Intelligent monitoring using hazard identification technique and multi-sensor data fusion for crude distillation column

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Abstract

Hazard assessment techniques and multi-sensor fusion are used for intelligent systematic monitoring. Firstly, a hazard identification technique is considered using failure mode and effect analysis and advantages of using a combined hazard technique is discussed. Data sources are identified considering component failures and some sensors associated with potential failure. Possible consequences in a hazardous situation are identified using failure mode and effect analysis to choose suitable safety measures. Failure mode and effect analysis is systematically considers how sequences of events can lead to accidents by looking at components and faults recorded by sensors and anomalies. Data were presented based on their threat levels using a traffic light color code system. Refineries use sensors to observe the process of crude refining and the monitoring system uses real-time data to access information provided by sensors. Understanding hazard assessments, sensor multi-fusion and sensor pattern recognition in a distillation column could help to identify trends, flag major regions of growing malfunction, model risk threat of a crude distillation column and help to systematically make decisions. The decisions could improve design regulations, eliminate anomalies, improve monitoring and reduce threat levels.
Original languageEnglish
Title of host publicationIntelligent Systems and Applications
Subtitle of host publicationProceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
PublisherSpringer
Pages730-741
ISBN (Electronic)978-3-030-55190-2
ISBN (Print)978-3-030-55189-6
DOIs
Publication statusPublished - 25 Aug 2020
EventIntelligent Systems Conference - London, United Kingdom
Duration: 3 Sep 20204 Sep 2020

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume1252
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceIntelligent Systems Conference
Abbreviated titleIntelliSys 2020
Country/TerritoryUnited Kingdom
CityLondon
Period3/09/204/09/20

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