Modelling the Time-Dependent Reactivity of Catalysts by Experiments and Artificial Intelligence

The description of heterogeneous catalysis is challenged by the intricacy of numerous multi-scale processes that govern the performance of catalyst materials. The chemical environment of the catalytic process and the kinetics of structural changes create configurations of typically unknown local geometries and chemistry. These may result in significant changes in activity or selectivity within minutes, hours, or longer, during the so-called induction period. Here, we use experimental data together with a focused artificial-intelligence (AI) approach based on subgroup discovery and symbolic regression to model the evolution of the catalyst reactivity with time on stream. We consider palladium-based alloys synthesized mechanochemically and applied in the selective hydrogenation of concentrated acetylene streams resulting from a hypothetical electric plasma-assisted methane-to-ethylene process. Our AI approach starts with the identification of descriptions of materials and reaction conditions relevant to acetylene conversion. Then, a model for time-on-stream-dependent selectivity focused on situations associated to noticeable acetylene conversion is obtained by the sure-independence-screening-and-sparsifying-operator (SISSO) approach. Our AI approach identifies relationships between the measured catalyst reactivity and only few, key parameters, from 21 measured and calculated bulk, surface, and mesoscopic materials' properties and reaction parameters offered as candidate descriptive parameters. These identified parameters highlight the crucial influence of surface and subsurface carbon and hydrogen on the selectivity towards ethylene formation. Guided by the AI models, new, highly selective bimetallic and trimetallic systems are designed and tested experimentally.