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.
Journal article
Prediction of heavy oil viscosity using a radial basis function neural network
Petroleum Science and Technology, Vol.34(21), pp.1742-1748
2016
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Abstract
Details
- Title
- Prediction of heavy oil viscosity using a radial basis function neural network
- Creators
- Afshin Tatar - Islamic Azad University, North TehranAli Barati-Haroni - Islamic Azad UniversitySiyamak Moradi - Petroleum University of TechnologySaeid Nasery - Islamic Azad UniversityAdel Najafi-marghmaleki - Islamic Azad UniversityMoonong Lee - Yeungnam UniversityLe Thi Kim Phung - Hochiminh City UniversityAlireza Bahadori - Southern Cross University
- Publication Details
- Petroleum Science and Technology, Vol.34(21), pp.1742-1748
- Identifiers
- 4059; 991012821873402368
- Academic Unit
- School of Environment, Science and Engineering; Faculty of Science and Engineering
- Resource Type
- Journal article