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

Abdulilah Mohammad Mayet, Karina Shamilyevna Nurgalieva, Ali Awadh Al-Qahtani, Igor M. Narozhnyy, Hala H. Alhashim, Ehsan Nazemi () and Ilya M. Indrupskiy
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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

Mathematics, 2022, vol. 10, issue 16, 1-20

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.

Keywords: gamma-ray attenuation technique; Multilayer Perceptron (MLP) neural network; feature extraction; frequency domain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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