On the prediction of Watson characterization factor of hydrocarbons

Abstract This study aims to develop four methods, namely GA-RBF, MLP-NN, CSA-LSSVM and CHPSO-ANFIS methods, to accurately estimate the Watson characterization factor for various hydrocarbons. The obtained results were analyzed graphically and statistically to check the validity and the reliability of the proposed models. Results reveal that the proposed models are robust and accurate for estimation of experimental data. Moreover, the GA-RBF model is superior to three other developed models in prediction of experimental data. Furthermore, the results of the GA-RBF model as the most accurate model in the present work are compared with the results of a previously published model in literature and it is concluded that the GA-RBF model outperforms the literature model thanks to higher values of correlation coefficient and lower values of Root Mean Squared Error (RMSE).

[1]  Farhad Gharagheizi,et al.  Prediction of the Watson Characterization Factor of Hydrocarbon Components from Molecular Properties , 2008 .

[2]  A. Katritzky,et al.  How Chemical Structure Determines Physical, Chemical, and Technological Properties: An Overview Illustrating the Potential of Quantitative Structure−Property Relationships for Fuels Science , 2005 .

[3]  Amir H. Mohammadi,et al.  Integrating a robust model for predicting surfactant–polymer flooding performance , 2016 .

[4]  Shahab D. Mohaghegh,et al.  Virtual-Intelligence Applications in Petroleum Engineering: Part 2—Evolutionary Computing , 2000 .

[5]  Majid Moosavi,et al.  Density prediction of liquid alkali metals and their mixtures using an artificial neural network method over the whole liquid range , 2014 .

[6]  Surjya K. Pal,et al.  Flank wear prediction in drilling using back propagation neural network and radial basis function network , 2008, Appl. Soft Comput..

[7]  S. Azizi,et al.  Prediction of heat transfer coefficient during condensation of R134a in inclined tubes using artificial neural network , 2016 .

[8]  Elham Heidari,et al.  Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN) , 2016 .

[9]  C. H. Whitson,et al.  Characterizing hydrocarbon plus fractions , 1983 .

[10]  Alireza Bahadori,et al.  Assessing the Dynamic Viscosity of Na–K–Ca–Cl–H2O Aqueous Solutions at High-Pressure and High-Temperature Conditions , 2014 .

[11]  Amal Elkilani,et al.  Fundamentals of Petroleum Refining , 2009 .

[12]  Abdulsamet Hasiloglu,et al.  Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow , 2004 .

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  Tareq A. Albahri.  Molecularly Explicit Characterization Model (MECM) for Light Petroleum Fractions , 2005 .

[15]  M. Riazi.  Characterization and Properties of Petroleum Fractions , 2005 .

[16]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[17]  Mehdi Bagheri,et al.  BPSO-MLR and ANFIS based modeling of lower flammability limit , 2012 .

[18]  M. Ahmadi,et al.  New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept , 2012 .

[19]  Alireza Bahadori,et al.  Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models , 2016 .

[20]  Haifeng Chen,et al.  Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression , 2004, J. Chem. Inf. Model..