Monitoring of an industrial dearomatisation process
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A4 Artikkeli konferenssijulkaisussa
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Date
2002
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en
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IFAC PROCEEDINGS VOLUMES, Volume 35, issue 1, pp. 331-336
Abstract
Process monitoring methods have been studied widely in recent years, and several industrial applications have been published. Early detection and identification of abnormal and undesired process states and equipment failures are essential requirements for safe and reliable processes. This helps to reduce the amount of production losses during abnormal events. In this paper, statistical multivariate methods and neural networks applied in monitoring of an industrial dearomatisation process are compared. No appriori process knowledge for the methods were assumed. The data for the comparison were generated with a dynamic simulator model of the process. Special emphasis was put on a case of internal leak in a heat exchangerDescription
Keywords
chemical industry, fault diagnosis, neural networks, statistical process control
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Citation
Bergman, S, Sourander, M & Jämsä-Jounela, S-L 2002, ' Monitoring of an industrial dearomatisation process ', IFAC PROCEEDINGS VOLUMES, vol. 35, no. 1, pp. 331-336 . https://doi.org/10.3182/20020721-6-ES-1901.01364