Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks
Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma so...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/14/14/2852 |
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| author | Abdulilah Mohammad Mayet Seyed Mehdi Alizadeh Zana Azeez Kakarash Ali Awadh Al-Qahtani Abdullah K. Alanazi John William Grimaldo Guerrero Hala H. Alhashimi Ehsan Eftekhari-Zadeh |
| author_facet | Abdulilah Mohammad Mayet Seyed Mehdi Alizadeh Zana Azeez Kakarash Ali Awadh Al-Qahtani Abdullah K. Alanazi John William Grimaldo Guerrero Hala H. Alhashimi Ehsan Eftekhari-Zadeh |
| author_sort | Abdulilah Mohammad Mayet |
| collection | DOAJ |
| description | Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids. |
| first_indexed | 2024-03-09T10:12:04Z |
| format | Article |
| id | doaj.art-015e4d2a99d44953aba70b271deee7f8 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-09T10:12:04Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-015e4d2a99d44953aba70b271deee7f82023-12-01T22:36:38ZengMDPI AGPolymers2073-43602022-07-011414285210.3390/polym14142852Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural NetworksAbdulilah Mohammad Mayet0Seyed Mehdi Alizadeh1Zana Azeez Kakarash2Ali Awadh Al-Qahtani3Abdullah K. Alanazi4John William Grimaldo Guerrero5Hala H. Alhashimi6Ehsan Eftekhari-Zadeh7Electrical Engineering Department, King Khalid University, P.O. Box 394, Abha 61411, Saudi ArabiaPetroleum Engineering Department, Australian College of Kuwait, West Mishref 13015, KuwaitDepartment of Computer Science, Kurdistan Technical Institute, Sulaymaniyah 46001, IraqElectrical Engineering Department, King Khalid University, P.O. Box 394, Abha 61411, Saudi ArabiaDepartment of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Energy, Universidad de la Costa, Barranquilla 080001, ColombiaDepartment of Physics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaInstitute of Optics and Quantum Electronics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743 Jena, GermanyInstantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.https://www.mdpi.com/2073-4360/14/14/2852detection systemfeature extractionRBF neural networkoil and polymeric fluidsdual-energy gamma source |
| spellingShingle | Abdulilah Mohammad Mayet Seyed Mehdi Alizadeh Zana Azeez Kakarash Ali Awadh Al-Qahtani Abdullah K. Alanazi John William Grimaldo Guerrero Hala H. Alhashimi Ehsan Eftekhari-Zadeh Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks Polymers detection system feature extraction RBF neural network oil and polymeric fluids dual-energy gamma source |
| title | Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks |
| title_full | Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks |
| title_fullStr | Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks |
| title_full_unstemmed | Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks |
| title_short | Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks |
| title_sort | increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma ray attenuation time domain feature extraction and artificial neural networks |
| topic | detection system feature extraction RBF neural network oil and polymeric fluids dual-energy gamma source |
| url | https://www.mdpi.com/2073-4360/14/14/2852 |
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