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.  

Recent News

Boukouvala Lab Summer 2019 Updates

Atlanta, GA

August 19, 2019

Conferences and Presentations During the summer, Boukouvala lab members attended 3 conferences: Machine Learning in Science and Engineering (MLSE) conference held at Atlanta, GA, the Foundations of Computer-Aided Process Design (FOCAPD) conference held at Copper Mountain, CO, and the inaugural Foundations of…

William successfully defends PhD Proposal

Atlanta, GA

August 29, 2019

CONGRATULATIONS to William, who successfully defended his PhD proposal. His work will focus on developing data-driven hybrid models for chemical processes.

New publication!

Atlanta, GA

March 25, 2019

Jackie published her first paper on variable selection algorithm for surrogate modeling. The article can be found here. Congratulations!

Latest Publications

Managing Uncertainty in Data-Driven Simulation-Based Optimization

Computers & Chemical Engineering 2019

Gordon Hullen Jianyuan Zhai Sun Hye Kim Anshuman Sinha Matthew Realff Fani Boukouvala

Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and…

Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Optimization Letters 2019

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear…