Development of a Predictive Tool for the Estimation of True Vapor Pressure of Volatile Petroleum Products

REFERENCES

• American Petroleum Institute (API). 1997. Vapor Pressure in Technical Data Book—Petroleum Refining, 6th Ed. Washington, DC: American Petroleum Institute.
   Google Scholar
• Arrhenius, S. 1889. Uber die reaktionsgeschwindigkeit der inversion von rohrzucker durch saeuren. Z. Phys. Chem. 4:226–248.
   Google Scholar
• Bahadori, A. 2009. Estimation of hydrate inhibitor loss in hydrocarbon liquid phase. Petrol. Sci. Technol. 27:943–951.
   Web of Science ®Google Scholar
• Bahadori, A., and Vuthaluru, H. B. 2009. New method accurately predicts carbon dioxide equilibrium adsorption isotherms. Int. J. Greenhouse Gas Control 3:768–772.
   Web of Science ®Google Scholar
• Bair, E., Hastie, T., Debashis, P., and Tibshirani, R. 2006. Prediction by supervised principal components. J. Am. Stat. Assoc. 101:119–137.
   Web of Science ®Google Scholar
• Baird, C. T. 1981. Crude Oil Yields and Product Properties Ch. De Lahaute-Belotte 6, Cud Thomas Baird IV, Geneva, Switzerland.
   Google Scholar
• Bird, W. L., and Kimball, J. L. 1975. Application of gas chromatographic distillation to motor gasoline blending. In: Calculation of Physical Properties of Petroleum Products from Gas Chromatographic Analyses, Green, L. E., and Albert, D. K. (Eds.). Philadelphia, PA: American Society for Testing and Materials, pp. 51–64.
   Google Scholar
• Civan, F. 2007. Critical modification to the Vogel–Tammann–Fulcher equation for temperature effect on the density of water. Indus. Eng. & Chem. Res. J. 46:5810–5814.
   Web of Science ®Google Scholar
• Civan, F. 2008. Use exponential functions to correlate temperature dependence. Chem. Eng. Progr. 104:46–52.
   Web of Science ®Google Scholar
• Fulcher, G. S. 1925. Analysis of recent data of the viscosity of glasses. J. Am. Ceram. Soc. 8:339–355.
   Web of Science ®Google Scholar
• Fulton, W., and Harris, J. 1991. Representation Theory: A First Course (Graduate Texts in Mathematics, Readings in Mathematics). New York: Springer-Verlag, 551 pp.
   Google Scholar
• Gas Processors Suppliers Association (GPSA). 2004. Engineering Data Book, 12th Ed. Tulsa, OK: Gas Processors Suppliers Association.
   Google Scholar
• Geladi, P., and Esbensen, K. 1991. Regression on multivariate images: Principal component regression for modeling, prediction and visual diagnostic tools. J. Chemometrics 5:111.
   Google Scholar
• Horn, R. A., and Johnson, C. R. 1991. Topics in Matrix Analysis. Cambridge, UK: Cambridge University Press, Sect. 6.1.
   Google Scholar
• Jenkins, G. I., and White, M. M. 1969. Calculation of Reid vapor pressure of motor gasolines and aviation fuels from distillation data. J. Inst. Pet. 55:153.
   Web of Science ®Google Scholar
• Kadleca, P., Gabrys, B., and Strandt, S. 2009. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33:795–814.
   Web of Science ®Google Scholar
• Kaspar, M. H., and Ray, W. H. 1993. Partial least squares modelling as successive singular value decompositions. Comput. Chem. Eng. 17:985–989.
   Web of Science ®Google Scholar
• Kourti, T. 2002. Process analysis and abnormal situation detection: From theory to practice. Contr. Sys. Mag., IEEE 22:10–25.
   Web of Science ®Google Scholar
• MATLAB. 2006. MATLAB, Version 7.3.0.267. Natick, MA: MathWorks Company.
   Google Scholar
• Riazi, M. R., Albahri, T. A., and Alqattan, A. H. 2005. Prediction of Reid vapor pressure of petroleum fuels. Pet. Sci. Technol. 23:75–86.
   Web of Science ®Google Scholar
• Tammann, G., and Hesse, W. 1926. Die abhängigkeit der viskosität von der temperature bei unterkühlten flüssigkeiten. Z. Anorg. Allg. Chem. 156:245–257.
   Google Scholar
• Vazquez-Esparragoza, J. J., Iglesias-Silva, G. A., Hlavinka, M. W., and Bullin, J. 1992. How to estimate RVP of blends. Hydrocarbon Proc. 71:135–138.
   Web of Science ®Google Scholar
• Vogel, H. 1921. Das temperature-abhängigketsgesetz der viskosität von flüssigkeiten. Phys. Z. 22:645–646.
   Google Scholar
• Warne, K., Prasad, G., Rezvani, S., and Maguire, L. 2004. Statistical and computational intelligence techniques for inferential model development: A comparative evaluation and a novel proposition for fusion. Eng. Appl. Artific. Intell. 17:871–885.
   Web of Science ®Google Scholar