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Adaptive Consensus Principal Component Analysis for On-Line Batch Process Monitoring

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Abstract

As the regulations of effluent quality are increasingly stringent, the on-line monitoring of wastewater treatment processes becomes very important. Multivariate statistical process control such as principal component analysis (PCA) has found wide applications in process fault detection and diagnosis using measurement data. In this work, we propose a consensus PCA algorithm for adaptive wastewater treatment process monitoring. The method overcomes the problem of changing operating conditions by updating the covariance structure recursively. The algorithm does not require any estimation compared to typical multiway PCA models. With this method process disturbances are detected in real time and the responsible measurements are directly identified. The presented methodology is successfully applied to a pilot-scale sequencing batch reactor for wastewater treatment.

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References

  • Chang, C. H. and Hao, O. J.: 1996, 'Sequencing batch reactor system for nutrient removal: ORP and pH profiles', J. Chem. Tech. Biotechnol. 67, 27–38.

    Google Scholar 

  • Demuynck, C., Vanrolleghem, P. A., Mingneau, C., Liessens, J. and Verstraete,W.: 1994, 'NDBEPR process optimization in SBRs: Reduction of external carbon source and oxygen supply', Water Sci. Tech. 30(4), 169–179.

    Google Scholar 

  • Geladi, P. and Kowalski, B. R.: 1986, 'Partial least squares regression: A tutorial', Anal. Chim. Acta. 185, 1–17.

    Google Scholar 

  • Lee, D. S., Jeon, C. O., Park, J. M. and Chang, K. S.: 2002, 'Hybrid neural network modeling of a full-scale industrial wastewater treatment process', Biotechnol. Bioeng. 78(6), 670–682.

    PubMed  Google Scholar 

  • Lee, D. S. and Park, J. M.: 1998, 'Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor', J. Biotechnol. 75, 229–239.

    Google Scholar 

  • Lee, D. S. and Vanrolleghem, P. A.: 2003, 'Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis', Biotechnol. Bioeng. 82(4), 489–497.

    PubMed  Google Scholar 

  • Lennox, B., Montague, G. A., Hiden, H. G., Kornfeld, G. and Goulding, P. R.: 2001, 'Process monitoring of an industrial fed-batch fermentation', Biotechnol. Bioeng. 74, 125–135.

    PubMed  Google Scholar 

  • Li, W., Yue, H., Valle-Cervants, S. and Qin, S. J.: 2000, 'Recursive PCA for adaptive process monitoring', J. Proc. Cont. 10, 471–486.

    Google Scholar 

  • Mace, S. and Mata-Alvarez, J.: 2002, 'Utilization of SBR technology for wastewater treatment: An overview', Ind. Eng. Chem. Res. 41, 5539–5553.

    Google Scholar 

  • MacGregor, J. F. and Kourti, T.: 1995, 'Statistical process control of multivariate processes', Cont. Eng. Practice 3, 403–414.

    Google Scholar 

  • Nomikos, P. and MacGregor, J. F.: 1994, 'Monitoring batch processes using multiway principal component analysis', AIChE J. 40, 1361–1375.

    Google Scholar 

  • Nomikos, P. and MacGregor, J. F.: 1995, 'Multivariate SPC charts for monitoring batch processes', Technometrics 37, 41–59.

    Google Scholar 

  • Nopens, I., Biggs, C. A., Clercq, B. D., Govoreanu, R., Wilen, B. M., Lant, P. and Vanrolleghem, P. A.: 2002, 'Modeling the activated sludge flocculation process combining laser diffraction particle sizing and population balance modeling (PBM)', Water Sci. Tech. 45(6), 41–49.

    Google Scholar 

  • Ränner, S., MacGregor, J. F. and Wold, S.: 1998, 'Adaptive batch monitoring using hierarchical PCA', Chemom. Intell. Lab. Syst. 41, 73–81.

    Google Scholar 

  • Rosen, C. and Lennox, J. A.: 2001, 'Multivariate and multiscale monitoring of wastewater treatment operation', Water Res. 35, 3402–3410.

    PubMed  Google Scholar 

  • Rosen, C. and Olsson, G.: 1998, 'Disturbance detection in wastewater treatment systems', Water Sci. Tech. 37(12), 197–205.

    Google Scholar 

  • Savitzky, A. and Golay, M. J. E.: 1964, 'Smoothing and differentiation of data by simplified least squares procedures', Anal. Chem. 36, 1627–1639.

    Google Scholar 

  • Tates, A. A., Louwerse, D. J., Smilde, A. K., Koot, G. L. M. and Berndt, H.: 1999, 'Monitoring a PVC batch process with multivariate statistical process control charts', Ind. Eng. Chem. Res. 38, 4769–4776.

    Google Scholar 

  • Westerhuis, J. A., Kourti, T. and MacGregor, J. F.: 1998, 'Analysis of multiblock and hierarchical PCA and PLA models', J. Chemom. 12, 301–312.

    Google Scholar 

  • Wise, B. M. and Gallagher, N. B.: 1996, 'The process chemometrics approach to process monitoring and fault detection,' J. Proc. Cont. 6, 329–348.

    Google Scholar 

  • Wold, S., Hellberg, S., Lundstedt, T., Sjostrom, M. and Wold, H.: 1987, 'PLS Modeling with Latent Variables in Two or More Dimensions', Proceedings of Symposium on PLS Model Building: Theory and Application, Frankfurt am Main, Germany, September 1987, pp. 23-25.

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Authors
  1. Dae Sung Lee
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  2. Peter A. Vanrolleghem
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Lee, D.S., Vanrolleghem, P.A. Adaptive Consensus Principal Component Analysis for On-Line Batch Process Monitoring. Environ Monit Assess 92, 119–135 (2004). https://doi.org/10.1023/B:EMAS.0000014498.72455.18

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  • DOI: https://doi.org/10.1023/B:EMAS.0000014498.72455.18

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