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

Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Karina Shamilyevna Nurgalieva, Robert Hanus, Ehsan Nazemi and Igor M. Narozhnyy
Additional contact information
Abdulilah Mohammad Mayet: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Seyed Mehdi Alizadeh: Petroleum Engineering Department, Australian College of Kuwait, Kuwait City 13015, Kuwait
Karina Shamilyevna Nurgalieva: Department of Development and Operation of Oil and Gas Fields, Saint-Petersburg Mining University, 199106 Saint-Petersburg, Russia
Robert Hanus: Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland
Ehsan Nazemi: Imec-Vision Laboratory, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium
Igor M. Narozhnyy: Department of Commercialization of Intellectual Activity Resultse Center for Technology Transfer of RUDN University, Mining Oil and Gas Department, RUDN University, 117198 Moscow, Russia

Energies, 2022, vol. 15, issue 6, 1-19

Abstract: 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.

Keywords: computational intelligence; monitoring characteristics; oil and petrochemical fluids; feature extraction; radiation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/6/1986/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/6/1986/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:6:p:1986-:d:767033

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().