Application of novel ANFIS-PSO approach to predict asphaltene precipitation-Reference-Cited by-同舟云学术

Application of novel ANFIS-PSO approach to predict asphaltene precipitation

Published:2017-12-21 Issue:2 Volume:36 Page:154-159
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Keybondorian Ebrahim¹,Taherpour Alireza²,Bemani Amin²,Hamule Touba³

Affiliation:

1. Department of Chemical and Petroleum Engineering, Sharif University of Technology, Iran

2. Department of Petroleum Engineering, Petroleum University of Technology, Iran

3. Department of Basic Science, Petroleum University of Technology, Iran

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2017.1411948

Reference17 articles.

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

2. Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique

3. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches

4. Application of LSSVM strategy to estimate asphaltene precipitation during different production processes

5. Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

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