A new approach to assess the dependency of extant half-saturation coefficients on maximum process rates and estimate intrinsic coefficients

Integrated control strategy for dual sludge ages in the high-concentration powder carrier bio-fluidized bed (HPB) technology: Enhancing municipal wastewater treatment efficiency

The high-concentration powder carrier bio-fluidized bed (HPB) technology is an emerging approach that enables on-site upgrading of wastewater treatment plants (WWTPs). HPB technology promotes the formation of biofilm sludge with micron-scale composite powder carriers as the core and suspended sludge mainly composed of flocs surrounding the biofilm sludge. This study proposed a novel integrated strategy for assessing and controlling the sludge ages in suspended/bio-film activated sludge supported by micron-scale composite powder carrier. Utilizing the cyclone unit and the corresponding theoretical model, the proposed strategy effectively addresses the sludge ages contradiction between denitrifying bacteria and polyphosphate-accumulating organisms (PAOs), thereby enhancing the efficiency of municipal wastewater treatment. The sludge age of the suspended (25 d) and bio-film (99 d) sludge, calculated using the model, contribute to the simultaneous removal of nitrogen and phosphorus. Meanwhile, the model further estimates distinct contributions of suspended and bio-film sludge to chemical oxygen demand (COD) and total nitrogen (TN), which are 55% and 42% for COD, 20% and 57% for TN of suspended sludge and bio-film sludge, respectively. This suggests that the contribution of suspended sludge and bio-film sludge to COD and TN removal efficiency can be determined and controlled by the operational conditions of the cyclone unit. Additionally, the simulation values for COD, ammonia nitrogen (NH4+-N), TN and total phosphorus (TP) closely align with the actual values of WWTPs over 70 days (p < 0.001) with the correlation coefficients (R2) of 0.9809, 0.9932, 0.9825, and 0.837, respectively. These results support the theoretical foundation of HPB technology for simultaneous nitrogen and phosphorus removal in sewage treatment plants. Therefore, this model serves as a valuable tool to guide the operation, design, and carrier addition in HPB technology implementation.

The best location for the application of static magnetic fields based on biokinetic coefficients in complete-mix activated sludge process

The use of the kinetic coefficients for the mathematical expression of the biochemical processes and the relationship between the effective parameters is importance. Change of the biokinetic coefficients in the complete-mix activated sludge processes were calculated for 1 month operation of the activated sludge model (ASM) in a Lab-scale in three series. 15 mT intensity of static magnetic fields (SMFs) applied on the aeration reactor (ASM 1), clarifier reactor (ASM 2) and, sludge returning systems (ASM 3) for 1 h, daily. During the operation of the systems, five basic biokinetic coefficients such as maximum specific substrate utilization rate (k), heterotrophic half-saturation substrate concentration (K_s), decay coefficient (k_d), yield coefficient (Y) and, maximum specific microbial growth rate (μ_max) were determined. The rate of k (g COD/g Cells.d) in ASM 1 was 2.69% and, 22.79% higher than ASM 2 and, ASM 3. The value of K_s (mg COD/L) was 54.44 and, 71.13 (mg/L) lower than the ASM 2 and, ASM 3. The rate of k_d ASM 1, ASM 2 and, ASM 3 was 0.070, 0.054 and, 0.516 (d^-1). The value of Y (kg VSS/kg COD) in ASM 1 was 0.58% and, 0.48% lower than ASM 2 and, ASM 3. The rate of μ_max (d^-1) in ASM 1 was 0.197, this value for ASM 2 and ASM 3 were 0.324 and 0.309 (d^-1). Related to the biokinetic coefficients analyses the best location for the application of 15 mT SMFs was the aeration reactor, where the present of oxygen, substrate and, SMFs have the greatest impact on the positive changes of these coefficients.

Reactive and microbial inhibitory mechanisms depicting the panoramic view of pH stress effect on common biological nitrification

pH is a crucial factor of microbial nitrification, which often combines with high-strength ammonium to influence nitrogen removal pathway in wastewater treatment. However, the detailed inhibitory mechanisms of pH stress are not sufficiently disclosed yet. In this study, the pH stress effect on nitrification was comprehensively studied by a set of experiments which identified the reactivity of nitrification processes and activity of nitrifiers, the time dependence of inhibition effect and the hybrid pH stress effect with ammonium. The results revealed two distinct inhibitory mechanisms dominating in alkaline and acid ranges. In alkaline range (pH > 8), pH stress causes physiological damages on microorganisms which is named as microbial inhibition. It has the features of less recoverability of nitrifiers, time-dependent inhibition effect and low pH-tolerance of nitrite oxidation bacteria. Free ammonia enhanced microbial inhibition and greatly promoted nitrite accumulation. A novel reactive inhibition mechanism dominated in acid range (pH < 7) was disclosed. It only impedes ammonia oxidation process (AOP) but not impair microbial activity obviously and the effect is time-independent. The mechanism was clarified from H⁺ transport because AOP involved H⁺ production. The H⁺ transport was impeded under acid stress owing to the decrease of pH gradient across cell membrane. The two mechanisms formed a panoramic view of pH stress effect on nitrification advancing the understanding of nitrifier adaptability and nitritation regulation in wastewater treatment processes.

Impacts of co-contaminants and dilution on perchlorate biodegradation using various carbon sources

This research investigates the biodegradation of perchlorate in the presence of the co-contaminants nitrate and chlorate using soluble and slow-release carbon sources. In addition, the impact of bio-augmentation and dilution, which results in lower total dissolved salts (TDS) and contaminant levels, is examined. Laboratory microcosms were conducted using actual groundwater and soils from a contaminated aquifer. The results revealed that both soluble and slow-release carbon sources support biodegradation of contaminants in the sequence nitrate > chlorate > perchlorate. Degradation rates, including and excluding lag times, revealed that the overall impact of the presence of co-contaminants depends on degradation kinetics and the relative concentrations of the contaminants. When the lag time caused by the presence of the co-contaminants is considered, the degradation rates for chlorate and perchlorate were two to three times slower. The results also show that dilution causes lower initial contaminant concentrations, and consequently, slower degradation rates, which is not desirable. On the other hand, the dilution resulting from the injection of amendments to support remediation promotes desirably lower salinity levels. However, the salinity associated with the presence of sulfate does not inhibit biodegradation. The naturally occurring bacteria were able to support the degradation of all contaminants. Bio-augmentation was effective only in diluted microcosms. Proteobacteria and Firmicutes were the dominant phyla identified in the microcosms.

Mainstream short-cut N removal modelling: current status and perspectives

This work gives an overview of the state-of-the-art in modelling of short-cut processes for nitrogen removal in mainstream wastewater treatment and presents future perspectives for directing research efforts in line with the needs of practice. The modelling status for deammonification (i.e., anammox-based) and nitrite-shunt processes is presented with its challenges and limitations. The importance of mathematical models for considering N₂O emissions in the design and operation of short-cut nitrogen removal processes is considered as well. Modelling goals and potential benefits are presented and the needs for new and more advanced approaches are identified. Overall, this contribution presents how existing and future mathematical models can accelerate successful full-scale mainstream short-cut nitrogen removal applications.

Physiological Acclimation Extrapolates the Kinetics and Thermodynamics of Methanogenesis From Laboratory Experiments to Natural Environments

Chemotrophic microorganisms face the steep challenge of limited energy resources in natural environments. This observation has important implications for interpreting and modeling the kinetics and thermodynamics of microbial reactions. Current modeling frameworks treat microbes as autocatalysts, and simulate microbial energy conservation and growth with fixed kinetic and thermodynamic parameters. However, microbes are capable of acclimating to the environment and modulating their parameters in order to gain competitive fitness. Here we constructed an optimization model and described microbes as self-adapting catalysts by linking microbial parameters to intracellular metabolic resources. From the optimization results, we related microbial parameters to the substrate concentration and the energy available in the environment, and simplified the relationship between the kinetics and the thermodynamics of microbial reactions. We took as examples Methanosarcina and Methanosaeta – the methanogens that produce methane from acetate – and showed how the acclimation model extrapolated laboratory observations to natural environments and improved the simulation of methanogenesis and the dominance of Methanosaeta over Methanosarcina in lake sediments. These results highlight the importance of physiological acclimation in shaping the kinetics and thermodynamics of microbial reactions and in determining the outcome of microbial interactions.

Modeling anaerobic digestion metabolic pathways for antibiotic-contaminated wastewater treatment

Anaerobic digestion has been used to treat antibiotic-contaminated wastewaters. However, it is not always effective, since biodegradation is the main removal mechanism and depends on the compound chemical characteristics and on how microbial metabolic pathways are affected by the reactor operational conditions and hydrodynamic characteristics. The aim of this study was to develop a mathematical model to describe 16 metabolic pathways of an anaerobic process treating sulfamethazine-contaminated wastewater. Contois kinetics and a useful reaction volume term were used to represent the biomass concentration impact on bed porosity in a N continuously stirred tank modeling approach. Two sulfamethazine removal hypotheses were evaluated: an apparent enzymatic reaction and a cometabolic degradation. Additionally, long-term modeling was developed to describe how the operational conditions affected the performance of the process. The best degradation correlations were associated with the consumption of carbohydrates, proteins and it was inversely related to acetic acid production during acidogenesis.

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.

From the affinity constant to the half-saturation index: understanding conventional modeling concepts in novel wastewater treatment processes

The "affinity constant" (KS) concept is applied in wastewater treatment models to incorporate the effect of substrate limitation on process performance. As an increasing number of wastewater treatment processes rely on low substrate concentrations, a proper understanding of these so-called constants is critical in order to soundly model and evaluate emerging treatment systems. In this paper, an in-depth analysis of the KS concept has been carried out, focusing on the different physical and biological phenomena that affect its observed value. By structuring the factors influencing half-saturation indices (newly proposed nomenclature) into advectional, diffusional and biological, light has been shed onto some of the apparent inconsistencies present in the literature. Particularly, the importance of non-ideal mixing as a source of variability between observed KS values in different systems has been illustrated. Additionally, discussion on the differences existent between substrates that affect half-saturation indices has been carried out; it has been shown that the observed KS for some substrates will reflect transport or biological limitations more than others. Finally, potential modeling strategies that could alleviate the shortcomings of the KS concept have been provided. These could be of special importance when considering the evaluation and design of emerging wastewater treatment processes.

Implementation of a demand-side approach to reduce aeration requirements of activated sludge systems: directed acclimation of biomass and its effect at the process level

Conventionally, increased oxygen requirements of biological wastewater treatment systems have been addressed through supply-side approaches that focus on upgrading the oxygen mass transfer capabilities of aeration devices. Such approaches entail high aeration energy penalties for relatively low performance improvements, as well as causing decreased oxygen mass transfer efficiencies. In this study, a nitrifying community acclimated to low dissolved oxygen (DO) conditions has been demonstrated to yield similar treatment performance as a conventional nitrifying process with 20% less aeration requirements and 20% improvement in mass transfer efficiency. Such a demand-side approach has been shown to be successful due to an improvement of the half saturation constant of the acclimated biomass that has, in turn, been linked to the expression of hemoglobin proteins. Directed acclimation of nitrifying communities could help address the operational requirement to carry out ammonia oxidation at consistently low DO conditions in the mainstream anammox and simultaneous nitrification-denitrification processes.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters.