Ignacio Grossmann
Prof. Dr. Ignacio Grossmann
Session: Systems Engineering and Computational Methods
Title: Recent Advances in
Computational Methods for the
Discrete and Continuous Optimization
of Energy Systems
Ignacio E. Grossmann is the R. R. Dean University Professor in the Department of Chemical Engineering, and former department head at Carnegie Mellon University. He obtained his B.S. degree at the Universidad Iberoamericana, Mexico City, in 1974, and his M.S. and Ph.D. at Imperial College in 1975 and 1977, respectively. He is a member and director of the Center for Advanced Process Decision-making, an industrial consortium that involves about 20 petroleum, chemical, engineering, and software companies. He is a member of the National Academy of Engineering, and associate editor of AIChE Journal. He has received the following AIChE awards: Computing in Chemical Engineering, William H. Walker for Excellence in Publications, Warren Lewis for Excellence in Education, Research Excellence in Sustainable Engineering, Founders Award for Outstanding Contributions to the Field of Chemical Engineering. In 2015 he was the first recipient of the Sargent Medal by the IChemE. He has honorary doctorates from Abo Akademi in Finland, University of Maribor in Slovenia, Technical University of Dortmund in Germany, University of Cantabria in Spain, Russian Kazan National Research Technological University, Universidad Nacional del Litoral, Argentina, and Universidad de Alicante in Spain. He is a 2019 top cited scientist in Computer Science and Electronics: 53 Worldwide, 38 National. He has authored more than 600 papers, several monographs on design cases 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. He has also organized the virtual loibrary on process systems engineering. Grossmann has graduated 67 Ph.D. and 24 M.S. students.
Recent Advances in Computational Methods for the Discrete and Continuous Optimization of Energy Systems
In this talk we give an overview of recent models and algorithms for the discrete and continuous optimization of a variety of challenging applications in Energy Systems. We provide an overview of applications of deterministic models based on mixed-integer linear/nonlinear programming (MILP/MINLP), Generalized Disjunctive Programming (GDP) and global optimization to highlight the progress that has been made in the application of these optimization techniques to energy systems. We first consider applications of MILP that include optimization of hydrogen supply chains for vehicle use, and long term planning of electric power systems with high penetration of renewables. Next, we consider applications of MINLP that include optimization of shale gas infrastructures. We then consider recent algorithms for rigorous global optimization for which we consider applications in optimal process water networks that involve reuse and recycle, and optimal design of centralized and distributed manufacturing facilities for biomass production. All the examples illustrate the expanding scope of the proposed optimization models and algorithms.