Refinery production planning optimization under crude oil quality uncertainty

Li, Fupei; Qian, Feng; Du, Wenli; Yang, Minglei; Long, Jian; Mahalec, Vladimir

doi:10.1016/j.compchemeng.2021.107361

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…To prevent this, Equation (16) prescribes that the amount of inventory EOD u,T within tank u U IT at the end of the final time period T must be equal to the stock EOD u,0 at the beginning of the planning horizon (t = 0). Upper and lower limits on the stock EOD u,t of a tank u U IT are imposed via Equation (17).…”

Section: Multiperiod Formulationmentioning

confidence: 99%

“…Li, Misener, and Floudas 16 applied a robust optimization framework to a nonlinear crude‐oil scheduling problem under demand uncertainty. Li et al 17 formulated a two‐stage stochastic blending problem, where the first‐stage feedstock quantities are optimized under uncertainty in the crude‐oil properties. They sampled a finite number of discrete scenarios via the random vector sampling approach.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of a multiperiod refinery planning problem under uncertainty

et al. 2022

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Uncertainty in refinery planning presents a significant challenge in determining the day-to-day operations of an oil refinery. Deterministic modeling techniques often fail to account for this uncertainty, potentially resulting in reduced profit. The stochastic programming framework explicitly incorporates parameter uncertainty in the problem formulation, thus giving preference to robust solutions. In this work, a nonlinear, multiperiod, industrial refinery problem is extended to a two-stage stochastic problem, formulated as a mixed-integer nonlinear program. A crude-oil sequencing case study is developed with binary scheduling decisions in both stages of the stochastic programming problem. Solution via a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full-space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, a feasible mixed-integer solution is obtained that is more robust to uncertainty than its deterministic counterpart.

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Section: Multiperiod Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of a multiperiod refinery planning problem under uncertainty

et al. 2022

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“…The production planning problem has subsequently been solved using many approaches, for instance, linear programming [15] and nonlinear programming [16], based on the considered conditions, such as random parameters. According to studies by Yazdani et al [17]- [20, sustainability problems could also be solved by implementing multiobjective optimization models.…”

Section: Introductionmentioning

confidence: 99%

The Joint Decision-Making Support through Piecewise Objective Optimization Model for Integrated Supplier Selection, Inventory Management, and Production Planning Involving Discounts

Widowati

Sutrisno

Utomo

2023

JOIV : Int. J. Inform. Visualization

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The decision-makers in manufacturing industries continuously optimize every supply-chain part to achieve optimal profit. In this paper, three crucial activities in the supply chain are observed as profit contributors: supplier selection, inventory management, and production planning. Decision-making support is needed to optimize those activities, especially when prices/costs involve discounts. Therefore, this study aims to develop integrated decision-making support for supplier selection, inventory management, and production planning involving discounted prices. The problem was considered with multi-supplier, multi-raw material, multi-product, and multi-observation time instant. The objective was based on maximizing the profit for the entire activity, i.e., from the raw material procurement and storage to the production. This supply chain was modeled as mixed-integer linear programming with a piecewise objective function representing the profit, which was maximized. It was also modeled with a bunch of constraint functions, including product demand satisfaction. The proposed model was tested with computational simulations using randomly generated supply chain data. The primal simplex algorithm was also employed to calculate the real value of the optimal decision, which was combined with the Branch-and-Bound approach to calculate the appropriate integer solution. The results showed that the optimal decision was achieved, namely (1) The optimal quantity of raw materials ordered to each supplier, (2) The optimal production quantity, and (3) The optimal inventory level, which provided the maximal profit for the whole optimization time horizon. This indicated that the proposed decision-making support model is implementable for industrial decision-makers.

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“…Nevertheless, various uncertainties in the data still exist in practical situations and are ignored by deterministic modeling approaches . The uncertainties include inherent properties (e.g., kinetic rates, heat transfer constants, blending effects, and intermolecular forces), − process operation fluctuations (e.g., recipe variations, temperatures, and equipment efficiencies), , and stream properties (e.g., density, sulfur content, and composition), as well as external uncertainties, such as resources, prices, and demand. , …”

Section: Introductionmentioning

confidence: 99%

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

et al. 2022

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With the increasing challenges in environmental protection, product quality, and market profit, the development of a feasible optimization approach coupling accurate oil property online prediction has become crucial to the intelligent production of refineries. However, traditional property modeling with near-infrared (NIR) spectroscopy cannot dynamically extract the synergistic effect between wavelengths and cause inaccurate property prediction. The success rate of the data-driven robust optimization (DDRO) of the refining process is influenced by process uncertainty. Thus, we proposed a modified hybrid strategy on online NIR prediction modeling with adjustable characteristic wavelength selection and DDRO under uncertainty for the refining process. An adjustable feature space variable extraction (AFSVE) method was first proposed to dynamically select the characteristic wavelengths for constructing accurate property prediction models. A data-driven robust optimization of gasoline blending model was rendered through dual transformation using the uncertainty set of oil property derived from principal component analysis combined with robust kernel density estimation. An industrial application showed the best prediction accuracy of property models with the AFSVE method, and a high blending success rate was obtained by the proposed modified hybrid strategy, which hedges against uncertainties, including measuring error and blending effect, and boosts environmentally friendly and intelligent process operation.

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Refinery production planning optimization under crude oil quality uncertainty

Cited by 25 publications

References 20 publications

Optimization of a multiperiod refinery planning problem under uncertainty

Optimization of a multiperiod refinery planning problem under uncertainty

The Joint Decision-Making Support through Piecewise Objective Optimization Model for Integrated Supplier Selection, Inventory Management, and Production Planning Involving Discounts

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

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