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Abstract.
A large number of statistical methods are being developed to solve the problem of natural gas composition analysis. Statistical models are used in these methods for determination of natural gas composition by its known physical parameters. The choice of a statistical model for the method under discussion is a difficult task. No general algorithm has been found for selecting a model for a specific task. Basic statistical models, that are often used in practice, are studied in the article. The comparative analysis of the models is carried out according to a number of important criteria for solving the discussed problem. As a result, it is concluded that the neural network model is the most effective model for the natural gas composition analysis. Recommendations are given on choosing a statistical model in the tasks of natural gas quality analysis that are similar to the problem under consideration.
Keywords:
machine learning, statistical models, neural network analysis, composition analysis, natural gas.
PP. 34-43.
DOI 10.14357/20718632200104 References
1. GOST 31369-2008. Gaz prirodnyiy. Vyichislenie teplotyi sgoraniya, plotnosti, otnositelnoy plotnosti i chisla Vobbe na osnove komponentnogo sostava [Natural gas. Calculation of the calorific value, density, relative density and Wobbe number based on the composition.]. Moscow: Standartinform. 2008. 30 p. 2. Dörr H., Koturbash T., Kutcherov V. Review of impacts of gas qualities with regard to quality determination and energy metering of natural gas // Measurement Science and Technology. 2019. V. 30, №2. P. 1-20. 3. Koturbash T.T., Brokarev I.A. Metod opredeleniya svoystv i sostava prirodnogo gaza po izmereniyam ego fizicheskih parametrov [Method for determining the properties and composition of natural gas by measuring its physical parameters] // Datchiki i sistemyi [Sensors and systems]. 2018. № 6. C. 43-50. 4. Kostin V.N., Tishina N.A. Statisticheskie metodyi i modeli [Statistical methods and models]. Orenburg: GOU OGU, 2004. 138 p. 5. GOST Р 8.662-2009 (ISO 20765-1:2005) Gaz prirodnyiy. Termodinamicheskie svoystva gazovoy fazyi. Metodyi raschetnogo opredeleniya dlya tseley transportirovaniya i raspredeleniya gaza na osnove fundamentalnogo uravneniya sostoyaniya AGA8 [Natural gas. Thermodynamic properties of the gas phase. Calculation methods for the transportation and distribution of gas based on the fundamental equation of state AGA8]. Moscow: Standartinform. 2010. 43 p. 6. Koturbash T.T., Brokarev I.A. Sravnitelnyiy analiz fizicheskih svoystv prirodnogo gaza i ekvivalentnyih emu psevdogazovyih smesey [Comparative analysis of the physical properties of natural gas and equivalent pseudogas mixtures] // Datchiki i sistemyi [Sensors and systems]. №3. 2019. P. 7-13. 7. Koturbash T., Bicz A., Bicz W. New instrument for measuring velocity of sound and quantitative characterization of binary gas mixtures composition // Measurement Automation Monitoring. 2016. Р. 254-258. 8. Löfqvist T., Delsing J., Sokas K. Speed of sound measurements in gas — mixtures at varying composition using an ultrasonic gas flow meter with silicon based transducers // International Conference on Flow Measurement. Groningen, Netherlands. 2003. 9. Thermal Conductivity Gauge. Available at: http://www.xensor.nl (Accessed December 1, 2019). 10. Dynament Infrared Gas Sensors. Available at: https://www.dynament.com (Accessed December 1, 2019). 11. Bright Sensors BlueEye. Available at: https://www.brightsensors.com (Accessed December 1, 2019). 12. Mirzaei-Paiaman A., Salavati S. The application of artificial neural networks for the prediction of oil production flow rate // Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2012. No. 34:19. P. 1834-1843. 13. Hribar R., Potočnik P., Šilc J., Papa G. A comparison of models for forecasting the residential natural gas demand of an urban area // Energy. 2018. Vol. 167. P. 511-522. 14. Vondráček J., Pelikán E., Konár O., Čermáková J., Eben, K., Malý, M., Brabec, M. A statistical model for the estimation of natural gas consumption // Applied Energy. 2008. No. 85(5). P. 362-370. 15. Aleardi, M. Analysis of different statistical models in probabilistic joint estimation of porosity and litho-fluid facies from acoustic impedance values // Geosciences. 2018. No. 8(11). P. 386-388. 16. Mitchell T. M. Machine Learning // McGraw-Hill Science/Engineering/Math. 1997. 17. Graybill F.A., Iyer H.K. Regression analysis // Concepts and applications, Duxbury Print. 1994. 18. Strizhov V.V., Kryimova E.A. Metodyi vyibora regressionnyih modeley [Regression model selection methods]. Moscow: VTS RAN, 2010. 60 p. 19. Hastie T., Tibshirani R., Friedman J. The Elements Of Statistical Learning: Data Mining, Inference and Prediction // Springer. 2009. 20. Rasmussen C. E., Williams C. K. Gaussian Processes for Machine Learning // The MIT Press. 2006. 21. Brokarev I.A. Iskusstvennyie neyronnyie seti dlya resheniya zadachi analiza komponentnogo sostava gazovyih smesey [Artificial neural networks for solving the problem of analyzing the composition of gas mixtures] // Upravlenie bolshimi sistemami [Large-scale Systems Control]. V. 80. Moscow: IPU RAN, 2019. P.98-115. 22. Callan R. The essence of neural networks (The essence of computing series) // Prentice Hall Europe. 1999. 23. Hochreiter S., Schmidhuber J. Long short-term memory // Neural computation. 1997. Vol. 9(8). P. 1735-1780.
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