On prediction of asphaltene precipitation in different operational conditions utilization of LSSVM algorithm-Reference-Cited by-同舟云学术

On prediction of asphaltene precipitation in different operational conditions utilization of LSSVM algorithm

Published:2018-05-15 Issue:16 Volume:36 Page:1292-1297
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Sedaghat Milad¹,Rouhibakhsh Karim²

Affiliation:

1. Department of Petroleum Engineering, Marvdasht branch, Islamic Azad University, Marvdasht, Fars, Iran

2. Department of Petroleum Engineering, School of chemical, petroleum and Gas Eng., Shiraz University, Shiraz, Fars, 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/10.1080/10916466.2018.1471491

Reference22 articles.

1. Development of an Artificial Neural Network Algorithm for the Prediction of Asphaltene Precipitation

2. Comparison of scaling equation with neural network model for prediction of asphaltene precipitation

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

4. Phase equilibrium modelling of natural gas hydrate formation conditions using LSSVM approach

5. Parametric analysis of the effect of operation parameters on asphaltene deposition: An experimental study

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A gear fault diagnosis method based on variational mode decomposition and multi-scale discrete entropy;Journal of Vibroengineering;2024-01-21

2. Connectionist Models for Asphaltene Precipitation Prediction by n-Alkane Titration─Pressure and Crude Oil Properties Considered;Industrial & Engineering Chemistry Research;2023-08-18

3. Applying ANFIS and LSSVM Models for the Estimation of Biochar Aromaticity;International Journal of Chemical Engineering;2022-01-30

4. Application of intelligent models in reservoir and production engineering;Applications of Artificial Intelligence Techniques in the Petroleum Industry;2020