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Item type:Publication, Simultaneous occurrences and false-positives analysis in discrete event dynamic systems(Elsevier, 2020-07-01)Simultaneous occurrences of events have been a crucial and hard problem since the beginning of the research about automaton and simulation theories of discrete event systems, for more than 50 years. This article addresses some diagnosis problems in industrial processes, situations such as simultaneity of events, false positives, and partial recognition of event sequences. V-nets are presented as a means to model dynamic processes without the state machine concept and, the robustness and capability to identify different sequences of discrete events. With the V-nets formalism, it is possible to identify the evolution of the discrete events, simultaneous occurrences of events, partial recognition, counting the number of times that each discrete event occurred in a temporal sequence and this formalism also has the capability to model sequences of sequences. An example of one industrial application is presented and a comparative analysis of the Time Petri Nets, Timed Automata, and Chronicles with the V-nets is exposed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An additional layer of protection through superalarms with diagnosis capability(Ecopetrol S.A.ctyf@ecopetrol.com.co, 2020-06-01)An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms furthermore, a case study of the petrochemical sector using CBAM is presented to build the chronicles of the normal startup, abnormal start-up, and normal shutdown scenarios. Finally, the scenario validation is performed for an abnormal start-up, showing how a super-alarm is generated. - Some of the metrics are blocked by yourconsent settings
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, V-nets(Elsevier B.V., 2024-06-01)This article presents an actualization of the formalism V-nets which is a tool of supervision that deals with diagnosis problems of industrial processes, situations such as simultaneity, and false positives in sequences of discrete events. In particular, in fault detection applications, industrial processes need the dependability of their control and supervisory systems. Since the start of the investigation into automata and simulation theories, almost 50 years ago, simultaneous occurrences have been a crucial and challenging subject. This situation is a false problem that comes from the notion of "state", that is to say, that model Petri Nets, DEVS, and State Charts, among others, do not come from the concrete process under consideration. Therefore, this new formalism is based on the model of Chronicles and improved with other tools and elements that permit an increase in its functionality. V-nets are provided as a way to represent dynamic processes without using the state machine paradigm, with robustness and the capacity to distinguish discrete event sequences. An analysis of alarm management in a Green Hydrogen Plant (GHP) is presented and concludes with a comparative analysis of Time Petri Nets (TPN), Timed Automata (TA), Chronicles, and V-nets. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Prediction of Prostate Cancer Biochemical Recurrence by Using Discretization Supports the Critical Contribution of the Extra-Cellular Matrix Genes(Nature Research, 2023-12-01)Due to its complexity, much effort has been devoted to the development of biomarkers for prostate cancer that have acquired the utmost clinical relevance for diagnosis and grading. However, all of these advances are limited due to the relatively large percentage of biochemical recurrence (BCR) and the limited strategies for follow up. This work proposes a methodology that uses discretization to predict prostate cancer BCR while optimizing the necessary variables. We used discretization of RNA-seq data to increase the prediction of biochemical recurrence and retrieve a subset of ten genes functionally known to be related to the tissue structure. Equal width and equal frequency data discretization methods were compared to isolate the contribution of the genes and their interval of action, simultaneously. Adding a robust clinical biomarker such as prostate specific antigen (PSA) improved the prediction of BCR. Discretization allowed classifying the cancer patients with an accuracy of 82% on testing datasets, and 75% on a validation dataset when a five-bin discretization by equal width was used. After data pre-processing, feature selection and classification, our predictions had a precision of 71% (testing dataset: MSKCC and GSE54460) and 69% (Validation dataset: GSE70769) should the patients present BCR up to 24 months after their final treatment. These results emphasize the use of equal width discretization as a pre-processing step to improve classification for a limited number of genes in the signature. Functionally, many of these genes have a direct or expected role in tissue structure and extracellular matrix organization. The processing steps presented in this study are also applicable to other cancer types to increase the speed and accuracy of the models in diverse datasets. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling energy efficiency in industrial plants: A novel diagnostic approach(Elsevier Ltd, 2025-02-15)In industrial plants, diagnosing energy efficiency issues is essential to achieve sustainable operations and reduce costs. This paper introduces a novel diagnostic approach using advanced modeling techniques to identify inefficiencies in energy consumption within industrial environments. The proposed method uses discrete event analysis to detect and characterize abnormal energy usage patterns, providing a systematic framework for diagnosing performance issues in complex systems. Two case studies involving high-performance computing (HPC) systems illustrate the practical application of the approach, showcasing its ability to uncover critical inefficiencies and inform energy management strategies. The research addresses a significant gap in current methodologies by providing a detailed diagnostic tool customized to the unique challenges of industrial energy management. This study paves the way for future research into advanced diagnostic techniques, strengthening the importance of precise and actionable information on energy use for industrial stakeholders.1