Monitoring morphological changes from activated sludge to aerobic granular sludge under distinct organic loading rates and increasing minimal imposed sludge settling velocities through quantitative image analysis. (January 2022)

• Record Type: Journal Article
• Authors: Silva, Sérgio A.; Val del Río, Angeles; Amaral, António L.; Ferreira, Eugénio C.; Madalena Alves, M.; Mesquita, Daniela P.
• Is Part Of: Chemosphere. Volume 286:Part 2(2022)
• Publication Date: 2022-01
• Subjects: Aerobic granulation process -- Sequencing batch reactors -- Image processing -- Partial least squares -- Principal component analysis; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2021.131637
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Quantitative image analysis (QIA) was used for monitoring the morphology of activated sludge (AS) during a granulation process and, thus, to define and quantify, unequivocally, structural changes in microbial aggregates correlated with the sludge properties and granulation rates. Two sequencing batch reactors fed with acetate at organic loading rates of 1.1 ± 0.6 kgCOD m −3 d −1 (R1) and 2.0 ± 0.2 kgCOD m −3 d −1 (R2) and three minimal imposed sludge settling velocities (0.27 m h −1, 0.53 m h −1, and 5.3 m h −1 ) induced distinct granulation processes and rates. QIA results evidenced the turning point from flocculation to granulation processes by revealing the differences in the aggregates' stratification patterns and quantifying the morphology of aggregates with equivalent diameter (Deq) of 200 μm ≤ Deq ≤ 650 μm. Multivariate statistical analysis of the QIA data allowed to distinguish the granulation status in both systems, by clustering the observations according to the sludge aggregation and granules maturation status, and successfully predicting the sludge volume index measured at 5 min (SVI5 ) and 30 min (SVI30 ). These results evidence the possibility of defining unequivocally the granulation rate and anticipating the sludge settling properties at early stages of the process using QIA data. Hence, QIA could be used to predict episodes of granules disruption and hindered settling ability in aerobic granulation sludge processes. Graphical abstract: Image 1 Highlights: QIA is a suitable tool to access aerobic sludge granulation rates. The morphology of small granules reflected the degree of sludge aggregation. Principal component analysis allowed to discriminate the granulation process. SVI5 and SVI30 were successfully predicted by partial least square regression. The sludge morphological changes correlated best with the SVI5 .