Enhancing recursive-PCA-based process monitoring using t-SNE visualization
OKTORIFO Gardiola, ABDULHALIM SHAH Maulud, MUHAMMAD Nawaz, NABILA FARHANA Jamaludin
Abstract. Certain industrial chemical processes are known to change over time but are still regarded as normal; this can lead to false alarms when monitored using a non-adaptive process monitoring technique. Adaptive process monitoring techniques can improve product quality and minimize downtime by reducing false alarms. Since most industrial chemical processes are time-variant, standard principal component analysis (PCA) is not the best method for monitoring a slowly changing process. It is crucial to predetermine the data’s characteristics to choose the best process monitoring technique. It has been discovered that t-distributed stochastic neighbour embedding (t-SNE) is a valuable data visualisation method. This paper uses t-SNE to visualise simulated data from a continuously stirred tank reactor (CSTR) process. It has been found that the visualized data using t-SNE is worth monitoring with recursive PCA due to its non-linear data characteristics. Consequently, sample-wise recursive PCA is utilized to identify slow changes caused by variations in ambient temperature during the day and night. Compared to standard PCA, the recursive PCA model performs noticeably better in reducing false alarms due to its effective adaptation to slow changes.
Keywords
Adaptive Process Monitoring, Process Monitoring, PCA, Recursive PCA, t-SNE
Published online 4/25/2025, 6 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: OKTORIFO Gardiola, ABDULHALIM SHAH Maulud, MUHAMMAD Nawaz, NABILA FARHANA Jamaludin, Enhancing recursive-PCA-based process monitoring using t-SNE visualization, Materials Research Proceedings, Vol. 53, pp 458-463, 2025
DOI: https://doi.org/10.21741/9781644903575-47
The article was published as article 47 of the book Decarbonization Technology
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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