Variable contribution identification and visualization in multivariate statistical process monitoring

dc.contributor.authorRossouw, R. F.en_ZA
dc.contributor.authorCoetzer, R. L. J.en_ZA
dc.contributor.authorLe Roux, N. J.en_ZA
dc.date.accessioned2022-05-30T09:20:47Z
dc.date.available2022-05-30T09:20:47Z
dc.date.issued2020-01
dc.descriptionCITATION: Rossouw, R. F.; Coetzer, R. L. J. & Le Roux, N. J. 2020. Variable contribution identification and visualization in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 198. doi:10.1016/j.chemolab.2019.103894
dc.descriptionThe original publication is available at https://www.sciencedirect.com/journal/chemometrics-and-intelligent-laboratory-systems
dc.description.abstractMultivariate statistical process monitoring (MSPM) has received book-length treatments and wide spread application in industry. In MSPM, multivariate data analysis techniques such as principal component analysis (PCA) are commonly employed to project the (possibly many) process variables onto a lower dimensional space where they are jointly monitored given a historical or specified reference set that is within statistical control. In this paper, PCA and biplots are employed together in an innovative way to develop an efficient multivariate process monitoring methodology for variable contribution identification and visualization. The methodology is applied to a commercial coal gasification production facility with multiple parallel production processes. More specifically, it is shown how the methodology is used to specify the optimal principal component combinations and biplot axes for visualization and interpretation of process performance, and for the identification of the critical variables responsible for performance deviations, which yielded direct benefits for the commercial production facility.en_ZA
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S0169743919305088?via%3Dihub
dc.description.versionPublishers version
dc.format.extent19 pages : illustrations
dc.identifier.citationRossouw, R. F.; Coetzer, R. L. J. & Le Roux, N. J. 2020. Variable contribution identification and visualization in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 198. doi:10.1016/j.chemolab.2019.103894
dc.identifier.issn0169-7439 (print)
dc.identifier.otherdoi:10.1016/j.chemolab.2019.103894
dc.identifier.urihttp://hdl.handle.net/10019.1/125263
dc.language.isoen_ZAen_ZA
dc.publisherElsevier
dc.rights.holderElsevier B. V.
dc.subjectMultivariate analysis -- Graphic methodsen_ZA
dc.subjectGraphical modeling (Statistics)en_ZA
dc.subjectStatistics -- Graphic methodsen_ZA
dc.titleVariable contribution identification and visualization in multivariate statistical process monitoringen_ZA
dc.typeArticleen_ZA
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