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Author Ghosha, Kaushik ♦ Ramteke, Manojkumar ♦ Srinivasan, Rajagopalan
Source IIT Gandhinagar
Content type Text
Publisher Elsevier
File Format PDF
Language English
Subject Domain (in DDC) Technology ♦ Chemical engineering
Subject Keyword Fault detection ♦ Optimization ♦ Process control ♦ Safety ♦ Systems engineeringq ♦ Tennessee Eastman Process
Abstract In a typical large-scale chemical process, hundreds of variables are measured. Since statistical process monitoring techniques typically involve dimensionality reduction, all measured variables are often provided as input without weeding out variables. Here, we demonstrate that incorporating measured variables that do not provide any additional information about faults degrades monitoring performance. We propose a stochastic optimization-based method to identify an optimal subset of measured variables for process monitoring. The benefits of the reduced monitoring model in terms of improved false alarm rate, missed detection rate, and detection delay is demonstrated through PCA based monitoring of the benchmark Tennessee Eastman Challenge problem.
ISSN 00981354
Learning Resource Type Article