New correlations for predicting pure and impure natural gas viscosity

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@Article{Izadmehr:2016:JNGSE,

• author = "Mojtaba Izadmehr and Reza Shams and Mohammad Hossein Ghazanfari",
• title = "New correlations for predicting pure and impure natural gas viscosity",
• journal = "Journal of Natural Gas Science and Engineering",
• year = "2016",
• volume = "30",
• pages = "364--378",
• month = mar,
• ISSN = "1875-5100",
• URL = "http://www.sciencedirect.com/science/article/pii/S1875510016300713",
• DOI = "doi:10.1016/j.jngse.2016.02.026",
• keywords = "genetic algorithms, genetic programming, Pure/impure natural gas viscosity, New correlations, Empirical models, Design of experiments, Leverage value statistics",
• abstract = "Accurate determination of natural gas viscosity is important for successful design of production, transportation, and gas storage systems. However, most of available models/correlations suffer from complexity, robustness, and inadequate accuracy especially when wide range of pressure and temperature is applied. Present study illustrates development of two novel models for predicting natural gas viscosity for pure natural gas (CH4) as well as natural gas containing impurities. For this purpose, 6484 data points have been gathered and analysed from the open literature covering wide range of pressure, temperature, and specific gravity levels, temperature ranges from -262.39 to 620.33 degree F (109.6 to 600 K), pressure ranges from 1.4508 to 29,000 psi (0.0100-199.94801 MPa), and gas specific gravity ranges from 0.553 to 1.5741. Sensitivity analysis on the collected data points through design of experiments algorithm showed that pseudo reduced pressure and pseudo reduced temperature are the most effective parameters as the inputs of the models. The Leverage Value Statistics is applied and doubtful data points are determined. The average absolute relative error and the coefficient of determination of the proposed models for predicting pure/impure natural gas viscosity on a wide range of conditions are 5.67percent and 1.87percent, 0.9826 and 0.9953, respectively. Reliable accuracy of proposed models in comparison to eight commonly used correlations makes them attractive for possible implementing in natural gas simulation/modelling applications.",
• notes = "Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran",

}

Genetic Programming entries for Mojtaba Izadmehr Reza Shams Mohammad Hossein Ghazanfari

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