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

Optimization Letters 2017 Best Paper Award

Atlanta, GA

December 05, 2018

Fani won the 2017 Best Paper Award in Optimization Letters for her paper on the ARGONAUT algorithm for global optimization of grey-box problems. The OPTL Best Paper Award carries a 500 USD prize. The co-author of this paper is the…

Data-Driven PSE lab at INFORMS

Phoenix, AZ

November 04, 2018

Dr. Boukouvala gave a talk at the Annual Informs meeting in Phoenix. The talk focused on recent advances of the lab on data-driven optimization concepts and algorithms.

Data-Driven PSE lab at AIChE

Pittsburgh, PA

November 01, 2018

Jackie and Sophie successfully gave their first talks at AIChE conference. Jackie presented her work on "Spatial Branch-and-bound (sBB) Algorithm for Surrogate-based Optimization" and Sophie presented her work on "Optimization of Data-Dependent Mixed-Integer Nonlinear Problems".

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…