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.
DDPSE welcomes new undergrad student!
Atlanta, GA
January 23, 2023
The DD-PSE lab has grown! Riddhi Bhattacharya joins as a new undergraduate researcher and will work with Elisavet on the supply-chain of plastic recycling. Welcome, Riddhi!
Sid successfully passed his qualification exam!
Atlanta, GA
January 20, 2023
Sid successfully passed his quals! Congratulations!
DDPSE team at AIChE 2022
Phoenix
November 20, 2022
DDPSE team gave a total of 4 talks and 2 posters at AIChE held at Phoenix, AZ. 2 of them were focused on modeling of mechanocatalytic plastic recycling, 1 talk was on data-driven optimization and rest of the talks on…
Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization
Optimization and Engineering 2022
Black-box surrogate-based optimization has received increasing attention due to the growing interest in solving optimization problems with embedded simulation data. The main challenge in surrogate-based optimization is the lack of consistently convergent behavior, due to the variability introduced by initialization,…
Training Stiff Dynamic Process Models via Neural Differential Equations
Computer Aided Chemical Engineering 2022
A common step in developing generalizable, dynamic mechanistic models is to fit unmeasured parameters to measured data. Fitting differential equation-based models can be computationally expensive due to the presence of nonlinearity and stiffness. This work proposes a two-stage indirect approach…
