ORION - Oil and Renewables Refining Industry Optimization and SyNergies

Clarissa Bergman-Fonte, Fernanda Guedes, Frédéric Lantz, Pedro Rochedo and Alexandre Szklo
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Clarissa Bergman-Fonte: UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil], IFPEN - IFP Energies nouvelles, IFP School
Fernanda Guedes: UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil], IFPEN - IFP Energies nouvelles, IFP School
Frédéric Lantz: IFPEN - IFP Energies nouvelles, IFP School
Pedro Rochedo: UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil], Khalifa University
Alexandre Szklo: UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil]

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Abstract: This paper introduces ORION, a GAMS-based Mixed Integer Linear Programming model designed to optimize the refining sector. ORION is a multiplant model and considers the spatial distribution of supply, demand, and process units. It addresses not only conventional refinery operations, but also focuses on novel opportunities for integrating the sector into a context of carbon intensity reduction: processing of both fossil and renewable feedstocks, and the construction of small-scale modular refineries. Moreover, the model allows a two-step optimization to identify refineries with low utilization factors and subsequently evaluate how the industry responds to their closure. Those features of the model are demonstrated in a case study for Brazil, illustrating that ORION is useful for assessing risks (e.g., stranded assets) and opportunities (e.g., biomass co-processing, small-scale refineries) in decarbonization scenarios.

Keywords: ORION; Analyzing; Refining (search for similar items in EconPapers)
Date: 2024-02-01
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