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Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops

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Publicado en:Processes vol. 12, no. 3 (2024), p. 561
Autor principal: Ahmednooh, Mahmoud
Otros Autores: Menezes, Brenno
Publicado:
MDPI AG
Materias:
Catalytic cracking
Hydrocarbons
Gasoline
Petroleum refineries
Optimization techniques
Supervised learning
Diesel fuels
Least squares method
Formulations
Machine learning
Decision making
Nonlinear programming
Refineries
Scheduling
Simulation
Blending
Raw materials
Petroleum
Batch processes
Optimization
Distillation
Literature reviews
Mixed integer
Mixtures
Kerosene
Petroleum refining
Inventory
Decomposition
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Resumen:A major operation in petroleum refinery plants, blend scheduling management of stocks and their mixtures, known as blend-shops, is aimed at feeding process units (such as distillation columns and catalytic cracking reactors) and production of finished fuels (such as gasoline and diesel). Crude-oil, atmospheric residuum, gasoline, diesel, or any other stream blending and scheduling (or blend scheduling) optimization yields a non-convex mixed-integer nonlinear programming (MINLP) problem to be solved in ad hoc propositions based on decomposition strategies. Alternatively, to avoid such a complex solution, trial-and-error procedures in simulation-based approaches are commonplace. This article discusses solutions for blend scheduling (BS) in petroleum refineries, highlighting optimization against simulation, continuous (simultaneous) and batch (sequential) mixtures, continuous- and discrete-time formulations, and large-scale and complex-scope BS cases. In the latter, ordinary least squares regression (OLSR) using supervised machine learning can be utilized to pre-model blending of streams as linear and nonlinear constraints used in hierarchically decomposed blend scheduling solutions. Approaches that facilitate automated decision-making in handling blend scheduling in petroleum refineries must consider the domains of quantity, logic, and quality variables and constraints, in which the details and challenges for industrial-like blend-shops, from the bulk feed preparation for the petroleum processing until the production of finished fuels, are revealed.
ISSN:2227-9717
DOI:10.3390/pr12030561
Fuente:Materials Science Database