Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Zana Azeez Kakarash, Ali Awadh Al-Qahtani, Abdullah K. Alanazi, Hala H. Alhashimi, Ehsan Eftekhari-Zadeh and Ehsan Nazemi
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Abdulilah Mohammad Mayet: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Seyed Mehdi Alizadeh: Petroleum Engineering Department, Australian College of Kuwait, West Mishref 13015, Kuwait
Zana Azeez Kakarash: Department of Information Technology, University of Human Development, Sulaymaniyah 07786, Iraq
Ali Awadh Al-Qahtani: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Abdullah K. Alanazi: Department of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Hala H. Alhashimi: Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Ehsan Eftekhari-Zadeh: Institute of Optics and Quantum Electronics, Friedrich-Schiller-University Jena, Max-Wien-Platz 1, 07743 Jena, Germany
Ehsan Nazemi: Imec-Vision Laboratory, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium

Mathematics, 2022, vol. 10, issue 10, 1-13

Abstract: When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent.

Keywords: pipeline’s scale; feature extraction; GMDH neural network; two-phase flow (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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