perform a kinetics-free simulation of hydrogen production processes using predicted catalyst performances and evaluate early-state catalysts from an industrial perspective by identifying the optimal operating conditions and the catalyst structure for economic and energy-efficient hydrogen production. As a result, the processes over Pt/Co(10 wt %)/Al2O3, Pt/Co(20 wt %)/Al2O3, and Pt/Ce(5 wt %)/TiO2 show the best performance to produce high-purity hydrogen.