Process Systems Engineering

Making use of process tomography data for multivariate statistical process control

  1. Bundit Boonkhao1,
  2. Rui F. Li1,
  3. Xue Z. Wang1,*,
  4. Richard J. Tweedie2,
  5. Ken Primrose2

Article first published online: 9 NOV 2010

DOI: 10.1002/aic.12443

Issue

AIChE Journal

AIChE Journal

Volume 57, Issue 9, pages 2360–2368, September 2011

Additional Information

How to Cite

Boonkhao, B., Li, R. F., Wang, X. Z., Tweedie, R. J. and Primrose, K. (2011), Making use of process tomography data for multivariate statistical process control. AIChE J., 57: 2360–2368. doi: 10.1002/aic.12443

Author Information

  1. 1

    Institute of Particle Science and Engineering, School of Process, Environmental and Material Engineering, University of Leeds, Leeds LS2 9JT, U.K.

  2. 2

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

*Institute of Particle Science and Engineering, School of Process, Environmental and Material Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Publication History

  1. Issue published online: 3 AUG 2011
  2. Article first published online: 9 NOV 2010
  3. Accepted manuscript online: 24 SEP 2010 11:04AM EST
  4. Manuscript Revised: 26 AUG 2010
  5. Manuscript Received: 3 DEC 2009

Funded by

  • Technology Strategy Board. Grant Number: TP/SC/6/I/10097
  • UK Engineering and Physical Sciences Research Council. Grant Numbers: EP/E040624/1, EP/H008853/1

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Keywords:

  • electrical resistance tomography;
  • multivariate statistical process control;
  • principal component analysis;
  • emulsion

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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