A Mixed-Integer Linear Programming Model for Optimizing the Scheduling and Assignment of Tank Farm Operations

Terrazas-Moreno, Sebastian; Grossmann, Ignacio E.; Wassick, John M.

doi:10.1021/ie202217v

Cited by 13 publications

(10 citation statements)

References 18 publications

(58 reference statements)

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“…The work of Terrazas-Moreno, Grossmann, and Wassick also focuses on a tank farm operation model. The referenced authors indicate a significant set of operational issues that made the tank farm management a complex problem.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The work developed in ref approaches the Tank Farm Operation Problem (TFOP) by applying mathematical programming with a continuous time representation. The authors address the problem of tanks’ assignment in order to meet the continuous production and local demand, respecting structural limits of resources and the operational characteristics of the process, which make the tank farm management a complex activity.…”

Section: Introductionmentioning

confidence: 99%

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MILP Model for the Tank Farm Operation Problem of Finished Products in Refineries

Schneider

Neves

Magatão

et al. 2016

Ind. Eng. Chem. Res.

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This paper presents a mixed-integer linear programming (MILP) model with a continuous time representation to address the Tank Farm Operation Problem (TFOP) of finished products in refineries. Real scenarios are considered, which were obtained from the planning of refineries and the external pipeline network scheduling, proposed by Ind. Eng. Chem. Res.2010495661. The developed MILP model determines the scheduling of loading and unloading operations in the tank farm of finished products at each refinery, but is subjected to time-window constraints. A decomposition approach has been applied, and multiproduct scenarios proposed by Ind. Eng. Chem. Res.2010495661 were broken in single product scenarios. Each one of these scenarios is related with the tank farm in a specific refinery. Therefore, they are presenting volumes and values of stored inventories, maximum capacity tanks, and start and end times to product movements at the refinery interfaces (production, demand, and pipelines). The proposed MILP model searches a scheduling that minimizes the movements within the refinery tank farm in order to respect the imposed operational and structural constraints. Further, for making feasible the scheduling in a smaller computational time, an iterative algorithm is developed and a new model approach, named MILP-IA, is added within the solution process. The results allow us to analyze the model computational time, the temporal and structural violations, and the number of product movements for each scenario. For the studied cases, we can also check for attending to time and monthly volume constraints for each interface. Finally, the results also indicate that the proposed MILP-IA approach finds solutions in computational times on the order of minutes. The obtained solutions contribute to improve the transfer and storage activities (TS) on two main points: (i) they minimize the number of movements, facilitating the plant operational tasks (searching for routes); and (ii) they provide feedback to the pipeline scheduling, creating a collaborative integration between refinery subsystems, linking all information about internal and external product movements at refineries.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MILP Model for the Tank Farm Operation Problem of Finished Products in Refineries

Schneider

Neves

Magatão

et al. 2016

Ind. Eng. Chem. Res.

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“…We take advantage of the knowledge of scheduling optimization procedures that have been published recently. The reviews by Mendez et al, Floudas and Lin, Kallrath and Barbosa‐Povoa highlighted the importance of scheduling optimization methods in the process industry, whereas other authors have successfully applied such techniques to solve various manufacturing problems (for example, Mouret et al introduced a methodology to solve large‐scale MINLP problems for the oil refining industry considering planning and scheduling, then Mouret et al incorporated priority slots; Sharda and Vazquez et al presented a discrete event simulation decision support system to evaluate tank farm operations; Terrazas‐Moreno et al proposed a MILP formulation for the tank farm operation problem; Gutiérrez‐Limón et al developed a multiobjective formulation dealing with simultaneous scheduling and control issues in processing systems; Erdirik‐Dogan and Grossmann presented a multi‐period MILP model for the planning and scheduling of single‐stage plants; Barbosa‐Povoa and Macchietto proposed a formulation for the optimal selection of equipment units and the network of connections for multipurpose batch plants; Lin and Floudas synthesized a multipurpose batch plant considering scheduling issues; Castro et al presented a general mathematical formulation for the simultaneous design and scheduling of multipurpose plants). However, there have not been yet works for optimizing simultaneously both the design and operation strategy of short‐term heat storage in solar‐driven absorption refrigerators integrated with heat exchanger networks for the optimal solar energy conservation considering an average day per month and a discretization for time of 1 h accounting for 24 slots.…”

Section: Solution Strategymentioning

confidence: 99%

Optimum heat storage design for solar‐driven absorption refrigerators integrated with heat exchanger networks

et al. 2013

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A methodology is presented for optimizing hybrid renewable energy-fossil fuel systems with short-term heat storage. The considered system is an absorption-refrigeration (AR) cycle integrated with a heat exchanger network (HEN) requiring cooling below ambient temperature. The AR cycle can be driven by multiple energy sources including excess energy from hot process streams, renewable energy sources (solar and biofuels), and fossil fuels. A two-step approach based on mixed integer nonlinear programming methods is used for the optimization. First, the problem of optimal energy integration in the hybrid energy system without heat storage is solved on a monthly basis by minimizing simultaneously the total annual cost and the overall greenhouse gas emissions. In the second step, the multi-tank thermal energy storage (TES) design problem is solved. The design involves the identification of the optimal number of storage tanks, their sizes, configuration and operation policies. The TES optimization is carried out on an hourly basis while incorporating the design targets determined by the first step. V C 2013 American Institute of Chemical Engineers AIChE J, 60: 909-930, 2014

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“…Only a few studies have been conducted − on the scheduling of the third subsystem shipping of refinery, which is similar to the considered problem addressed in this paper. The tank farm receives products from the blending units and delivers them to the local markets.…”

Section: Introductionmentioning

confidence: 99%

Operation Scheduling Optimization of a Transfer Hub with Multiple Tank Farms Based on a New Continuous Time Representation

Wang

Huang

et al. 2018

Ind. Eng. Chem. Res.

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This study addresses the operation-scheduling problem of a transfer hub with multiple tank farms (M-TH) connected to a multiproduct pipeline network. As part of the pipeline network, the THs serve as links that receive, store, and deliver oil products. Previous scheduling optimization works on pipeline networks with oil products largely focused on the batch plans of each pipeline, without considering the operation schedule of the THs. In this study, the main task is to optimize the loading and unloading scheduling operations of the tanks in the M-TH, which are subjected to given schedules of upstream/downstream pipelines. The schedules of pipelines that carry different types of products can be decomposed into several independent single product scenarios. A mixed-integer linear programming (MILP) formulation is presented. The proposed formulation takes into account the capacities of the tanks, operational rules of the tanks and tank farms, structural constraints, and settling time after the loading operation, while minimizing the switch operations between the tanks and switchovers between the tank farms. Moreover, the MILP formulation can be solved for one separate products at one time as there is no interference among the single-product scheduling problems within the transfer hub. The formulation is based on a new continuous time representation with static and dynamic time slots. The scheduling horizon is partitioned into several static time slots on the basis of the time points of batches arriving at and leaving from the M-TH or the flow rate changed. Each static time slot is divided into several dynamic time slots, whose duration and starting/ending points are determined by the optimization process. The proposed formulation is validated using the optimal results of a real-world case. A time horizon partitioning strategy is employed to deal with the long-term horizon scenario. The results help verify that we can substantially save computational time and obtain a satisfactory solution; however, the optimality is compromised.

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A Mixed-Integer Linear Programming Model for Optimizing the Scheduling and Assignment of Tank Farm Operations

Cited by 13 publications

References 18 publications

MILP Model for the Tank Farm Operation Problem of Finished Products in Refineries

MILP Model for the Tank Farm Operation Problem of Finished Products in Refineries

Optimum heat storage design for solar‐driven absorption refrigerators integrated with heat exchanger networks

Operation Scheduling Optimization of a Transfer Hub with Multiple Tank Farms Based on a New Continuous Time Representation

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