Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Numéro d'article 42
Nombre de pages 17
DOI https://doi.org/10.2516/ogst/2021020
Publié en ligne 14 juin 2021
  • BP (2019) BP statistical review of world energy-Natural gas, Technical Report British Petroleum (BP). [Google Scholar]
  • Üster H., Dilaveroğlu Ş. (2014) Optimization for design and operation of natural gas transmission networks, Appl. Energy 133, 56–69. [Google Scholar]
  • Di Lullo G., Oni A.O., Gemechu E., Kumar A. (2020) Developing a greenhouse gas life cycle assessment framework for natural gas transmission pipelines, J. Nat. Gas Sci. Eng. 75, 103136. [Google Scholar]
  • Demissie A., Zhu W., Belachew C.T. (2017) A multi-objective optimization model for gas pipeline operations, Comput. Chem. Eng. 100, 94–103. [Google Scholar]
  • Saidur R., Rahim N.A., Hasanuzzaman M. (2010) A review on compressed-air energy use and energy savings, Renew. Sustain. Energy Rev. 14, 1135–1153. [Google Scholar]
  • Ahmadian Behrooz H. (2016) Managing demand uncertainty in natural gas transmission networks, J. Nat. Gas Sci. Eng. 34, 100–111. [Google Scholar]
  • Mikolajková M., Saxén H., Pettersson F. (2018) Linearization of an MINLP model and its application to gas distribution optimization, Energy 146, 156–168. [Google Scholar]
  • Liang Y., Hui C. (2018) Convexification for natural gas transmission networks optimization, Energy 158, 1001–1016. [Google Scholar]
  • Botros K., Sennhauser D., Jungowski K., Poissant G., Golshan H., Stoffregen J. (2004) Multi-objective optimization of large pipeline networks using genetic algorithm, in: International Pipeline Conference, Calgary, Canada, pp. 2005–2015. [Google Scholar]
  • Kashani A.H.A., Molaei R. (2014) Techno-economical and environmental optimization of natural gas network operation, Chem. Eng. Res. Des. 92, 2106–2122. [Google Scholar]
  • Panda D., Ramteke M. (2019) Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm, Appl. Energy 235, 68–82. [Google Scholar]
  • Fettaka S., Thibault J. (2013) Pipeline optimization using a novel hybrid algorithm combining front projection and the non-dominated sorting genetic algorithm-II (FP-NSGA-II), in: IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 697–704. [Google Scholar]
  • Yang Y., Liu J., Tan S., Wang H. (2018) Application of constrained multi-objective evolutionary algorithm in multi-source compressed-air pipeline optimization problems, IFAC-PapersOnLine 51, 168–173. [Google Scholar]
  • Hu Y., Bie Z., Ding T., Lin Y. (2016) An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning, Appl. Energy 167, 280–293. [Google Scholar]
  • Qu K., Yu T., Zhang X., Li H. (2019) Homogenized adjacent points method: A novel Pareto optimizer for linearized multi-objective optimal energy flow of integrated electricity and gas system, Appl. Energy 233–234, 338–351. [Google Scholar]
  • Zheng J., Wu Q., Jing Z. (2017) Coordinated scheduling strategy to optimize conflicting benefits for daily operation of integrated electricity and gas networks, Appl. Energy 192, 370–381. [Google Scholar]
  • Zan T.T.T., Gupta P., Wang M., Dauwels J., Ukil A. (2018) Multi-objective optimal sensor placement for low-pressure gas distribution networks, IEEE Sens J. 18, 6660–6668. [Google Scholar]
  • Sheikh Alivand M., Farhadi F. (2018) Multi-objective optimization of a multi-layer PTSA for LNG production, J. Nat. Gas Sci. Eng. 49, 435–446. [Google Scholar]
  • Wang X., Zhou F., Xia T., Xu M. (2016) A multi-objective optimization model to enhance the comprehensive performance of underground gas drainage system, J. Nat. Gas Sci. Eng. 36, 852–864. [Google Scholar]
  • Vesterstrom J., Thomsen R. (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, in: IEEE Congress on Evolutionary Computation, Portland, USA, pp. 1980–1987. [Google Scholar]
  • Das S., Suganthan P.N. (2011) Differential evolution: A survey of the state-of-the-art, IEEE Trans Evol. Comput. 15, 4–31. [Google Scholar]
  • Brest J., Greiner S., Boskovic B., Mernik M., Zumer V. (2006) Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Trans Evol. Comput. 10, 646–657. [Google Scholar]
  • Yazdi J., Choi Y.H., Kim J.H. (2017) Non-dominated sorting harmony search differential evolution (NS-HS-DE): A hybrid algorithm for multi-objective design of water distribution networks, Water 9, 587. https://doi.org/10.3390/w9080587. [Google Scholar]
  • Ewees A.A., Abd Elaziz M., Oliva D. (2021) A new multi-objective optimization algorithm combined with opposition-based learning, Expert Syst. Appl. 165, 113844. [Google Scholar]
  • Yu W., Wen K., Li Y., Huang W., Gong J. (2018) A methodology to assess the gas supply capacity and gas supply reliability of a natural gas pipeline network system, in: International Pipeline Conference, Calgary, Canada. [Google Scholar]
  • Su H., Zio E., Zhang J., Li X., Chi L., Fan L., Zhang Z. (2019) A method for the multi-objective optimization of the operation of natural gas pipeline networks considering supply reliability and operation efficiency, Comput. Chem. Eng. 131, 106584. [Google Scholar]
  • Yu W., Gong J., Song S., Huang W., Li Y., Zhang J., Hong B., Zhang Y., Wen K., Duan X. (2019) Gas supply reliability analysis of a natural gas pipeline system considering the effects of underground gas storages, Appl. Energy 252, 113418. [Google Scholar]
  • Shinstine D.S., Ahmed I., Lansey K.E. (2002) Reliability/availability analysis of municipal water distribution networks: case studies, J. Water Resour. Plan. Manag. 128, 140–151. [Google Scholar]
  • Yu W., Song S., Li Y., Min Y., Huang W., Wen K., Gong J. (2018) Gas supply reliability assessment of natural gas transmission pipeline systems, Energy 162, 853–870. [Google Scholar]
  • Li J., Liu W. (2006) Large-scale urban network seismic reliability analysis and optimization, Earthq. Eng. Eng. Vib. 26, 172–175 (in Chinese). [Google Scholar]
  • Shabazbegian V., Ameli H., Ameli M.T., Strbac G. (2020) Stochastic optimization model for coordinated operation of natural gas and electricity networks, Comput. Chem. Eng. 142, 107060. [Google Scholar]
  • Zheng F., Simpson A.R., Zecchin A.C. (2013) A decomposition and multistage optimization approach applied to the optimization of water distribution systems with multiple supply sources, Water Resour Res. 49, 380–399. [Google Scholar]
  • Su Y., Mays L.W., Duan N., Lansey K.E. (1987) Reliability-based optimization model for water distribution systems, J. Hydraul. Eng. 113, 1539–1556. [Google Scholar]
  • Li J., Qin C., Yan M., Ma J., Yu J. (2016) Hydraulic reliability analysis of an urban loop high-pressure gas network, J. Nat. Gas Sci. Eng. 28, 372–378. [Google Scholar]
  • Saeid M., Poe W.A. (2012) Sales gas transmission, in: Handbook of natural gas transmission and processing (second edition), Gulf Professional Publishing, Boston, pp. 425–450. [Google Scholar]
  • Liu E., Lv L., Yi Y., Xie P. (2019) Research on the steady operation optimization model of natural gas pipeline considering the combined operation of air coolers and compressors, IEEE Access 7, 83251–83265. [Google Scholar]
  • Wang P., Ao S., Yu B., Han D., Xiang Y. (2019) An efficiently decoupled implicit method for complex natural gas pipeline network simulation, Energies 12. [Google Scholar]
  • Wang P., Yu B., Han D., Sun D., Xiang Y. (2018) Fast method for the hydraulic simulation of natural gas pipeline networks based on the divide-and-conquer approach, J. Nat. Gas Sci. Eng. 50, 55–63. [Google Scholar]
  • Benedict M., Webb G.B., Rubin L.C. (1940) An empirical equation for thermodynamic properties of light hydrocarbons and their mixtures I. Methane, Ethane, Propane and n-Butane, J. Chem. Phys. 8, 334–345. [Google Scholar]
  • Deb K., Pratap A., Agarwal S., Meyarivan T. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans, Evol. Comput. 6, 182–197. [Google Scholar]
  • Yuan Q., Li J., Liu H., Yu B., Sun D., Deng Y. (2019) Parametric regression of a multiparameter thixotropic model for waxy crude oil based on multiobjective strategy, J. Pet. Sci. Eng. 173, 287–297. [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.