Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AI

Describing heterogeneous catalysis is complicated by the intricate interplay of processes that govern catalyst performance. The evolving chemical environment and the kinetics of catalyst’s structural changes during reactions often lead to unknown local geometries and chemistry, which can shift reactivity over time. Here, we perform systematic experiments and apply a focused artificial-intelligence (AI) approach to model the measured time-on-stream-dependent reactivity of palladium-based bimetallic catalysts. These materials are synthesized via mechanochemistry and applied in the selective hydrogenation of concentrated acetylene streams(>14.0 vol %)under industrially relevant pressures (10 bar), resulting from a hypothetical electric plasma-assisted methane-to-ethylene process. Unlike the well-established hydrogenation of diluted acetylene (0.1 to 2.0 vol %) streams of naphtha steam cracking, the hydrogenation of concentrated acetylene streams remains largely underexplored due to the harsh reaction conditions and the explosive nature of acetylene. This precludes <i>operando</i> characterization or atomistic simulations to investigate catalyst time-on-stream behavior under realistic conditions. Our AI approach first uses subgroup discovery to identify descriptions of materials and reaction conditions resulting in noticeable acetylene conversion. Then, it models time-dependent selectivity focused on high acetylene conversion via the sure-independence-screening-and-sparsifying operator symbolic-regression approach. AI identifies key experimental and theoretical physicochemical descriptive parameters correlated with the reactivity, which highlight the critical interplay between the material structure and the chemical potential of the reaction mixture. The AI models enable the design of bimetallic and trimetallic catalysts, which are experimentally validated.