Evaluation of Floc Settling Velocity and Morphology and Their Link with Bulk Settling Parameters for Different Activated Sludge Types
Urbanization and increasingly stringent effluent standards are placing growing demands on wastewater treatment plants (WWTPs), with clarifier capacity often emerging as a critical bottleneck to process intensification. Understanding and enhancing sludge settleability is critical for improving clarifier capacity and treatment efficiency. As the dominant settling regime in secondary clarifiers, understanding flocculent settling is essential for a comprehensive understanding of sludge settleability. Although parameters such as the Sludge Volume Index (SVI) and Threshold of Flocculation (TOF) are commonly used to assess settleability, their relationship with flocculent settling characteristics remains insufficiently understood. This study applied an image-based analysis method to characterize floc shape and settling velocity distributions under realistic clarifier conditions, and evaluated their relationships with bulk settleability metrics including SVI30 and TOF. Results show that SVI30 correlated very well with floc relative density, size and circularity. Although individual settleability parameters could not directly predict floc settling velocity, TOF showed a linear relationship with settling velocity within certain SVI30 ranges. Specifically, this relationship was driven by floc relative density when SVI30 was below 60 mL/gTSS, and by floc size when SVI30 ranged from 70 to 90 mL/gTSS. However, when SVI30 exceeded 100 mL/gTSS, TOF no longer served as a reliable predictor, while the correlation between floc size and settling velocity persisted. These findings highlight the complex dynamics of floc settling and reveal how floc characteristics differentially influence settling velocity across the SVI30 spectrum. The study presents the first comprehensive dataset linking floc morphology, settling velocity, and bulk settling parameters, offering valuable insights for improved clarifier modeling and capacity estimation.