Ignacio E. Grossmann

This work evaluates a systematic comparison between the production of methanol and methane using CO2 and renewable hydrogen. CO2 is captured from point and dilute sources using aqueous MEA solutions and a conventional DAC process. Hydrogen is obtained through water electrolysis, powered by PV panels and wind turbines. First, a techno-economic evaluation is developed to detail the characteristics of the production facilities and the renewable energy systems. Finally, a Facility Location Problem (FLP) is developed to determine the centralized and decentralized CO2 use across Spain. This supply network is formulated as a mixed-integer linear programming (MILP) problem, selecting the optimal amount of CO2 to capture, the number and location of the facilities, the distribution of the PV panels for a fixed available area in the territory, and the number of wind turbines across the 47 Spanish peninsular …

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, considering system uncertainties, assessing local benefits and the impact on carbon removal potential. This study investigates how uncertainty in electricity demand affects the optimal design of integrated carbon removal and power generation systems using multistage stochastic programming. Given the model complexity, we propose a tailored decomposition algorithm by extending previous work on the shrinking horizon approach that reduces the computational time by 90%, enabling insights into various European scenarios. A combination of conventional technologies and biomass could satisfy the electricity demand with up to 9 Gt of net CO2 removal from the atmosphere …

This paper presents a Multi-Echelon Inventory Optimization (MEIO) framework, based on the Guaranteed-Service Model (GSM), to allocate safety stocks across a supply chain with several locations and products, minimizing costs while meeting service level objectives. Extending previous work by Achkar et al. (2023), this paper enhances the Mixed-Integer Quadratically Constrained Program (MIQCP) with a highly efficient solution approach. The model introduces a piecewise linear approximation, significantly improving computational efficiency and the accuracy of the approximation for the fill rate function. It also introduces a different and more efficient approach to account for stochastic lead times using a discrete function. Moreover, an extension of the approach to account for non-normally distributed demands is proposed. The model is applied to several instances of a real-world case study from a pharmaceutical …

Energy industries face the challenge on how to design networks to gather flows from unconventional assets due to the relative short lifetime of the wells. The optimal design of the network of pipelines and processing facilities is a challenging problem from both combinatorial and nonlinear viewpoints. To date, real instances of this problem cannot be solved to global optimality unless simplifying assumptions are made. We propose a systematic algorithm to address the optimal planning of gathering networks in a multiperiod horizon. It relies on the strategic selection of links on which fluid-dynamic equations are imposed. By relaxing constraints, the resulting mixed-integer programming models are still complex from the combinatorial standpoint, but they can be solved in reasonable computational times. The algorithm progressively adds constraints to the potential arcs, seeking for the global optimal solution by …

We propose a rigorous approach for the operational planning of supply chains considering carbon intensity limits in final products. Accurately tracking of these indicators along the value chain can be challenging when dealing with bulk products. Production, transportation, blending and separation of components yield a complex pooling problem within a network structure. A novel mixed-integer nonlinear model is developed to optimize shipping schedules, inventories, processing and distribution of products while complying with environmental constraints of different markets. The model allows for rigorous monitoring and managing of carbon indicators, as well as for drawing important conclusions about sourcing strategies. A decomposition algorithm is presented for this problem, which permits obtaining high quality solutions in reasonable times. A global scale case study is solved to illustrate the economic impact of …

Modeling and optimization of large-scale refinery scheduling problems is challenging because of their complexity and size. Herein, we propose a mathematical model to represent such problems more accurately and realistically, and a state-of-the-art optimization framework for its solution. The framework leverages the use of mathematical optimization and algorithmic methods by combining modeling approaches (process design, model decompositions), solving strategies (rescheduling, heuristics), and machine learning regression (surrogate models). An industrial-size refinery scheduling problem (2 blenders, 4 feed tanks, distillation network with 5 towers, processing network with FCC, hydrotreaters, debutanizers, superfractionator, catalytic reformer) is formulated as a hierarchical nonconvex mixed-integer nonlinear programming (MINLP) model and is successfully optimized, providing higher profitability and more …

Multi-echelon inventory optimization (MEIO) plays a key role in a supply chain seeking to achieve specified customer service levels with a minimum capital in inventory. In this work, we propose a generalized MEIO model based on the Guaranteed Service approach to allocate safety stock levels across the network at the lowest holding cost. This model integrates several existing and some novel features that are usually present in pharmaceutical multi-echelon supply chains into a single model: review periods, manufacturing facilities, hybrid nodes (nodes with both internal and external demand), minimum order quantities (MOQ), and different service level performance indicators (fill rate and cycle service levels). We include a polynomial regression to approximate fill rates as a possible target measure to set safety stocks. To improve efficiency, we propose a nonlinear programming model to support decision making …

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 many real life applications, it can be interesting to have a pool of solutions to compare what operations should be executed and what is the cost/benefit of doing it. To obtain a specified number of these alternate solutions in the increasing order of objective values, we propose an iterative MILP algorithm in which we successively add integer cuts on inactive constraints. We demonstrate the application and effectiveness of this algorithm on a 2 dimensional LP and on small and large supply chain problems. The proposed iterative MILP algorithm provides an effective approach for finding a specified number of alternate optima in LP models, which provides a useful tool in a variety of …