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Home All issues Volume 74 (2019) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 74 (2019) 31 References
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Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Article Number 31
Number of page(s) 8
DOI https://doi.org/10.2516/ogst/2019006
Published online 25 March 2019
  • Farzaneh-Gord M., Farsiani M., Khosravi A., Arabkoohsar A., Dashtie F. (2015) A novel method for calculating natural gas density based on joule Thomson coefficient, J. Nat. Gas Sci. Eng. 26, 1018–1029. doi: 10.1016/j.jngse.2015.07.029. [Google Scholar]
  • Froysa K.E., Lunde P. (2005) Density and calorific value measurement in natural gas using ultrasonic flow meters, 23rd International North Sea Flow Measurement Workshop, 18–21 October 2005, Norway. [Google Scholar]
  • AlQuraishi A.A., Shokir E.M. (2009) Viscosity and density correlations for hydrocarbon gases and pure and impure gas mixture, Pet. Sci. Technol. 27, 1674–1689. doi: 10.1080/10916460802456002. [CrossRef] [Google Scholar]
  • Saeedi Dehaghani A.H., Badizad M.H. (2017) A soft computing approach for prediction of P-ρ-T behavior of natural gas using adaptive neuro-fuzzy inference system, Petroleum 3, 4, 447–453. doi: 10.1016/j.petlm.2016.12.004. [CrossRef] [Google Scholar]
  • Hammond R.H. (2001) Ultrasonic measurement system with molecular weight determination, Patent US6216091B1. [Google Scholar]
  • Watson J.W., White F.A. (1982) Acoustic measurement for gas BTU content, Oil Gas J. 80,14, 217–225. [Google Scholar]
  • Dell’Isola M., Cannizzo M., Diritti M. (1982) Acoustic measurement for gas BTU content, Measurement 20, 2, 75–89. doi: 10.1016/S0263-2241(97)00016-X. [CrossRef] [Google Scholar]
  • Buonanno G., Carotenuto A., Dell’Isola M. (1998) The influence of reference condition correction on natural gas flow measurement, Measurement 23, 2, 77–91. doi: 10.1016/S0263-2241(98)00009-8. [CrossRef] [Google Scholar]
  • Nishiumi H., Saito S. (1975) An improved generalized BWR equation of state applicable to low reduced temperatures, J. Chem. Eng. Jpn. 8, 5, 356–360. doi: 10.1252/jcej.8.356. [CrossRef] [Google Scholar]
  • Farzaneh-Gord M., Rahbari H. (2011) Developing novel correlations for calculating natural gas thermodynamic properties, Chem. Process Eng. 32, 4, 435–452. [CrossRef] [Google Scholar]
  • Dranchuk P., Abou-Kassem J. (1975) Calculation of Z-factors for natural gases using equations of state, J. Can. Petrol. Technol. 14, 3, 14–34. doi: 10.2118/75-03-03. [Google Scholar]
  • Londono F., Aicher R., Blasingame T. (2002) Simplified correlations for hydrocarbon gas viscosity and gas density validation and correlation behavior using a large scale database, SPE Gas Technology Symposium, 30 April–2 May, Calgary, Canada. doi: 10.2118/75721-MS. [Google Scholar]
  • Farzaneh-Gord M., Khamforoush A., Hashemi S., Pourkhadem N. (2010) Computing thermal properties of natural gas by utilizing AGA8 equation of state, IJCEA 1, 20–24. doi: 10.7763/IJCEA.2010.V1.4. [CrossRef] [Google Scholar]
  • Farzaneh-Gord M., Rahbari H. (2012) Numerical procedures for natural gas accurate thermodynamics properties calculation, J. Eng. Thermophys. 21, 4, 213–234. doi: 10.1134/S1810232812040017. [CrossRef] [Google Scholar]
  • Kunz O., Wagner W. (2012) The GERG-2008 wide-range equation of state for natural gases and other mixtures: An expansion of GERG-2004, J. Chem. Eng. Data 57, 11, 3032–3091. doi: 10.1021/je300655b. [Google Scholar]
  • AGA8 (1992) Compressibility and super compressibility gas and other hydrocarbon gases, Transmission Measurement Committee Report, Number 8, AGA Catalogue No. XQ 1285, Arlington. [Google Scholar]
  • Rostami A., Shokrollahi A., Ghazanfari M.H. (2018) New method for predicting n-tetradecane/bitumen mixture density: correlation development, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 73, 35, doi: 10.2516/ogst/2018037. [Google Scholar]
  • Mohamadi-Baghmolaei M., Azin R., Osfouri S., Mohamadi-Baghmolaei R., Zarei Z. (2015) Prediction of gas compressibility factor using intelligent models, Nat. Gas Ind. B 2 2, 4, 283–294. doi: 10.1016/j.ngib.2015.09.001. [CrossRef] [Google Scholar]
  • Rostami A., Kalantari-Meybodi M., Karimi M., Tatar A., Mohammadi A.H. (2018) Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding, Oil Gas Sci. Technol. – Rev. IFP. Energies nouvelles 73, 22. doi: 10.2516/ogst/2018006. [Google Scholar]
  • Bashipour F., Nouri Khorasani S., Rahimi A. (2015) H2S reactive absorption from off-gas in a spray column: Insights from experiments, Chem. Eng. Techol. 38, 12, 2137–2145. doi: 10.1002/ceat.201500233. [CrossRef] [Google Scholar]
  • Mohammadi A.H., Richon D. (2007) Use of artificial neural networks for estimating water content of natural gases, Ind. Eng. Chem. Res. 46, 1431–1438. doi: 10.1021/ie060494u. [Google Scholar]
  • Chouai A., Laugier S., Richon D. (2002) Modeling of thermodynamic properties using neural networks: Application to refrigerants, Fluid Phase Equilib. 199, 1–2, 53–62. doi: 10.1016/S0378-3812(01)00801-9. [Google Scholar]
  • Karpash O., Darvay I., Karpash M. (2010) New approach to natural gas quality determination, J. Petrol. Sci. Eng. 71, 133–137. doi: 10.1016/j.petrol.2009.12.012. [CrossRef] [Google Scholar]
  • AlQuraishi A.A., Shokir E.M. (2011) Artificial neural networks modeling for hydrocarbon gas viscosity and density estimation, JKSUES 23, 123–129. doi: 10.1016/j.jksues.2011.03.004. [Google Scholar]
  • Sipöcz N., Tobiesen F.A., Assadi M. (2011) The use of artificial neural network models for CO2 capture plants, Appl. Energy 88, 7, 2368–2376. doi: 10.1016/j.apenergy.2011.01.013. [Google Scholar]
  • Shahsavand A., DerakhshanFard F., Sotoudeh F. (2011) Application of artificial neural networks for simulation of experimental CO2 absorption data in a packed column, J. Nat. Gas Sci. Eng. 3, 3, 518–529. doi: 10.1016/j.jngse.2011.05.001. [Google Scholar]
  • Fast M., Assadi M., De S. (2009) Development and multi-utility of an ANN model for an industrial gas turbine, Appl. Energy 86, 1, 9–17. doi: 10.1016/j.apenergy.2008.03.018. [Google Scholar]
  • Akcayol M.A., Cinar C. (2005) Artificial neural network based modeling of heated catalytic converter performance, Appl. Therm. Eng. 25, 2341–2350. doi: 10.1016/j.applthermaleng.2004.12.014. [Google Scholar]
  • Fausett L.V. (1993) Fundamentals of neural networks: Architectures, algorithms and applications, Prentice-Hall, Englewood Cliffs. [Google Scholar]
  • Bashipour F., Rahimi A., Nouri Khorasani S., Naderinik A. (2017) Experimental optimization and modeling of sodium sulfide production from H2S-Rich off-gas via response surface methodology and artificial neural network, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 72, 9. doi: 10.2516/ogst/2017004. [Google Scholar]
  • Holland J.H. (1975) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, University of Michigan Press, Michigan, USA. [Google Scholar]
  • Darwin C., Bynum W.F. (2009) The origin of species by means of natural selection: Or, the preservation of favored races in the struggle for life, Penguin Classics, London. [Google Scholar]
  • Xiucheng D., Shouchun W., Renjin S., Suoqi Z. (2010) Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil, Pet. Sci. 7, 118–122. doi: 10.1007/s12182-010-0015-y. [Google Scholar]
  • Čapla L., Buryan P., Jedelský J., Rottner M., Linek J. (2002) Isothermal pVT measurements on gas hydrocarbon mixtures using a vibrating-tube apparatus, J. Chem. Thermodyn. 34, 5, 657–667. doi: 10.1006/jcht.2001.0935. [Google Scholar]
  • Atilhan M., Aparicio S., Ejaz S., Cristancho D., Hall K.R. (2011) PρT behavior of a lean synthetic natural gas mixture using magnetic suspension densimeters and an isochoric apparatus: Part I, J. Chem. Eng. Data 56, 2, 212–221. doi: 10.1021/je100676j. [Google Scholar]
  • Hwang C.A., Simon P.P., Hou H., Hall K.R., Holste J.C., Marsh K.N. (1997) Burnett and pycnometric (p, Vm, T) measurements for natural gas mixtures, J. Chem. Thermodyn. 29, 12, 1455–1472. doi: 10.1006/jcht.1997.0258. [Google Scholar]
  • Patil P., Ejaz S., Atilhan M., Cristancho D., Holste J.C., Hall K.R. (2007) Accurate density measurements for a 91% methane natural gas-like mixture, J. Chem. Thermodyn. 39, 8, 1157–1163. doi: 10.1016/j.jct.2007.01.002. [Google Scholar]
  • Tampe S.S., Kulkarni B.D., Desphande P.B. (1996) Elements of artificial networks with selected applications in chemical engineering and chemical and biological sciences, Simulation and Advanced Controls Ltd., Louisville. [Google Scholar]
  • Hornik K., Stinchcoombe M., White H. (1989) Multi layer feed forward networks are universal approximators, Neural Netw. 2, 359–366. doi: 10.1016/0893-6080(89)90020-8. [Google Scholar]
  • Kolmogorov A.N. (1957) On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, Dokl. Akad. Nauk USSR 114, 5, 953–956. [Google Scholar]

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