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Related Experiment Video

Updated: Jul 2, 2025

Extraction of Structural Extracellular Polymeric Substances from Aerobic Granular Sludge
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A novel method for identifying aerobic granular sludge state using sorting, densification and clarification dynamics

Zhi-Hua Li1, Ruo-Lan Wang1, Meng Lu1

  • 1Key Laboratory of Northwest Water Resource, Environment, and Ecology, MOE, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; Xi'an Key Laboratory of Intelligent Equipment Technology for Environmental Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Water Research
|February 21, 2024
PubMed
Summary

Dynamic texture entropy from settling images predicts aerobic granular sludge stability. This method offers a robust, biologically-based approach for monitoring wastewater treatment processes and ensuring operational efficiency.

Keywords:
Aerobic granular sludgeImage processingRespirogramSettling abilityTextural features

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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.