Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

ORCID
0000-0001-7739-0105
Affiliation
Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Mayet, Abdulilah Mohammad;
ORCID
0000-0002-2942-7839
Affiliation
College of Management and Design, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Chen, Tzu-Chia;
ORCID
0000-0003-0951-174X
Affiliation
Petroleum Engineering Department, Australian University, Kuwait City 13015, Kuwait
Alizadeh, Seyed Mehdi;
Affiliation
Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Al-Qahtani, Ali Awadh;
ORCID
0000-0003-0968-5483
Affiliation
Department Electrical and of Electronic Engineering, College of Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia
Qaisi, Ramy Mohammed Aiesh;
Affiliation
Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
Alhashim, Hala H.;
GND
1231322179
ORCID
0000-0003-1480-1450
Affiliation
Institute of Optics and Quantum Electronics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, Germany
Eftekhari-Zadeh, Ehsan

Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241 Am and 133 Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations.

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