Simulated Annealing Optimization for Hydrocarbon Pipeline Networks

Rodríguez, Diego; Oteiza, Paola Patricia; Brignole, Nélida Beatríz

doi:10.1021/ie400022g

Cited by 21 publications

(5 citation statements)

References 38 publications

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“…They found that the hybrid GASA has better performance than the improved complex algorithm. Rodríguez et al [14] presented a research of using a metaheuristic algorithm called Simulated Annealing with GAMS (SAG) to optimize the layout of long-distance hydrocarbon pipelines. They show that SAG is robust because a high percentage of the near-optimal solutions can be found.…”

Section: Simulated Annealing (Sa) Algorithmmentioning

confidence: 99%

“…Recently, a number of successful industry projects associated with applications of stochastic algorithms have been reported, including applications of the Genetic Algorithm (GA) [12,13], Ant Colony Optimization (ACO) [13], Simulated Annealing (SA) optimization [14], Particle Swarm Optimization (PSO) [15], and their extensions. These achievements show prospective ways of efficient solving the operation optimization model of natural gas pipelines.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

He³

et al. 2018

Mathematical Problems in Engineering

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Operation optimization of natural gas pipelines has received increasing attentions, due to such advantages as maximizing the operating economic benefit and the gas delivery amount. This paper provides a review on the most relevant research progress related to the steady-state operation optimization models of natural gas pipelines as well as corresponding solution methods based on stochastic optimization algorithms. The existing operation optimization model of the natural gas pipeline is a mixed-integer nonlinear programming (MINLP) model involving a nonconvex feasible region and mixing of continuous, discrete, and integer optimization variables, which represents an extremely difficult problem to be solved by use of optimization algorithms. A survey on the state of the art demonstrates that many stochastic algorithms show better performance of solving such optimization models due to their advantages of handling discrete variables and of high computation efficiency over classical deterministic optimization algorithms. The essential progress mainly with regard to the applications of the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA) algorithms, and their extensions is summarized. The performances of these algorithms are compared in terms of the quality of optimization results and the computation efficiency. Furthermore, the research challenges of improving the optimization model, enhancing the stochastic algorithms, developing an online optimization technology, researching the transient optimization, and studying operation optimization of the integrated energy network are discussed.

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Section: Simulated Annealing (Sa) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

He³

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…Since then the algorithm has been widely used in the literature, and some recent examples include [9] in finance, [10] in machine learning, [11] in chemical engineering and [12] in production line machine scheduling.…”

Section: Optimization Techniquesmentioning

confidence: 99%

A Simulated Annealing Based Optimization Algorithm

Tenne¹

2017

Computational Optimization in Engineering - Paradigms and Applications

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“…The optimization framework presented here can, in principle, collaborate with any single and multiobjective method using transitions between the system solutions (see ref 21). It is also possible to consider hybridization of SA with deterministic optimizers to achieve improvement in the CPU time, as proposed in the work of Rodriguez et al 22 3.4. Convergence of SA Algorithms.…”

Section: Pipeline Optimization Frameworkmentioning

confidence: 99%

Design of Optimal Pipeline Systems Using Internal Corrosion Models and GIS Tools

Marcoulaki

Tsoutsias

Papazoglou

2014

Ind. Eng. Chem. Res.

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Corrosion is among the principal causes of damage and failure in pipeline systems and is linked to various economic and environmental risks. The consideration of corrosion phenomena at the early stages of pipeline system design can help quantify these risks and include risk reduction alternatives in the analysis. The present work builds upon previous research on the optimization of pipeline systems and to consider corrosion factors in making preliminary decisions on routing and equipment selection. The optimization tool is herein extended by including corrosion models and upgrading the equipment design and cost functions. The proposed developments are tested against a case study involving a crude oil pipeline system and a range of corrosion related properties for the transmitted fluid.

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Simulated Annealing Optimization for Hydrocarbon Pipeline Networks

Cited by 21 publications

References 38 publications

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

A Simulated Annealing Based Optimization Algorithm

Design of Optimal Pipeline Systems Using Internal Corrosion Models and GIS Tools

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