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Identifying different types of bulking in an activated sludge system through quantitative image analysis
D P Mesquita1, A L Amaral, E C Ferreira
1IBB-Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
Chemosphere
|August 16, 2011
Summary
This study introduces an image analysis method to detect various disturbances in wastewater treatment activated sludge systems. The approach predicts mixed liquor suspended solids (MLSS) and sludge volume index (SVI) under different operational conditions.
Area of Science:
- Environmental Science
- Biotechnology
- Water Treatment Engineering
Background:
- Activated sludge systems are crucial for wastewater treatment.
- System disturbances can negatively impact effluent quality and operational efficiency.
- Existing image analysis methods primarily focus on filamentous bulking detection.
Purpose of the Study:
- To develop and validate an image analysis methodology for identifying diverse disturbances in activated sludge systems.
- To predict key operational parameters like mixed liquor suspended solids (MLSS) and sludge volume index (SVI) under various disturbance scenarios.
- To analyze biomass characteristics, including morphology, content, Gram status, and viability, in relation to system disturbances.
Main Methods:
- Image analysis techniques were employed to study activated sludge.
- Four experimental conditions were simulated: filamentous bulking, zoogleal/viscous bulking, pinpoint floc formation, and normal operation.
- Biomass content, morphology, Gram staining, and viability were assessed using image analysis alongside MLSS and SVI measurements.
Main Results:
- The proposed image analysis methodology successfully identified different types of disturbances.
- The study established correlations between image-derived biomass characteristics and MLSS/SVI values.
- Distinct image features were associated with specific disturbances, enabling their differentiation.
Conclusions:
- Image analysis offers a promising tool for early detection and monitoring of various activated sludge disturbances.
- Predicting MLSS and SVI through image analysis can aid in proactive process control.
- This methodology enhances the understanding of biomass behavior under different operational stresses in wastewater treatment.
