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

first_pagesettingsOrder Article Reprints Open AccessArticle 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 by Abdulilah Mohammad Mayet 1ORCID,Tzu-Chia Chen 2,3,*ORCID,Ijaz Ahmad 4,*,Elsayed Tag Eldin 5ORCID,Ali Awadh Al-Qahtani 1,Igor M. Narozhnyy 6,John William Grimaldo Guerrero 7ORCID andHala H. Alhashim 8 1 Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia 2 College of Management and Design, Ming Chi University of Technology, New Taipei City 243303, Taiwan 3 International College, Krirk University, Bangkok, 3 Ram Inthra Rd, Khwaeng Anusawari, Khet Bang Khen, Krung Thep Maha Nakhon 10220, Thailand 4 Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences (UCAS), Shenzhen 518055, China 5 Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt 6 Department of Commercialization of Intellectual Activity Resultse Center for Technology Transfer of RUDN University, Mining Oil and Gas Department, RUDN University, 117198 Moscow, Russia 7 Department of Energy, Universidad de la Costa, Barranquilla 080001, Colombia 8 Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia * Authors to whom correspondence should be addressed. Mathematics 2022, 10(19), 3544; https://doi.org/10.3390/math10193544 Received: 3 August 2022 / Revised: 15 September 2022 / Accepted: 17 September 2022 / Published: 28 September 2022 (This article belongs to the Special Issue Application of Artificial Neural Network as Mathematical Tool in Engineering and Management Problems) Download Browse Figures Versions Notes Abstract Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source (241Am and 133Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241Am and 133Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry.

• Autores: Mayet, Abdulilah; Tzu-Chia, Chen; Ahmad, Ijaz; Tag elDin, Elsayed; Al-Qahtani, Ali Awadh; Igor, Narozhnyy; Grimaldo Guerrero, John William; alhashim, Hala
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2022
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/9761
• Acceso en línea: https://hdl.handle.net/11323/9761; https://repositorio.cuc.edu.co/
• Palabra clave: Three-phase flow; Scale layer thickness; Volume fraction independent; MLP neural network
• Rights: openAccess
• License: Atribución 4.0 Internacional (CC BY 4.0)