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Predicting aerobic granular sludge structural instability: An intelligent early-warning framework integrating
Jie Lei1, Zhanfei Wu2, Zhe Liu3
1School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an, 710055, China.
Journal of Environmental Management
|February 28, 2026
Summary
An intelligent early-warning model (EPS-ResNet) predicts aerobic granular sludge (AGS) structural destabilization 6 days in advance. This model analyzes extracellular polymeric substances (EPS) fluorescence, enabling early detection of critical states in wastewater treatment.
Area of Science:
- Environmental Biotechnology
- Wastewater Treatment
- Microbial Ecology
Background:
- Aerobic granular sludge (AGS) offers advantages over activated sludge but suffers from structural instability and lacks early-warning systems for critical states.
- Existing methods fail to predict destabilization events, hindering the reliable implementation of AGS technology in wastewater treatment.
Purpose of the Study:
- To develop an intelligent early-warning model for predicting structural destabilization in aerobic granular sludge (AGS).
- To identify key fluorescence characteristics and microbial communities associated with AGS instability.
- To establish a scalable and mechanistically interpretable framework for early warning in biological treatment systems.
Main Methods:
- Development of an early-warning model (EPS-ResNet) utilizing multi-view convolutional neural networks.
- Analysis of fluorescence characteristics in Excitation-Emission-Matrix Spectra (EEMs) of loosely/tightly bound extracellular polymeric substances (LB-EPS/TB-EPS).
- Integration of occlusion sensitivity analysis, fluorescence region segmentation, microbiome analysis, and Mantel tests.
Main Results:
- The EPS-ResNet model achieved a 6±1-day advance prediction of AGS structural destabilization with 97.6% accuracy.
- Key fluorescence regions (TB-EPS Region I, TB-EPS Region IV, LB-EPS Region IV) were identified as critical for early warning.
- Microbiome analysis linked model performance to the dynamic succession of dominant phyla (Bacteroidota, Patescibacteria) and genera (Flavobacterium, Thauera).
Conclusions:
- The developed EPS-ResNet model provides a reliable and accurate early-warning system for AGS structural destabilization.
- The framework integrates fluorescence analysis and microbiome dynamics, offering mechanistic insights into AGS instability.
- The early-warning framework is extensible to other biological treatment systems, promoting intelligent operation and maintenance.
