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Proposing a High-Precision Petroleum Pipeline Monitoring System for Identifying the Type and Amount of Oil Products Using Extraction of Frequency Characteristics and a MLP Neural Network

Author

Listed:
  • Abdulilah Mohammad Mayet

    (Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia)

  • Karina Shamilyevna Nurgalieva

    (Department of Development and Operation of Oil and Gas Fields, Saint-Petersburg Mining University, 199106 Saint-Petersburg, Russia)

  • Ali Awadh Al-Qahtani

    (Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia)

  • 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)

  • Hala H. Alhashim

    (Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)

  • Ehsan Nazemi

    (Imec-Vision Laboratory, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium)

  • Ilya M. Indrupskiy

    (Mining Oil and Gas Department, RUDN University, 117198 Moscow, Russia)

Abstract

Setting up pipelines in the oil industry is very costly and time consuming. For this reason, a pipe is usually used to transport various petroleum products, so it is very important to use an accurate and reliable control system to determine the type and amount of oil product. In this research, using a system based on the gamma-ray attenuation technique and the feature extraction technique in the frequency domain combined with a Multilayer Perceptron (MLP) neural network, an attempt has been made to determine the type and amount of four petroleum products. The implemented system consists of a dual-energy gamma source, a test pipe to simulate petroleum products, and a sodium iodide detector. The signals received from the detector were transmitted to the frequency domain, and the amplitudes of the first to fourth dominant frequency were extracted from them. These characteristics were given to an MLP neural network as input. The designed neural network has four outputs, which is the percentage of the volume ratio of each product. The proposed system has the ability to predict the volume ratio of products with a maximum root mean square error (RMSE) of 0.69, which is a strong reason for the use of this system in the oil industry.

Suggested Citation

  • Abdulilah Mohammad Mayet & Karina Shamilyevna Nurgalieva & Ali Awadh Al-Qahtani & Igor M. Narozhnyy & Hala H. Alhashim & Ehsan Nazemi & Ilya M. Indrupskiy, 2022. "Proposing a High-Precision Petroleum Pipeline Monitoring System for Identifying the Type and Amount of Oil Products Using Extraction of Frequency Characteristics and a MLP Neural Network," Mathematics, MDPI, vol. 10(16), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2916-:d:887410
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    References listed on IDEAS

    as
    1. Abdullah M. Iliyasu & Abdulilah Mohammad Mayet & Robert Hanus & Ahmed A. Abd El-Latif & Ahmed S. Salama, 2022. "Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines," Energies, MDPI, vol. 15(12), pages 1-12, June.
    2. Abdullah K. Alanazi & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & John William Grimaldo Guerrero & Hala M. Abo-Dief & Ehsan Eftekhari-Zadeh & Ehsan Nazemi & Igor M. Narozhnyy, 2021. "Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    3. Anatoliy Andreevich Isaev & Mekhrali Mirzali Ogly Aliev & Alexander Nikolaevich Drozdov & Yana Alekseevna Gorbyleva & Karina Shamilyevna Nurgalieva, 2022. "Improving the Efficiency of Curved Wells’ Operation by Means of Progressive Cavity Pumps," Energies, MDPI, vol. 15(12), pages 1-14, June.
    4. Mohammed Balubaid & Mohammad Amir Sattari & Osman Taylan & Ahmed A. Bakhsh & Ehsan Nazemi, 2021. "Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products," Mathematics, MDPI, vol. 9(24), pages 1-14, December.
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