112
Views
5
CrossRef citations to date
0
Altmetric
Articles

Modeling vaporization enthalpy of pure hydrocarbons and petroleum fractions using LSSVM approach

, & ORCID Icon
Pages 569-576 | Received 29 Oct 2018, Accepted 05 Jan 2019, Published online: 04 Mar 2019

References

  • Aghayari, R., H. Maddah, M. H. Ahmadi, W. M. Yan, and N. Ghasemi. 2018. Measurement and artificial neural network modeling of electrical conductivity of CuO/glycerol nanofluids at various thermal and concentration conditions. Energies 11 (5):1190. doi:10.3390/en11051190.
  • Ahmadi, M. A. 2016. Toward reliable model for prediction drilling fluid density at wellbore conditions: A LSSVM model. Neurocomputing 211:143–49. doi:10.1016/j.neucom.2016.01.106.
  • Ahmadi, M. A., M. H. Ahmadi, M. F. Alavi, M. R. Nazemzadegan, R. Ghasempour, and S. Shamshirband. 2018. Determination of thermal conductivity ratio of CuO/ethylene glycol nanofluid by connectionist approach. Journal of the Taiwan Institute of Chemical Engineers 91:383–95. doi:10.1016/j.jtice.2018.06.003.
  • Ahmadi, M. A., and A. Bahadori. 2015. A LSSVM approach for determining well placement and conning phenomena in horizontal wells. Fuel 153:276–83. doi:10.1016/j.fuel.2015.02.094.
  • Ahmadi, M. A., M. Z. Hasanvand, and A. Bahadori. 2015. A LSSVM approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems. International Journal of Ambient Energy 38:122–29. doi:10.1080/01430750.2015.1055515.
  • Ahmadi, M. H., M. A. Ahmadi, S. A. Sadatsakkak, and M. Feidt. 2015. Connectionist intelligent model estimates output power and torque of stirling engine. Renewable and Sustainable Energy Reviews 50:871–83. doi:10.1016/j.rser.2015.04.185.
  • Ahmadi, M. H., M. A. Nazari, R. Ghasempour, H. Madah, M. B. Shafii, and M. A. Ahmadi. 2018b. Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods. Colloids and Surfaces A: Physicochemical and Engineering Aspects 541:154–64. doi:10.1016/j.colsurfa.2018.01.030.
  • Ahmadi, M. H., A. Tatar, M. A. Nazari, R. Ghasempour, A. J. Chamkha, and W. M. Yan. 2018a. Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks. International Journal of Heat and Mass Transfer 126:1079–86. doi:10.1016/j.ijheatmasstransfer.2018.06.085.
  • Ali Ahmadi, M., and A. Ahmadi. 2016. Applying a sophisticated approach to predict CO2 solubility in brines: Application to CO2 sequestration. International Journal of Low-Carbon Technologies 11 (3):325–32. doi:10.1093/ijlct/ctu034.
  • Baghban, A., P. Abbasi, and P. Rostami. 2016. Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm. Petroleum Science and Technology 34 (20):1698–704. Review of. doi:10.1080/10916466.2016.1219748.
  • Baghban, A., M. A. Ahmadi, B. Pouladi, and B. Amanna. 2015. Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique. The Journal of Supercritical Fluids 101( Review of):184–92. doi:10.1016/j.supflu.2015.03.004.
  • Baghban, A., M. A. Ahmadi, and B. H. Shahraki. 2015. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. The Journal of Supercritical Fluids 98( Review of):50–64. doi:10.1016/j.supflu.2015.01.002.
  • Baghban, A., A. Bahadori, A. H. Mohammadi, and A. Behbahaninia. 2017. Prediction of CO 2 loading capacities of aqueous solutions of absorbents using different computational schemes. International Journal of Greenhouse Gas Control 57( Review of):143–61. doi:10.1016/j.ijggc.2016.12.010.
  • Baghban, A., M. Bahadori, A. S. Lemraski, and A. Bahadori. 2016a. Prediction of solubility of ammonia in liquid electrolytes using least square support vector machines. Ain Shams Engineering Journal.
  • Baghban, A., M. Bahadori, J. Rozyn, M. Lee, A. Abbas, A. Bahadori, and A. Rahimali. 2016b. Estimation of air dew point temperature using computational intelligence schemes. Applied Thermal Engineering 93( Review of):1043–52. doi:10.1016/j.applthermaleng.2015.10.056.
  • Baghban, A., and A. Khoshkharam. 2016. Application of LSSVM strategy to estimate asphaltene precipitation during different production processes. Petroleum Science and Technology 34 (22):1855–60. Review of. doi:10.1080/10916466.2016.1237966.
  • Baghban, A., A. H. Mohammadi, and M. S. Taleghani. 2017. Rigorous modeling of CO2 equilibrium absorption in ionic liquids. International Journal of Greenhouse Gas Control 58: 19–41. doi:10.1016/j.ijggc.2016.12.009.
  • Baghban, A., S. Namvarrechi, L. T. K. Phung, M. Lee, A. Bahadori, and T. Kashiwao. 2016c. Phase equilibrium modelling of natural gas hydrate formation conditions using LSSVM approach. Review Of. Petroleum Science and Technology 34 (16):1431–38. doi:10.1080/10916466.2016.1202966.
  • Chickos, J. S., and W. Hanshaw. 2004. Vapor pressures and vaporization enthalpies of the n-alkanes from C21 to C30 at T= 298.15 K by correlation gas chromatography. Journal of Chemical & Engineering Data 49 (1):77–85. Review of. doi:10.1021/je0301747.
  • Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20 (3):273–97. Review of. doi:10.1007/BF00994018.
  • Fang, W., Q. Lei, and R. Lin. 2003. Enthalpies of vaporization of petroleum fractions from vapor pressure measurements and their correlation along with pure hydrocarbons. Fluid Phase Equilibria 205 (1):149–61. Review of. doi:10.1016/S0378-3812(02)00277-7.
  • Gopinathan, N., and D. N. Saraf. 2001. Predict heat of vaporization of crudes and pure components: Revised II. Fluid Phase Equilibria 179 (1):277–84. Review of. doi:10.1016/S0378-3812(00)00501-X.
  • Kahani, M., M. H. Ahmadi, A. Tatar, and M. Sadeghzadeh. 2018. Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO2/water nanofluid flows through non-straight pathways. Numerical Heat Transfer, Part A: Applications 74 (4):1190–206. doi:10.1080/10407782.2018.1523597.
  • Liebman, J. F., and Y. Panshin. 1988. Estimations of the heats of vaporization of simple hydrocarbon derivatives at 298 K. Journal of Organic Chemistry 53( Review of):3424–29. doi:10.1021/jo00250a004.
  • Mohammadi, A. H., and D. Richon. 2007. New predictive methods for estimating the vaporization enthalpies of hydrocarbons and petroleum fractions. Industrial & Engineering Chemistry Research 46 (8):2665–71. Review of. doi:10.1021/ie0613927.
  • Osborne, N. S., and D. C. Ginnings. 1947. Measurements of heat of vaporization and heat capacity of a number of hydrocarbons. Journal of Research Past Papers 39( Review of):453–77. doi:10.6028/jres.039.031.
  • Parhizgar, H., M. R. Dehghani, and A. Eftekhari. 2013. Modeling of vaporization enthalpies of petroleum fractions and pure hydrocarbons using genetic programming. Journal of Petroleum Science and Engineering 112( Review of):97–104. doi:10.1016/j.petrol.2013.10.012.
  • Riazi, M. R., and T. E. Daubert. 1980. Simplify property predictions. Hydrocarbon Processing 60 (3):115–16. Review of.
  • Shafiei, A., M. A. Ahmadi, S. H. Zaheri, A. Baghban, A. Amirfakhrian, and R. Soleimani. 2014. Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach. The Journal of Supercritical Fluids 95:525–34. doi:10.1016/j.supflu.2014.08.011.
  • Smola, A., and V. Vapnik. 1997. Support vector regression machines. Advances in Neural Information Processing Systems 9:155–61. Review of.
  • Suykens, J. A. K., and J. Vandewalle. 1999. Least squares support vector machine classifiers. Neural Processing Letters 9 (3):293–300. Review of. doi:10.1023/A:1018628609742.
  • Wang, L. 2005. Support vector machines: theory and applications, Vol. 177. Springer-Verlag Berlin Heidelberg: Springer Science & Business Media.
  • Wilhoit, R. C., and B. J. Zwolinski. 1971. Handbook of vapor pressures and heats of vaporization of hydrocarbons and related compounds. College Station, Texas: Texas A&M Research Foundation.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.