Phase behavior modeling of asphaltene precipitation utilizing RBF-ANN approach

• Published in: Petroleum science and technology 2019-08, Vol.37 (16), p.1861-1867
• Main Authors: Kardani, Mohammad Navid, Baghban, Alireza, Hamzehie, Mohammad Ehsan, Baghban, Mohammad
• Format: Article
• Language: English
• Subjects: Alkanes; Artificial neural networks; asphaltene; Asphaltenes; Basis functions; Dilution; dilution ratio; heavy n-alkane; Molecular weight; Radial basis function; RBF-ANN; Refineries; temperature
• ISSN: 1091-6466; 1532-2459
• DOI: 10.1080/10916466.2017.1289222

Precipitation of heavy hydrocarbons, particularly asphaltenes, is the reason for numerous operational and production problems in the petroleum industry. Hence, knowing the amount of asphaltene precipitation is a critical commission for petroleum engineers to overcome its problems. The aim of this study was to predict the amount of asphaltene precipitation as a function of temperature, dilution ratio, and molecular weight of different n-alkanes utilizing radial basis function artificial neural network (RBF-ANN). Additionally, this model has been compared with previous correlations, and its great accuracy was proved to predict the precipitated asphaltene. The values of R-squared and mean squared error obtained were 0.998 and 0.007, respectively. The efforts confirmed brilliant forecasting skill of RBF-ANN for the approximation of the precipitated asphaltene as a function of temperature, dilution ratio, and molecular weight of different n-alkanes.