High rate nitrogen removal by the CANON process at ambient temperature

Completely autotrophic nitrogen removal over nitrite (CANON) is a cost-effective nitrogen removal process. Implementation of the CANON process relies on the cooperation of ammonium-oxidizing and Anammox bacteria, as well as the inhibition of nitrite-oxidizing bacteria. Strict limitations on dissolved oxygen (DO) concentration in the reactor, and the addition of sufficient inorganic carbon in the influent, were adopted as the main operational strategies. The reactor was fed with synthetic inorganic wastewater composed mainly of NH(4)(+)-N, and operated for 106 days. Stable nitrogen removal rates (NRR) of around 1.4 kg N m(-3) d(-1) were obtained at ambient temperature. Morphological characteristics and analysis of bacterial community confirmed the formation of functional outer aerobic and inner anaerobic granular sludge, providing evidence of stable nitrogen removal.     

Critical modeling parameters identified for 3D CFD modeling of rectangular final settling tanks for New York City wastewater treatment plants

New York City Environmental Protection is in the process of incorporating biological nitrogen removal (BNR) in its wastewater treatment plants (WWTPs) which entails operating the aeration tanks with higher levels of mixed liquor suspended solids (MLSS) than a conventional activated sludge process. The objective of this paper is to discuss two of the important parameters introduced in the 3D CFD model that has been developed by the City College of New York (CCNY) group: (a) the development of the 'discrete particle' measurement technique to carry out the fractionation of the solids in the final settling tank (FST) which has critical implications in the prediction of the effluent quality; and (b) the modification of the floc aggregation (K(A)) and floc break-up (K(B)) coefficients that are found in Parker's flocculation equation (Parker et al. 1970, 1971) used in the CFD model. The dependence of these parameters on the predictions of the CFD model will be illustrated with simulation results on one of the FSTs at the 26th Ward WWTP in Brooklyn, NY.     

Protein analysis as a measure of active biomass in activated sludge

Conventional methods to determine the biomass in activated sludge are the measurement of total suspended solids (TSS) and volatile suspended solids (VSS). Such methods do not distinguish between active biomass and inactive organic material. In this study, biomass was determined with both conventional methods and also through measuring the protein content with the modified Lowry method. In order to investigate the relationship between activity and biomass concentration in terms of TSS, VSS and protein content, some starvation experiments were conducted. It was found that the protein fraction of VSS differs under different starvation conditions. The biological activity of the activated sludge was measured as oxygen uptake rate (OUR). The strongest correlation could be measured between protein and OUR under various conditions. The results show that protein is an appropriate parameter for the measurement of the biological activity.     

Denitrification with corncob as carbon source and biofilm carriers

In this research the agricultural by-product corncob was investigated as a carbon source as well as a biofilm carrier to remove organic matter, expressed as chemical oxygen demand (COD) and nitrate nitrogen (nitrate-N), from wastewater in a batch laboratory reactor. The performance of a reactor with corncob as the carbon source and the biofilm carrier was compared with a control batch reactor with suspended plastic carriers and glucose as the sole carbon source. With 60 vol% of corncob carriers inside the reactor, a soluble COD/N ratio of 4.2 g COD g N(-1) was enough for total denitrification, nearly half of the control reactor (9.5 g COD g N(-1)), at 23 h reaction time. The specific denitrification rate decreased with increasing soluble COD consumption for both reactors. Nitrate and COD removal efficiencies decreased with shorter retention times, with accentuated effects in the reactor. This study suggested corncob as a feasible carbon source and that reaction time was a limiting factor with corncob used as the carbon source for denitrification.     

Inorganic fractions in extracellular polymeric substance extracted from activated sludge and biofilm samples by different methods

This study highlighted the inorganic fractions in the extracellular polymeric substance (EPS) extract from two activated sludges and one biofilm. Nine EPS extraction methods (centrifugation, sonication, cation exchange resin (CER) + sonication, CER, heating, formaldehyde + heating, formaldehyde + NaOH, ethanol and EDTA) were used in the study. The EPS extracts had large inorganic fractions, which ranged from 28% to 94% of the EPS dry weight. The EPS inorganic fraction was dependent on the source of the sludge and wastewater, the kinds of bacteria and the extraction method. The EPS extracts obtained by heating and sonication had smaller inorganic fractions than those obtained by centrifugation. The compositions of the inorganic fraction of EPS extracts obtained with CER and sonication + CER showed similar trends. The chemical extraction methods could contaminate the inorganic composition of EPS extracts by impurities, carrying over of the extractant itself or by changing the pH of the solution. Ethanol was the most effective extractant for obtaining inorganic ions.     

Impact of the surface characteristics of rainwater tank material on biofilm development

In order to determine the impact of rainwater construction material on the development of a biofilm, three materials were tested: concrete, clay, and PVC. The biofilm attachment was initially more effective on clay coupons, but, after a period of three days, concrete coupons produced a greater quantity of biofilm than clay and PVC, in that order. The heterotrophic plate count in the rainwater indicated that this quantity tended to first increase following a rainfall, and then decrease. The new materials seemed to attach themselves to the existing biofilm on the wall and/or sediment in the form of small particles. The presence of fecal coliforms in the biofilm coupons was noted after major rainfall events, and this was correlated with the increase in fecal coliforms in the water. This study concluded that the most favorable support for biofilm development is concrete, clay, and PVC, in that order.     

Adsorption of phenanthrene by quaternary ammonium surfactant modified peat and the mechanism involved

Removal of phenanthrene (PHE) from aqueous solution by adsorption onto quaternary ammonium surfactant modified peat was studied. The results show that surfactant modification enhanced the PHE adsorption capacity of peat. Low temperature and neutral pH favored PHE adsorption. Peat modified with long carbon chain surfactant performed better than peat modified with short carbon chain surfactant. The magnitude of PHE adsorption capacity followed the order of MP-HPB>MP-HTAB>MP-TBAB>RP, ranged from 924 to 1,228 μg g(-1). A negative trend between adsorption capacity (y) and (O+N)/C ratio of biosorbent (x) was observed (y = -1,369.6x + 2,176), which confirmed the negative effect of polarity on polycyclic aromatic hydrocarbon (PAH) removal. The study provides a guide to modify raw materials to enhance adsorption of hydrophobic organics.     

Enhancement of nitrate removal in synthetic groundwater using wheat rice stone

To enhance the efficiency of nitrate removal from synthetic groundwater, wheat rice stone (WRS) and granular activated carbon (GAC) were employed as biofilm carriers for denitrification under different HRT (hydraulic retention time) and C/N ratios. Four different ratios of GAC to WRS (0, 0.5, 1.0, and 2.0) were investigated to determine the most appropriate ratio of GAC and WRS. The NO(3)(-)-N, NO(2)(-)-N, COD levels and pH of the effluent were also investigated under various HRT and C/N ratios. The results showed that the column at a GAC/WRS ratio of 1.0 performed best under a C/N ratio of 0.9 and an HRT of 8 h, with 99% nitrate being removed. In addition, little nitrite accumulation and chemical oxygen demand (COD) were observed in effluent under these conditions. These results demonstrated that, with no addition of phosphor in the influent, the nitrate removal efficiency can be enhanced by WRS because WRS can leach trace elements and phosphor to promote the growth of bacteria.     

Learning invariance from natural images inspired by observations in the primary visual cortex

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.     

Improved generative semisupervised learning based on finely grained component-conditional class labeling

We introduce new inductive, generative semisupervised mixtures with more finely grained class label generation mechanisms than in previous work. Our models combine advantages of semisupervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieve accurate classification in the vicinity of labeled samples or prototypes. For our NN-based method, we propose a novel two-stage stochastic data generation, with all samples first generated using a standard finite mixture and then all class labels generated, conditioned on the samples and their components of origin. This mechanism entails an underlying Markov random field, specific to each mixture component or cluster. We invoke the pseudo-likelihood formulation, which forms the basis for an approximate generalized expectation-maximization model learning algorithm. Our NP-based model overcomes a problem with the NN-based model that manifests at very low labeled fractions. Both models are advantageous when within-component class proportions are not constant over the feature space region "owned by" a component. The practicality of this scenario is borne out by experiments on UC Irvine data sets, which demonstrate significant gains in classification accuracy over previous semisupervised mixtures and also overall gains, over KNN classification. Moreover, for very small labeled fractions, our methods overall outperform supervised linear and nonlinear kernel support vector machines.