LAPSE:2023.5903
Published Article
LAPSE:2023.5903
Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products
February 23, 2023
Abstract
Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.
Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.
Record ID
Keywords
MCNP code, neural network, oil products monitoring, X-ray spectrum
Suggested Citation
Roshani GH, Ali PJM, Mohammed S, Hanus R, Abdulkareem L, Alanezi AA, Sattari MA, Amiri S, Nazemi E, Eftekhari-Zadeh E, Kalmoun EM. Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products. (2023). LAPSE:2023.5903
Author Affiliations
Roshani GH: Electrical Engineering Department, Kermanshah University of Technology, Kermanshah 6715685420, Iran [ORCID]
Ali PJM: Department of Software Engineering, Faculty of Engineering, Koya University, Koya KOY45, Kurdistan Region, Iraq [ORCID]
Mohammed S: Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho Box 12, Kurdistan Region, Iraq [ORCID]
Hanus R: Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland [ORCID]
Abdulkareem L: Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho Box 12, Kurdistan Region, Iraq
Alanezi AA: Department of Chemical Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training (PAAET), P.O. Box 42325, Shuwaikh 70654, Kuwait [ORCID]
Sattari MA: Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah 6714414971, Iran
Amiri S: Razi University, Kermanshah 6714414971, Iran
Nazemi E: Imec-Vision Lab, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium [ORCID]
Eftekhari-Zadeh E: Institute of Optics and Quantum Electronics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, Germany [ORCID]
Kalmoun EM: Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha 2713, Qatar
Ali PJM: Department of Software Engineering, Faculty of Engineering, Koya University, Koya KOY45, Kurdistan Region, Iraq [ORCID]
Mohammed S: Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho Box 12, Kurdistan Region, Iraq [ORCID]
Hanus R: Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland [ORCID]
Abdulkareem L: Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho Box 12, Kurdistan Region, Iraq
Alanezi AA: Department of Chemical Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training (PAAET), P.O. Box 42325, Shuwaikh 70654, Kuwait [ORCID]
Sattari MA: Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah 6714414971, Iran
Amiri S: Razi University, Kermanshah 6714414971, Iran
Nazemi E: Imec-Vision Lab, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium [ORCID]
Eftekhari-Zadeh E: Institute of Optics and Quantum Electronics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, Germany [ORCID]
Kalmoun EM: Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha 2713, Qatar
Journal Name
Processes
Volume
9
Issue
5
First Page
828
Year
2021
Publication Date
2021-05-09
ISSN
2227-9717
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PII: pr9050828, Publication Type: Journal Article
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LAPSE:2023.5903
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https://doi.org/10.3390/pr9050828
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Feb 23, 2023
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