Microbial community in an anaerobic membrane bioreactor and its performance in treating organic solid waste under controlled and deteriorated conditions.

The adoption of anaerobic membrane bioreactors (AnMBRs) for organic solid waste management is important for the recovery of energy and high-quality treated water. However, few studies have focused on AnMBR treatment of high-strength organic solid waste and the microorganisms involved under deteriorated operating conditions. In the present study, a 15-L bench-scale AnMBR was operated using a model slurry of high-strength organic solid waste with the organic loading rate (OLR) increasing from 2.3 g chemical oxygen demand (COD) L-1 day-1 (represented as a controlled condition) to 11.6 g COD L-1 day-1 (represented as a deteriorated condition), and microbial community dynamics over 120 days of operation were analyzed. The abundances of methanogens and bacteria that were dominant under the controlled condition decreased as a result of both high organic loading and sludge withdrawal under the deteriorated condition and did not recover thereafter. Instead, numbers of putative volatile fatty acid (VFA)-producing bacterial operational taxonomic units (OTUs) related to the genus Prevotella increased rapidly, reaching a relative abundance of 43.2%, leading to the deterioration of methanogenic AnMBR operation. Considering that the sequences of these OTUs exhibited relatively low sequence identity (91-95%) to those of identified Prevotella species, the results strongly suggest that the accumulation of VFAs by novel VFA-producing bacteria in the digestion sludge promotes the disruption of the methanogen community under deteriorated conditions.

Unexpected diversity of acetate degraders in anaerobic membrane bioreactor treating organic solid waste revealed by high-sensitivity stable isotope probing.

In anaerobic membrane bioreactor (AnMBR) treating organic solid waste, acetate is one of the most important precursors to CH4. However, the identity and diversity of anaerobic acetate degraders are largely unknown, possibly due to their slow growth rates and low abundances. Here, we identified acetate-degrading microorganisms in the AnMBR sludges by high-sensitivity stable isotope probing. Degradation of the amended 13C-acetate coincided with production of 13CH4 and 13CO2 during the sludge incubation. High-throughput sequencing of RNA density fractions indicated that the aceticlastic and hydrogenotrophic methanogens, i.e., Methanosaeta sp. (acetate dissimilator) and Methanolinea sp. (acetate assimilator), incorporated 13C-acetate significantly. Remarkably, 22 bacterial species incorporating 13C-acetate were identified, whereas their majority was distantly related to the cultured representatives. Only two of them were the class Deltaproteobacteria-affiliated lineages with syntrophic volatile fatty acid oxidation activities. Phylogenetic tree analysis and population dynamics tracing revealed that novel species of the hydrolyzing and/or fermenting taxa, such as the phyla Bacteroidetes, Chloroflexi and Lentisphaerae, exhibited low relative abundances comparable to that of Methanolinea sp. (0.00011%) during the AnMBR operation, suggesting that these bacteria were involved in anaerobic acetate assimilation. Meanwhile, novel species of the phyla Firmicutes, Synergistetes and Caldiserica, the candidate phyla Aminicenantes and Atribacteria and the candidate division GOUTA4-related clade, as well as the known Deltaproteobacteria members, existed at relatively high abundances (0.00031%-0.31121%) in the reactor, suggesting that these bacterial species participated in anaerobic dissimilation of acetate, e.g., syntrophic acetate oxidation. The results of this study demonstrated the unexpected diversity and ecophysiological features of the anaerobic acetate degraders in the AnMBR treating organic solid waste.

Clarifying prokaryotic and eukaryotic biofilm microbiomes in anaerobic membrane bioreactor by non-destructive microscopy and high-throughput sequencing.

Anaerobic membrane bioreactor (AnMBR) is used for the treatment of organic solid waste. Clogging of filtration membrane pores, called membrane fouling, is one of the most serious issues for the sustainable operation of AnMBR. Although the physical and chemical mechanisms of the membrane fouling have been widely studied, the biological mechanisms are still unclear. The biofilm formation and development on the membrane might cause the membrane fouling. In this study, the prokaryotic and eukaryotic microbiomes of the membrane-attached biofilms in an AnMBR treating a model slurry of organic solid waste were investigated by non-destructive microscopy and high-throughput sequencing of 16S and 18S rRNA genes. The non-destructive visualization indicated that the biofilm was layered with different structures. The lowermost residual fouling layer was mesh-like and composed of filamentous microorganisms, while the upper cake layer was mainly the non-dense and non-cell region. The principal coordinate and phylogenetic analyses of the sequence data showed that the biofilm microbiomes were different from the sludge. The lowermost layer consisted of operational taxonomic units that were related to Leptolinea tardivitalis and Methanosaeta concilii (9.53-10.07% and 1.14-1.64% of the total prokaryotes, respectively) and Geotrichum candidum (30.22-82.31% of the total eukaryotes), all of which exhibited the filamentous morphology. Moreover, the upper layer was inhabited by the presumably cake-degrading bacteria and predatory eukaryotes. The biofilm microbiome features were consistent with the microscope-visualized structure. These results demonstrated that the biofilm structure and microbiome were the layer specific, which provides better understanding of biological mechanisms of membrane fouling in the AnMBR.

Computational design of molecules for an all-quinone redox flow battery.

Inspired by the electron transfer properties of quinones in biological systems, we recently showed that quinones are also very promising electroactive materials for stationary energy storage applications. Due to the practically infinite chemical space of organic molecules, the discovery of additional quinones or other redox-active organic molecules for energy storage applications is an open field of inquiry. Here, we introduce a high-throughput computational screening approach that we applied to an accelerated study of a total of 1710 quinone (Q) and hydroquinone (QH2) (i.e., two-electron two-proton) redox couples. We identified the promising candidates for both the negative and positive sides of organic-based aqueous flow batteries, thus enabling an all-quinone battery. To further aid the development of additional interesting electroactive small molecules we also provide emerging quantitative structure-property relationships.

Inhibition of total oxygen uptake by silica nanoparticles in activated sludge.

Nanoparticle toxicity to biological activities in activated sludge is largely unknown. Among the widely used nanoparticles, silica nanoparticles (SNP) have a limited number of studies associated with inhibition to the activated sludge process (ASP). We demonstrated SNP inhibition of activated sludge respiration through oxygen uptake rate (OUR) measurement. Based on the percentage inhibition of total oxygen consumption (IT), we observed that smaller SNPs (12 nm, IT=33 ± 3%; 151 nm, IT=23 ± 2%) were stronger inhibitors than larger SNPs (442 and 683 nm, IT=5 ± 1%). Transmission electron micrographs showed that some of the SNPs were adsorbed on and/or apparently embedded somewhere in the microbial cell membrane. Whether SNPs are directly associated with the inhibition of total oxygen uptake warrants further studies. However, it is clear that SNPs statistically significantly altered the composition of microbial membrane lipids, which was more clearly described by principal component analysis and weighted Euclidian distance (PCA-ED) of the fatty acid methyl ester (FAME) data. This study suggests that SNPs potentially affect the biological activity in activated sludge through the inhibition of total oxygen uptake.

A metal-free organic-inorganic aqueous flow battery.

As the fraction of electricity generation from intermittent renewable sources--such as solar or wind--grows, the ability to store large amounts of electrical energy is of increasing importance. Solid-electrode batteries maintain discharge at peak power for far too short a time to fully regulate wind or solar power output. In contrast, flow batteries can independently scale the power (electrode area) and energy (arbitrarily large storage volume) components of the system by maintaining all of the electro-active species in fluid form. Wide-scale utilization of flow batteries is, however, limited by the abundance and cost of these materials, particularly those using redox-active metals and precious-metal electrocatalysts. Here we describe a class of energy storage materials that exploits the favourable chemical and electrochemical properties of a family of molecules known as quinones. The example we demonstrate is a metal-free flow battery based on the redox chemistry of 9,10-anthraquinone-2,7-disulphonic acid (AQDS). AQDS undergoes extremely rapid and reversible two-electron two-proton reduction on a glassy carbon electrode in sulphuric acid. An aqueous flow battery with inexpensive carbon electrodes, combining the quinone/hydroquinone couple with the Br2/Br(-) redox couple, yields a peak galvanic power density exceeding 0.6 W cm(-2) at 1.3 A cm(-2). Cycling of this quinone-bromide flow battery showed >99 per cent storage capacity retention per cycle. The organic anthraquinone species can be synthesized from inexpensive commodity chemicals. This organic approach permits tuning of important properties such as the reduction potential and solubility by adding functional groups: for example, we demonstrate that the addition of two hydroxy groups to AQDS increases the open circuit potential of the cell by 11% and we describe a pathway for further increases in cell voltage. The use of π-aromatic redox-active organic molecules instead of redox-active metals represents a new and promising direction for realizing massive electrical energy storage at greatly reduced cost.

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process.

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called 'particle'. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.

Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network.

This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.

Visualization of high-dimensional combinatorial catalysis data.

The role of various techniques for visualization of high-dimensional data is demonstrated in the context of combinatorial high-throughput experimentation (HTE). Applying visualization tools, we identify which constituents of catalysts are associated with final products in a huge combinatorially generated data set of heterogeneous catalysts, and catalytic activity regions are identified with respect to pentanary composition spreads of catalysts. A radial visualization scheme directly visualizes pentanary composition spreads in two-dimensional (2D) space and catalytic activity of a final product by combining high-throughput results from five slate libraries. A glyph plot provides many possibilities for visualizing high-dimensional data with interactive tools. For catalyst discovery and lead optimization, this work demonstrates how large multidimensional catalysis data sets are visualized in terms of quantitative composition activity relationships (QCAR) to effectively identify the relevant key role of compositions (i.e., lead compositions) of catalysts.

Variation of ADM1 by using temperature-phased anaerobic digestion (TPAD) operation.

The objective of the study was to examine the application of the Anaerobic Digestion Model No. 1 (ADM1) developed by the IWA task group for mathematical modelling of anaerobic process. Lab-scale temperature-phased anaerobic digestion (TPAD) process were operated continuously, and were fed with co-substrate composed of dog food and flour. The model platform implemented in the simulation was a derivative of the ADM1. Sensitivity analysis showed that k(m.process) (maximum specific uptake rate) and K(S.process) (half saturation value) had high sensitivities to model components. Important parameters including maximum uptake rate for propionate utilisers (k(m.pro)) and half saturation constant for acetate utilisers (K(S.ac)) in the thermophilic digester and maximum uptake rate for acetate utilisers (k(m.ac)) in the mesophilic digester were estimated using iterative methods, which optimized the parameters with experimental results. Simulation with estimated parameters showed good agreement with experimental results in the case of methane production, uptake of acetate, soluble chemical oxygen demand (SCOD) and total chemical oxygen demand (TCOD). Under these conditions, the model predicted reasonably well the dynamic behavior of the TPAD process for verifying the model.

Sequential modeling of fecal coliform removals in a full-scale activated-sludge wastewater treatment plant using an evolutionary process model induction system.

We propose an evolutionary process model induction system that is based on the grammar-based genetic programming to automatically discover multivariate dynamic inference models that are able to predict fecal coliform bacteria removals using common process variables instead of directly measuring fecal coliform bacteria concentration in a full-scale municipal activated-sludge wastewater treatment plant. A sequential modeling paradigm is also proposed to derive multivariate dynamic models of fecal coliform removals in the evolutionary process model induction system. It is composed of two parts, the process estimator and the process predictor. The process estimator acts as an intelligent software sensor to achieve a good estimation of fecal coliform bacteria concentration in the influent. Then the process predictor yields sequential prediction of the effluent fecal coliform bacteria concentration based on the estimated fecal coliform bacteria concentration in the influent from the process estimator with other process variables. The results show that the evolutionary process model induction system with a sequential modeling paradigm has successfully evolved multivariate dynamic models of fecal coliform removals in the form of explicit mathematical formulas with high levels of accuracy and good generalization. The evolutionary process model induction system with sequential modeling paradigm proposed here provides a good alternative to develop cost-effective dynamic process models for a full-scale wastewater treatment plant and is readily applicable to a variety of other complex treatment processes.

Analysis and application of ADM1 for anaerobic methane production.

An anaerobic model for the serum bottle test was developed and analyzed with sensitivities of stoichiometric and kinetic parameters to the components in order to establish a basis for appropriate application of the model. Anaerobic glucose degradation in a serum bottle was selected as an example. The anaerobic model was developed based on the anaerobic digestion model no. 1 (ADM1), which had five processes with 17 kinetic and stoichiometric parameters. Sensitivity analysis showed that the yield of product on the substrate (f) has high sensitivities to model components, and that the methane concentration was the most sensitive component. Important parameters including yield of product on the substrate (f), yield of biomass on the substrate (Y), and half-saturation values (K) were estimated using genetic algorithms, which optimized the parameters with experimental results. The Monod maximum specific uptake rate (k) was, however, so strongly associated with the concentration of biomass, that values could not be estimated individually. Simulation with estimated parameters showed good agreement with experimental results in the case of methane production. However, there were some differences in acetate and propionate concentrations.