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Prediction of heavy oil viscosity using a radial basis function neural network |
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| Authors: | Afshin Tatar Ali Barati-Harooni Siyamak Moradi Saeid Nasery Adel Najafi-Marghmaleki Moonyong Lee |
| Affiliation: | 1. Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran;2. Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;3. Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran;4. School of Chemical Engineering, Yeungnam University, Gyeongsan, Korea |
| Abstract: | Heavy oil and extra heavy oil resources comprise about 75% of petroleum resources. The most important characteristic of heavy oils is their viscosity. Consequently, to extract and prepare these kinds of crude oil for use, great emphasis should be put on viscosity. The present study highlights the application of intelligent model named radial basis function (RBF) network optimized by genetic algorithm for estimation of diluted heavy oil viscosity in presence on kerosene. The input parameters of model were temperature and mass fraction of kerosene. The output of model was viscosity of heavy oil. Genetic algorithm was utilized to optimize the tuning parameters of RBF model. The outcomes of this study showed that the proposed model is accurate in estimation of target data. |
| Keywords: | Genetic algorithm heavy oil radial basis function viscosity |