Short-term scheduling of diesel blending and distribution

Dimas, Diovanina; Murata, V. V.; Neiro, Sérgio Mauro da Silva

doi:10.3182/20131218-3-in-2045.00051

Cited by 1 publication

(2 citation statements)

References 4 publications

(6 reference statements)

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“…Crude-oil [23][24][25][26][27][28], 18 [29][30][31][32][33][34], [16,[35][36][37][38][39] Gasoline [40][41][42][43][44][45], 16 [46][47][48][49], [50][51][52][53][54] Other Distilled Fuel 3 [55][56][57] 3.2. Crude-Oil (Petroleum) Blend Scheduling…”

Section: #Publications Referencementioning

confidence: 99%

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

Ahmednooh,

Menezes

2024

Processes

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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.

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Section: #Publications Referencementioning

confidence: 99%

“…Although this model provides valuable insights, there is room for improvement, particularly in enhancing interface identification constraints to increase its applicability. Additionally, penalties can be introduced to account for pump interruptions, especially in short-term scheduling scenarios [55].…”

Section: Other Distillate Blend Schedulingmentioning

confidence: 99%

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

Ahmednooh,

Menezes

2024

Processes

View full text Add to dashboard Cite

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Short-term scheduling of diesel blending and distribution

Cited by 1 publication

References 4 publications

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

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

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