Volume 5, Issue 5, September 2017, Page: 65-71
Correction of Terminal Velocity Prediction Model for CO2-Kerosene and Air-Kerosene Systems by Artificial Intelligence
Sadra Mahmoudi, Department of Gas Engineering, Ahwaz Faculty of Petroleum, Petroleum University of Technology, Ahwaz, Iran
Bahram Hashemi Shahraki, Department of Gas Engineering, Ahwaz Faculty of Petroleum, Petroleum University of Technology, Ahwaz, Iran
Masoud Aghajani, Department of Gas Engineering, Ahwaz Faculty of Petroleum, Petroleum University of Technology, Ahwaz, Iran
Received: Nov. 28, 2017;       Accepted: Dec. 7, 2017;       Published: Jan. 2, 2018
DOI: 10.11648/j.se.20170505.11      View  1150      Downloads  36
Abstract
In this study the essential factors of rising air and CO2 bubbles in distillated water and kerosene investigate with the experimental and theoretical attitude. Many formulas developed by pervious investigators for bubble terminal velocity prediction in air-water system. By using PSO (particle swarm optimization) algorithm and plotting experimental data of terminal velocity against the size of gas bubbles, suitable was chosen. Results showed that Jamialahmadi model is more practical for air-water and CO2-water system. The main aim of this paper is to validate and correct Jamialahmadi model for predicting of bubble’s terminal velocities in air-kerosene and CO2-kerosene systems. Jamialahmadi model requires a modification to be utilized for air-kerosene and CO2-kerosene system. The developed PSO algorithm model is accurate for prediction of experimental data with an average R2 value of 0.976.
Keywords
PSO Algorithm, Kerosene, Distillated Water, Carbon Dioxide, Bubble Column
To cite this article
Sadra Mahmoudi, Bahram Hashemi Shahraki, Masoud Aghajani, Correction of Terminal Velocity Prediction Model for CO2-Kerosene and Air-Kerosene Systems by Artificial Intelligence, Software Engineering. Vol. 5, No. 5, 2017, pp. 65-71. doi: 10.11648/j.se.20170505.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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