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Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

Author

Listed:
  • Mohammed Balubaid

    (Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia)

  • Mohammad Amir Sattari

    (Friedrich Schiller University Jena, Fürstengraben 1, 07743 Jena, Germany)

  • Osman Taylan

    (Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia)

  • Ahmed A. Bakhsh

    (Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia)

  • Ehsan Nazemi

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

Abstract

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3215-:d:701241
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    Cited by:

    1. 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.
    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 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.
    4. 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.
    5. 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.

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