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Making use of process tomography data for multivariate statistical process control

Authors

  • Bundit Boonkhao,

    1. Institute of Particle Science and Engineering, School of Process, Environmental and Material Engineering, University of Leeds, Leeds LS2 9JT, U.K.
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  • Rui F. Li,

    1. Institute of Particle Science and Engineering, School of Process, Environmental and Material Engineering, University of Leeds, Leeds LS2 9JT, U.K.
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  • Xue Z. Wang,

    Corresponding author
    1. Institute of Particle Science and Engineering, School of Process, Environmental and Material Engineering, University of Leeds, Leeds LS2 9JT, U.K.
    • Institute of Particle Science and Engineering, School of Process, Environmental and Material Engineering, University of Leeds, Leeds LS2 9JT, U.K.
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  • Richard J. Tweedie,

    1. Industrial Tomography Systems Limited, Speakers House, 39 Deansgate, Manchester M3 2BA, U.K.
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  • Ken Primrose

    1. Industrial Tomography Systems Limited, Speakers House, 39 Deansgate, Manchester M3 2BA, U.K.
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Abstract

A novel strategy for making effective use of on-line process tomography measurements for process monitoring is described. The electrical resistance tomography (ERT) sensing system equipped with sixteen electrodes provides 104 conductivity measurements every 25 ms. The data has traditionally been used for construction of images for display purpose. In this study, ERT data was used for multivariate statistical process control. Data at predefined normal operational conditions was processed using principal component analysis. The compressed data was used to derive two statistics, T2 and squared prediction error (SPE). T2 and SPE charts predict the probability that the process being monitored has undergone statistically significant changes from previous state or the so-called normal operational state, in terms of mixing quality. The methodology is illustrated by reference to a case study of a sunflower oil/water emulsion process. © 2010 American Institute of Chemical Engineers AIChE J, 2011

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