Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops
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| Wydane w: | Processes vol. 12, no. 3 (2024), p. 561 |
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| 1. autor: | |
| Kolejni autorzy: | |
| Wydane: |
MDPI AG
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| Hasła przedmiotowe: | |
| Dostęp online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Ahmednooh, Mahmoud |u Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 34110, Qatar; Production Planning and Scheduling, Um Said Refinery, Qatar Energy, Doha P.O. Box 3212, Qatar; Blend-Shops Company, Qatar Science and Technological Park, Qatar Foundation, Doha P.O. Box 34110, Qatar | |
| 245 | 1 | |a Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Catalytic cracking | ||
| 653 | |a Hydrocarbons | ||
| 653 | |a Gasoline | ||
| 653 | |a Petroleum refineries | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Supervised learning | ||
| 653 | |a Diesel fuels | ||
| 653 | |a Least squares method | ||
| 653 | |a Formulations | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision making | ||
| 653 | |a Nonlinear programming | ||
| 653 | |a Refineries | ||
| 653 | |a Scheduling | ||
| 653 | |a Simulation | ||
| 653 | |a Blending | ||
| 653 | |a Raw materials | ||
| 653 | |a Petroleum | ||
| 653 | |a Batch processes | ||
| 653 | |a Optimization | ||
| 653 | |a Distillation | ||
| 653 | |a Literature reviews | ||
| 653 | |a Mixed integer | ||
| 653 | |a Mixtures | ||
| 653 | |a Kerosene | ||
| 653 | |a Petroleum refining | ||
| 653 | |a Inventory | ||
| 653 | |a Decomposition | ||
| 700 | 1 | |a Menezes, Brenno |u Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 34110, Qatar; Blend-Shops Company, Qatar Science and Technological Park, Qatar Foundation, Doha P.O. Box 34110, Qatar | |
| 773 | 0 | |t Processes |g vol. 12, no. 3 (2024), p. 561 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3003410679/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3003410679/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3003410679/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |