Phase behavior modeling of asphaltene precipitation utilizing RBF-ANN approach

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 st...

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Bibliographic Details
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
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Summary: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.
ISSN:1091-6466
1532-2459
DOI:10.1080/10916466.2017.1289222