A novel method for identifying aerobic granular sludge state using sorting, densification and clarification dynamics...

Area of Science:

• Environmental Science
• Biotechnology
• Wastewater Treatment

Background:

• Aerobic granular sludge is a key technology for wastewater treatment.
• Maintaining the stability of aerobic granular sludge remains a significant challenge.
• Predicting the state of granular sludge is crucial for stable operations.

Purpose of the Study:

• To explore dynamic texture entropy from settling images as a predictive tool for granular sludge state.
• To assess the relationship between settling dynamics and granule characteristics.
• To develop a biologically-based monitoring approach for granular sludge.

Main Methods:

• Utilized dynamic texture entropy derived from settling images.
• Analyzed granule behavior during traditional thickening, clarification, and particle sorting.
• Correlated texture entropy features with respirogram data (specific endogenous and quasi-endogenous respiration rates).

Main Results:

• Rapid particle sorting during settling indicates stable aerobic granular sludge.
• A clear relationship was found between sorting time and granule heterogeneity.
• Dynamic texture entropy features showed high correlation with respiration rates (R² = 0.86–0.91).
• Classification accuracy using dynamic texture entropy exceeded 0.90, outperforming conventional features.

Conclusions:

• Dynamic texture entropy is a promising tool for predicting aerobic granular sludge stability.
• This approach provides a biologically-based method for monitoring treatment processes.
• Findings support the development of robust tools for maintaining stable granular sludge operations.