Phase behavior modeling of asphaltene precipitation utilizing RBF-ANN approach |
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Authors: | Mohammad Navid Kardani Mohammad Ehsan Hamzehie Mohammad Baghban |
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Affiliation: | 1. Department of Petroleum Engineering, University of Tehran, Tehran, Iran;2. Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran.;3. Yoaung Researcher and Elite Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran |
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Abstract: | 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. |
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Keywords: | asphaltene dilution ratio heavy n-alkane RBF-ANN temperature |
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