fellow

Ignacio E. Grossmann

2024-2025
Home institution
Carnegie Mellon University
Country of origin (home institution)
United States
Discipline(s)
Chemistry; Earth, environmental and climate sciences; Engineering
Theme(s)
Energy & Renewable Resources; Environment, Sustainability & Biodiversity
Fellowship dates
Biography

Ignacio E. Grossmann is the R. R. Dean University Professor in the Department of Chemical Engineering, and former department head at Carnegie Mellon University. His main research interests lie in the areas of discrete/continuous optimization, particularly mixed-integer nonlinear programming and generalized disjunctive programming, optimal synthesis and planning of chemical processes through superstructure optimization. His research further engages with questions around the optimization of expansion planning of reliable and resilient power systems with high penetration of renewables, the logistics and field management of CO2 capture and sequestration, optimization models for digital supply chain optimization, and stochastic programming methods for optimization under uncertainty.

He obtained his BS degree at the Universidad Iberoamericana, Mexico City, in 1974, and his MS and PhD at Imperial College in 1975 and 1977, respectively. He has authored more than 700 papers, several monographs on design case studies, the recent textbook Advanced Optimization in Process Systems Engineering, and the textbook Systematic Methods of Chemical Process Design, which he co-authored with Larry Biegler and Art Westerberg.

Research Project
Optimal Design of Sustainable Process and Energy Systems

This project aims to develop optimization models using mixed-integer linear/nonlinear programming (MILP/MINLP), Generalized Disjunctive Programming (GDP), and global optimization techniques for the synthesis and design of sustainable process and energy systems. The goal is to integrate life-cycle analysis models to assess the environmental impact of the optimized systems. A specific example involves deploying negative emissions technologies in the European Union power system to anticipate uncertainties through stochastic programming models.

The project will include teaching the graduate-level course “Advanced Optimization for Process Systems Engineering” at ETH Zurich.

Research Interests:

process systems engineering; mathematical optimisation; mixed-integer programming; nonlinear programming; disjunctive programming; global optimisation; sustainable energy systems; life-cycle analysis.