Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-19T05:25:45.029Z Has data issue: false hasContentIssue false
  • English
  • Français

Detection of Filamentous Bulking Problems: Developing an Image Analysis System for Sludge Composition Monitoring

Published online by Cambridge University Press:  18 January 2007

Rika Jenné ,
Ephraim Noble Banadda ,
Ilse Smets ,
Jeroen Deurinck  and
Jan Van Impe
Rika Jenné
Affiliation:
BioTeC-Bioprocess Technology and Control, Katholieke Universiteit Leuven, Department of Chemical Engineering, W. de Croylaan 46, B-3001 Leuven, Belgium
Ephraim Noble Banadda
Affiliation:
BioTeC-Bioprocess Technology and Control, Katholieke Universiteit Leuven, Department of Chemical Engineering, W. de Croylaan 46, B-3001 Leuven, Belgium
Ilse Smets
Affiliation:
BioTeC-Bioprocess Technology and Control, Katholieke Universiteit Leuven, Department of Chemical Engineering, W. de Croylaan 46, B-3001 Leuven, Belgium
Jeroen Deurinck
Affiliation:
BioTeC-Bioprocess Technology and Control, Katholieke Universiteit Leuven, Department of Chemical Engineering, W. de Croylaan 46, B-3001 Leuven, Belgium
Jan Van Impe
Affiliation:
BioTeC-Bioprocess Technology and Control, Katholieke Universiteit Leuven, Department of Chemical Engineering, W. de Croylaan 46, B-3001 Leuven, Belgium
Get access

Abstract

This article describes a fully automatic image analysis procedure for fast and reliable characterization of the activated sludge composition, that is, the floc and filament features. The algorithms developed for each of the analysis steps, that is, segmentation, object recognition, and characterization, are described in detail. Although the application range of the recognition method is a priori expanded by introducing a number of control parameters, the procedure proves to be intrinsically robust as it produces satisfactory results for a fixed set of parameter values for a wide variety of image types.

Keywords

activated sludge characterizationfilamentous bulkingimage analysissettleability monitoring
Type
Research Article
Information
Microscopy and Microanalysis , Volume 13 , Issue 1: Special Issue: Environmental, Industrial, and AppliedMicrobiology , February 2007 , pp. 36 - 41
Copyright
2007 Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alves, M., Cavaleiro, A.J., Ferreira, E.C., Amaral, A.L., Mota, M., da Motta, M., Vivier, H. & Pons, M.-N. (2000). Characterisation by image analysis of anaerobic sludge under shock conditions. Water Sci Technol 41, 207–214.Google Scholar
Cenens, C., Van Beurden, K.P., Jenné, R. & Van Impe, J.F. (2002). On the development of a novel image analysis technique to distinguish between flocs and filaments in activated sludge images. Water Sci Technol 46, 381–387.Google Scholar
Dagot, C., Pons, M.N., Casellas, M., Guibaud, G., Dollet, P. & Baudu, M. (2001). Use of image analysis and rheological studies for the control of settleability of filamentous bacteria: Application in SBR reactor. Water Sci Technol 43, 27–33.Google Scholar
Da Motta, M., Pons, M.N. & Roche, N. (2001). Automated monitoring of activated sludge in a pilot plant using image analysis. Water Sci Technol 43, 91–96.Google Scholar
Glasbey, C.A. & Horgan, G.W. (1995). Image Analysis for the Biological Sciences. New York: John Wiley & Sons.
Govoreanu, R., Seghers, D., Nopens, I., De Clercq, B., Saveyn, H., Capalozza, C., Van der Meeren, P., Verstraete, W., Top, E. & Vanrolleghem, P.A. (2003). Linking floc structure and settling properties to activated sludge population dynamics in an SBR. Water Sci Technol 47, 9–18.Google Scholar
Grijspeerdt, K. & Verstraete, W. (1996). A sensor for the secondary clarifier based on image analysis. Water Sci Technol 33, 61–70.Google Scholar
Grijspeerdt, K. & Verstraete, W. (1997). Image analysis to estimate the settleability and concentration of activated sludge. Water Res 31, 1126–1133.Google Scholar
Heine, W., Sekoulov, I., Burkhardt, H., Bergen, L. & Behrendt, J. (2002). Early warning-system for operation failures in biological stages of WWTPS by on-line image analysis. Water Sci Technol 46, 117–124.Google Scholar
Jenné, R., Banadda, E.N., Smets, I.Y., Gins, G., Mys, M. & Van Impe, J.F. (2004). Developing an early warning tool for filamentous bulking problems based on image analysis. In Proceedings of the Second International IWA Conference on Automation in Water Quality Monitoring, AutMoNet 2004, Vienna, Austria, Langergraber, G., Winkler, S., Fleischmann, N., Pressl, A. & Haberl, R. (Eds.), pp. 221–228.
Jenné, R., Cenens, C., Geeraerd, A.H. & Van Impe, J.F. (2002). Towards on-line quantification of flocs and filaments by image analysis. Biotechnol Lett 24, 931–935.Google Scholar
Klonowski, W. (2000). Signal and image analysis using chaos theory and fractal geometry. Mach Graph Vision 9, 403–431.Google Scholar
Russ, J.C. (1995). The Image Processing Handbook. 3rd ed. Boca Raton, FL: CRC Press.