Optimal pipe-sizing problem of tree-shaped gas distribution networks

Shiono, Naoshi; Suzuki, Honoh

doi:10.1016/j.ejor.2016.01.008

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…It is well known that optimization problems on potential-driven networks get more difficult to solve the more cycles they contain because the existence of cycles leads to more complex patterns of flow directions (cf. the literature review in Shiono and Suzuki (2016)). To systematically test our models also with respect to this type of difficulty, we successively increase the circuit rank by adding up to ten new pipelines to the Belgian network, which results in five new networks Belgium + (2, 4, 6, 8, 10) arcs, see Fig.…”

Section: Comparison Of Discrete Models (Models a B And C)mentioning

confidence: 99%

“…During the past 40 years of research on capacity problems in potential-driven networks, three different modelling approaches have been considered for the looping problem (cf. Shiono and Suzuki 2016): (1) a direct approach where the optimal diameters for the loops are chosen from the set of commercially available diameters (discrete approach), (2) a continuous approach where continuous diameters are typically used to approximate the problem and (3) an extended approach where the entire length of the pipeline to be looped is split into several segments of variable lengths each of which may have its own diameter from the discrete set of available diameters (split-pipe approach).…”

Section: Introductionmentioning

confidence: 99%

“…Osiadacz and Górecki (1995) apply sequential quadratic programming to the continuous relaxation of a gas network design problem and round the solution to the closest available diameter size. Shiono and Suzuki (2016) introduce an analytic approach that calculates the optimal diameter costs for the pipe-sizing problem of a tree-shaped gas network with continuous diameters, which are then heuristically converted to discrete pipe diameters. As the present paper is concerned with models that lead to exact solutions, we will not further consider this line of research here.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of capacity expansion in potential-driven networks including multiple looping: a comparison of modelling approaches

Lenz

Becker

2021

OR Spectrum

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In commodity transport networks such as natural gas, hydrogen and water networks, flows arise from nonlinear potential differences between the nodes, which can be represented by so-called potential-driven network models. When operators of these networks face increasing demand or the need to handle more diverse transport situations, they regularly seek to expand the capacity of their network by building new pipelines parallel to existing ones (“looping”). The paper introduces a new mixed-integer nonlinear programming model and a new nonlinear programming model and compares these with existing models for the looping problem and related problems in the literature, both theoretically and experimentally. On this basis, we give recommendations to practitioners about the circumstances under which a certain model should be used. In particular, it turns out that one of our novel models outperforms the existing models with respect to computational time, the number of solutions found, the number of instances solved and cost savings. Moreover, the paper extends the models for optimizing over multiple demand scenarios and is the first to include the practically relevant option that a particular pipeline may be looped several times.

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Section: Comparison Of Discrete Models (Models a B And C)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of capacity expansion in potential-driven networks including multiple looping: a comparison of modelling approaches

Lenz

Becker

2021

OR Spectrum

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“…This simplification also contributes to the investigation of BP identification, which will be included in following sections of this paper. In this case, the gas transmission pipeline network is properly generalized as tree-shaped model (Babonneau et al, 2012;Shiono and Suzuki, 2016), which could be built based on following rules for pipe connection:…”

Section: A Tree-shaped Model Of Gas Networkmentioning

confidence: 99%

Application of Particle Swarm Optimization (PSO) algorithm for Black Powder (BP) source identification in gas pipeline network based on 1-D model

Shi

Al‐Durra

Matraji

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Black Powder (BP) is a worldwide challenge that spans all stages of the natural gas industry from the producing wells to the consuming points. It can endanger the pipeline operations, damage instruments and contaminate customer supplies. The formation of BP inside natural gas pipeline mainly results from the corrosion of internal walls of the pipeline, which is a complex chemical reaction. This work aims to develop a novel algorithm for BP source identification within gas pipelines network based on a 1-D model of BP transport and deposition. The optimization algorithm for BP source identification is developed based on the well-known Particle Swarm Optimization (PSO) algorithm, which can solve constrained optimization problems. By applying this optimization algorithm on the gas transmission pipeline network, the BP source at different junctions could be identified and quantified simultaneously. Extensive simulation studies are conducted to validate the effectivity of the optimization algorithm.

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“…Network optimization has attracted an increasing interest, and people have found many applications in the real world in recent years [1,5]. Many logistics management problems are related to the optimal tree structures in networks [3,6,7,9,11]. Given a graph G, we call it a forest if it is acyclic, and we call it a tree if it is connected and acyclic.…”

Section: Introductionmentioning

confidence: 99%

Computational Methods for Logistics Problems Related to Optimal Trees

Wu¹,

Qin²,

Yang³

2017

ANZIAM J.

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In recent years, balanced network optimization problems play an important role in practice, especially in information transmission, industry production and logistics management. In this paper, we consider some logistics optimization problems related to the optimal tree structures in a network. We show that the most optimal subtree problem is NP-hard by transforming the connected dominating set problem into this model. By constructing the network models of the most balanced spanning tree problem with edge set restrictions, and by finding the optimal subtrees in special networks, we present efficient computational methods for solving some logistics problems.

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Optimal pipe-sizing problem of tree-shaped gas distribution networks

Cited by 16 publications

References 21 publications

Optimization of capacity expansion in potential-driven networks including multiple looping: a comparison of modelling approaches

Optimization of capacity expansion in potential-driven networks including multiple looping: a comparison of modelling approaches

Application of Particle Swarm Optimization (PSO) algorithm for Black Powder (BP) source identification in gas pipeline network based on 1-D model

Computational Methods for Logistics Problems Related to Optimal Trees

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