Data-driven Process Systems Engineering Lab
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.
Atlanta, GA
June 15, 2022
Congrats to Suryateja for successfully defending his PhD Proposal! His work will be focused on developing derivative-free optimization techniques and data-driven hybrid modeling for process systems.
Atlanta, GA
May 10, 2022
Dr. Boukouvala gave 4 seminars in March and April'22 on 'Integrating Chemical-Engineering Principles with Data-Driven Methods for Modeling & Optimization' at Auburn University, Carnegie Mellon University, University of Manchester and University of Minnesota
Atlanta, GA
May 06, 2022
Congrats to William for graduating with her PhD in Chemical and Biomolecular Engineering!
Computers & Chemical Engineering 2022
Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster…
Industrial & Engineering Chemistry Research 2021
Modeling physiochemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation-intensive…