Data-driven Process Systems Engineering Lab
Welcome to the webpage of the Data-Driven Process Systems Engineering 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.
The DDPSE lab welcomes two new members
Atlanta, GA
November 14, 2020
The DD-PSE lab welcomes to new PhD students, Elisavet Anglou and Suryateja Ravutla. Elisavet joins the lab in October 2020 and will be working on modeling and optimization for plastics recycling. Suryateja will join the lab in January 2021 and…
NSF award to reduce plastic waste
Atlanta, GA
November 14, 2020
Fani Boukouvala is a co-PI on a grant awarded by NSF for reducing plastic waste. The role of the Data-Driven PSE lab on this proposal will be to model and optimize the integrated process that transforms plastic waste of various…
ARPA-E award for flexible carbon capture and storage
Atlanta, GA
November 14, 2020
An ARPA-E FLExible Carbon Capture and Storage (FLECCS) award has been given to Matthew Realff (PI), Chris Jones, Ryan Lively, Joe Scott and Fani Boukouvala to develop a modular direct air capture (DAC) process to be integrated with flexible natural gas-fired…
Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization
Archive 2020
In this paper, we present a novel approach that uses convex underestimators of data and a branch-and-bound procedure to obtain globally optimal solutions of simulation-based problems.
Surrogate-Based Optimization for Mixed-Integer Nonlinear Problems
Computers & Chemical Engineering 2020
