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 successfully defends PhD proposal!
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.
Boukouvala Lab Spring 22 updates
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
William graduates!
Atlanta, GA
May 06, 2022
Congrats to William for graduating with her PhD in Chemical and Biomolecular Engineering!
Perspectives on the Integration between First-Principles and Data-Driven Modeling
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…
Two-Stage Approach to Parameter Estimation of Differential Equations Using Neural ODEs
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…
