Introduction: At the Intersection of Optimization and Uncertainty Quantification

Our group applies mathematical modeling, computational optimization, and uncertainty quantification paradigms to several chemical engineering application related to sustainable energy. Our work spans diverse length and timescales and is highly collaborative.

Across the diverse applications listed below, our group is especially interested in problem formulations, algorithms, and software that integrate Bayesian uncertainty quantification and mathematical programming paradigms. We focus on Bayesian statistical learning techniques to rigorously inform optimization under uncertainty. This is especially important for multiscale and multiphysics applications, where simplification or surrogate models are routinely developed ad-hoc to enable computational tractability. Emerging machine learning can enable new ways to rigorously quantify and account for information loss from model reduction needed for multiscale optimization.

Application Theme 1: Molecules-to-Systems Engineering for Enhanced Separations

Through three collaborative projects at Notre Dame, we are developing mathematical and computational frameworks to systematically integration molecular and nanoengineering with unit operation and process scale optimization.

Integrated Molecular and Separation System Design to Enable Sustainable Recycling of Hydrofluorocabons

Bridgette Befort (PhD student). Co-advised with Prof. Edward Maginn, Notre Dame Chemical and Biomolecular Engineering.

Modeling and Optimization of Directional Solvent Extraction for Sustainable Desalination

Alejandro Garciadiego (PhD student). Project in collaboration with Prof. Tengfei Luo, Notre Dame Aerospace and Mechanical Engineering

Multiscale Modeling and Optimization of Novel Membrane Separations

Elvis Eugene (PhD student). Project in collaboration with Prof. William Phillip, Notre Dame Chemical and Biomolecular Engineering.

Application Theme 2: Multiscale Optimization of Alkane Transformations

We also contribute to the NSF Center for Innovative and  Strategic Transformation of Alkane Resources (CISTAR, through two projects.

Optimal Deployment of Modular Natural Gas Upgrading Systems

Kanishka Ghosh (PhD student). Project in collaboration with Mark Stadtherr and David Allen at UT Austin Chemical Engineering.

Modeling and Optimization of Oligomerization Reactors

Kanishka Ghosh (PhD student) and Alejandro Garciadiego (PhD student). Project in collaboration with Rakesh Agarwal at Purdue Chemical Engineering and Linda Broadbelt at Northwestern Chemical & Biological Engineering.

Application Theme 3: Uncertainty Modeling and Stochastic Optimization of Energy Systems

Optimal Bidding Strategies for Energy Markets Under Uncertainty

Xian Gao (PhD Student). Project is part of the Institute for Design of Advanced Energy Systems (IDAES,

Forecasting Energy Prices with Dynamic Mode Decomposition

Clay Elmore (Undergraduate Researcher).