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• W2211756078 endingPage "1882" @default.
• W2211756078 startingPage "1875" @default.
• W2211756078 abstract "The precipitation of asphaltene, a polar fraction of crude oil, during oil production has unfavorable impacts on many parts of the petroleum industry. Within the upstream processes, asphaltene precipitation occurs in crude oil, forming solid deposits in the reservoir during enhanced oil recovery operations and natural depletion. This significantly influences the porosity and permeability of the reservoir, reducing the effectiveness of the recovery process. Precipitation and deposition in downstream processes causes noticeable increases in production costs. Therefore, it is essential to predict the amount of asphaltene precipitation based on pressure, temperature and liquid phase composition using a dependable, precise, and robust strategy. However, the experimental measurement techniques used to estimate amounts are expensive and time consuming, while the thermodynamic models available are also somewhat complex. The authors propose an innovative approach for the simple and prompt prediction of asphaltene precipitation, employing an artificial neural network. The results show that the predicted values were in agreement with the experimental data, with the maximum absolute error deviation for the proposed model no more than 2.46%. A comparison of the proposed model with previously presented models highlight the superiority of the model developed in this study." @default.
• W2211756078 created "2016-06-24" @default.
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• W2211756078 date "2015-12-17" @default.
• W2211756078 modified "2023-10-15" @default.
• W2211756078 title "Application of Radial Basis Function Neural Network for Prediction of Titration-Based Asphaltene Precipitation" @default.
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• W2211756078 doi "https://doi.org/10.1080/10916466.2015.1108984" @default.
• W2211756078 hasPublicationYear "2015" @default.
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