Modeling hydrocarbon surface tension using MLP and RBF neural networks and evolutionary optimization algorithms-Reference-Cited by-同舟云学术

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Modeling hydrocarbon surface tension using MLP and RBF neural networks and evolutionary optimization algorithms

Published:2022-07-04 Issue: Volume: Page:1-19
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Rafie Saleh¹^ORCID,Hajipour Mastaneh¹^ORCID,Biniaz Delijani Ebrahim¹

Affiliation:

1. Department of Petroleum Engineering, Faculty of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, 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/10.1080/10916466.2022.2094952

Reference44 articles.

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

2. Proposing a modified mechanism for determination of hydrocarbons dynamic viscosity, using artificial neural network

3. Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

4. Colonial competitive algorithm

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1. Surface tension prediction of pure organic components: An artificial neural network approach;Fuel;2025-01

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