Oil Development Engineering Company, Tehran, Iran

Document Type : Research Paper

Authors

1 Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

2 Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran\Mechateronic & Artificial Intelligence Research Center, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

3 Engineering Devision, Reseach Institute of Petroleum Industry, Tehran, Iran

10.22078/jpst.2024.5526.1950

Abstract

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.

Keywords

References

  1. Schumacher, A., Nemeth, T., & Sihn, W. (2020). Roadmapping towards industrial digitalization based on Industry 4.0. Procedia CIRP, 93, 204-209. DOI: 10.1016/j.procir.2020.03.051.##
  2. Leitão, P., Colombo, A. W., & Karnouskos, S. (2020). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11-25. DOI: 10.1016/j.compind.2020.04.003. ##
  3. Fernandes, F. A., & Souza, F. R. (2022). Evaluating pipeline performance metrics using real-time monitoring. Journal of Pipeline Engineering, 38(2), 112-124. DOI: 10.1007/s11356-022-20540-7. ##
  4. Coito, T., Firme, B., Martins, M. S., Vieira, S. M., Figueiredo, J., & Sousa, J. M. (2021). Intelligent sensors for real-Time decision-making. Automation, 2(2), 62-82. doi: 10.3390/automation2020007. ##
  5. Melo, P.F., et al., Open source control device for industry 4.0 based on RAMI 4.0. Electronics, 2021. 10(7): p. 869. doi: 10.3390/electronics10070869. ##
  6. Lindner, M., Bank, L., Schilp, J., & Weigold, M. (2023). Digital twins in manufacturing: A RAMI 4.0 compliant concept. Sci, 5(4), 40. doi: 10.3390/sci5040040. ##
  7. Galvis, É.N.R., Analysis and Evaluation of 50 Key Performance Indicators from Colombian Transmission Pipelines. ##
  8. Mishra, N. & A. (2019). Saraf. leak detection system applicability for offshore and onshore oil and gas pipelines. in SPE Oil and Gas India Conference and Exhibition. Society of Petroleum Engineers. doi: 10.2118/194500-MS. ##
  9. Burr, A.S., C.M. Frazier, & S.D. (2021). Toth. API pipeline safety management system PSMS third-party assessment program: a valuable tool to help industry implement PSMS. in Abu Dhabi International Petroleum Exhibition and Conference. SPE. doi: 10.2118/207842-MS. ##
  10. Zhang, Y., & Zhou, W. (2021). Advanced pipeline strain analysis methods in Industry 4.0 environments. Journal of Structural Engineering, 147(4), 04521010. doi: 10.1061/(ASCE)ST.1943-541X.0003072. ##
  11. Dhirani, L.L., E. Armstrong, and T. Newe, Industrial IoT, cyber threats, and standards landscape: Evaluation and Roadmap. Sensors, 2021. 21(11): p. 3901. doi: 10.3390/s21113901. ##
  12. Schreieck, M., Wiesche, M., & Krcmar, H. (2020). Data privacy in continuous integration pipelines: Balancing compliance and innovation. Information Systems Journal, 30(3), 509-534. doi: 10.1111/isj.12324. ##
  13. Rauh, L., M. Reichardt, & H.D. Schotten. (2022). AI Asset Management: a Case Study with the Asset Administration Shell (AAS). in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE. doi: 10.1109/ETFA52439.2022.9921481. ##
  14. Beden, S., Q. Cao, & Beckmann A. (2021). Semantic asset administration shells in industry 4.0: A survey. in 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). . IEEE. doi: 10.1109/ICPS49255.2021.9468182. ##
  15. Marcon, P., Diedrich, C., Zezulka, F., Schröder, T., Belyaev, A., Arm, J., Benesl, T., Bradac, Z. & Vesely, I., (2018). December. The asset administration shell of operator in the platform of industry 4.0. In 2018 18th international conference on mechatronics-mechatronika (me) (pp. 1-5). IEEE. doi: 10.1109/MECHATRONIKA.2018.8644577. ##
  16. Inigo, M. A., Porto, A., Kremer, B., Perez, A., Larrinaga, F., & Cuenca, J. (2020, April). Towards an Asset Administration Shell scenario: a use case for interoperability and standardization in industry 4.0. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-6). IEEE. doi: 10.1109/NOMS47738.2020.9110441. ##
  17. Liang, J., Ma, L., Liang, S., Zhang, H., Zuo, Z., & Dai, J. (2023). Data-driven digital twin method for leak detection in natural gas pipelines. Computers and Electrical Engineering, 110, 108833. doi: 10.1016/j.compeleceng.2023.108833. ##
  18. Priyanka, E. B., & Thangavel, S. (2022). Multi-type feature extraction and classification of leakage in oil pipeline network using digital twin technology. Journal of Ambient Intelligence and Humanized Computing, 13(12), 5885-5901. doi: 10.1007/s12652-022-03810-7. ##
  19. Wang, D., Shi, S., Lu, J., Hu, Z., & Chen, J. (2023). Research on gas pipeline leakage model identification driven by digital twin. Systems Science & Control Engineering, 11(1), 2180687. doi: 10.1080/21642583.2023.2180687. ##
  20. Osaretin, C. A., Zamanlou, M., Iqbal, M. T., & Butt, S. (2020, November). Open source iot-based scada system for remote oil facilities using node-red and arduino microcontrollers. In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0571-0575). IEEE. doi: 10.1109/IEMCON51383.2020.9284856. ##
  21. Baiji, Y. & P. Sundaravadivel. (2019). ILoLeak-detect: An IoT-based LoRAWAN-enabled oil leak detection system for smart cities. in 2019 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS). 2019. IEEE. doi: 10.1109/iSES47678.2019.00031. ##
  22. Torres, L., Jiménez-Cabas, J., González, O., Molina, L., & López-Estrada, F. R. (2020). Kalman filters for leak diagnosis in pipelines: Brief history and future research. Journal of Marine Science and Engineering, 8(3), 173. doi: 10.3390/jmse8030173. ##
  23. Mohammadi, M., & Jafari, M. (2023). Integrating data sensing and real-time modeling for leak detection in industrial pipelines. Energy Technology, 11(3), 120-130. doi: 10.1002/ente.202200985. ##
  24. Tajalli, S. A. M., Moattari, M., Naghavi, S. V., & Salehizadeh, M. R. (2024). A novel hybrid internal pipeline leak detection and location system based on modified real-time transient modelling. Modelling, 5(3), 1135-1157. doi: 10.3390/modelling5030087. ##
  25. Yağlı, H., Koç, Y., Köse, Ö., Koç, A., & Yumrutaş, R. (2021). Optimisation of simple and regenerative organic Rankine cycles using jacket water of an internal combustion engine fuelled with biogas produced from agricultural waste. Process Safety and Environmental Protection, 155, 17-31. doi: 10.1016/j.psep.2021.08.035. ##
  26. Quy, T. B., & Kim, J. M. (2021). Real-Time Leak Detection for a Gas Pipeline Using Ak-NN Classifier and Hybrid AE Features. Sensors, 21(2), 367. doi: 10.3390/s21020367. ##
  27. Sadeghioon, A. M., Metje, N., Chapman, D. N., & Anthony, C. J. (2014). SmartPipes: Smart wireless sensor networks for leak detection in water pipelines. Journal of sensor and Actuator Networks, 3(1), 64-78. doi: 10.3390/jsan3010064. ##
  28. Smith, K., & Jones, R. (2021). A framework for corrosion evaluation in oil pipelines using KPIs. Journal of Materials Performance, 60(5), 256-269. doi: 10.1016/j.jmatprotec.2021.11.003. ##
  29. Andrade, A., & Lopes, R. (2022). KPI-driven integrity management for oil and gas pipelines. International Journal of Pressure Vessels and Piping, 195, 104597. DOI: 10.1016/j.ijpvp.2022.104597. ##
  30. Răileanu, S., Borangiu, T., Len-oiu, I., Anton, F., & Negoiţă, R. (2023). Data Acquisition System for Developing Digital Twin Solutions: A Practical Guide. In International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing (pp. 38-49). Cham: Springer Nature Switzerland. doi: 10.1007/978-3-030-27477-1_11. ##
  31. Lu, H., Guo, L., Azimi, M., & Huang, K. (2019). Oil and Gas 4.0 era: A systematic review and outlook. Computers in Industry, 111, 68-90. doi: 10.1016/j.compind.2019.06.007. ##
  32. Pelleg, J., & Pelleg, J. (2022). Cyclic deformation in nanostructures. cyclic deformation in oxides, carbides and nitrides: alumina, Magnesia, Yttria, SiC, B4C and Si3N4, 479-494. doi: 10.1007/978-3-030-86118-6_14. ##
  33. Lázaro, O., Alonso, J., Figueiras, P., Costa, R., Graça, D., Garcia, G., Canepa, A., Calefato, C., Vallini, M., Fournier, F. and Hazout, N., (2022). Big data-driven industry 4.0 service engineering large-scale trials: the boost 4.0 experience. In Technologies and Applications for Big Data Value (pp. 373-397). Cham: Springer International Publishing. doi: 10.1007/978-3-030-86118-6_17. ##
  34. Srikonda, R., Rastogi A., & Oestensen H. (2020). Increasing facility uptime using machine learning and physics-based hybrid analytics in a dynamic digital twin. in Offshore Technology Conference. OTC. doi: 10.4043/30417-MS. ##
  35. Johansen, S.T., Unal, P., Albayrak, Ö., Ikonen, E., Linnestad, K.J., Jawahery, S., Srivastava, A.K. and Løvfall, B.T., (2023). Hybrid and cognitive digital twins for the process industry. Open Engineering, 13(1), p.20220418. doi: 10.1515/eng-2022-0418. ##
  36. Meththananda, R. G. U. I., Ganegoda, N. C., Perera, S. S. N., Erandi, K. K. W. H., Jayathunga, Y., & Peiris, H. O. W. (2021). On timeline of enhancing testing-capacity of COVID-19: A case study via an optimal replacement model. Journal of Process Control, 105, 204-213. doi: 10.1016/j.jprocont.2021.08.002. ##
  37. Pivoto, D. G., De Almeida, L. F., da Rosa Righi, R., Rodrigues, J. J., Lugli, A. B., & Alberti, A. M. (2021). Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review. Journal of Manufacturing Systems, 58, 176-192. doi: 10.1016/j.jmsy.2020.11.011. ##
  38. Ye, X., Hong, S. H., Song, W. S., Kim, Y. C., & Zhang, X. (2021). An industry 4.0 asset administration shell-enabled digital solution for robot-based manufacturing systems. Ieee Access, 9, 154448-154459. doi: 10.1109/ACCESS.2021.3129433. ##
  39. Arm, J., Benesl, T., Marcon, P., Bradac, Z., Schröder, T., Belyaev, A., Werner, T., Braun, V., Kamensky, P., Zezulka, F. and Diedrich, C., (2021). Automated design and integration of Asset Administration Shells in components of Industry 4.0. Sensors, 21(6), p.2004. doi: 10.3390/s21062004. ##
  40. Ye, X., Song, W. S., Hong, S. H., Kim, Y. C., & Yoo, N. H. (2022). Toward data interoperability of enterprise and control applications via the industry 4.0 asset administration shell. IEEE Access, 10, 35795-35803. doi: 10.1109/ACCESS.2022.3163374. ##
  41. Jacoby, M., et al., An approach for realizing hybrid digital twins using asset administration shells and apache StreamPipes. Information, 2021. 12(6): p. 217. DOI: 10.3390/info12060217. doi: 10.1515/auto-2021-0125. ##
  42. J Jacoby, M., Jovicic, B., Stojanovic, L., & Stojanović, N. (2021). An approach for realizing hybrid digital twins using asset administration shells and apache StreamPipes. Information, 12(6), 217. doi: 10.3390/info12060217. ##
  43. Afonso, L., & Oliveira, P. (2021). Node-RED for industrial automation: Real-world applications and challenges. Automation in Industry, 19(3), 201-212. doi: 10.1016/j.auind.2021.05.010. ##
  44. Hansen, T., & Sorensen, M. (2022). Digital twin frameworks for hydraulic systems leak detection. Journal of Hydraulic Engineering, 148(5), 04122005. doi: 10.1061/(ASCE)HY.1943-7900.0002010. ##
  45. Abdelsattar, A., Park, E. J., & Marzouk, A. (2022). An OPC UA client/gateway-based digital twin architecture of a SCADA system with embedded system connections. In 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 798-803). IEEE. doi: 10.1109/AIM52237.2022.9863364. ##
  46. Santos-Ruiz, I., Bermúdez, J.R., López-Estrada, F.R., Puig, V., Torres, L. and Delgado-Aguiñaga, J.A., (2018). Online leak diagnosis in pipelines using an EKF-based and steady-state mixed approach. Control Engineering Practice, 81, pp.55-64. doi: 10.1016/j.conengprac.2018.09.006. ##
  47. Henao, F.A., (2021). Risk-based decisions: Implementing the asset integrity program. Process Safety Progress, 40: p. S24-S31. doi: 10.1002/prs.12243. ##
  48. Maciel Thom, F. C., Bastos Zoghbi, J. R., da Rocha Freitas, M. S., & Rossoni Sisquini, G. (2020). Dynamic risk calculation model applied to gas compressors. REM-International Engineering Journal, 73(1). doi: 10.1590/0370-44672019730096. ##
  49. Liu, Y. (2020). Safety barriers: Research advances and new thoughts on theory, engineering and management. Journal of Loss Prevention in the Process Industries, 67, 104260. doi: 10.1016/j.jlp.2020.104260. ##
  50. Priyanta, D., & Zaman, M. B. (2021). The development of a risk-based maintenance flowchart to select the correct methodology to develop maintenance strategies of oil and gas equipment. In IOP Conference Series: Materials Science and Engineering. 1052, (1): 012042. IOP Publishing. doi: 10.1088/1757-899X/1081/1/012057. ##
  51. Akrama, F., & Jahankhani, H. (2023). An Investigation into the State of Cybersecurity Preparedness with Respect to Operational Technology. In Wireless Networks: Cyber Security Threats and Countermeasures (pp. 301-348). Cham: Springer International Publishing. doi: 10.1007/978-3-031-11300-2_15. ##
  52. Campari, A., Darabi, M. A., Ustolin, F., Alvaro, A., & Paltrinieri, N. (2022). Applicability of risk-based inspection methodology to hydrogen technologies: A preliminary review of the existing standards. In Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022). doi: 10.3850/978-981-18-5183-4_R13-01-095-cd. ##
  53. Koziolek, H., Gruener, S., & Ashiwal, V. (2023, September). Chatgpt for plc/dcs control logic generation. In 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-8). IEEE. doi: 10.48550/arXiv.2305.15809. ##
  54. Cerrolaza, J.P., Obermaisser, R., Abella, J., Cazorla, F.J., Grüttner, K., Agirre, I., Ahmadian, H. and Allende, I., 2020. Multi-core devices for safety-critical systems: A survey. ACM Computing Surveys (CSUR), 53(4), pp.1-38. doi: 10.1145/3374606. ##
  55. Gomes, J.V., S. Shamshy, and R. Ostebo. DeepStar Projects Impact on Industry Standardization. in Offshore Technology Conference. (2023). OTC. DOI: 10.4043/32623-MS. ##
  56. Eun, J. C. T. (2020). Handbook of engineering practice of materials and corrosion. Springer Nature. doi: 10.1007/978-3-030-42602-5. ##
  57. Tanaka, T., & Sato, K. (2023). Integrated ISO management systems for improved industrial performance: Case studies from Asia. International Journal of Quality & Reliability Management, 40(4), 509-530. doi: 10.1108/IJQRM-11-2022-0391. ##
  58. Davis, R., & King, T. (2021). Enforcement and deterrence in environmental compliance: A study under the Clean Water Act. Environmental Law Review, 45(3), 325-340. doi: 10.1016/j.envlawrev.2021.05.008. ##
  59. Taylor, L., & Roberts, M. (2023). Political dynamics in the enforcement of environmental regulations in the US. Policy Studies Journal, 51(2), 567-589. doi: 10.1016/j.psj.2023.04.004. ##

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