Environmental Engineering Research

Korean Society of Environmental Engineers (대한환경공학회)
권호 표지
DOI QR코드

DOI QR Code

FAULT DETECTION, MONITORING AND DIAGNOSIS OF SEQUENCING BATCH REACTOR FOR INTEGRATED WASTEWATER TREATMENT MANAGEMENT SYSTEM

  • Yoo, Chang-Kyoo (School of Environmental Science and Engineering/Department of Chemical Engineering, Pohang University of Science and Technology) ;
  • Vanrolleghem, Peter A. (BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University) ;
  • Lee, In-Beum (School of Environmental Science and Engineering/Department of Chemical Engineering, Pohang University of Science and Technology)
  • Published : 2006.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

Keywords

References

  1. Olsson, G. and Newell, B., Wastewater Treatment Systems: Modelling, diagnosis and Control, IWA, UK (1999)
  2. Choi, K. S., Kim, B. G., Han, G. B., and Kim, C. W., 'Detection of toxicity of wastewater in the activated sludge processes,' Environ. Eng. Res., 4(1), 59-70 (1999)
  3. Kim, I. S., Young, J. C., Kim, S. Y., and Kim, S. M., 'Development of monitoring methodology to fingerprint the activated sludge processes using oxygen uptake rate,' Environ. Eng. Res., 6(4), 251-259 (2001)
  4. Yoo, C.K., Lee, D. S. and Vanrolleghem, P.A., 'Application of multiway ICA for on-line monitoring of a sequencing batch reactor,' Wat. Res., 38(7), 1715-1732 (2004) https://doi.org/10.1016/j.watres.2004.01.006
  5. Yoo, C.K., Son, H. R., and Lee, I. B., 'Modeling and multiresolution analysis in a full-scale industrial plant,' Environ Eng. Res., 10(2), 88-103 (2005) https://doi.org/10.4491/eer.2005.10.2.088
  6. He, Q. P., Qin, S. J., and Wang, J. A., 'new fault diagnosis method using fault directios in fisher discriminatn analysis,' AIChE J., 51(2), 555-571 (2005) https://doi.org/10.1002/aic.10325
  7. Lee, D. S., and Vanrolleghem, P. A., 'Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis,' Biotech. & Bioeng, 82, 489-497 (2003) https://doi.org/10.1002/bit.10589
  8. Nomikos, P. and MacGregor, J. F., 'Monitoring batch processes using multiway principal component analysis,' AIChE J. 40(8), 1361-1375 (1994) https://doi.org/10.1002/aic.690400809
  9. Nomikos, P. and MacGregor, J. F., 'Multivariate SPC charts for monitoring batch processes, Technometrics, 37, 41-59 (1995) https://doi.org/10.2307/1269152
  10. Hyvarinen, A., 'Fast and robust fixed-point algorithms for independent component analysis,' IEEE Trans. on Neural Networks., 10, 626-634 (1999) https://doi.org/10.1109/72.761722
  11. Hyvarinen, A., 'Survey on independent component analysis,' Neural Computing Surveys, 2, 94-128 (1999)
  12. Hyvarinen, A., Karhunen, J., and Oja, E., Independent component analysis, John Wiley & Sons, INC., USA, (2001)
  13. Yoo, C. K., Lee, J., Vanrolleghem, P. A., and Lee, I. B., 'On-line monitoring of batch processes using multiway independent component analysis,' Chemom. and Intel. Lab. Sys., 71(2), 151-163 (2004) https://doi.org/10.1016/j.chemolab.2004.02.002
  14. Martin, E. B. and Morris, A. J., 'Non-parametric confidence bounds for process performance monitoring charts,' J. Proc. Cont., 6(6) 349-358 (1996) https://doi.org/10.1016/0959-1524(96)00010-8
  15. Chang, C. H. and Hao, O. J., 'Sequencing batch reactor system for nutrient removal: ORP and pH profiles,' J. Chem. Tech. Biotechnol. 67, 27-38 (1996) https://doi.org/10.1002/(SICI)1097-4660(199609)67:1<27::AID-JCTB430>3.0.CO;2-2
  16. Yoo, C. K., Vanrolleghem, P. A., and Lee, I. B., 'Integrated framework of model-based monitoring, control and optimization for a sustainable biological wastewater treatment operation,' Proceedings of KOSENV Fall Meeting (2004)

Cited by

  1. Fuzzy rule-based inference of reasons for high effluent quality in municipal wastewater treatment plant vol.28, pp.3, 2011, https://doi.org/10.1007/s11814-010-0428-8