Recognizing the state of aerobic granular sludge over its life-cycle in a continuous-flow membrane bioreactor with an artificial intelligence approach.
Journal:
Journal of environmental management
PMID:
40315654
Abstract
The continuous-flow aerobic granular sludge-membrane bioreactor (AGS-MBR) system represents an efficient and sustainable technology for wastewater treatment. AGS, a spherical or ellipsoidal granular sludge formed through microbial self-aggregation under aerobic conditions, progresses through four distinct life-cycle stages in the AGS-MBR system: initial, growth, mature, and cleaved. Accurate identification and classification of these stages are crucial for optimizing AGS-MBR operations and maintaining system stability; however, traditional monitoring methods are labor-intensive and error-prone. This study utilized Artificial Intelligence (AI) to develop a machine learning model based on the You Only Look Once (YOLOv8) algorithm for automated AGS monitoring and classification. Trained on 862 annotated images, the model achieved average precision of 0.985 at an Intersection over Union (IoU) threshold of 0.5 (mAP50), and the mAP50-95 of 0.837, demonstrating high accuracy in AGS classification. The t-distributed Stochastic Neighbor Embedding (t-SNE) revealed distinct clusters of AGS features across life-cycle stages, while SHapley Additive exPlanations (SHAP) demonstrated that the model focused on global features of small-grained images and edge features of large-grained images, both confirming the robustness of classification. The model's statistical functionality, supported by global variables, enabled real-time AGS monitoring in MBR system. This study provides a powerful tool for detecting and classifying the AGS life-cycle, offering guidance for the operation and maintenance of AGS-MBR system and demonstrating the potential applications of AI in wastewater treatment.