142
Views
9
CrossRef citations to date
0
Altmetric
Original Articles

Determination of efficient surfactants in the oil and gas production units using the SVM approach

&

References

  • Ahmadi, M. A., and Baghban, A. (2015). Evolving simple-to-apply models for estimating thermal conductivity of supercritical CO2. Int. J. Ambient Energy. doi: 10.1080/01430750.2015.1086682.
  • Amedi, H. R., Baghban, A., and Ahmadi, M. A. (2016). Evolving machine learning models to predict hydrogen sulfide solubility in the presence of various ionic liquids. J. Mol. Liq. 216:411–422.
  • Antón, R. E., Andérez, J. M., Bracho, C., Vejar, F., and Salager, J.-L. (2008). Practical surfactant mixing rules based on the attainment of microemulsion–oil–water three-phase behavior systems. Adv. Polym. Sci. 218:83–113.
  • Baghban, A., Ahmadi, M. A., Pouladi, B., and Amanna, B. (2015a). 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. J. Supercrit. Fluids 101:184–192.
  • Baghban, A., Ahmadi, M. A., and Shahraki, B. H. (2015b). Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. J. Supercrit. Fluids 98:50–64.
  • Baghban, A., Bahadori, M., Ahmad, Z., Kashiwao, T., and Bahadori, A. (2016a). Modeling of true vapor pressure of petroleum products using ANFIS algorithm. Pet. Sci. Technol. 34:933–939.
  • Baghban, A., Bahadori, M., Kashiwao, T., and Bahadori, A. (2016b). Modelling of gas to hydrate conversion for promoting CO2 capture processes in the oil and gas industry. Pet. Sci. Technol. 34:642–651.
  • Baghban, A., Bahadori, M., Rozyn, J., Abbas, A., Bahadori, A., and Rahimali, A. (2015c). Estimation of air dew point temperature using computational intelligence schemes. Appl. Therm. Eng. 93:1043–1052.
  • Baghban, A., Kashiwao, T., Bahadori, M., Ahmad, Z., and Bahadori, A. (2016c). Estimation of natural gases water content using adaptive neuro-fuzzy inference system. Pet. Sci. Technol. 34:891–897.
  • Bahadori, A., Baghban, A., Bahadori, M., Kashiwao, T., and Vafaee Ayouri, M. (2016a). Estimation of emission of hydrocarbons and filling losses in storage containers using intelligent models. Pet. Sci. Technol. 34:145–152.
  • Bahadori, A., Baghban, A., Bahadori, M., Lee, M., Ahmad, Z., Zare, M., and Abdollahi, E. (2016b). Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems. Appl. Therm. Eng. 102:432–446.
  • Bansal, S., Roy, S., and Larachi, F. (2012). Support vector regression models for trickle bed reactors. Chem. Eng. J. 207–208:822–831.
  • Basak, D., Pal, S., and Patranabis, D. C. (2007). Support vector regression. Neural Information Process.-Lett. Rev. 11:203–224.
  • Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine Learning 20:273–297.
  • Gandhi, A. B., and Joshi, J. B. (2010). Estimation of heat transfer coefficient in bubble column reactors using support vector regression. Chem. Eng. J. 160:302–310.
  • Griffin, W. C. (1946). Classification of surface-active agents by “HLB”. J. Soc. Cosm. Chem. 1:311–26.
  • Gunn, S. R. (1998). Support vector machines for classification and regression. ISIS Technical Report 14.
  • Matsumoto, S., Hatakawa, Y., and Nakajima, A. (1991). Hydrophilic-lipophilic balance. EP0348883 A2.
  • Poprawski, J., Catte, M., Marquez, L., Marti, M.-J., Salager, J.-L., and Aubry, J.-M. (2003). Application of hydrophilic–lipophilic deviation formulation concept to microemulsions containing pine oil and nonionic surfactant. Polym. Int. 52:629–632.
  • Queste, S., Salager, J. L., Strey, R., and Aubry, J. M. (2007). The EACN scale for oil classification revisited thanks to fish diagrams. J. Colloid Interface Sci. 312:98–107.
  • Ramli, M. A. M., Twaha, S., and Al-Turki, Y. A. (2015). Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study. Energy Convers. Manage. 105:442–452.
  • Salager, J. L., Bourrel, M., Schechter, R. S., and Wade, W. H. (1979). Mixing rules for optimum phase-behavior formulations of surfactant/oil/water systems. SPE J. 19:271–278.
  • Shafiei, A., Ahmadi, M. A., Zaheri, S. H., Baghban, A., Amirfakhrian, A., and Soleimani, R. (2014). Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach. J. Supercrit. Fluids 95:525–534.
  • Shojai, K. N., Mohammadi, N., and Ashrafizadeh, S. N. (2009). Prediction of cell voltage and current efficiency in a lab scale chlor-alkali membrane cell based on support vector machines. Chem. Eng. J. 147:161–172.
  • Smola, A., and Vapnik, V. (1997). Support vector regression machines. Adv. Neural Information Process. Syst. 9:155–61.
  • Solairaj, S., Britton, C., Lu, J., Kim, D. H., Weerasooriya, U., and Pope, G. A. (2012). New correlation to predict the optimum surfactant structure for EOR. SPE-154262-MS, SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, April 14–18.
  • Welling, M.. (2004). Support vector regression. PhD thesis. Toronto, Canada: Department of Computer Science, University of Toronto.
  • Winsor, P. A. (1954). Solvent properties of amphiphilic compounds. London, England: Butterworths Scientific.

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.