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 group funded by NSF

Altanta, GA

September 01, 2018

The Boukouvala group was awarded $300,000 by the NSF to develop new optimization algorithms for data-dependent systems. This new class of algorithms aims to identify optimal solutions to complex design problems, using a combination of data analysis and mathematical programming. The work will envolve…

Sophie successfully defends PhD Proposal

Atlanta, GA

August 06, 2018

CONGRATULATIONS to Sophie, who successfully defended her PhD Proposal defense. Her work will focus on development of algorithms for mixed-integer optimization of data-dependent problems for process intensification. Her work will be funded by RAPID-DOE.

Jackie successfully defends PhD Proposal

Atlanta, GA

July 24, 2018

CONGRATULATIONS to Jackie, who successfully defended and passed her PhD Proposal defense. Her work will involve development of novel spatial branch-and-bound algorithms for data-driven optimization. Her work will be funded by NSF. 

Latest Publications

Optimization of black-box problems using Smolyak grids and polynomial approximations

Journal of Global Optimization 2018

C.A. Kieslich F. Boukouvala C.A. Floudas

A surrogate-based optimization method is presented, which aims to locate the global optimum of box-constrained problems using input–output data. The method starts with a global search of the n-dimensional space, using a Smolyak (Sparse) grid which is constructed using Chebyshev extrema…

Global optimization of grey-box computational systems using surrogate functions and application to highly constrained oil-field operations

Computers & Chemical Engineering 2018

B. Beykal F. Boukouvala C.A. Floudas N. Sorek H. Zalavadia E. Gildin

This work presents recent advances within the AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems (ARGONAUT) framework, developed for optimization of systems which lack analytical forms and derivatives. A new parallel version of ARGONAUT (p-ARGONAUT) is introduced to solve…