Morphology and physiology of anaerobic granular sludge exposed to an organic solvent

Abstract

The use of quantitative image analysis techniques, together with physiological information might be used to monitor and detect operational problems in advance to reactor performance failure. Industrial organic solvents, such as White Spirit, are potentially harmful to granular sludge. In preliminary batch assays, 33 mg L(-1) of solvent caused 50% relative biomass activity loss. In an expanded granular sludge blanket reactor fed with 40 mg L(-1) of solvent, during 222h, the reactor performance seemed to be unaffected, presenting COD removal efficiency consistently >95%. However, in the last days of exposure, the biogas production and the methane content were inhibited. Afterwards, already during recovery phase, the COD removal efficiency decreased to 33%, probably because the reactor was underloaded and the biomass became saturated in solvent only at this stage. In the first hours of exposure the specific acetoclastic and the specific hydrogenotrophic methanogenic activities decreased 29% and 21%, respectively. The % of aggregates projected area with equivalent diameter (D(eq))>1mm decreased from 81% to 53%. The mean D(eq) of the aggregates > or =0.2mm decreased, as well as the settling velocity, showing that the granules experienced fragmentation phenomenon caused by the solvent shock load. The ratio between total filaments length and total aggregates projected area (LfA) increased 2 days before effluent volatile suspended solids, suggesting that LfA could be an early-warning indicator of washout events.

Sludge volume index and suspended solids estimation of mature aerobic granular sludge by quantitative image analysis and chemometric tools

“…Although QIA techniques have already been successfully applied to monitor anaerobic granular sludge [29][30][31], to the authors' knowledge, studies regarding the application of QIA to AGS systems are still scarce. Nonetheless, the application of QIA can be a useful tool to characterize, for example, the start-up of AGS systems and the structure of mature AGS, as well as to monitor the balance between suspended and granular fractions.…”

Morphological and physiological response of anaerobic granular sludge during the enhanced biohydrogen process via cascade acid stress

“…Interestingly, no significant size reduction of anaerobic granules in an EGSB reactor could be observed in the larger aggregates in the case of two groups after detergent shocks [9]. Moreover, it was found that anaerobic granules experienced fragmentation, and about 50% loss of specific acetoclastic activity occurred suffering 33 mg/L of solvent, while the overall reactor performance was unaffected by a White Spirit shock load of 9 days at 40 mg/L [10]. To accurately predict the overall performance of the anaerobic granules in the presence of environment fluctuations, integration of morphological and physiological changes might be indispensable.…”

Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment: a review

“…In a different experiment, a commercial detergent and a solvent were fed to lab-scale expanded granular sludge bed (EGSB) reactors to detect the effects of toxic compounds from the cleaning stages (Costa et al 2007(Costa et al , 2009b. The inhibitory effects were dependent on the surfactant concentration and exposure time.…”

“…Furthermore, the LfA parameter acted as an early-warning indicator of the biomass washout, since it increased 3 and 5 days before effluent VSS increase, respectively, in shock with 150 and 300 mg COD L −1 of detergent (Costa et al 2007). During the solvent shock load (Costa et al 2009b), the percentage of the aggregates projected area with D eq >1 mm decreased from 81 to 53 %. Furthermore, the mean D eq of the aggregates >0.2 mm decreased, as well as the settling velocity, showing that the granules underwent a fragmentation process (Fig.…”

“…Its use, coupled with multivariate statistical techniques, represents a significant advance in the monitoring and control of the granule morphology stability and helps to define parameters to detect in real time problematic situations. Quantitative image analysis techniques have already been used to describe micro-and macrostructural changes of anaerobic granular sludge during transient periods, organic load disturbances (Costa et al 2009a), and toxic load shocks (Costa et al 2007(Costa et al , 2009b. Later, the application of PCA to these datasets, obtained during the transient states, determined the latent variables, encompassing a weighted sum of performance, physiological, and morphological information.…”