Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops
<p dir="ltr">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 gaso...
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2024
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| _version_ | 1864513510232293376 |
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| author | Mahmoud Ahmednooh (19237120) |
| author2 | Brenno Menezes (17933786) |
| author2_role | author |
| author_facet | Mahmoud Ahmednooh (19237120) Brenno Menezes (17933786) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mahmoud Ahmednooh (19237120) Brenno Menezes (17933786) |
| dc.date.none.fl_str_mv | 2024-03-13T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/pr12030561 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Blend_Scheduling_Solutions_in_Petroleum_Refineries_towards_Automated_Decision-Making_in_Industrial-like_Blend-Shops/26389036 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Manufacturing engineering Resources engineering and extractive metallurgy Information and computing sciences Machine learning blend scheduling blend-shops petroleum management quantity–quality preservation |
| dc.title.none.fl_str_mv | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Processes<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/pr12030561" target="_blank">https://dx.doi.org/10.3390/pr12030561</a></p><p dir="ltr">Additional institutions affiliated with: Blend-Shops Company - QSTP</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f73879b23f3de2c90bb226f527b2fc6e |
| identifier_str_mv | 10.3390/pr12030561 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26389036 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-ShopsMahmoud Ahmednooh (19237120)Brenno Menezes (17933786)EngineeringManufacturing engineeringResources engineering and extractive metallurgyInformation and computing sciencesMachine learningblend schedulingblend-shopspetroleum managementquantity–quality preservation<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Processes<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/pr12030561" target="_blank">https://dx.doi.org/10.3390/pr12030561</a></p><p dir="ltr">Additional institutions affiliated with: Blend-Shops Company - QSTP</p>2024-03-13T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/pr12030561https://figshare.com/articles/journal_contribution/Blend_Scheduling_Solutions_in_Petroleum_Refineries_towards_Automated_Decision-Making_in_Industrial-like_Blend-Shops/26389036CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263890362024-03-13T09:00:00Z |
| spellingShingle | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops Mahmoud Ahmednooh (19237120) Engineering Manufacturing engineering Resources engineering and extractive metallurgy Information and computing sciences Machine learning blend scheduling blend-shops petroleum management quantity–quality preservation |
| status_str | publishedVersion |
| title | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| title_full | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| title_fullStr | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| title_full_unstemmed | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| title_short | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| title_sort | Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops |
| topic | Engineering Manufacturing engineering Resources engineering and extractive metallurgy Information and computing sciences Machine learning blend scheduling blend-shops petroleum management quantity–quality preservation |