Implementing radial basis function neural networks for prediction of saturation pressure of crude oils-Reference-Cited by-同舟云学术

Implementing radial basis function neural networks for prediction of saturation pressure of crude oils

Published:2016-03-03 Issue:5 Volume:34 Page:454-463
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Tatar A.,Najafi-Marghmaleki A.,Barati-Harooni A.,Gholami A.,Ansari H. R.,Bahadori M.,Kashiwao T.,Lee M.,Bahadori A.

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

http://www.tandfonline.com/doi/pdf/10.1080/10916466.2016.1141217

Reference47 articles.

1. Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling

2. A Regression Technique With Dynamic-Parameter Selection or Phase Behavior Matching

3. Phase Equilibrium Modeling of Clathrate Hydrates of Carbon Dioxide + 1,4-Dioxine Using Intelligent Approaches

4. Recovery Rate of Vapor Extraction in Heavy Oil Reservoirs—Experimental, Statistical, and Modeling Studies

5. New tools to determine bubble point pressure of crude oils: Experimental and modeling study

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