Ignacio E. Grossmann
YOU?
Author Swipe
View article: Pareto optimal solutions for decarbonization of oil refineries under different electricity grid decarbonization scenarios
Pareto optimal solutions for decarbonization of oil refineries under different electricity grid decarbonization scenarios Open
In response to global efforts to reduce carbon emissions, the oil refining sector, a major source of industrial emissions, has set ambitious decarbonization targets. This study analyzes trade-offs between minimizing CO2 emissions and costs…
View article: GRAPSE: Graph-Based Retrieval Augmentation for Process Systems Engineering
GRAPSE: Graph-Based Retrieval Augmentation for Process Systems Engineering Open
Large Language Models have demonstrated potential in accelerating scientific discovery, but they face challenges when making inferences in rapidly evolving and niche domains like Process Systems Engineering (PSE). To address this, we propo…
View article: Multiscale analysis through the use of biomass residues and CO2 towards energetic security at country scale via methane production
Multiscale analysis through the use of biomass residues and CO2 towards energetic security at country scale via methane production Open
The growing demand for sustainable energy has driven research into renewable methane production to reduce greenhouse gas emissions and reliance on fossil fuels. Promising feedstocks include lignocellulosic dry residues, wet waste, and capt…
View article: Optimization models and algorithms for the Unit Commitment problem
Optimization models and algorithms for the Unit Commitment problem Open
The unit commitment problem determines the optimal strategy to meet the electricity demand at minimum cost by committing power generation units at each point of time. Solving the unit commitment problem gives rise to a challenging optimiza…
View article: Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees Open
We propose Conformal Mixed-Integer Constraint Learning (C-MICL), a novel framework that provides probabilistic feasibility guarantees for data-driven constraints in optimization problems. While standard Mixed-Integer Constraint Learning me…
View article: Computational Strategies for RTN Model for Supply Logistics of Carbon Dioxide for Carbon Capture and Storage
Computational Strategies for RTN Model for Supply Logistics of Carbon Dioxide for Carbon Capture and Storage Open
This journal contribution is published Open Access by the publisher. Follow the DOI link to retrieve a copy of the full text.
View article: Optimal Retrofit of Carbon Capture and Electrification Technologies in Oil Refineries for Reducing Direct CO<sub>2</sub> Emissions
Optimal Retrofit of Carbon Capture and Electrification Technologies in Oil Refineries for Reducing Direct CO<sub>2</sub> Emissions Open
This journal contribution is published Open Access by the publisher. Follow the DOI link to retrieve a copy of the full text.
View article: Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions
Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions Open
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors su…
View article: RTN Model for Supply Logistics of Carbon Dioxide in the Field Management for Carbon Capture and Storage
RTN Model for Supply Logistics of Carbon Dioxide in the Field Management for Carbon Capture and Storage Open
This journal contribution is published Open Access by the publisher. Follow the DOI link to retrieve a copy of the full text.
View article: Optimal Reactive Operation of General Topology Supply Chain and Manufacturing Networks under Disruptions
Optimal Reactive Operation of General Topology Supply Chain and Manufacturing Networks under Disruptions Open
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors su…
View article: A Computational Framework for Evaluating and Optimizing Critical Mineral Recovery Opportunities in Produced Water Networks
A Computational Framework for Evaluating and Optimizing Critical Mineral Recovery Opportunities in Produced Water Networks Open
In this presentation, we show a quadratically constrained optimization model to assess how operational decisions in a produced water network can meet minimum Lithium recovery concentration requirements while minimizing operating costs. We …
View article: Optimization Model and Algorithm for Capacity Planning and Operation of Reliable and Carbon-neutral Power Systems with High Penetration of Renewable Generation
Optimization Model and Algorithm for Capacity Planning and Operation of Reliable and Carbon-neutral Power Systems with High Penetration of Renewable Generation Open
In this work, we propose a Generalized Disjunctive Programming (GDP) model that optimizes both long-term capacity planning (such as the number and size of dispatchable/renewable generators, batteries, and transmission lines) and hourly ope…
View article: Two-stage Stochastic Generalized Disjunctive Programming (GDP) Model for Proactive Planning and Reactive Operations of Resilient Power Systems under Disruptions
Two-stage Stochastic Generalized Disjunctive Programming (GDP) Model for Proactive Planning and Reactive Operations of Resilient Power Systems under Disruptions Open
In this work, we propose a Generalized Disjunctive Programming (GDP) model that optimizes both long-term capacity planning (such as the number and size of dispatchable/renewable generators, batteries, and transmission lines) and hourly ope…
View article: Solving the security constrained unit commitment problem: Three novel approaches
Solving the security constrained unit commitment problem: Three novel approaches Open
This work proposes three novel approaches to speed up the solution of the Security Constrained Unit Commitment problem: an improvement of an active-set iterative approach taken from literature, an approach using solver callback functions f…
View article: Optimal Membrane Cascade Design for Critical Mineral Recovery through Logic-based Superstructure Optimization
Optimal Membrane Cascade Design for Critical Mineral Recovery through Logic-based Superstructure Optimization Open
In this work, we extend the superstructure model proposed by Wamble et al. (2022) that considers feed input locations, recycling strategies, split fractions, stage numbers, and membrane area. We include the total number of stages as a deci…
View article: Optimal Membrane Cascade Design for Critical Mineral Recovery Through Logic-based Superstructure Optimization
Optimal Membrane Cascade Design for Critical Mineral Recovery Through Logic-based Superstructure Optimization Open
Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recover…
View article: Mixed-Integer Nonlinear Programming Model for Optimal Field Management for Carbon Capture and Storage
Mixed-Integer Nonlinear Programming Model for Optimal Field Management for Carbon Capture and Storage Open
This work proposes a multiperiod mixed-integer nonlinear programming (MINLP) model to optimize the complex operations of CO2 buffering in tanks, transport through pipe networks, and storage in depleted reservoirs via injection wells. The m…
View article: Angel Irabien Festschrift: Honoring His Impact on the Profession
Angel Irabien Festschrift: Honoring His Impact on the Profession Open
ADVERTISEMENT RETURN TO ARTICLES ASAPPREVEditorialNEXTAngel Irabien Festschrift: Honoring His Impact on the ProfessionIgnacio GrossmannIgnacio GrossmannMore by Ignacio Grossmannhttps://orcid.org/0000-0002-7210-084X, Richard DartonRichard D…
View article: Decomposition methods for multi-horizon stochastic programming
Decomposition methods for multi-horizon stochastic programming Open
Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structur…
View article: Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs
Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs Open
The optimization of chemical processes is challenging due to the nonlinearities arising from process physics and discrete design decisions. In particular, optimal synthesis and design of chemical processes can be posed as a Generalized Dis…
View article: Iterative MILP algorithm to find alternate solutions in linear programming models
Iterative MILP algorithm to find alternate solutions in linear programming models Open
We address in this paper linear programming (LP) models in which it is desired to find a finite set of alternate optima. An LP may have multiple alternate solutions with the same objective value or with increasing objective values. For man…
View article: A tailored decomposition approach for optimization under uncertainty of carbon removal technologies in the EU power system
A tailored decomposition approach for optimization under uncertainty of carbon removal technologies in the EU power system Open
The broad portfolio of negative emissions technologies calls for integrated analyses to explore the synergies between them and the power sector, with which they display strong links. These analyses should be conducted at a regional level, …
View article: Decomposition methods for multi-horizon stochastic programming
Decomposition methods for multi-horizon stochastic programming Open
Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structur…