Ignacio Grossmann

About

Ignacio Grossmann is the R. R. Dean University Professor in the Department of Chemical Engineering at Carnegie Mellon University and a former department head. He obtained his B.S. degree at the Universidad Iberoamericana in Mexico City in 1974, followed by an M.S. and Ph.D. at Imperial College in 1975 and 1977, respectively. His main research interests include discrete continuous optimization, optimal synthesis and planning of chemical processes and energy systems, and supply chain optimization. Grossmann has authored over 700 papers, several monographs, and textbooks such as 'Advanced Optimization in Process Systems Engineering' and 'Systematic Methods of Chemical Process Design.' He is a member of the National Academy of Engineering and has received numerous awards from AIChE, including the Computing in Chemical Engineering award, the William H. Walker award, and the Founders Award for Outstanding Contributions to the Field of Chemical Engineering. Grossmann has also been recognized internationally with honorary doctorates from multiple universities and was the first recipient of the Sargent Medal by IChemE in 2015. His contributions extend to industrial collaborations, including leading the Center for Advanced Process Decision-making, and he is ranked among the top cited scientists in computer science and electronics.

Research topics

• Computer Science
• Mathematics
• Algorithm
• Theoretical computer science
• Engineering
• Programming language
• Mathematical optimization
• Artificial Intelligence
• Industrial engineering
• Software engineering

Selected publications

• A review on superstructure optimization approaches in process system engineering

  Computers & Chemical Engineering · 2020 · 212 citations

  Senior authorCorresponding

  In this paper, we survey the main superstructure-based approaches in process system engineering, with a particular emphasis on the existing literature for automated superstructure generation. We examine both classical and more recent representations in terms of generality, ease of use, and tractability. We also discuss the implications that different representations may have on strategies for algebraic modeling and optimization. We then review the state-of-the-art in software implementations to…

  DOI

• A deep reinforcement learning approach for chemical production scheduling

  Computers & Chemical Engineering · 2020 · 187 citations

  This work examines applying deep reinforcement learning to a chemical production scheduling process to account for uncertainty and achieve online, dynamic scheduling, and benchmarks the results with a mixed-integer linear programming (MILP) model that schedules each time interval on a receding horizon basis. An industrial example is used as a case study for comparing the differing approaches. Results show that the reinforcement learning method outperforms the naive MILP approaches and is competi…

  DOI

• Mixed-Integer Nonlinear Programming

  Cambridge University Press eBooks · 2021 · 99 citations

  1st authorCorresponding

  Based on the author's forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic…

  DOI

• Multiscale analysis through the use of biomass residues and CO2 towards energetic security at country scale via methane production

  Energy · 2025-02-05 · 10 citations

  articleSenior author
  PublisherDOI

• RTN Model for Supply Logistics of Carbon Dioxide in the Field Management for Carbon Capture and Storage

  Industrial & Engineering Chemistry Research · 2025-01-20 · 9 citations

  articleOpen accessSenior authorCorresponding

  We present a mixed-integer linear programming (MILP) model for a maritime inventory routing (MIR) problem for carbon capture and storage (CCS). The model is formulated using an extension of the resource-task network (RTN) representation that minimizes sailing costs. The proposed model can accommodate any number of vessels and emitters. The discrete-time model considers accurate task durations relative to the load of CO2. The model can generate an hourly inventory profile and detailed scheduling…

  PublisherDOI

Recent grants

• GOALI: Multiscale Decomposition Techniques for the Integration of Optimal Planning and Scheduling of Batch and Continuous Multiproduct Process Systems

  NSF · $278k · 2006–2010

• GOALI: Multi-scale Optimization for the Design, Capacity Planning and Operation of Power Intensive Process Networks under Uncertain Electricity Prices and Market Demands

  NSF · $302k · 2012–2016

• Advanced Computational Models for Multistage Stochastic Optimization of Process Systems with Renewable Resources

  NSF · $272k · 2005–2009

Frequent coauthors

• Mariano Martı́n

  66 shared

• José A. Caballero

  57 shared

• Pedro M. Castro

  50 shared

• Zdravko Kravanja

  38 shared

• José M. Pinto

  38 shared

• John M. Wassick

  36 shared

• Iiro Harjunkoski

  Hitachi (Germany)

  32 shared

• Diego C. Cafaro

  31 shared

Education

• B.S., Chemical Engineering

  Universidad Iberoamericana

  1974

• M.S., Chemical Engineering

  Imperial College, University of London

  1975

• Ph.D., Chemical Engineering

  Imperial College, University of London

  1977

Awards & honors

• William H. Walker Award for Excellence in Publications
• Warren Lewis Award for Excellence in Education
• Research Excellence in Sustainable Engineering
• Founders Award for Outstanding Contributions to the Field of…
• John M. Prausnitz AIChE Institute Lecturer

Similar researchers at Carnegie Mellon University

• Neil Donahueh-116
• Lorenz Bieglerh-92
• Robert Tiltonh-59