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

Intelligent monitoring using hazard identification technique and multi-sensor data fusion for crude distillation column
Intelligent monitoring using hazard identification technique and multi-sensor data fusion for crude distillation column
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.
Crude, Distillation, Hazard, Intelligent, Monitoring, Sensor, Sensors
2194-5357
730-741
Springer
Omoarebun, Peter
d2bac592-82c7-450c-830c-49a7334fda1e
Sanders, David
54f2f76a-f55a-42d7-ac56-2e9fd8d92de4
Ikwan, Favour
504e862a-ecff-41ce-85ba-38d95e34aad1
Hassan, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Thabet, Mohamad
d6026667-fef0-44a5-99d1-1520a35dfbd3
Piner, Jake
039a370a-055e-41e8-918c-c463ac4bec54
Shah, Amjad
acbfbb5c-fe5d-4703-8f1b-2397f18e80d8
Arai, Kohei
Kapoor, Supriya
Bhatia, Rahul
Omoarebun, Peter
d2bac592-82c7-450c-830c-49a7334fda1e
Sanders, David
54f2f76a-f55a-42d7-ac56-2e9fd8d92de4
Ikwan, Favour
504e862a-ecff-41ce-85ba-38d95e34aad1
Hassan, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Thabet, Mohamad
d6026667-fef0-44a5-99d1-1520a35dfbd3
Piner, Jake
039a370a-055e-41e8-918c-c463ac4bec54
Shah, Amjad
acbfbb5c-fe5d-4703-8f1b-2397f18e80d8
Arai, Kohei
Kapoor, Supriya
Bhatia, Rahul

Omoarebun, Peter, Sanders, David, Ikwan, Favour, Hassan, Mohamed, Haddad, Malik, Thabet, Mohamad, Piner, Jake and Shah, Amjad (2021) Intelligent monitoring using hazard identification technique and multi-sensor data fusion for crude distillation column. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (eds.) In Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3. vol. 1252 AISC, Springer. pp. 730-741 . (doi:10.1007/978-3-030-55190-2_61).

Record type: Conference or Workshop Item (Paper)

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.

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More information

e-pub ahead of print date: 25 August 2020
Published date: 2021
Keywords: Crude, Distillation, Hazard, Intelligent, Monitoring, Sensor, Sensors

Identifiers

Local EPrints ID: 444158
URI: http://eprints.soton.ac.uk/id/eprint/444158
ISSN: 2194-5357
PURE UUID: 021be89e-4d4b-4d42-9d00-dcee11ee457d
ORCID for Mohamed Hassan: ORCID iD orcid.org/0000-0003-3729-4543

Catalogue record

Date deposited: 29 Sep 2020 17:40
Last modified: 18 Feb 2021 17:41

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Contributors

Author: Peter Omoarebun
Author: David Sanders
Author: Favour Ikwan
Author: Mohamed Hassan ORCID iD
Author: Malik Haddad
Author: Mohamad Thabet
Author: Jake Piner
Author: Amjad Shah
Editor: Kohei Arai
Editor: Supriya Kapoor
Editor: Rahul Bhatia

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