Building a better chemical building block
NSF awards ChemE professor $355K for work on olefins
Credit: CANELa Lab at the University of Pittsburgh
PITTSBURGH (Aug. 2, 2019) — Olefins, simple compounds of hydrogen and carbon, serve as the building blocks in chemical industry and are important for the synthesis of materials, including polymers, plastics and more. However, creating them can be problematic: it requires the use of fossil fuels, energy intensive “cracking” facilities, and limited production control. But engineers at the University of Pittsburgh are using advanced computing to develop more efficient means of production.
The National Science Foundation has awarded Giannis (Yanni) Mpourmpakis, PhD, bicentennial alumni faculty fellow and assistant professor of chemical engineering at the University of Pittsburgh’s Swanson School of Engineering, $354,954 to continue his research into a promising but poorly understood method of creating olefins: the dehydrogenation of alkanes on metal oxides. The team in Dr. Mpourmpakis’s CANELa lab will use computational modeling and machine learning to understand how the reaction takes place, and use that knowledge to screen a wide range of metal oxides and their properties for use in the process.
“The success of shale gas in the U.S. has transformed the chemical market and have made light alkanes a great feedstock for the production of olefins. However, there is a knowledge gap in the understanding of the mechanism behind turning alkanes into olefins,” says Dr. Mpourmpakis. “Determining how this reaction takes place will allow us to computationally screen metal oxide catalysts and identify the exact active sites on the catalyst, limiting costly and lengthy trial-and-error experiments in the lab.”
The advancement of catalyst discovery will have wide-ranging impacts for the chemical industry and the U.S. economy as a whole, enabling more efficient and cost-effective chemical production using the nation’s abundant natural gas reserves.
Dr. Mpourmpakis’ team will work with the RAPID Manufacturing Institute and Pitt’s Center for Research Computing on this project from September 2019 through August 2022.