Green Process Engineering

Gyorgy Szekely

Assistant Professor, Chemical Engineering

Nanofiltration Molecular Imprinting Green Process Engineering Hybrid Processes Membrance Reactors

Professor Szekely's research focuses on sustainable separations through the synergistic combination of materials science and chemical engineering. Sustainable production of chemicals, pharmaceuticals, and clean water is largely impacted by the efficiency of separation processes in product supply chains. The conventional separation processes can account for as much as 80% of the total manufacturing costs, contributing to approx. 10% of the world's energy consumption. In particular, the group's research investigates the potential of advanced membrane materials for efficient purification and sustainable processing of fine chemicals and water. They use applied machine learning and deep learning techniques to bridge the gap between material science and artificial intelligence. The primary focus is to solve challenging separation problems, which are considered impossible with traditional approaches. His group also combines wet-lab and in-silico data generation with building databases used in downstream prediction tasks or generative models. They have been using machine learning related to organic solvents, solvent-resistant nanofiltration, metal–organic frameworks, and covalent organic frameworks.