Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Authors
Mayet A.M.1 , Alizadeh S.M.2 , Nurgalieva K.S.3 , Hanus R.4 , Nazemi E.5 , Narozhnyy I.M. 6
Journal
Publisher
MDPI AG
Number of issue
6
Language
English
Status
Published
Number
1986
Volume
15
Year
2022
Organizations
  • 1 Electrical Engineering Department, King Khalid University, Abha, 61411, Saudi Arabia
  • 2 Petroleum Engineering Department, Australian College of Kuwait, Kuwait City, 13015, Kuwait
  • 3 Department of Development and Operation of Oil and Gas Fields, Saint-Petersburg Mining University, Saint-Petersburg, 199106, Russian Federation
  • 4 Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Rzeszów, 35-959, Poland
  • 5 Imec-Vision Laboratory, Department of Physics, University of Antwerp, Antwerp, 2610, Belgium
  • 6 Department of Commercialization of Intellectual Activity Resultse Center for Technology Transfer of RUDN University, Mining Oil and Gas Department, RUDN University, Moscow, 117198, Russian Federation
Keywords
Computational intelligence; Feature extraction; Monitoring characteristics; Oil and petrochemical fluids; Radiation
Date of creation
06.07.2022
Date of change
06.07.2022
Short link
https://repository.rudn.ru/en/records/article/record/83799/
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