Multi-block methods in multivariate process control

J. Kohonen, S.P. Reinikainen, K. Aaljoki, A. Perkio, T. Vaananen, Agnar Høskuldsson

    Research output: Contribution to journalConference articleResearchpeer-review

    Abstract

    In chemometric studies all predictor variables are usually collected in one data matrix X. This matrix is then analyzed by PLS regression or other methods. When data from several different sub-processes are collected in one matrix, there is a possibility that the effects of some sub-processes may vanish. If there is, for instance, mechanic data from one process and spectral data from another, the influence of the mechanic sub-process may not be detected. An application of multi-block (MB) methods, where the X-data are divided into several data blocks is presented in this study. By using MB methods the effect of a sub-process can be seen and an example with two blocks, near infra-red, NIR, and process data, is shown. The results show improvements in modelling task, when a MB-based approach is used. This way of working with data gives more information on the process than if all data are in one X-matrix. The procedure is demonstrated by an industrial continuous process, where knowledge about the sub-processes is available and X-matrix can be divided into blocks between process variables and NIR spectra.
    Original languageEnglish
    JournalJournal of Chemometrics
    Volume22
    Issue number11-12
    Pages (from-to)580-586
    ISSN0886-9383
    DOIs
    Publication statusPublished - 2008
    Event10th Scandinavian Symposium on Chemometrics - Lappeenranta, Finland
    Duration: 11 Jun 200715 Jun 2007
    Conference number: 10

    Conference

    Conference10th Scandinavian Symposium on Chemometrics
    Number10
    Country/TerritoryFinland
    CityLappeenranta
    Period11/06/200715/06/2007

    Keywords

    • priority regression
    • CovProc
    • multi-block PLS
    • oil refining
    • process control

    Fingerprint

    Dive into the research topics of 'Multi-block methods in multivariate process control'. Together they form a unique fingerprint.

    Cite this