Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines
Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise and dependable control system to identify the kind and quantity of oil products being transported. T...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823009286 |
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| author | Tzu-Chia Chen Hani Almimi Mohammad Sh. Daoud John William Grimaldo Guerrero Rafał Chorzępa |
| author_facet | Tzu-Chia Chen Hani Almimi Mohammad Sh. Daoud John William Grimaldo Guerrero Rafał Chorzępa |
| author_sort | Tzu-Chia Chen |
| collection | DOAJ |
| description | Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise and dependable control system to identify the kind and quantity of oil products being transported. This study attempts to identify four petroleum products by using an X-ray tube-based system, feature extraction in the frequency and temporal domains, and feature selection using Particle Swarm Optimization (PSO) in conjunction with a Group Method of Data Handling (GMDH) neural network. A sodium iodide detector, a test pipe that simulates petroleum compounds, and an X-ray source make up the implemented system. The detector's output signals were transmitted to the frequency domain, where the amplitudes of the top five dominant frequencies could be determined. Furthermore, the received signals were analyzed to extract five temporal characteristics-MSR, 4th order moment, skewness, WL, and kurtosis. The PSO system takes into account the extracted time and frequency features as input in order to introduce the optimal combination. Four different GMDH neural networks were constructed, and the chosen characteristics were used as inputs for those networks. Finding the volume ratio of each product was the responsibility of each neural network. The four designed neural networks were able to predict the amount of ethylene glycol, crude oil, gasoil, and gasoline with RMSE of 0.26, 0.17, 0.19, and 0.23, respectively. One compelling argument for using the proposed approach in the oil industry is that it can calculate the volume ratio of products with a root mean square error of no more than 0.26. The adoption of a feature selection method to choose the best ones is credited with this remarkable degree of precision. By providing appropriate inputs to neural networks, the control system has significantly outperformed its predecessors in terms of precision and efficiency. |
| first_indexed | 2024-03-11T13:29:06Z |
| format | Article |
| id | doaj.art-06e2d62b10e0456d958c27b78ff7cef2 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-11T13:29:06Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-06e2d62b10e0456d958c27b78ff7cef22023-11-03T04:15:04ZengElsevierAlexandria Engineering Journal1110-01682023-11-0182518530Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelinesTzu-Chia Chen0Hani Almimi1Mohammad Sh. Daoud2John William Grimaldo Guerrero3Rafał Chorzępa4Department of Artificial Intelligence, Tamkang University, New Taipei City, 251301, Taiwan; Corresponding authors.Cybersecurity Department, Al-Zaytoonah University of Jordan, Faculty of Science and Information Technology, JordanCollege of Engineering, Al Ain University, Abu Dhabi, United Arab EmiratesDepartment of Energy, Universidad de la Costa, Barranquilla 080001, ColombiaFaculty of Electrical and Computer Engineering, Rzeszow University of Technology, Powstancow Warszawy 12, 35-959 Rzeszow, Poland; Corresponding authors.Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise and dependable control system to identify the kind and quantity of oil products being transported. This study attempts to identify four petroleum products by using an X-ray tube-based system, feature extraction in the frequency and temporal domains, and feature selection using Particle Swarm Optimization (PSO) in conjunction with a Group Method of Data Handling (GMDH) neural network. A sodium iodide detector, a test pipe that simulates petroleum compounds, and an X-ray source make up the implemented system. The detector's output signals were transmitted to the frequency domain, where the amplitudes of the top five dominant frequencies could be determined. Furthermore, the received signals were analyzed to extract five temporal characteristics-MSR, 4th order moment, skewness, WL, and kurtosis. The PSO system takes into account the extracted time and frequency features as input in order to introduce the optimal combination. Four different GMDH neural networks were constructed, and the chosen characteristics were used as inputs for those networks. Finding the volume ratio of each product was the responsibility of each neural network. The four designed neural networks were able to predict the amount of ethylene glycol, crude oil, gasoil, and gasoline with RMSE of 0.26, 0.17, 0.19, and 0.23, respectively. One compelling argument for using the proposed approach in the oil industry is that it can calculate the volume ratio of products with a root mean square error of no more than 0.26. The adoption of a feature selection method to choose the best ones is credited with this remarkable degree of precision. By providing appropriate inputs to neural networks, the control system has significantly outperformed its predecessors in terms of precision and efficiency.http://www.sciencedirect.com/science/article/pii/S1110016823009286Particle Swarm OptimizationX-ray tube-based systemGroup method of Data Handling (GMDH) neural networkFeature extractionFeature selection technique |
| spellingShingle | Tzu-Chia Chen Hani Almimi Mohammad Sh. Daoud John William Grimaldo Guerrero Rafał Chorzępa Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines Alexandria Engineering Journal Particle Swarm Optimization X-ray tube-based system Group method of Data Handling (GMDH) neural network Feature extraction Feature selection technique |
| title | Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines |
| title_full | Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines |
| title_fullStr | Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines |
| title_full_unstemmed | Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines |
| title_short | Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines |
| title_sort | selection of effective combination of time and frequency features using pso based technique for monitoring oil pipelines |
| topic | Particle Swarm Optimization X-ray tube-based system Group method of Data Handling (GMDH) neural network Feature extraction Feature selection technique |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823009286 |
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