Albert Sabadell-Rendón, Kamila Kaźmierczak, Santiago Morandi, Florian Euzenat, Daniel Curulla-Ferré, Núria López
Author Information
Albert Sabadell-Rendón: Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, (BIST) Av. Paisos Catalans 16 Tarragona 43007 Spain asabadell@iciq.es nlopez@iciq.es.
Kamila Kaźmierczak: TotalEnergies, TotalEnergies One Tech Belgium Zone industrielle C, 7181 Feluy Belgium.
Santiago Morandi: Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, (BIST) Av. Paisos Catalans 16 Tarragona 43007 Spain asabadell@iciq.es nlopez@iciq.es. ORCID
Florian Euzenat: TotalEnergies Research and Technology Gonfreville, Route Industrielle, Carrefour 4, Port 4864 76700 Rogerville France.
Daniel Curulla-Ferré: TotalEnergies, TotalEnergies One Tech Belgium Zone industrielle C, 7181 Feluy Belgium.
Núria López: Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, (BIST) Av. Paisos Catalans 16 Tarragona 43007 Spain asabadell@iciq.es nlopez@iciq.es. ORCID
Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challenges in the multiscale modeling of reactors can be grouped into two main categories: catalytic complexity and the difference between time and length scales of chemical and transport phenomena. Here we introduce the Automated MUltiscale Simulation Environment AMUSE, a workflow that starts from Density Functional Theory (DFT) data, automates the analysis of the reaction networks through graph theory, prepares it for microkinetic modeling, and subsequently integrates the results into a standard open-source Computational Fluid Dynamics (CFD) code. We demonstrate the capabilities of AMUSE by applying it to the unimolecular iso-propanol dehydrogenation reaction and then, increasing the complexity, to the pre-commercial Pd/InO catalyst employed for the CO hydrogenation to methanol. The results show that AMUSE allows the computational investigation of heterogeneous catalytic reactions in a comprehensive way, providing essential information for catalyst design from the atomistic to the reactor scale level.
