Oil Development Engineering Company, Tehran, Iran
This paper delves into the transformative implications of Digital Twin (DT) technology on pipeline management within Industry 4.0, emphasizing its pivotal role in ensuring integrity, efficiency, and leak detection for oil, gas, and water transportation. The proposed pipeline management platform adop...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Reaserch Institute of Petroleum Industry
2024-05-01
|
| Series: | Journal of Petroleum Science and Technology |
| Subjects: | |
| Online Access: | https://jpst.ripi.ir/article_1449_ec4246f6117f4cd33a25415a6d346c51.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850148413789700096 |
|---|---|
| author | Seyed Ali Mohammad Tajalli Mazda Moattari Vahid Naghavi Mohammad Reza Salehizadeh |
| author_facet | Seyed Ali Mohammad Tajalli Mazda Moattari Vahid Naghavi Mohammad Reza Salehizadeh |
| author_sort | Seyed Ali Mohammad Tajalli |
| collection | DOAJ |
| description | This paper delves into the transformative implications of Digital Twin (DT) technology on pipeline management within Industry 4.0, emphasizing its pivotal role in ensuring integrity, efficiency, and leak detection for oil, gas, and water transportation. The proposed pipeline management platform adopts a conceptual DT architecture, integrating key components such as the Asset Administration Shell (AAS), Admin-Shell-IO, Node-RED, Apache StreamPipes, SimCenter, MATLAB, and Ignition software.The platform focuses on automation, operational optimization, safety, and regulatory compliance through this integration. To achieve these goals, the paper introduces the Modified Real-Time Transient Modeling (MRTTM) framework, which aims to swiftly and accurately detect and locate leaks. Furthermore, the operational procedure of this framework involves three key stages. In the “Data Collection” phase, sensor data are monitored by observing nodes. In the subsequent “Detection” stage, leaks are identified, and in the concluding “Decision-making” module, the exact magnitude and location of the leakage are determined using MRTTM. Leveraging a hybrid approach that combines the Extended Kalman Filter (EKF), Real-Time Transient Modeling (RTTM), and machine learning algorithms, the framework offers accurate insights into the pipeline’s operational status. Moreover, machine learning models, including K-nearest neighbors (KNN) and support vector machines (SVM), enhance anomaly detection precision, allowing for early identification and localization of potential leaks.Ultimately, the proposed framework brings several key benefits to pipeline management, including early anomaly detection, real-time data integration, predictive maintenance, and regulatory compliance. By identifying potential leaks and anomalies early on, operators can take measures to prevent failures, respond quickly to disruptions, and comply with environmental and safety regulations. |
| format | Article |
| id | doaj-art-7cc08fb6f275443aadb12aa18bb56b3c |
| institution | OA Journals |
| issn | 2251-659X 2645-3312 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Reaserch Institute of Petroleum Industry |
| record_format | Article |
| series | Journal of Petroleum Science and Technology |
| spelling | doaj-art-7cc08fb6f275443aadb12aa18bb56b3c2025-08-20T02:27:15ZengReaserch Institute of Petroleum IndustryJournal of Petroleum Science and Technology2251-659X2645-33122024-05-01142395010.22078/jpst.2024.5526.19501449Oil Development Engineering Company, Tehran, IranSeyed Ali Mohammad Tajalli0Mazda Moattari1Vahid Naghavi2Mohammad Reza Salehizadeh3Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, IranDepartment of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran\Mechateronic & Artificial Intelligence Research Center, Marvdasht Branch, Islamic Azad University, Marvdasht, IranEngineering Devision, Reseach Institute of Petroleum Industry, Tehran, IranDepartment of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, IranThis paper delves into the transformative implications of Digital Twin (DT) technology on pipeline management within Industry 4.0, emphasizing its pivotal role in ensuring integrity, efficiency, and leak detection for oil, gas, and water transportation. The proposed pipeline management platform adopts a conceptual DT architecture, integrating key components such as the Asset Administration Shell (AAS), Admin-Shell-IO, Node-RED, Apache StreamPipes, SimCenter, MATLAB, and Ignition software.The platform focuses on automation, operational optimization, safety, and regulatory compliance through this integration. To achieve these goals, the paper introduces the Modified Real-Time Transient Modeling (MRTTM) framework, which aims to swiftly and accurately detect and locate leaks. Furthermore, the operational procedure of this framework involves three key stages. In the “Data Collection” phase, sensor data are monitored by observing nodes. In the subsequent “Detection” stage, leaks are identified, and in the concluding “Decision-making” module, the exact magnitude and location of the leakage are determined using MRTTM. Leveraging a hybrid approach that combines the Extended Kalman Filter (EKF), Real-Time Transient Modeling (RTTM), and machine learning algorithms, the framework offers accurate insights into the pipeline’s operational status. Moreover, machine learning models, including K-nearest neighbors (KNN) and support vector machines (SVM), enhance anomaly detection precision, allowing for early identification and localization of potential leaks.Ultimately, the proposed framework brings several key benefits to pipeline management, including early anomaly detection, real-time data integration, predictive maintenance, and regulatory compliance. By identifying potential leaks and anomalies early on, operators can take measures to prevent failures, respond quickly to disruptions, and comply with environmental and safety regulations.https://jpst.ripi.ir/article_1449_ec4246f6117f4cd33a25415a6d346c51.pdfpipeline managementtheft/leak detectionindustry 4.0digital twinasset administration shellmodified real-time transient modelin |
| spellingShingle | Seyed Ali Mohammad Tajalli Mazda Moattari Vahid Naghavi Mohammad Reza Salehizadeh Oil Development Engineering Company, Tehran, Iran Journal of Petroleum Science and Technology pipeline management theft/leak detection industry 4.0 digital twin asset administration shell modified real-time transient modelin |
| title | Oil Development Engineering Company, Tehran, Iran |
| title_full | Oil Development Engineering Company, Tehran, Iran |
| title_fullStr | Oil Development Engineering Company, Tehran, Iran |
| title_full_unstemmed | Oil Development Engineering Company, Tehran, Iran |
| title_short | Oil Development Engineering Company, Tehran, Iran |
| title_sort | oil development engineering company tehran iran |
| topic | pipeline management theft/leak detection industry 4.0 digital twin asset administration shell modified real-time transient modelin |
| url | https://jpst.ripi.ir/article_1449_ec4246f6117f4cd33a25415a6d346c51.pdf |
| work_keys_str_mv | AT seyedalimohammadtajalli oildevelopmentengineeringcompanytehraniran AT mazdamoattari oildevelopmentengineeringcompanytehraniran AT vahidnaghavi oildevelopmentengineeringcompanytehraniran AT mohammadrezasalehizadeh oildevelopmentengineeringcompanytehraniran |