Framework Rapidly Identifies Promising Catalysts for Processing Shale Gas
Outcome/Accomplishment
Scientists developed a computational screening framework that combines graph theory, database tools, and mechanistic descriptors to rapidly identify promising alloy catalysts for propane dehydrogenation (PDH). The research was supported by the Center for Innovative and Strategic Transformation of Alkane Resources (NSF CISTAR), an NSF-funded Engineering Research Center (ERC) based at Purdue University.
Impact/Benefits
The framework has enabled faster discovery of catalyst capabilities through computer modeling, including the rapid screening of thousands of alloys for selective PDH. The system already identified new catalysts that can achieve up to 98 percent propylene selectivity, and can be applied to other complex catalytic systems.
Explanation/Background
Data-driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting and interpreting chemistry mechanisms. PDH, meanwhile, has garnered significant attention in recent years for production of propylene due to the increase in shale gas production.
But leading catalysts for PDH, including platinum and palladium, suffer losses in selectivity and activity mainly due to the high temperatures (∼550° C) required for PDH. Computational methods could be useful in finding new families of promising alloys, but in-depth mechanistic studies have not been scalable across a large set of alloy candidates.
The Center’s innovative strategy combined key mechanistic insights from previous studies and utilized graph theory-based algorithms, along with python-based databasing, to identify promising catalytic combinations for PDH chemistry.
Location
West Lafayette, Indianawebsite
Start Year
Energy and Sustainability
Energy and Smart Infrastructure
Lead Institution
Core Partners
Fact Sheet
Outcome/Accomplishment
Scientists developed a computational screening framework that combines graph theory, database tools, and mechanistic descriptors to rapidly identify promising alloy catalysts for propane dehydrogenation (PDH). The research was supported by the Center for Innovative and Strategic Transformation of Alkane Resources (NSF CISTAR), an NSF-funded Engineering Research Center (ERC) based at Purdue University.
Location
West Lafayette, Indianawebsite
Start Year
Energy and Sustainability
Energy and Smart Infrastructure
Lead Institution
Core Partners
Fact Sheet
Impact/benefits
The framework has enabled faster discovery of catalyst capabilities through computer modeling, including the rapid screening of thousands of alloys for selective PDH. The system already identified new catalysts that can achieve up to 98 percent propylene selectivity, and can be applied to other complex catalytic systems.
Explanation/Background
Data-driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting and interpreting chemistry mechanisms. PDH, meanwhile, has garnered significant attention in recent years for production of propylene due to the increase in shale gas production.
But leading catalysts for PDH, including platinum and palladium, suffer losses in selectivity and activity mainly due to the high temperatures (∼550° C) required for PDH. Computational methods could be useful in finding new families of promising alloys, but in-depth mechanistic studies have not been scalable across a large set of alloy candidates.
The Center’s innovative strategy combined key mechanistic insights from previous studies and utilized graph theory-based algorithms, along with python-based databasing, to identify promising catalytic combinations for PDH chemistry.