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Item type:Publication, Super-alarms with diagnosis proficiency used as an additional layer of protection applied to an oil transport system(MDPI AG, 2021-02-01)In automated plants, particularly in the petrochemical, energy, and chemical industries, the combined management of all of the incidents that can produce a catastrophic accident is required. In order to do this, an alarm management methodology can be formulated as a discrete event sequence recognition problem, in which time patterns are used to identify the safe condition of the process, especially in the start-up and shutdown stages. In this paper, a new layer of protection (a Super-Alarm), based on the diagnostic stage to industrial processes is presented. The alarms and actions of the standard operating procedures are considered to be discrete events involved in sequences; the diagnostic stage corresponds to the recognition of the situation when these sequences occur. This provides operators with pertinent information about the normal or abnormal situations induced by the flow of the alarms. Chronicles Based Alarm Management (CBAM) is the methodology used in this document to build the chronicles that will permit us to generate the Super-Alarms; in addition, a case study of the petrochemical sector using CBAM is presented in order to build one chronicle that represents the scenario of an abnormal start-up of an oil transport system. Finally, the scenario’s validation for this case is performed, showing the way in which, a Super-Alarm is generated. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing alarm management in green hydrogen plants: A comprehensive analysis of the V-NETS-based methodology(Ecopetrol S.A., 2024-12-01)This paper presents a novel V-nets-Based Alarm Management (VBAM) methodology designed to enhance supervision and safety in Green Hydrogen Plants (GHPs). The proposed approach integrates visual modeling and temporal pattern analysis to accurately detect and manage alarms, seeking to reduce false positives and optimize response times. The methodology starts with a Preliminary Hazard and Operability (HAZOP) analysis to identify potential hazards and critical operational conditions, which are the foundation for constructing V-nets that map the temporal relationships between discrete events. By systematically capturing event sequences and their interdependencies, the VBAM approach allows for early fault detection and a proactive alarm management system fit for varying operational scenarios. A case study of the EL30N Green Hydrogen Plant proves the efficacy of the VBAM methodology in reducing downtime, improving system safety, and enhancing overall operational efficiency. This work provides a comprehensive framework for addressing discrete event challenges in alarm management, paving the way for safer and more resilient practices in green hydrogen production. Future directions will include expanding the application of VBAM to other operational phases and incorporating real-time analytics for further performance optimization. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Alarm management approach for supervision of green hydrogen plants(Elsevier Ltd, 2024-10-20)Amid the increasing significance of renewable energy sources, Green Hydrogen Plants (GHPs) have emerged as pivotal contributors to sustainable energy solutions. However, efficient and reliable alarm management in such complex systems remains a significant challenge. This paper presents a novel methodology called V-nets-based alarm management (VBAM), designed to improve supervision by addressing the intricacies of discrete event management in industrial applications such as GHPs. VBAM offers a powerful formalism that integrates visual modeling and temporal patterns, enabling the precise detection and handling of alarms. The proposed methodology is applied to a case study in a GHP, where critical operational parameters are continuously monitored. By analyzing discrete events and their temporal patterns, V-nets facilitate early fault detection, minimize false positives, and optimize alarm response. The theoretical application of VBAM demonstrates its efficacy in improving system safety, reducing downtime, and enhancing overall operational efficiency within GHPs. The contributions of this work to the state of the art include the development of a comprehensive VBAM methodology tailored to GHPs, as well as the theoretical and practical demonstration of its potential impact on alarm management within GHP operations. The outcomes from the experiments showcase the adaptability and effectiveness of VBAM in addressing the complexities of alarm management in GHPs, thereby contributing to enhanced safety, efficiency, and resilience in the operation of GHPs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing Safety in Lithium Mines: Super Alarm Generation Using V-Nets for Autonomous Vehicles(Elsevier BV, 2025-10-01)The deployment of autonomous vehicles in lithium mining operations faces significant safety challenges due to the complexity and unpredictability of mining environments. Reliable hazard detection and response are critical to preventing accidents and operational disruptions. Super alarms, an advanced alarm management strategy, have shown promise in identifying critical event patterns. However, conventional methods are limited by high false alarm rates and poor adaptability in dynamic, real-time systems. This study proposes using V-nets, a formalism for managing discrete event sequences, as a novel approach to generating and simulating super alarms for autonomous vehicles in lithium mines. V-nets structure event sequences to enhance the accuracy and adaptability of super alarm systems, addressing the limitations of traditional rule-based or statistical models. The proposed approach enables a context-aware and predictive alarm mechanism, reducing false alarms while improving the detection of hazardous situations. A simulation framework models autonomous vehicle trajectories in a mining environment, integrating V-net-based super alarm generation. Performance is evaluated using key metrics such as detection accuracy, response time, and false alarm rates. Results demonstrate that V-nets significantly improve alarm precision, reducing unnecessary alerts by 77% while achieving 96.3% accuracy in detecting critical safety events. These findings highlight the potential of V-nets in advancing alarm management for industrial automation, offering a scalable and intelligent solution for safety-critical mining applications. This research provides a foundation for future implementations of V-nets in real-world autonomous mining operations, contributing to improved risk management, operational efficiency, and safety in the rapidly evolving mining industry.2