Mejora de la gestión de alarmas en plantas de hidrógeno verde: un análisis completo de la metodología basada en V-NETS
Resumen
Este artículo presenta una nueva metodología de gestión de alarmas basada en redes V (VBAM) diseñada para mejorar la supervisión y la seguridad en plantas de hidrógeno verde (GHP). El enfoque propuesto integra el modelado visual y el análisis de patrones temporales para detectar y gestionar alarmas con precisión, con el objetivo de reducir los falsos positivos y optimizar los tiempos de respuesta. La metodología comienza con un análisis preliminar de peligros y operabilidad (HAZOP) para identificar peligros potenciales y condiciones operativas críticas, formando la base para construir redes virtuales que mapeen las relaciones temporales entre eventos discretos. Al capturar sistemáticamente secuencias de eventos y sus interdependencias, el enfoque VBAM permite la detección temprana de fallas y un sistema de gestión de alarmas proactivo que se adapta a diferentes escenarios operativos. Un estudio de caso de la planta de hidrógeno verde EL30N demuestra la eficacia de la metodología VBAM para reducir el tiempo de inactividad, mejorar la seguridad del sistema y mejorar la eficiencia operativa general. Este trabajo proporciona un marco integral para abordar los desafíos de eventos discretos en la gestión de alarmas, allanando el camino para prácticas más seguras y resilientes en la producción de hidrógeno verde. Las direcciones futuras incluyen ampliar la aplicación de VBAM a otras fases operativas e incorporar análisis en tiempo real para una mayor optimización del rendimiento.
Referencias bibliográficas
Ajanovic, A., Sayer, M., & Haas, R. (2024). On the future relevance of green hydrogen in Europe. Applied Energy, 358, 122586. https://doi.org/10.1016/j.apenergy.2023.122586
Capacho, J. V., Subias, A., Trave-Massuyes, L., & Jimenez, F. (2017). Alarm management via temporal pattern learning. Engineering Applications of Artificial Intelligence, 65, 506–516. https://doi.org/10.1016/j.engappai.2017.07.008
Capacho, J. W. V., Zuñiga, C. G. P., Maldonado, Y. A. M., & Castro, A. O. (2020). Simultaneous occurrences and false-positives analysis in discrete event dynamic systems. Journal of Computational Science, 44, 101162. https://doi.org/10.1016/j.jocs.2020.101162
Cong, L., Yang, J., Shi, L., Zhou, X., Liu, L., Jiang, F., & Zhang, A. (2023). Intelligent operation and maintenance business process optimization design of integrated energy system. In 2023 5th Asia Energy and Electrical Engineering Symposium (AEEES), IEEE, 1741–1746. https://doi.org/10.1109/AEEES56888.2023.10114192
Fanti, M. P., Mininel, S., Ukovich, W., & Vatta, F. (2012). Modelling alarm management workflow in healthcare according to IHE framework by coloured Petri nets. Engineering Applications of Artificial Intelligence, 25(4), 728–733. https://doi.org/10.1016/j.engappai.2010.11.003
Feng, L., Gu, Y., Pang, J., Jiang, L., Liu, J., Zhou, H., & Babaee, S. (2024). Risk identification and safety technology for hydrogen production from natural gas reforming. ChemBioEng Reviews, 11(2), 386-405. https://doi.org/10.1002/cben.202300049
Görgülü, H., & Özkasap, Ö. (2023, July). Machine Learning Methods for Alarm Prediction in Industrial Informatics: Review and Benchmark. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 21-30). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-38616-9_3
International Energy Agency (IEA 50). (June 2019). The future of hydrogen. Tech. rep. Paris. https://www.iea.org/reports/the-future-of-hydrogen
Jansons, L., Zemite, L., Zeltins, N., Bode, I., Geipele, I., & Kiesners, K. (2022). The green hydrogen and the EU gaseous fuel diversification risks. Latvian Journal of Physics and Technical Sciences, 59(4), 53-70. https://doi.org/10.2478/lpts-2022-0033
Jin, H., Gao, Z., Zuo, Z., Zhang, Z., Wang, Y., & Zhang, A. (2023). A combined model-based and data-driven fault diagnosis scheme for lithium-ion batteries. IEEE Transactions on Industrial Electronics. https://ieeexplore.ieee.org/document/10203013
Kovač, A., Paranos, M., & Marciuš, D. (2021). Hydrogen in energy transition: A review. International Journal of Hydrogen Energy, 46(16), 10016-10035. https://doi.org/10.1016/j.ijhydene.2020.11.256
Kumar, S., Arzaghi, E., Baalisampang, T., Garaniya, V., & Abbassi, R. (2023). Insights into decision-making for offshore green hydrogen infrastructure developments. Process Safety and Environmental Protection, 174, 805-817. https://doi.org/10.1016/j.psep.2023.04.042
Kumar, S., Baalisampang, T., Arzaghi, E., Garaniya, V., Abbassi, R., & Salehi, F. (2023). Synergy of green hydrogen sector with offshore industries: Opportunities and challenges for a safe and sustainable hydrogen economy. Journal of Cleaner Production, 384, 135545. https://doi.org/10.1016/j.jclepro.2022.135545
Ni, C., & Li, S. C. (2024). Machine learning enabled industrial IoT security: Challenges, trends, and solutions. Journal of Industrial Information Integration, 100549. https://doi.org/10.1016/j.jii.2023.100549
Noussan, M., Raimondi, P. P., Scita, R., & Hafner, M. (2020). The role of green and blue hydrogen in the energy transition—A technological and geopolitical perspective. Sustainability, 13(1), 298. https://doi.org/10.3390/su13010298
NTRS-NASA. Technical Reports Server (January 1997). Safety standard for hydrogen and hydrogen systems: Guidelines for hydrogen system design, materials selection, operations, storage and transportation. (TM) NASA-TM-112540. https://ntrs.nasa.gov/citations/19970033338
Rajapriya, R., & Dangate, M. S. (2023). Hydrogen as a fuel cell. Integrated Green Energy Solutions, 1, 45–59. https://doi.org/10.1002/9781119847564.ch4
Scheller, F., Wald, S., Kondziella, H., Gunkel, P. A., & Bruckner, T. (2023). Future role and economic benefits of hydrogen and synthetic energy carriers in Germany: A review of long-term energy scenarios. Sustainable Energy Technologies and Assessments, 56, 103037. https://doi.org/10.1016/j.seta.2023.103037
Shein, G. S., Brodie, R., & Mintz, Y. (2023). Human-Machine Collaboration in AI-Assisted Surgery: Balancing Autonomy and Expertise. Artificial Intelligence in Medicine and Surgery-An Exploration of Current Trends, Potential Opportunities, and Evolving Threats-Volume 1. https://www.intechopen.com/chapters/87002
Superchi, F., Mati, A., Carcasci, C., & Bianchini, A. (2023). Techno-economic analysis of wind-powered green hydrogen production to facilitate the decarbonization of hard-to-abate sectors: A case study on steelmaking. Applied Energy, 342, 121198. https://doi.org/10.1016/j.apenergy.2023.121198
Vasquez Capacho, J. W., Perez Zuñiga, C. G., Muñoz Maldonado, Y. A., & Ospino Castro, A. J. (2020, June 30). An additional layer of protection through superalarms with diagnosis capability. CT&F - Ciencia, Tecnología y Futuro, 10(1), 45–66. https://doi.org/10.29047/01225383.168
Vasquez, J. W., Travé-Massuyès, L., Subias, A., Jiménez, F., & Agudelo, C. (2015, August). Chronicle based alarm management in startup and shutdown stages. In 26th International Workshop on Principles of Diagnosis (pp. 277-280). https://ceur-ws.org/Vol-1507/dx15paper31.pdf
Zheng, W., Zheng, X., & Zhu, X. (2024). Promoting integration of industry and vocational education: Exploring stakeholder intentions of hydrogen energy industry. International Journal of Hydrogen Energy, 52, 454-464. https://doi.org/10.1016/j.ijhydene.2023.06.072
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