Monitoring of an industrial dearomatisation process

Samuli Bergman, Mauri Sourander, Sirkka-Liisa Jämsä-Jounela

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    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 exchanger
    Original languageEnglish
    Pages (from-to)331-336
    Issue number1
    Publication statusPublished - 2002
    MoE publication typeA4 Article in a conference publication
    EventIFAC World Congress - Barcelona, Spain
    Duration: 21 Jul 200226 Jul 2002
    Conference number: 19


    • chemical industry
    • fault diagnosis
    • neural networks
    • statistical process control

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