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Improving natural gas supply chain profitability: A multi-methods optimization study

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  • Arya, Adarsh Kumar
  • Kumar, Adarsh
  • Pujari, Murali
  • Pacheco, Diego A.de J.

Abstract

Developing effective optimization models to improve pipeline network profitability in oil and gas supply chains is one of the most promising research areas in this industry. Because of the substantial advantages natural gas networks’ operations have realized, this industry has become more competitive and eager to develop robust supply optimization decision models. However, although several models and techniques have been developed to reduce natural gas consumption, only very few studies have focused on comparing the performance of these models and the implications of the distinct optimization performances. Consequently, the generalizability of the research in the area is still problematic, representing a research area not sufficiently explored. Taking this into account, this paper compares the fuel consumption values in a French gas pipeline by analyzing the Genetic algorithms (GA), Generalized reduced gradient (GRG), and Ant colony optimization (ACO) models. Overall, our findings show significant differences in gas consumption when the ACO and GA are compared with the GRG technique. Furthermore, the findings indicate that ACOs are competitive with GA and GRG in computational efficiency in finding near-global optimized solutions. The article can assist decision-makers and policymakers in discovering the most profitable operational parameters to minimize gas consumption and increase the profitability of natural gas networks.

Suggested Citation

  • Arya, Adarsh Kumar & Kumar, Adarsh & Pujari, Murali & Pacheco, Diego A.de J., 2023. "Improving natural gas supply chain profitability: A multi-methods optimization study," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223020534
    DOI: 10.1016/j.energy.2023.128659
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    References listed on IDEAS

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    1. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    2. Munksgaard, Jesper & Christoffersen, Line Block & Keiding, Hans & Pedersen, Ole Gravgard & Jensen, Trine S., 2007. "An environmental performance index for products reflecting damage costs," Ecological Economics, Elsevier, vol. 64(1), pages 119-130, October.
    3. Shan, Xiaofang & Wang, Peng & Lu, Weizhen, 2017. "The reliability and availability evaluation of repairable district heating networks under changeable external conditions," Applied Energy, Elsevier, vol. 203(C), pages 686-695.
    4. Tesch, Stefanie & Morosuk, Tatiana & Tsatsaronis, George, 2016. "Advanced exergy analysis applied to the process of regasification of LNG (liquefied natural gas) integrated into an air separation process," Energy, Elsevier, vol. 117(P2), pages 550-561.
    5. Praks, Pavel & Kopustinskas, Vytis & Masera, Marcelo, 2015. "Probabilistic modelling of security of supply in gas networks and evaluation of new infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 254-264.
    6. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    7. McCarthy, Ryan W. & Ogden, Joan M. & Sperling, Daniel, 2007. "Assessing reliability in energy supply systems," Energy Policy, Elsevier, vol. 35(4), pages 2151-2162, April.
    8. Debora Mahlke & Alexander Martin & Susanne Moritz, 2007. "A simulated annealing algorithm for transient optimization in gas networks," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(1), pages 99-115, August.
    9. Chong, Zheng Rong & Yang, She Hern Bryan & Babu, Ponnivalavan & Linga, Praveen & Li, Xiao-Sen, 2016. "Review of natural gas hydrates as an energy resource: Prospects and challenges," Applied Energy, Elsevier, vol. 162(C), pages 1633-1652.
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