Super-alarms with diagnosis proficiency used as an additional layer of protection applied to an oil transport system

• Autores: Vásquez J.W., Pérez-Zuñiga G., Sotomayor-Moriano J., Ospino A.
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/2398
• Enlace del recurso: https://hdl.handle.net/20.500.12390/2398; https://doi.org/10.3390/e23020139
• Nivel de acceso: acceso abierto
• Materia: Super-alarm; Alarm management; Diagnosis; Protection layers; Safe-process; http://purl.org/pe-repo/ocde/ford#2.07.03

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.