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Original Articles

Application of the ANFIS strategy to estimate vaporization enthalpies of petroleum fractions and pure hydrocarbons

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.
  • Ahmadi, M. A., and Shadizadeh, S. R. (2012). New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel 102:716–723.
  • Ahmadi, M. A., Soleimani, R., and Bahadori, A. (2014). A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems. Fuel 137:145–154.
  • Ahmadi, M. A., Soleimani, R., Lee, M., Kashiwao, T., and Bahadori, A. (2015). Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Petroleum 1:118–132.
  • 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.
  • 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., Rozyn, J., Abbas, A., Bahadori, A., and Rahimali, A. (2016). Estimation of air dew point temperature using computational intelligence schemes. Appl. Therm. Eng. 93:1043–1052.
  • 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.
  • Cachadina, I., and Mulero, A. (2009). New corresponding states model for the estimation of the vaporization enthalpy of fluids. Fluid Phase Equilib. 287:33–38.
  • Esen, H., Inalli, M., Sengur, A., and Esen, M. (2008). Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS. Build. Environ. 43:2178–2187.
  • Fang, W., Lei, Q., and Lin, R. (2003). Enthalpies of vaporization of petroleum fractions from vapor pressure measurements and their correlation along with pure hydrocarbons. Fluid Phase Equilib. 205:149–161.
  • Gopinathan, N., and Saraf, D. N. (2001). Predict heat of vaporization of crudes and pure components: Revised II. Fluid Phase Equilib. 179:277–284.
  • Mohammadi, A. H., and Richon, D. (2007). New predictive methods for estimating the vaporization enthalpies of hydrocarbons and petroleum fractions. Ind. Eng. Chem. Res. 46:2665–2671.
  • Ozturk, A., Arslan, A., and Hardalac, F. (2008). Comparison of neuro-fuzzy systems for classification of transcranial Doppler signals with their chaotic invariant measures. Expert Syst. Appl. 34:1044–1055.
  • Parhizgar, H., Dehghani, M. R., and Eftekhari, A. (2013). Modeling of vaporization enthalpies of petroleum fractions and pure hydrocarbons using genetic programming. J. Pet. Sci. Eng. 112:97–104.
  • Perendeci, A., Arslan, S., Çelebi, S. S., and Tanyolaç, A. (2008). Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables. Chem. Eng. J. 145:78–85.
  • Razzak, S. A. (2012). Hydrodynamics modeling of an LSCFB riser using ANFIS methodology: Effects of particle shape and size. Chemical Engineering Journal 195–196:49–61.
  • Riazi, M. R., and Daubert, T. H. (1980). Simplify property predictions. Hydrocarbon Process 60:115–116.
  • Schmitt, L. M. (2001). Theory of genetic algorithms. Theoret. Comput. Sci. 259:1–61.
  • 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.
  • Strechan, A. A., Kabo, G. J., and Paulechka, Y. U. (2006). The correlations of the enthalpy of vaporization and the surface tension of molecular liquids. Fluid Phase Equilib. 250:125–130.
  • Verevkin, S. P. (2006). Vapour pressures and enthalpies of vaporization of a series of the linear n-alkyl-benzenes. J. Chem. Thermodynam. 38:1111–1123.
  • Vetere, A. (1979). New correlations for predicting vaporization enthalpies of pure compounds. Chem. Eng. J. 17:157–62.
  • Vetere, A. (1995). Methods to predict the vaporization enthalpies at the normal boiling temperature of pure compounds revisited. Fluid Phase Equilib. 106:1–10.
  • Vose, M. D. (1999). The simple genetic algorithm: foundations and theory (Vol. 12). Cambridge, MA: MIT Press.
  • Ying, L.-C., and Pan, M.-C. (2008). Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers. Manage. 49:205–211.

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