A novel method for identifying aerobic granular sludge state using sorting, densification and clarification dynamics during the settling process

• Published in: Water research (Oxford) Vol. 253; p. 121336
• Main Authors: Li, Zhi-Hua, Wang, Ruo-Lan, Lu, Meng, Wang, Xin, Huang, Yong-Peng, Yang, Jia-Wei, Zhang, Tian-Yu
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2024
• Subjects: Aerobic granular sludge; Image processing; Respirogram; Settling ability; Textural features; Aerobic granular sludge; Respirogram; Image processing; Settling ability; Textural features
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2024.121336

•Entropy of image texture reveals densification, clarification, and sorting of sludge.•Dynamic entropy effectively differentiates various types of granular sludge.•Prompt sorting during the settling process indicates stable granulation.•Image-based monitoring correlate with respirogram indexes. Aerobic granular sludge is one of the most promising biological wastewater treatment technologies, yet maintaining its stability is still a challenge for its application, and predicting the state of the granules is essential in addressing this issue. This study explored the potential of dynamic texture entropy, derived from settling images, as a predictive tool for the state of granular sludge. Three processes, traditional thickening, often overlooked clarification, and innovative particle sorting, were used to capture the complexity and diversity of granules. It was found that rapid sorting during settling indicates stable granules, which helps to identify the state of granules. Furthermore, a relationship between sorting time and granule heterogeneity was identified, helping to adjust selection pressure. Features of the dynamic texture entropy well correlated with the respirogram, i.e., R2 were 0.86 and 0.91 for the specific endogenous respiration rate (SOURe) and the specific quasi-endogenous respiration rate (SOURq), respectively, providing a biologically based approach for monitoring the state of granules. The classification accuracy of models using features of dynamic texture entropy as an input was greater than 0.90, significantly higher than the input of conventional features, demonstrating the significant advantage of this approach. These findings contributed to developing robust monitoring tools that facilitate the maintenance of stable granular sludge operations. [Display omitted]