ChemBE Seminar: Ioannis Economou

Description

Ioannis Economou (PhD '92), an executive associate dean and professor of chemical engineering at Texas A&M University at Qatar, will give a talk titled "Computational Molecular Engineering: A Powerful Tool for Chemical Process and Advanced Materials Design" for the Department of Chemical and Biomolecular Engineering. Refreshments will follow the lecture.

Abstract:

The unprecedented increase of computing power at affordable price and the development of advanced computational techniques spanning from the sub[1]molecular all the way to the macroscopic engineering level has made computational molecular engineering a very powerful tool for the design of novel chemical processes and advanced materials with tailor-made properties. In this presentation, I intend to provide a brief overview of various computational methods used widely today by chemical engineers, and I will then present two specific examples of industrial importance. The first one refers to the Fischer-Tropsch Synthesis (FTS) to transform natural gas into clean, high quality low emission transportation fuels through the so-called Gas-To-Liquid (GTL) process. The main FTS reaction products, namely water, wax, and small amounts of oxygenates (e.g., alcohols < 10 wt %), form a mixture through which the dissolved reactants diffuse, reach the catalytic nanoparticles and react. We have developed a model based on Molecular Dynamics (MD) simulations using state-of-the art realistic atomistic models. In addition, we developed a coarse grain methodology to simulate the system with the explicit presence of the catalytic nanoparticle. Simulation results at different levels are in excellent agreement with each other as well as with experimental data measured as part of this project. The second example refers to the development of a predictive model for the accurate design of highly selective Zeolitic-imidazolate Frameworks (ZIFs) for the separation of various gas mixtures, using a combination of density functional theory (DFT) calculations, molecular simulation (both MD and Grand Canonical Monte Carlo), and finally a machine learning (ML) model that allows massive screening of various structures whose relevant properties are generated by molecular simulation. This pore network engineering approach can rapidly and efficiently estimate the diffusivity of molecules in any possible ZIF structure with SOD topology by using readily accessible input information.

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