Biomass density and filament length synergistically affect activated sludge settling: systematic quantification and modeling.

Settling of the biomass produced during biological treatment of wastewater is a critical and often problematic process. Filamentous bacteria content is the best-known factor affecting biomass settleability in activated sludge wastewater treatment systems, and varying biomass density has recently been shown to play an important role as well. The objective of this study was to systematically determine how filament content and biomass density combine to affect microbial biomass settling, with a focus on density variations over the range found in full-scale systems. A laboratory-scale bioreactor system was operated to produce biomass with a range of filamentous bacterium contents. Biomass density was systematically varied in samples from this system by addition of synthetic microspheres to allow separation of filament content and density effects on settleability. Fluorescent in-situ hybridization indicated that the culture was dominated by Sphaerotilus natans, a common contributor to poor settling in full-scale systems. A simple, image-based metric of filament content (filament length per floc area) was linearly correlated with the more commonly used filament length per dry biomass measurement. A non-linear, semi-empirical model of settleability as a function of filament content and density was developed and evaluated, providing a better understanding of how these two parameters combine to affect settleability. Filament content (length per dry biomass weight) was nearly linearly related to sludge volume index (SVI) values, with a slightly decreasing differential, and biomass density exhibited an asymptotic relationship with SVI. The filament content associated with bulking was shown to be a function of biomass density. The marginal effect of filament content on settleability increased with decreasing biomass density (low density biomass was more sensitive to changes in filament content than was high density biomass), indicating a synergistic relationship between these factors. Consideration of both biomass density and filament content, as by the methods and model described herein, should improve operation and troubleshooting of settling processes for biological solids.

Is the whole the sum of its parts? Agent-based modelling of wastewater treatment systems.

Agent-based models (ABMS) simulate individual units within a system, such as the bacteria in a biological wastewater treatment system. This paper outlines past, current and potential future applications of ABMs to wastewater treatment. ABMs track heterogeneities within microbial populations, and this has been demonstrated to yield different predictions of bulk behaviors than the conventional, "lumped" approaches for enhanced biological phosphorus removal (EBPR) completely mixed reactors systems. Current work included the application of the ABM approach to bacterial adaptation/evolution, using the model system of individual EBPR bacteria that are allowed to evolve a kinetic parameter (maximum glycogen storage) in a competitive environment. The ABM approach was successfully implemented to a simple anaerobic-aerobic system and it was found the differing initial states converged to the same optimal solution under uncertain hydraulic residence times associated with completely mixed hydraulics. In another study, an ABM was developed and applied to simulate the heterogeneity in intracellular polymer storage compounds, including polyphosphate (PP), in functional microbial populations in enhanced biological phosphorus removal (EBPR) process. The simulation results were compared to the experimental measurements of single-cell abundance of PP in polyphosphate accumulating organisms (PAOs), performed using Raman microscopy. The model-predicted heterogeneity was generally consistent with observations, and it was used to investigate the relative contribution of external (different life histories) and internal (biological) mechanisms leading to heterogeneity. In the future, ABMs could be combined with computational fluid dynamics (CFD) models to understand incomplete mixing, more intracellular states and mechanisms can be incorporated, and additional experimental verification is needed.

Distributed microbial state effects on competition in enhanced biological phosphorus removal systems.

Computer simulation of activated sludge population dynamics is a useful tool in process design, operation, and troubleshooting, but currently available programs rely on the assumption of "lumped," or average, system characteristics in each reactor, such as microbial storage product contents. In reality, the states of individual bacteria are likely to vary due to variable residence times in reactors with completely mixed hydraulics. Earlier work by the present author introduced the MATLAB-based distributed state simulation program, Dissimulator 1.0, and demonstrated that distributed states may be particularly important in enhanced biological phosphorus removal (EBPR) systems, which rely on the cycling of bacteria through anaerobic and aerobic reactors to select for a population accumulating multiple microbial storage products. This paper explores the relationships between distributed state profiles, variable anaerobic and aerobic SRTs, and the process rates predicted by lumped and distributed approaches. Consistent with previous results, the lumped approach consistently predicted better EBPR performance than did the distributed approach. The primary reason for this was the presence of large fractions of polyphosphate accumulating organisms (PAOs) with depleted microbial storage product contents, which led to overestimation of process rates by the lumped approach. Distributed and lumped predictions were therefore most similar when microbial storage product depletion was minimal. The effects of variable anaerobic and aerobic SRTs on distributed profile characteristics and process rates are presented. This work demonstrated that lumped assumptions may overestimate EBPR performance, and the degree of this error is a function of the distributed state profile characteristics such as the degree to which fractions of the biomass contain depleted microbial storage product contents.

Effects of pH on enhanced biological phosphorus removal metabolisms.

Laboratory-scale sequencing batch reactors exhibiting enhanced biological phosphorus removal were analyzed for pH effects on anaerobic phosphorus (P) release, glycogen degradation, and acetate uptake. Samples with non-soluble P/total suspended solids values of either 0.13-0.14 mg/mg (HP) or 0.065-0.075 mg/mg (LP) were analyzed in anaerobic batch tests with excess acetate addition at pH values ranging from 5.2 to 9.5. A polyphosphate-accumulating metabolism (PAM) had a competitive advantage over a glycogen-accumulating metabolism (GAM) at pH > 7.0. Maximum acetate uptake rates by the HP and LP samples occurred at pH values 8.0 and 6.9, respectively. Anaerobic P release/acetate uptake increased with increasing pH at rates similar to previously reported values. Glycogen degradation/acetate uptake decreased with increasing pH above pH 7, which disagreed with previous reports that glycogen degradation/acetate increased or was unaffected by increasing pH. The results suggested that the acetate uptake mechanisms of GAM and PAM may be different.

Density separation and molecular methods to characterize enhanced biological phosphorus removal system populations.

A novel approach to the identification of microorganisms that accumulate high density microbial storage products based on density separation, denaturing gradient gel electrophoresis (DGGE), and DNA sequencing was developed and applied to bench and pilot scale enhanced biological phosphorus removal (EBPR) systems. Polyphosphate (PP), glycogen, and polyhydroxyalkanoates (PHAs), are all of higher density than a typical bacterial cell. PP-accumulating organisms (PAOs), the organisms responsible for EBPR, accumulate all three of these storage products. Density separation in a homogenous solution of Percoll produced a high-density biomass fraction with a relatively high concentration of PAOs, as determined by Neisser staining. DNA was extracted from these fractions, amplified, and separated by DGGE. DGGE profiles demonstrated some bacterial strains were present at a greater concentration in the high density fractions than in low density fractions. These strains were considered PAO candidates. 5 of 12 PAO candidates from high density fractions were gamma Proteobacteria and only 1 was a beta Proteobacterium. 2 PAO candidates were most similar to recently identified gamma Proteobacteria sequences obtained by DGGE analysis of a deteriorated benchtop EBPR system.

Microbial storage products, biomass density, and settling properties of enhanced biological phosphorus removal activated sludge.

The relationships between bacterial storage products, density, and settling characteristics were determined in a laboratory-scale sequencing batch reactor (SBR) enhanced biological phosphorus removal (EBPR) system. Both long-term and single anaerobic-aerobic cycle variations in these properties were studied. Increased polyphosphate (PP) content of the biomass during long-term operation resulted in improved sludge volume index (SVI) values. End-aerobic phase (after phosphate (P) uptake) SVI values were consistently lower than end-anaerobic phase (after P release) values. Neither filamentous nor slime bulking were evident by microscopic observations. Biomass density increased at a rate of 1.2 mg/L per each 1% increase in biomass P content. End-aerobic phase samples had an average 25% higher buoyant density than end-anaerobic phase samples; which was attributed to aerobic P uptake. Biomass density was negatively correlated with SVI values, and SVI values increased sharply at low biomass density. A mathematical model developed by Mas et al. (1985) was modified to predict total cell density based on literature values of PP, glycogen (GLY), and poly-b-hydroxybutyrate (PHB) densities. Model predictions were in good agreement with experimental results, although improved measurement of PP density is required to improve model predictions.