Modeling and Solution Approaches for Crude Oil Scheduling in a Refinery

Fragkogios, Antonios; Saharidis, Georgios K. D.

doi:10.1007/978-3-319-75690-5_12

Cited by 2 publications

(3 citation statements)

References 46 publications

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“…The objective of the MBSP is to find the least cost blending plan (or maximize profit), subject to various constraints such as raw material availability, operational rules, and product demand requirements. The MBSP has a wide variety of engineering applications such as crude or refined oil scheduling, − mine planning, , wastewater treatment, , copper concentrate blending, , and specialty chemicals’ manufacturing . While an optimal solution of the MBSP can bring significant economic benefits, − state-of-the-art methods can only solve instances of modest size.…”

Section: Introductionmentioning

confidence: 99%

“…Constraints ensure that during any time period, a blender may have multiple inlet flows or multiple outlet flows but not both. We note that such an operational rule is widely used for MBSPs. ,,, However, for specific applications such as crude-oil scheduling, a more restrictive operational rule may be applied, e.g., in one time period, a blender can have at most one inlet flow or one outlet flow but not both.…”

Section: Introductionmentioning

confidence: 99%

“…In such an MINLP, binary variables are employed to model decisions on material transfers, and bilinear terms are employed to model the composition consistency between blenders’ inventories and outflows. In terms of time representation, MINLPs for the MBSP are categorized into discrete- and continuous-time formulations; see Fragkogios and Saharidis for a review. In discrete-time formulations, the scheduling horizon is divided into predefined time periods, and all material transfer operations can only start or end at the boundaries of these time periods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linear Programming-Based Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling Problems

Song,

Maravelias

2024

Ind. Eng. Chem. Res.

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The multiperiod blend scheduling problem has a wide variety of engineering applications and is typically formulated as a nonconvex mixed-integer nonlinear program (MINLP). Such an MINLP is challenging to solve due to a large number of bilinear terms and binary variables. One prevalent solution method is branch and bound, whose efficiency heavily relies on the tightness of the convex relaxation of the MINLP. In this article, we propose new constraints that can be used for tightening such convex relaxation. These constraints are derived from the physical information lost due to relaxation and require solving linear programs during preprocessing. Extensive numerical tests are executed to examine the effectiveness of the proposed methods. The results show that even though hundreds of linear programs may be solved during preprocessing, our new methods can significantly reduce the overall computational time, including both the preprocessing and MINLP solver solution time. Further implications are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linear Programming-Based Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling Problems

Song,

Maravelias

2024

Ind. Eng. Chem. Res.

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A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

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Modeling and Solution Approaches for Crude Oil Scheduling in a Refinery

Cited by 2 publications

References 46 publications

Linear Programming-Based Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling Problems

Linear Programming-Based Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling Problems

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

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