Real-time sludge volume index estimation via microscopic image-based deep learning

Sludge bulking, a major challenge in wastewater treatment plants (WWTPs), is closely linked to sludge settleability, commonly quantified by the sludge volume index (SVI). However, conventional SVI measurement is labor-intensive and time-consuming, limiting real-time monitoring. This study introduces a deep learning-based framework for rapid SVI determination using microscopic images. Using our Real-time Online Microscopic Image Data Analysis System (ROMIDAS), 41,496 high-resolution sludge images were captured for qualitative and quantitative SVI identification. SVI-C established a qualitative correlation with 97.65% accuracy. For quantitative assessment, SVI-R-1 achieved an R² of 0.9893 in known conditions but dropped to 0.7240 in unfamiliar scenarios. Further training improved generalization, with SVI-R reaching an R² of 0.9297. These findings highlight deep learning's potential for efficient SVI measurement, real-time monitoring, and sludge bulking management, while emphasizing the need for improved robustness and transferability.