Gasoline blending is of pertinent importance in refinery operations owing to the fact that gasoline gives about 60 - 70 % of the refinery profit. The blending process is essential to obtain gasoline in the demanded quantities and ensure property specifications are met. Two model equations, multivariable nonlinear and multivariable exponential are proposed in this study which are useful in predicting three significant properties of a motor gasoline: research octane number, reid vapour pressure and specific gravity. Gasoline blend data obtained from four different streams: straight run gasoline, straight run naphtha, reformate and fluidized catalytically cracked gasoline have been subjected to multivariate regression analysis with the aid of a statistical software to ascertain the fitness of the proposed equations in predicting the research octane number, reid vapor pressure and the specific gravity of the resulting premium motor spirit. The results of the regression analysis showed that the nonlinear multivariable models proposed gave a good fit as evidenced by the value of the coefficient of determination R2 = 0.988 & 0.994 for the research octane number, 0.853 & 0.883 for the reid vapor pressure and 0.988 for specific gravity. In conclusion, the proposed model equations were fit to the data, found to be adequate, and therefore could be used for prediction of the blend gasoline properties.
| Published in |
American Journal of Chemical Engineering (Volume 3, Issue 2-1)
This article belongs to the Special Issue Developments in Petroleum Refining and Petrochemical Sector of the Oil and Gas Industry |
| DOI | 10.11648/j.ajche.s.2015030201.12 |
| Page(s) | 9-17 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Gasoline Blends, Modeling, Petroleum Refining, Octane Number, Reid Vapor Pressure, Multivariate Regression
| [1] | MathPro, An introduction to petroleum refining and the production of ultra low sulfur gasoline and diesel fuel, the International Council of Clean Transportation (ICCT), 2011. |
| [2] | C.Y. Jaja, Recent trends and patterns of gasoline consumption in Nigeria, Council for the Development of Social Science Research in Africa, pp. 159 – 177, 2010. |
| [3] | J. Wodausch, Investigation and prediction of autoignition during hot start conditions, M.Sc. Thesis, Nelson Mandela Metropolitan University, 2009. |
| [4] | M.H.T. Auckland and D.J. Charnock, The Development of Linear Blending Indices for Petroleum Properties, J. Inst. of Petroleum, 55(545), 322- 329, 1969. |
| [5] | M.B. Celik, Experimental determination of suitable ethanol-gasoline blend rate at high compression ratio for gasoline engine, Appl. Thermal Engrng., .28, 396–404, 2008. |
| [6] | A.E. Eiman, Improvement of gasoline octane number by blending gasoline with selective components, M.Sc Thesis, University of Technology, Republic of Iraq, 2008. |
| [7] | M.M. Farsibaf, M. Golchinpour, and A. Barzegar, Global optimization in order to find blend composition of gasoline of desired octane number considering ethanol as octane-booster, 41st International Conference on Computers & Industrial Engineering, 313 - 318, 2012. |
| [8] | S. Fernando, Internal combustion engines, PhD Thesis, Department of Aerospace and Mechanical Engineering University of Notre Dame, Notre Dame, 1998. |
| [9] | A. Hassan and Mourhaf, Correlation between the octane number of motor gasoline and its boiling range, J. King Saud Univ., Vol. 9, Eng .Sci. (2), pp. 311-318, 1996. |
| [10] | Jr. W.C. Healy, C.W. Maassen, and R.T. Peterson, A new approach to blending octanes, API Division of Refining, 24th midyear meeting, New York, 1959. |
| [11] | W.G. Lovell, J.M. Campbell, T.A. Boyd, Detonation characteristics of some aliphatic olefin hydrocarbons, Ind. Eng. Chem.. 555, 1931. |
| [12] | C.A. Mendez, I.E. Grossmann and I. Harjunkoski, Optimization techniques for blending and scheduling of oil-refinery operations, Carnegie Mellon University Pittsburgh, U.S.A. 2011. |
| [13] | D.S. Monder, Real-time optimization of gasoline blending with uncertain parameters, M.Sc Thesis, University of Alberta, Canada, 2001. |
| [14] | G. Najafi, B. Ghodadiam, T. Tavakoli, D.R. Buttsworth, T.F. Yusaf and M. Faizollahneja, Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network, Ind. Eng. Chem. Res, 86(5), pp. 630–639, 2009. |
| [15] | I.L. Nwaogazie, Probability and statistics for science and engineering practice, 3rd Edition, De-Adroit Innovation, Nigeria, 2011. |
| [16] | O.A. Francis, A model for blending motor gasoline, case study: Tema Oil Refinery Ltd., M.Sc. Thesis, Kwame Nkrumah University of Science and Technology, 2013. |
| [17] | Chorng H. Twu and John E. Coon, A Generalized Interaction Method for the Prediction of Octane Numbers for Gasoline Blends. Simulation Sciences Inc., 601 South Valencia Avenue, Brea, CA 92621 (USA). |
| [18] | http://www.osha.gov, OSHA Technical Manual. Accessed on 18th October, 2014 . |
| [19] | E. Paranghooshi and M. T. Sadeghi, Predicting octane numbers for gasoline blends using artificial neural networks, Hydrocarbon Processing, 2009. |
| [20] | W.F. Schoen and A.V. Mrstik, Calculating gasoline blend octane ratings, Ind. and Engr. Chem., 47(9), 1740-1742, 1955. |
| [21] | W.E. Stewart, Predict Octanes for Gasoline Blend, Petroleum Refiner, 38(12), 135-139, 1959. |
| [22] | M.H. Rusin, H.S. Chung, and J.F. Marshall, A transformation method for calculating the research and motor octane numbers of gasoline blends, Ind. Eng. Chem. Fundam., 20(3), pp. 195-204, 1981. |
| [23] | W.E Morris, “Interaction Approach to Gasoline Blending”, NPRA Paper AM-75-30, National Petroleum Refiners Association annual meeting, 1975. |
| [24] | N. Pasadakis, V. Gaganis, Ch. Foteinopoulos: “Octane number prediction for gasoline blends”, Fuel Processing Technology, 87, (2006) 505-509. |
| [25] | A. H. Zahed, S. A. Mullah, and M. D. Bashir. Predict Octane Number for Gasoline Blends. Hydroc Proc, 72(5), 1993, 85-87. |
| [26] | G. Prasenjeet, J.H. Karlton, and B.J. Stephen, Development of a detailed gasoline composition-based octane model, Ind. Eng. Chem. Res., pp. 337-345, 2006. |
| [27] | http://www.curvefitting.com, DataFitX version 8.2.79. |
APA Style
M. K. Oduola, A. I. Iyaomolere. (2015). Development of Model Equations for Predicting Gasoline Blending Properties. American Journal of Chemical Engineering, 3(2-1), 9-17. https://doi.org/10.11648/j.ajche.s.2015030201.12
ACS Style
M. K. Oduola; A. I. Iyaomolere. Development of Model Equations for Predicting Gasoline Blending Properties. Am. J. Chem. Eng. 2015, 3(2-1), 9-17. doi: 10.11648/j.ajche.s.2015030201.12
AMA Style
M. K. Oduola, A. I. Iyaomolere. Development of Model Equations for Predicting Gasoline Blending Properties. Am J Chem Eng. 2015;3(2-1):9-17. doi: 10.11648/j.ajche.s.2015030201.12
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Download@article{10.11648/j.ajche.s.2015030201.12,
author = {M. K. Oduola and A. I. Iyaomolere},
title = {Development of Model Equations for Predicting Gasoline Blending Properties},
journal = {American Journal of Chemical Engineering},
volume = {3},
number = {2-1},
pages = {9-17},
doi = {10.11648/j.ajche.s.2015030201.12},
url = {https://doi.org/10.11648/j.ajche.s.2015030201.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajche.s.2015030201.12},
abstract = {Gasoline blending is of pertinent importance in refinery operations owing to the fact that gasoline gives about 60 - 70 % of the refinery profit. The blending process is essential to obtain gasoline in the demanded quantities and ensure property specifications are met. Two model equations, multivariable nonlinear and multivariable exponential are proposed in this study which are useful in predicting three significant properties of a motor gasoline: research octane number, reid vapour pressure and specific gravity. Gasoline blend data obtained from four different streams: straight run gasoline, straight run naphtha, reformate and fluidized catalytically cracked gasoline have been subjected to multivariate regression analysis with the aid of a statistical software to ascertain the fitness of the proposed equations in predicting the research octane number, reid vapor pressure and the specific gravity of the resulting premium motor spirit. The results of the regression analysis showed that the nonlinear multivariable models proposed gave a good fit as evidenced by the value of the coefficient of determination R2 = 0.988 & 0.994 for the research octane number, 0.853 & 0.883 for the reid vapor pressure and 0.988 for specific gravity. In conclusion, the proposed model equations were fit to the data, found to be adequate, and therefore could be used for prediction of the blend gasoline properties.},
year = {2015}
}
TY - JOUR T1 - Development of Model Equations for Predicting Gasoline Blending Properties AU - M. K. Oduola AU - A. I. Iyaomolere Y1 - 2015/01/19 PY - 2015 N1 - https://doi.org/10.11648/j.ajche.s.2015030201.12 DO - 10.11648/j.ajche.s.2015030201.12 T2 - American Journal of Chemical Engineering JF - American Journal of Chemical Engineering JO - American Journal of Chemical Engineering SP - 9 EP - 17 PB - Science Publishing Group SN - 2330-8613 UR - https://doi.org/10.11648/j.ajche.s.2015030201.12 AB - Gasoline blending is of pertinent importance in refinery operations owing to the fact that gasoline gives about 60 - 70 % of the refinery profit. The blending process is essential to obtain gasoline in the demanded quantities and ensure property specifications are met. Two model equations, multivariable nonlinear and multivariable exponential are proposed in this study which are useful in predicting three significant properties of a motor gasoline: research octane number, reid vapour pressure and specific gravity. Gasoline blend data obtained from four different streams: straight run gasoline, straight run naphtha, reformate and fluidized catalytically cracked gasoline have been subjected to multivariate regression analysis with the aid of a statistical software to ascertain the fitness of the proposed equations in predicting the research octane number, reid vapor pressure and the specific gravity of the resulting premium motor spirit. The results of the regression analysis showed that the nonlinear multivariable models proposed gave a good fit as evidenced by the value of the coefficient of determination R2 = 0.988 & 0.994 for the research octane number, 0.853 & 0.883 for the reid vapor pressure and 0.988 for specific gravity. In conclusion, the proposed model equations were fit to the data, found to be adequate, and therefore could be used for prediction of the blend gasoline properties. VL - 3 IS - 2-1 ER -
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