Data-driven Process Systems Engineering Lab
Welcome to the webpage of the Data-Driven Process Systems Engineering (DDPSE) Lab! We are a computational research group in the School of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Our work lies in the Process Systems Engineering field, with applications in energy, process intensification and manufacturing of pharmaceuticals and bioproducts. Our aim is to integrate new developments in data-science with traditional chemical engineering fundamentals, to develop the new generation of modeling and optimization tools for complex multiscale systems.
Suryateja wins a best oral research presentation award at ChBE’s 36th Graduate Research Symposium
Atlanta, GA
February 04, 2024
Congrats to Suryateja for receiving 2nd place for the best oral presentations on research on complex systems at the 36th annual graduate research symposium!
Dr. Boukouvala receives the 2023 Scialog Collaborative Innovation Award
Atlanta, GA
January 25, 2024
Scialog: Negative Emissions Science Ends with Awards to 7 Teams Research Corporation for Science Advancement, the Alfred P. Sloan Foundation, and ClimateWorks Foundation have made awards to seven cross-disciplinary teams of early career scientists in the fourth and final year of Scialog: Negative…
Jinhyeun and Zach graduate!
Atlanta, GA
December 15, 2023
Congrats to Jinhyeun and Zach for graduating with PhD in Chemical and Biomolecular Engineering!
Process development and techno-economic analysis for mechanochemical recycling of poly(ethylene terephthalate)
Chemical Engineering Journal 2024
Chemical recycling of consumer plastics has garnered great attention recently towards achieving circular economy goals. Particularly in the case of PET waste, mechanochemical depolymerization in ball mill reactors has been identified as a very promising technology due to the high…
Machine learning in process systems engineering: Challenges and opportunities
Computer Aided Chemical Engineering 2023
This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27–29, 2022. The session included two invited talks…
