Nitrate residual as a key parameter to efficiently control partial denitrification coupling with anammox.

Despite the increased research efforts, full-scale implementation of shortcut nitrogen removal strategies has been challenged by the lack of consistent nitrite-oxidizing bacteria out-selection. This paper proposes an alternative path using partial denitrification (PdN) selection coupled with anaerobic ammonium-oxidizing bacteria (AnAOB). A nitrate residual concentration (>2 mg N/L) was identified as the crucial factor for metabolic PdN selection using acetate as a carbon source, unlike the COD/N ratio which was often suggested. Therefore, a novel and simple acetate dosing control strategy based on maintaining a nitrate concentration was tested in the absence and presence of AnAOB, achieving PdN efficiencies above 80%. The metabolic-based PdN selection allowed for flexibility to move between PdN and full denitrification when required to meet effluent nitrate levels. Due to the independence of this strategy on species selection and management of nitrite competition, this novel approach will guarantee nitrite availability for AnAOB under mainstream conditions unlike shortcut nitrogen removal approaches based on NOB out-selection. Overall, a COD addition of only 2.2 g COD/g TIN removed was needed for the PdN-AnAOB concept showing its potential for significant savings in external carbon source needs to meet low TIN effluent concentrations making this concept a competitive alternative. PRACTITIONER POINTS: Nitrate residual is the key control parameter for partial denitrification selection. Metabolic selection allowed for flexibility of moving from partial to full denitrification. 2.2 g COD/g TIN removed was needed for partial denitrification-anammox process.

Impact of carbon source and COD/N on the concurrent operation of partial denitrification and anammox.

In this study, concurrent operation of anammox and partial denitrification within a nonacclimated mixed culture system was proposed. The impact of carbon sources (acetate, glycerol, methanol, and ethanol) and COD/NO3- -N ratio on partial denitrification selection under both short- and long-term operations was investigated. Results from short-term testing showed that all carbon sources supported partial denitrification. However, acetate and glycerol were preferred due to their display of efficient partial denitrification selection, which may be related to their different electron transport pathways in comparison with methanol. Long-term operation confirmed results of batch tests by showing the contribution of partial denitrification to nitrate removal above 90% after acclimation in both acetate and glycerol reactors. In contrast, methanol showed challenges of maintaining efficient partial denitrification. COD/NO3- -N ratio mainly controlled the rate of nitrate reduction and not directly partial denitrification selection; thus, it should be used to balance between denitrification rate and anammox rate. PRACTITIONER POINTS: The authors aimed to investigate the impact of carbon sources and COD/NO3-N ratio on partial denitrification selection. All the carbon sources supported partial denitrification as long as the nitrite sink was available. 90% partial denitrification could be achieved with both acetate and glycerol in long-term operations. COD/NO3-N ratio did not directly control partial denitrification but can be used to balance between denitrification rate and anammox rate.

Supernatant organics from anaerobic digestion after thermal hydrolysis cause direct and/or diffusional activity loss for nitritation and anammox.

Treatment of sewage sludge with a thermal hydrolysis process (THP) followed by anaerobic digestion (AD) enables to boost biogas production and minimize residual sludge volumes. However, the reject water can cause inhibition to aerobic and anoxic ammonium-oxidizing bacteria (AerAOB & AnAOB), the two key microbial groups involved in the deammonification process. Firstly, a detailed investigation elucidated the impact of different organic fractions present in THP-AD return liquor on AerAOB and AnAOB activity. For AnAOB, soluble compounds linked to THP conditions and AD performance caused the main inhibition. Direct inhibition by dissolved organics was also observed for AerAOB, but could be overcome by treating the filtrate with extended aerobic or anaerobic incubation or with activated carbon. AerAOB additionally suffered from particulate and colloidal organics limiting the diffusion of substrates. This was resolved by improving the dewatering process through an optimized flocculant polymer dose and/or addition of coagulant polymer to better capture the large colloidal fraction, especially in case of unstable AD performance. Secondly, a new inhibition model for AerAOB included diffusion-limiting compounds based on the porter-equation, and achieved the best fit with the experimental data, highlighting that AerAOB were highly sensitive to large colloids. Overall, this paper for the first time provides separate identification of organic fractions within THP-AD filtrate causing differential types of inhibition. Moreover, it highlights the combined effect of the performance of THP, AD and dewatering on the downstream autotrophic nitrogen removal kinetics.

Dual substrate limitation modeling and implications for mainstream deammonification.

Substrate limitation occurs frequently in wastewater treatment and knowledge about microbial behavior at limiting conditions is essential for the use of biokinetic models in system design and optimization. Monod kinetics are well-accepted for modeling growth rates when a single substrate is limiting, but several models exist for treating two or more limiting substrates simultaneously. In this study three dual limitation models (multiplicative, minimum, and Bertolazzi) were compared based on experiments using nitrite-oxidizing bacteria (limited by dissolved oxygen and nitrite) and ANaerobic AMMonia-OXidizing bacteria or Aanammox (limited by ammonium and nitrite) within mixed liquor from deammonification pilots. A deterministic likelihood-based parameter estimation followed by Bayesian inference was used to estimate model-specific parameters. The minimum model outperformed the other two by a slight margin in three separate analyses. 1) Parameters estimated using the minimum model were closest to parameters estimated from single limitation batch tests. 2) Among simulations based on each model's own estimated parameters, the minimum model best described the experimental observations. 3) Among simulations based on parameters estimated from single limitation, the minimum model best described the experimental observations. The dual substrate model selected among the three studied can effect a 75% process performance variation based on simulations of a full-scale mainstream deammonification system.

Settling regimen transitions quantify solid separation limitations through correlation with floc size and shape.

This study monitored three different activated sludge systems from the Blue Plains Advanced Wastewater Plant for a 1 year period to explore the relationship between effluent quality and activated sludge settling and flocculation behavior. Hindered settling rates (ISV) and sludge volume index (SVI) measurements were collected weekly. Novel metrics based on the solids concentration at which the transition between settling regimens occurred were also collected weekly. The transitional metrics were Threshold of Flocculation (TOF), and Limit of Stokesian Settling (LOSS). They marked the transition from discreet to flocculant settling, and from flocculant to hindered settling, respectively. A pilot clarifier and settling column were run and filmed to determine floc morphological properties. SVI was found to lose sensitivity (r < 0.20) when characterizing ISV above a hindered settling rate of 3 m h-1. ISV and LOSS had a strong correlation (r = 0.71), but ISV was subject to change, depending on the solids concentration. Two sludge matrix limitations influencing effluent quality were characterized by transition concentrations; pinpoint floc formation, and loose floc formation. Pinpoint flocs had TOF values above 400 mg TSS L-1; loose floc formation sludge had TOF and LOSS values below 400 mg TSS L-1 and 900 mg TSS L-1, respectively. TOF was found to correlate with the particle size distribution while LOSS correlated to the settling velocity distribution. The use of both TOF and LOSS is a quick and effective way to characterize limitations effecting effluent quality.

WITHDRAWN: Reduced thrombin activatable fibrinolysis inhibitor and enhanced inflammatory markers in ACS.

Ahead of Print article withdrawn by publisher.

Deammonification for digester supernatant pretreated with thermal hydrolysis: overcoming inhibition through process optimization.

The thermal hydrolysis process (THP) has been proven to be an excellent pretreatment step for an anaerobic digester (AD), increasing biogas yield and decreasing sludge disposal. The goal of this work was to optimize deammonification for efficient nitrogen removal despite the inhibition effects caused by the organics present in the THP-AD sludge filtrate (digestate). Two sequencing batch reactors were studied treating conventional digestate and THP-AD digestate, respectively. Improved process control based on higher dissolved oxygen set-point (1 mg O2/L) and longer aeration times could achieve successful treatment of THP-AD digestate. This increased set-point could overcome the inhibition effect on aerobic ammonium-oxidizing bacteria (AerAOB), potentially caused by particulate and colloidal organics. Moreover, based on the mass balance, anoxic ammonium-oxidizing bacteria (AnAOB) contribution to the total nitrogen removal decreased from 97 ± 1 % for conventional to 72 ± 5 % for THP-AD digestate treatment, but remained stable by selective AnAOB retention using a vibrating screen. Overall, similar total nitrogen removal rates of 520 ± 28 mg N/L/day at a loading rate of 600 mg N/L/day were achieved in the THP-AD reactor compared to the conventional digestate treatment operating at low dissolved oxygen (DO) (0.38 ± 0.10 mg O2/L).

A new optimized GA-RBF neural network algorithm.

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.