A Comparison between bright field and phase-contrast image analysis techniques in activated sludge morphological characterization.

Different approaches using microscopy image analysis procedures were employed for characterization of activated sludge systems. The approaches varied mainly on the type of visualization and acquisition method used for collection of data. In this context, this study focused on the comparison of the two most common acquisition methods: bright field and phase-contrast microscopy. Images were acquired from seven different wastewater treatment plants for a combined period of two years. Advantages and disadvantages of each acquisition technique and the results are discussed. Bright field microscopy proved to be more simple and inexpensive and provided the best overall results.

[1]  Eugénio C. Ferreira,et al.  Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression , 2005 .

[2]  Awwa,et al.  Standard Methods for the examination of water and wastewater , 1999 .

[3]  A H Geeraerd,et al.  Use of image analysis for sludge characterisation: studying the relation between floc shape and sludge settleability. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[4]  D. Jassby,et al.  Filament content threshold for activated sludge bulking: artifact or reality? , 2007, Water research.

[5]  J F Van Impe,et al.  Image Analysis as a Monitoring Tool for Activated Sludge Properties in Lab-Scale Installations , 2003, Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering.

[6]  Eugénio C. Ferreira,et al.  Characterisation of Activated Sludge by Automated Image Analysis: Validation of Full-Scale Plants , 2001 .

[7]  A. L. Amaral Image analysis in biotechnological processes : applications to wastewater treatment , 2003 .

[8]  E. N. Banadda,et al.  Detection of Filamentous Bulking Problems: Developing an Image Analysis System for Sludge Composition Monitoring , 2007, Microscopy and Microanalysis.

[9]  E. N. Banadda,et al.  Predicting the onset of filamentous bulking in biological wastewater treatment systems by exploiting image analysis information , 2003, 2003 European Control Conference (ECC).

[10]  E. N. Banadda,et al.  Monitoring activated sludge settling properties using image analysis. , 2004, Water science and technology : a journal of the International Association on Water Pollution Research.

[11]  Jerzy J. Ganczarczyk Microbial aggregates in wastewater treatment , 1994 .

[12]  M. Pons,et al.  Biomass quantification by image analysis. , 2000, Advances in biochemical engineering/biotechnology.

[13]  A. L. Amaral,et al.  Monitoring of activated sludge settling ability through image analysis: validation on full-scale wastewater treatment plants , 2009, Bioprocess and biosystems engineering.

[14]  Anthony E. Walsby,et al.  Measurement of filamentous cyanobacteria by image analysis , 1996 .

[15]  J F Van Impe,et al.  On the development of a novel image analysis technique to distinguish between flocs and filaments in activated sludge images. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[16]  M M Alves,et al.  Quantitative image analysis as a diagnostic tool for monitoring structural changes of anaerobic granular sludge during detergent shock loads , 2007, Biotechnology and bioengineering.

[17]  M. Pons,et al.  Automated monitoring of activated sludge in a pilot plant using image analysis. , 2001, Water science and technology : a journal of the International Association on Water Pollution Research.

[18]  Willy Verstraete,et al.  Image analysis to estimate the settleability and concentration of activated sludge , 1997 .

[19]  M M Alves,et al.  Quantitative image analysis as a diagnostic tool for identifying structural changes during a revival process of anaerobic granular sludge. , 2007, Water research.

[20]  A. L. Amaral,et al.  Correlation between sludge settling ability and image analysis information using partial least squares. , 2009, Analytica chimica acta.