IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p2916-d887410.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/2916/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/16/2916/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abdullah M. Iliyasu & Dakhkilgova Kamila Bagaudinovna & Ahmed S. Salama & Gholam Hossein Roshani & Kaoru Hirota, 2023. "A Methodology for Analysis and Prediction of Volume Fraction of Two-Phase Flow Using Particle Swarm Optimization and Group Method of Data Handling Neural Network," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    2. Abdulilah Mohammad Mayet & Tzu-Chia Chen & Ijaz Ahmad & Elsayed Tag Eldin & Ali Awadh Al-Qahtani & Igor M. Narozhnyy & John William Grimaldo Guerrero & Hala H. Alhashim, 2022. "Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    3. Mohammed Balubaid & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Eftekhari-Zadeh & Ehsan Nazemi & Mohammed Alamoudi, 2022. "Central Nervous System: Overall Considerations Based on Hardware Realization of Digital Spiking Silicon Neurons (DSSNs) and Synaptic Coupling," Mathematics, MDPI, vol. 10(6), pages 1-20, March.
    4. 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.
    5. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    6. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & Robert Hanus & Ehsan Nazemi & Igor M. Narozhnyy, 2022. "Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems," Energies, MDPI, vol. 15(6), pages 1-19, March.
    7. Gulnur Zakirova & Vladimir Pshenin & Radmir Tashbulatov & Lyubov Rozanova, 2022. "Modern Bitumen Oil Mixture Models in Ashalchinsky Field with Low-Viscosity Solvent at Various Temperatures and Solvent Concentrations," Energies, MDPI, vol. 16(1), pages 1-18, December.
    8. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Zana Azeez Kakarash & Ali Awadh Al-Qahtani & Abdullah K. Alanazi & Hala H. Alhashimi & Ehsan Eftekhari-Zadeh & Ehsan Nazemi, 2022. "Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime," Mathematics, MDPI, vol. 10(10), pages 1-13, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2916-:d:887410. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.