Ignacio E. Grossmann

Carnegie Mellon University

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Mariano Martı́n

Universidad de Salamanca

José A. Caballero

University of Alicante

José M. Pinto

Rede de Química e Tecnologia

John M. Wassick

Carnegie Mellon University

Zdravko Kravanja

University of Maribor

Lorenz T. Biegler

Carnegie Mellon University

Pedro M. Castro

University of Lisbon

Iiro Harjunkoski

Hitachi (Germany)

Héctor D. Pérez

Carnegie Mellon University

David E. Bernal

Ames Research Center

Cooperative Institutions

Carnegie Mellon University

220

Massachusetts Institute of Technology

The University of Texas at Austin

National University of Singapore

Consejo Nacional de Investigaciones Científicas y Técnicas

ExxonMobil (United States)

Purdue University West Lafayette

Imperial College London

National Energy Technology Laboratory

Instituto de Desarrollo Tecnológico para la Industria Química

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An Efficient Multiperiod MINLP Model for Optimal Planning of Offshore Oil and Gas Field Infrastructure

Industrial & Engineering Chemistry Research (2012)

Vijay Gupta Ignacio E. Grossmann

In this paper, we present an efficient strategic/tactical planning model for offshore oilfield development problem that is fairly generic and can be extended to include other complexities. The proposed multiperiod nonconvex MINLP (mixed integer nonlinear programming) model for multifield site includes three components (oil, water, and gas) explicitly in the formulation using higher order polynomials, avoiding bilinear and other nonlinear terms. With the objective of maximizing total NPV for long-term planning horizon, the model involves decisions related to FPSO (floating production, storage, and offloading) installation and expansions, field–FPSO connections, well drilling, and production rates in each time period. The model can be solved effectively with DICOPT for realistic instances and gives good quality solutions. Furthermore, it can be reformulated into an MILP after piecewise linearization and exact linearization techniques that can be solved globally in an efficient way. Solutions of realistic instances involving 10 fields, 3 FPSOs, 84 wells, and 20 years planning horizon are reported, as well as comparisons between the computational performance of the proposed MINLP and MILP formulations.

Linearization

Time horizon

10.1021/ie202959w

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Citations (104)

Mixed-Integer Programming Models

Cambridge University Press eBooks (2021)

Ignacio E. Grossmann

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 background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential text for single-semester, graduate courses in process systems engineering in departments of chemical engineering.

10.1017/9781108917834.007

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Citations (0)

A computationally useful algebraic representation of nonlinear disjunctive convex sets using the perspective function

Computational Optimization and Applications (2020)

Kevin C. Furman Nicolas W. Sawaya Ignacio E. Grossmann

Proper convex function

Representation

10.1007/s10589-020-00176-0

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Citations (9)

Design of distillation sequences: from conventional to fully thermally coupled distillation systems

Computers & Chemical Engineering (2004)

José A. Caballero Ignacio E. Grossmann

Reboiler

Tray

Condenser (optics)

Zeotropic mixture

Sequence (biology)

Reactive distillation

10.1016/j.compchemeng.2004.04.010

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Citations (154)

Annals of Operations Research (1998)

José M. Pinto Ignacio E. Grossmann

Theory of computation

10.1023/a:1018929829086

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Citations (177)

Process Optimization of FT-Diesel Production from Lignocellulosic Switchgrass

Industrial & Engineering Chemistry Research (2011)

Mariano Martı́n Ignacio E. Grossmann

In this paper, we present the conceptual design for the optimization of FT-diesel production process from switchgrass via gasification of biomass. We propose a limited superstructure where the process starts with the gasification of the biomass. Two different alternatives are evaluated, direct and indirect gasification. The gas obtained is cleaned up and its composition adjusted in terms of the ratio CO/H2 considering a bypass, a PSA system and water gas shift reaction. Next, the removal of CO2 and H2S is performed using absorption in monoethanolamines and PSA. Once the syngas is prepared, the Fischer-Tropsch reaction is conducted and the products separated. Hydrocracking of the heavy products is also considered to increase the yield toward green diesel. The optimization of the system is formulated as an equation-based Mixed-Integer Nonlinear Programming (MINLP) problem that is solved for the optimal production of the biodiesel fraction while minimizing the energy consumption and the hydrogen consumption. The optimal solution requires the use of indirect gasification followed by steam reforming. The ratio of CO/H2 is left unchanged as the one obtained after gas reforming, while adjusting the temperature at the FT reactor and the operating conditions of the hydrocracking to increase the FT-diesel production. A promising production cost of 0.71 $/gallon is obtained, and only 0.15 gal Water/gal bioifuel of water consumption is required if air cooling is implemented in the plant.

Lignocellulosic Biomass

10.1021/ie201261t

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Citations (97)

Modeling of Purchase and Sales Contracts in Supply Chain Optimization

2006 SICE-ICASE International Joint Conference (2006)

Minhwan Park Sunwon Park Fernando D. Mele Ignacio E. Grossmann

This work presents a novel approach for modeling of different types of contracts that a company may sign with its suppliers and customers. The main objective is to expand the scope of current planning and supply chain optimization models by including the selection of the types of contracts as an additional decision. The solution approach relies on representing the decision of choosing different contracts using disjunctive programming for both short-term and long-term production planning models. The resulting formulation is converted into a mixed-integer linear programming (MILP) problem. The advantages of the proposed models are highlighted in two case studies of increasing complexity

Scope (computer science)

Decision model

Supply chain optimization

10.1109/sice.2006.315135

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Citations (2)

Using redundancy to strengthen the relaxation for the global optimization of MINLP problems

Computers & Chemical Engineering (2011)

Juan P. Ruiz Ignacio E. Grossmann

10.1016/j.compchemeng.2011.01.035

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Citations (29)