Hybrid Model-Based Framework for Alarm Anticipation

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dc.contributor.author Xu, Shichao
dc.contributor.author Adhitya, Arief
dc.contributor.author Srinivasan, Rajagopalan
dc.date.accessioned 2014-06-26T08:09:17Z
dc.date.available 2014-06-26T08:09:17Z
dc.date.issued 2014-04
dc.identifier.citation Xu, Shichao; Adhitya, Arief and Srinivasan, Rajagopalan, "Hybrid Model-Based Framework for Alarm Anticipation", Industrial & Engineering Chemistry Research, DOI: 10.1021/ie4014953, vol. 53, no. 13, pp. 5182-5193, Apr. 2014. en_US
dc.identifier.issn 0888-5885
dc.identifier.uri dx.doi.org/10.1021/ie4014953
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/1338
dc.description.abstract Modern chemical plants consist of a number of integrated and interlinked process units. When an abnormal situation occurs, the automation system alerts the operators through alarms. In this work, we introduce a new type of alarms, known as anticipatory alarms, aimed to enable operators to orient holistically to the abnormal situation. These anticipatory alarms are developed based on an alarm anticipation algorithm that utilizes dynamic process models to offer an accurate short-term prediction of the process state. In particular, these models predict the rate-of-change of process variables, which are then translated into predictions of time horizons for occurrence of various critical alarms. Anticipatory alarms seek to improve the sensemaking facilities offered to the operator through advance warning of impending alarms. As a result, operators can adopt a more proactive approach in managing abnormal situations. The benefits of anticipatory alarms have been demonstrated through six fault scenarios in a depropanizer unit case study. All alarms are successfully predicted, providing a diagnosis time benefit of around 35 s to the operators. en_US
dc.description.statementofresponsibility by Shichao Xu, Arief Adhitya and Rajagopalan Srinivasan
dc.format.extent Vol. 53, No. 13, pp. 5182-5193
dc.language.iso en en_US
dc.publisher American Chemical Society en_US
dc.subject Automation systems en_US
dc.subject Dynamic process en_US
dc.subject Fault scenarios en_US
dc.subject Pro-active approach en_US
dc.subject Process state en_US
dc.subject Process Variables en_US
dc.subject Rate of change en_US
dc.subject Short term prediction en_US
dc.title Hybrid Model-Based Framework for Alarm Anticipation en_US
dc.type Article en_US
dc.relation.journal Industrial & Engineering Chemistry Research

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