Modelling anaerobic, aerobic and partial nitritation-anammox granular sludge reactors - A review

Enhanced anammox performance under lower nitrite accumulation in modified partial nitritation-anammox (PN/A) process

Higher nitrite accumulation, which is challenging to achieve reliably, is always sought to obtain better nitrogen removal performance in traditional partial nitritation-anammox (PN/A) process. This study developed a modified PN/A process by introducing nitrite-oxidizing bacteria and endogenous metabolism. Advanced nitrogen removal performance of 95.5 % was achieved at a low C/N ratio of 2.7 under nitrite accumulation ratio (NAR) fluctuations. Higher nitrate accumulation at lower NAR (70 ∼ 40 %) resulted in superior anammox contribution (60 ∼ 75 %) and nitrogen removal performance (93 ∼ 98 %). This was attributed to the higher nitrogen removal efficiency of the post-anoxic endogenous partial denitrification coupling anammox process, although the PN/A process occurring first possessed a faster anammox rate of 2.0 mg NH4+-N /(g VSS⋅h). The introduction of nitrate allowed more nitrite flow to anammox, promoting a high enrichment of anammox bacteria (Ca. Brocadia, 0.3 % to 2.8 %). This study provides new insights into the practical application of the PN/A process.

Impact of benzalkonium chloride on anaerobic granules and its long-term effects on reactor performance

This study assessed the inhibitory and performance-degrading effects induced by the cationic surfactant benzalkonium chloride (BAC) on anaerobic granules during the long-term operation of a laboratory-scale expanded granular sludge bed (EGSB) reactor. To address the critical scientific problem of how BAC affects the efficiency of EGSB reactors, this research uniquely evaluated the long-term stress response to BAC by systematically comparing continuous and discontinuous inhibitor exposure scenarios. The novel comparison demonstrated that inhibitor concentration is of minor relevance compared to the biomass-specific cumulative inhibitor load in the reactor. After exceeding a critical biomass-specific cumulative inhibitor load of 6.1-6.5 mg BAC/g VS, continuous and discontinuous exposure to BAC caused comparable significant deterioration in reactor performance, including accumulation of volatile fatty acids (VFA), decreased removal efficiency, reduced methane production, as well as the wash-out, flotation, and disintegration of anaerobic granules. BAC exposures had a more detrimental effect on methanogenesis than on acidogenesis. Moreover, long-term stress by BAC led to an inhibition of protein production, resulting in a decreased protein-to-polysaccharide ratio of extracellular polymeric substances (EPS) that promoted destabilizing effects on the granules. Finally, hydrogenotrophic methanogenesis was triggered. Reactor performance could not be restored due to the severe loss of granular sludge.

A Review of Computational Modeling in Wastewater Treatment Processes

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

Strengthen high-loading operation of wastewater treatment plants by composite micron powder carrier: Microscale control of carbon, nitrogen, and sulfur metabolic pathways

The concentration of activated sludge is a crucial factor influencing the capacity and efficiency of sewage wastewater treatment plants (WWTPs). However, high sludge concentrations can lead to sludge loss in the secondary sedimentation tank, resulting in reduced processing capacity, particularly during low-temperature stages and sludge bulking. This study investigated the impact of adding composite micron powder carriers (CMPC) in high-concentration powder carrier biofluidized bed (HPB) technology to the biochemical units of WWTPs on sludge concentration and settling performance. For the traditional activated sludge method (ASM), its hydraulic retention time (HRT) was 8 h, with an average effluent total nitrogen (TN) of 15.14 mg/L. Sludge bulking was prone to occur in low-temperature environments, resulting in a high average sludge volume index (SVI) of 560 mL/g. Conversely, with a CMPC dosage of 4 g/L, the HRT of HPB technology was 4.8 h, and the average effluent TN was 11.40 mg/L, with a removal efficiency of 67.43 %. During operation of HPB technology under high sludge concentration conditions (8 g/L), the average SVI remained at 85 mL/g, indicating excellent settling characteristics. Moreover, in the sequencing batch reactor (SBR), the SVI value of bulking sludge decreased from the original 695 to 111 mL/g by the 9th day of operation with the CMPC dosage of 2 g/L. At the same time, the filamentous bacteria almost disappeared, suggesting that CMPC inhibit the growth of filamentous bacteria. Metagenomic analysis demonstrated that CPMC enhance the utilization of small molecular fatty acids in activated sludge and promote electron transfer between nitrate and nitrite, thereby improving wastewater treatment capacity. Additionally, CMPC enhanced the relative abundance of Saprospiraceae in sludge, which accelerate the degradation of polysaccharides in extracellular polymeric substances, weaken sludge's hydrophilic properties, and improve sludge's settling performance. Overall, these findings suggested that CMPC effectively strengthen the high-loading operation of WWTPs by improving sludge concentration and sedimentation performance.

Combination of sulfide-driven partial denitrification with anammox enhanced by zeolite powder for autotrophic nitrogen and sulfide removal from wastewater

Sulfide-driven partial denitrification and anaerobic ammonia oxidizing (anammox) (SPDA) is a high-efficiency technology to achieve simultaneous nitrogen and sulfide removal. Nitrite accumulation from sulfide-driven partial denitrification is the key to achieve SPDA. Zeolite powder was added to strengthen the competition of anammox bacteria against nitrite. The nitrogen removal rate (NRR) and partial denitrification efficiency in reactor was 5.18 kg-N m-3d-1 and 92.3% during 180 days of operation, higher than those without zeolite powder, indicating an improving contribution of zeolite powder. Metabolomics analysis revealed zeolite powder addition enhanced the metabolisms of amino acids, nicotinate and porphyrin through increasing glutamate content, and improved EPS secretion, heme c content and particle size. Besides, high ammonia enriched by zeolite powder was conducive to improve anammox activity and NRR. This study provides the metabolic insights into the mechanism of zeolite powder enhancing SPDA, which is meaningful towards overcoming the limitations in practical application of SDPA.

Free nitrous acid prediction in ANAMMOX process using hybrid deep neural network model

Free nitrous acid (FNA) is a critical metric for stabilization of ANAMMOX but can not be directly and immediately measured by sensors or chemical measurement method, which hinders the effective management and operation for ANAMMOX. This study focuses on FNA prediction using hybrid model based on temporal convolutional network (TCN) combined with attention mechanism (AM) optimized by multiobjective tree-structured parzen estimator (MOTPE), called MOTPE-TCNA. A case study in an ANAMMOX reactor is carried out. Results show that nitrogen removal rate (NRR) is highly correlated with FNA concentration, indicating that it can forecast the operational status by predicting FNA. Then, MOTPE successfully optimizes the hyperparameters of TCN, helping TCN achieve a high prediction accuracy, and AM furtherly improves model accuracy. MOTPE-TCNA obtains the highest prediction accuracy, whose R2 value gets 0.992, increasing 1.71-11.80% compared to other models. As a deep neural network model, MOTPE-TCNA has more advantages than traditional machine learning methods in FNA prediction, which is beneficial to maintain the stable operation and easy control for ANAMMOX process.

Roles and regulation of quorum sensing in anaerobic granular sludge: Research status, challenges, and perspectives

Anaerobic granular sludge (AnGS) has a complex and important internal microbial communication system due to its unique microbial layered structure. As a concentration-dependent communication system between bacterial cells through signal molecules, QS (quorum sensing) is widespread in AnGS and exhibits great potential to regulate microbial behaviors. Therefore, the universal functions of QS in AnGS have been systematically summarized in this paper, including the influence on the metabolic activity, physicochemical properties, and microbial community of AnGS. Subsequently, the common QS-based AnGS regulation approaches are reviewed and analyzed comprehensively. The regulation mechanism of QS in AnGS is analyzed from two systems of single bacterium and mixed bacteria. This review can provide a comprehensive understanding of QS functions in AnGS systems, and promote the practical application of QS-based strategies in optimization of AnGS treatment process.

Impacts of granular sludge properties on the bioreactor performing nitrate/nitrite-dependent anaerobic methane oxidation/anammox processes

In this work, a bioprocess model was applied to first determine the impacts of influent substrates conditions on the granular bioreactor performing nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) and anammox integrated processes and then investigate the roles of granular sludge properties in regulating the bioreactor performance and start-up process. The ideal influent substrates conditions were identified at NO2--N/NH4+-N of 1:1 and dissolved CH4 concentration of 85 g COD m-3, which achieved 98.6% total nitrogen removal and 87.7% dissolved CH4 utilization. Under such ideal influent conditions, the initial properties of granular sludge didn't significantly affect the granular bioreactor performance. However, inoculation of granular sludge with a relatively small granular sludge size and a high abundance of n-DAMO archaea or/and anammox bacteria could effectively shorten the bioreactor start-up. Meanwhile, reducing the diffusivity of solutes within granular sludge was also beneficial for expediting the start-up process and promoting dissolved CH4 utilization.

A predictive model for determining the nitrite concentration in the effluent of an anammox reactor using ensemble regression tree algorithm

Anaerobic ammonium oxidation (anammox) is a cost-effective biological nitrogen removal method for treating wastewater. Nitrite has strong negative effect on microbial activity of anammox bacteria, while the conventional equitment available for determining nitrite on-line is challenging due to high price. By knowing the concentration of nitrite in the effluent, its concentration in the reactor can be controlled accordingly. To investigate this, an ensemble regression tree algorithm was used to establish the predictive model proposed in the current work. Moreover, the Bayesian algorithm was adopted to systematically optimize various parameters of machine learning algorithms. The predicted concentrations of nitrite were in good agreement with the observed values, and the coefficient of determination (R2) and root mean squared error (RMSE) values reached 0.91 and 4.81, respectively. Furthermore, the model established by the ensemble regression tree algorithm was compared with models established by commonly used machine learning algorithms. Finally, the established models were applied to another anammox reactor, and the predicted results of ensemble regression tree model were found to be in good agreement with the experimental values with R2 and RMSE values of 0.84 and 6.34, respectively.

CFD simulation of anaerobic granular sludge reactors: A review

Anaerobic digestion processes can generate renewable energy in the form of biogas while treating organic wastewater. The generation of biogas within anaerobic digestion systems is directly linked to the mixing conditions inside the reactors. In high-rate reactors such as the upflow anaerobic sludge blanket (UASB) reactor, the expanded granular sludge bed (EGSB) reactor and the internal circulation (IC) reactor, the hydrodynamic behaviour will depend on the interactions between the wastewater, the biogas, and the biomass granules. Over the past few years, various researchers have used computational fluid dynamics (CFD) to study the hydrodynamic behaviour in these types of reactors. This review aims to present and critically discuss the state of the art in the use of CFD applied to anaerobic granular sludge reactors (AGSRs). It briefly introduces and discusses the various aspects of modelling. It also reviews the various papers which used CFD to model these reactors and critically analyses the models used for the simulations in terms of general approaches and single-phase vs multiphase studies. The methods used in the validation of the CFD models are also described and discussed. Based on the findings, the challenges and future perspectives for the CFD modelling of AGSRs are discussed and gaps in the knowledge are identified.

Sidestream characteristics in water resource recovery facilities: A critical review

This review compiles information on sidestream characteristics that result from anaerobic digestion dewatering (conventional and preceded by a thermal hydrolysis process), biological and primary sludge thickening. The objective is to define a range of concentrations for the different characteristics found in literature and to confront them with the optimal operating conditions of sidestream processes for nutrient treatment or recovery. Each characteristic of sidestream (TSS, VSS, COD, N, P, Al³⁺, Ca²⁺, Cl^-, Fe^2+/3+, Mg²⁺, K⁺, Na⁺, SO₄^2-, heavy metals, micro-pollutants and pathogens) is discussed according to the water resource recovery facility configuration, wastewater characteristics and implications for the recovery of nitrogen and phosphorus based on current published knowledge on the processes implemented at full-scale. The thorough analysis of sidestream characteristics shows that anaerobic digestion sidestreams have the highest ammonium content compared to biological and primary sludge sidestreams. Phosphate content in anaerobic digestion sidestreams depends on the type of applied phosphorus treatment but is also highly dependent on precipitation reactions within the digester. Thermal Hydrolysis Process (THP) mainly impacts COD, N and alkalinity content in anaerobic digestion sidestreams. Surprisingly, the concentration of phosphate is not higher compared to conventional anaerobic digestion, thus offering more attractive recovery possibilities upstream of the digester rather than in sidestreams. All sidestream processes investigated in the present study (struvite, partial nitrification/anammox, ammonia stripping, membranes, bioelectrochemical system, electrodialysis, ion exchange system and algae production) suffer from residual TSS in sidestreams. Above a certain threshold, residual COD and ions can also deteriorate the performance of the process or the purity of the final nutrient-based product. This article also provides a list of characteristics to measure to help in the choice of a specific process.

Start-up and optimization of a one-stage partial nitrification-anammox (PN-A) process treating low ammonium concentration wastewater: experimental results and modeling investigation

Although the partial nitrification-anammox process (PN-A) has achieved great success in nitrogen removal for the high ammonium concentration wastewater, its application is still limited in low ammonium concentration wastewater treatment due to its instability and low nitrogen removal efficiency. In this study, a sequencing batch reactor (SBR) with continuous aeration was employed to enrich ammonia oxidation bacteria (AOB) and suppress nitrite oxidation bacteria (NOB) first; then, the SBR was operated intermittently aerated SBR (IASBR), to which the anammox granular sludge (AMX) was added to achieve complete autotrophic nitrogen removal under low influent ammonium concentration of 100 mg/L. A mathematical model was used to optimize the IASBR aeration strategy to achieve sub-optimal nitrogen removal. The experimental results showed that high nitrite accumulation efficiency (above 80%) in the SBR and a fast start-up within 100 days and a stable TN (total nitrogen) removal efficiency of 70% were achieved in the IASBR. Meanwhile, the simulation results indicated that keeping aeration duration at 4 h, k_La (oxygen transfer coefficient) at 50 day^-1, or aeration duration at 2.5 h, k_La at 80 day^-1 could obtain a higher total nitrogen removal efficiency (TNR) (TNR > 80%), and the TN removal could also be improved by increasing hydraulic retention time (HRT) under the optimal oxygen supply rate.

Anammox-based granulation cycle for sustainable granular sludge biotechnology from mechanisms to strategies: A critical review

Anaerobic ammonium oxidation (anammox) granular sludge is a promising biotechnological process for treating low-carbon nitrogenous wastewater, and is featured with low energy consumption and footprint. Previous theoretical and experimental research on anammox granular sludge processes mainly focused on granulation (flocs → granules), but pay little attention to the granulation cycle including granulation and regeneration. This work reviewed the previous studies from the perspective of anammox granules lifecycle and proposed various sustainable formation mechanisms of anammox granules. By reviewing the anaerobic, aerobic, and anammox granulation mechanisms, we summarize the mechanisms of thermodynamic theory, heterogeneous growth, extracellular polymeric substance (EPS)-based adhesion, quorum sensing (QS)-based regulation, biomineralization-based growth, and stratification of microorganisms to understand anammox granulation. In the regeneration process, the formation of precursors for re-granulation is explained by the mechanisms of physical crushing, quorum quenching and dispersion cue sensing. Based on the granulation cycle mechanism, the rebuilding of the normal regeneration process is considered essential to avoid granule floatation and the wash-out of granules. This comprehensive review indicates that future research on anammox granulation cycle should focus on the effects of filamentous bacteria in denitrification-anammox granulation cycle, the role of QS/ quorum quenching (QQ)-based autoinducers, development of diversified mechanisms to understand the cycle and the cycle mechanisms of stored granules.

Phage Predation Promotes Filamentous Bacterium Piscinibacter Colonization and Improves Structural and Hydraulic Stability of Microbial Aggregates

Although bacteria-phage interactions have broad environmental applications and ecological implications, the influence of phage predation on bacterial aggregation and structural stability remains largely unexplored. Herein, we demonstrate that inefficient lytic phage predation can promote host filamentous bacterium Piscinibacter colonization onto non-host Thauera aggregates, improving the structural and hydraulic stability of the dual-species aggregates. Specifically, phage predation at 10³-10⁴ PFU/mL (i.e., multiplication of infection at 0.01-0.1) promoted initial Piscinibacter colonization by 10-15 folds and resulted in 29-31% higher abundance of Piscinibacter in the stabilized aggregates than that in the control aggregates without phage predation. Transcriptomic analysis revealed upregulated genes related to quorum sensing (by 15-92 folds) and polysaccharide secretion (by 10-90 folds) within the treated aggregates, which was consistent with 120-172% higher content of polysaccharides for the treated dual-species aggregates. Confocal laser scanning microscopic images further confirmed the increase of filamentous bacteria and polysaccharides (both with wider distribution) within the dual-species aggregates. Accordlingly, the aggregates' structural strength (via atomic force microscopes) and shear resistance (via hydraulic stress tests) increased by 77 and 42%, respectively, relative to the control group. In the long-term experiments, the enhanced hydraulic stability of the treated aggregates could facilitate dwelling bacteria propagation in flow-through conditions. Overall, our study demonstrates that phage predation can promote bacterial aggregation and enhance aggregate structural stability, revealing the beneficial role of lytic phage predation on bacterial symbiosis and environmental adaptivity.

A review on upgrading of the anammox-based nitrogen removal processes: Performance, stability, and control strategies

The anaerobic ammonia oxidation (anammox) process is a promising biological nitrogen removal technology. However, owing to the sensitivity and slow cell growth of anammox bacteria, long startup time and initially low nitrogen removal rate (NRR) are still limiting factors of practical applications of anammox process. Moreover, nitrogen removal efficiency (NRE) is often lower than 88 %. This review summarizes the most common methods for improving NRR by increasing microorganism concentration, and modifying reactor configuration. Recent integrated anammox-based systems were evaluated, including hydroxyapatite (HAP)-enhanced one-stage partial nitritation/anammox (PNA) process for a high NRR of over 2 kg N/m3/d at 25 °C, partial denitrification/anammox (PDA) process, and simultaneous partial nitrification, anammox, and denitrification process for a high NRE of up to 100 %. After discussing the challenges for the application of these systems critically, a combined system of anaerobic digestion, HAP-enhanced one-stage PNA and PDA is proposed in order to achieve a high NRR, high NRE, and phosphorus removal simultaneously.

Coupling sulfur-based denitrification with anammox for effective and stable nitrogen removal: A review

Anoxic ammonium oxidation (anammox) is an energy-efficient nitrogen removal process for wastewater treatment. However, the unstable nitrite supply and residual nitrate in the anammox process have limited its wide application. Recent studies have proven coupling of sulfur-based denitrification with anammox (SDA) can achieve an effective nitrogen removal, owing to stable provision of substrate nitrite from the sulfur-based denitrification, thus making its process control more efficient in comparison with that of partial nitrification and anammox process. Meanwhile, the anammox-produced nitrate can be eliminated through sulfur-based denitrification, thereby enhancing SDA's overall nitrogen removal efficiency. Nonetheless, this process is governed by a complex microbial system that involves both complicated sulfur and nitrogen metabolisms as well as multiple interactions among sulfur-oxidising bacteria and anammox bacteria. A comprehensive understanding of the principles of the SDA process is the key to facilitating the development and application of this novel process. Hence, this review is conducted to systematically summarise various findings on the SDA process, including its associated biochemistry, biokinetic reactions, reactor performance, and application. The dominant functional bacteria and microbial interactions in the SDA process are further discussed. Finally, the advantages, challenges, and future research perspectives of SDA are outlined. Overall, this work gives an in-depth insight into the coupling mechanism of SDA and its potential application in biological nitrogen removal.

Microbial bromate reduction following ozonation of bromide-rich wastewater in coastal areas

Ozonation of wastewater can reduce the release of organic micropollutants, but may result in the formation of undesirable by-products, such as bromate from bromide. Bromide is one of the most abundant ions in seawater, the primary precursor of bromate during ozonation, and the end product in microbial bromate reduction. Investigations were carried out to compare the concentration of bromide in wastewater in coastal and non-coastal catchment areas, to monitor bromate formation during ozonation, and to assess the potential for subsequent bromate reduction with denitrifying carriers. Higher bromide concentrations were systematically observed in wastewater from coastal catchment areas (0.2-2 mg Br^-/L) than in wastewater from non-coastal areas (0.06-0.2 mg Br^-/L), resulting in elevated formation of bromate during ozonation. Subsequent investigations of bromate reduction in contact with denitrifying carriers from two full-scale moving bed biofilm reactors (MBBRs) showed that 80 % of the bromate formed during ozonation could be reduced to bromide in 60 min with first-order rate constants of 0.3-0.8 L/(g_biomass·h). Flow-through experiments with denitrifying carriers also showed that combined reduction of bromate and nitrate could be achieved below a concentration of 2 mg NO_x^--N/L. These findings indicate that bromide-rich wastewater is more likely to be of concern when using ozonation in coastal than in non-coastal areas, and that bromate and nitrate reduction can be combined in a single biofilm reactor.

Model study on real-time aeration based on nitrite for effective operation of single-stage anammox

Anaerobic ammonia oxidation (Anammox) is an innovative technology for cost-efficient nitrogen removal without intensive aeration. However, effective control of the competition between nitrite oxidizing bacteria (X_NOB) and Anammox bacteria (X_ANA) for nitrite is a key challenge for broad applications of single-stage Anammox processes in real wastewater treatment. Therefore, a real-time aeration scheme was proposed to determine dissolved oxygen (DO) based on nitrite concentration for effective control of X_NOB growth while maintaining the X_ANA activity in a single-stage Anammox process. In this study, a non-steady state mathematical model was developed and calibrated using previously reported lab-scale Anammox results to investigate the efficiency of the proposed real-time aeration scheme in enhancing the Anammox process. Based on the calibrated model simulation results, DO of about 0.10 mg-O₂/L was found to be ideal for maintaining effective nitrite creation by ammonia oxidizing bacteria (X_AOB) while slowing down the growth of X_NOB. If DO is too low (e.g., 0.01 mg-O₂/L or lower), the overall rate of the ammonia removal is limited due to slow growth of X_AOB. On the other hand, high DO (e.g., 1.0 mg-O₂/L or higher) inhibits the growth of X_ANA, resulting in dominancy of X_AOB and X_NOB. According to the simulation results, nitrite concentration was found to be a rate-limiting parameter on effective nitrogen removal in single-stage Anammox processes. We also found that nitrite concentration can be used as a real-time switch for aeration in a single-stage Anammox process. A schematic aeration method based on real-time nitrite concentration was proposed and examined to control the competition between X_ANA and X_NOB. In the model simulation, the X_ANA activity was successfully maintained because the schematic aeration prevented an outgrowth of X_NOB, allowing energy-efficient nitrogen removal using single-stage Anammox processes.

Diffusion of soluble organic substrates in aerobic granular sludge: Effect of molecular weight

Aerobic granular sludge (AGS) is an advanced biofilm-based technology for wastewater treatment. Diffusion of substrates into the granules is a key aspect of this technology. Domestic wastewater contains soluble organic substrates of different sizes that could potentially diffuse into the granules. In this study, the relation between the molecular weight of a substrate and its diffusion coefficient within the granule was studied with model substrates (polyethylene glycols (PEGs) with a molecular weight between 62 and 10 000 Da). The diffusion coefficients of the model substrates within granules from a full-scale installation were measured with the 'transient uptake of a non-reactive solute' method. The diffusion coefficients in the granules were not significantly different from the diffusion coefficients in water, at least up to 4000 Da molecular weight. This indicates that these PEGs were not obstructed by the granule matrix. The 10 kDa PEG behaved differently from the lighter PEGs, as it could not penetrate the entire granule. Furthermore, the granule structure was characterized with Environmental Scanning Electron Microscopy (ESEM). The granules displayed an open structure with large macropores and semi-solid regions, which contained microbial cells. The diffusion results suggest that most diffusing molecules were unobstructed in the macropores and barely obstructed in the semi-solid regions. Only the diffusion of the 10 kDa PEG seemed to be hindered by the semi-solid regions, but not by the macropores. Lastly, the apparent molecular weight distribution of domestic wastewater soluble COD was determined with ultrafiltration membranes of 100, 10, and 1 kDa molecular weight cut-off. The influent fractionation revealed that a large part (61-69%) of the influent soluble COD was lighter than 1 kDa. As molecules lighter than 1 kDa diffuse easily, the majority of the influent soluble COD can be considered as diffusible COD. These findings provide new insight into the availability of influent COD for granular sludge.

Multiscale modelling of the start-up process of anammox-based granular reactors

This work proposes a mathematical model on partial nitritation/anammox (PN/A) granular bioreactors, with a particular interest in the start-up phase. The formation and growth of granular biofilms is modelled by a spherical free boundary problem with radial symmetry and vanishing initial value. Hyperbolic PDEs describe the advective transport and growth of sessile species inhabiting the granules. Parabolic PDEs describe the diffusive transport and conversion of soluble substrates, and the invasion process mediated by planktonic species. Attachment and detachment phenomena are modelled as continuous and deterministic fluxes at the biofilm-bulk liquid interface. The dynamics of planktonic species and substrates within the bulk liquid are modelled through ODEs. A simulation study is performed to describe the start-up process of PN/A granular systems and the development of anammox granules. The aim is to investigate the role that the invasion process of anaerobic ammonia-oxidizing (anammox) bacteria plays in the formation of anammox granules and explore how it affects the microbial species distribution of anaerobic ammonia-oxidizing, aerobic ammonia-oxidizing, nitrite-oxidizing and heterotrophic bacteria. Moreover, the model is used to study the role of two key parameters in the start-up process: the anammox inoculum size and the inoculum addition time. Numerical results confirm that the model can be used to simulate the start-up process of PN/A granular systems and to predict the evolution of anammox granular biofilms, including the ecology and the microbial composition. In conclusion, after being calibrated, the proposed model could provide quantitatively reliable results and support the start-up procedures of full-scale PN/A granular reactors.

On the mechanisms for aerobic granulation - model based evaluation

In this study a mathematical framework was developed to describe aerobic granulation based on 6 main mechanisms: microbial selection, selective wasting, maximizing transport of substrate into the biofilm, selective feeding, substrate type and breakage. A numerical model was developed using four main components; a 1D convection/dispersion model to describe the flow dynamics in a reactor, a reaction/diffusion model describing the essential conversions for granule growth, a setting model to track granules during settling and feeding, and a population model containing up to 100,000 clusters of granules to model the stochastic behaviour of the granulation process. With this approach the model can explain the dynamics of the granulation process observed in practice. This includes the presence of a lag phase and a granulation phase. Selective feeding was identified as an important mechanism that was not yet reported in literature. When aerobic granules are grown from activated sludge flocs, a lag phase occurs, in which not many granules are formed, followed by a granulation phase in which granules rapidly appear. The ratio of granule forming to non-granule forming substrate together with the feast/famine ratio determine if the transition from the lag phase to the granulation phase is successful. The efficiency of selective wasting and selective feeding both determine the rate of this transition. Brake-up of large granules into smaller well settling particles was shown to be an important source for new granules. The granulation process was found to be the combined result from all 6 mechanisms and if conditions for either one are not optimal, other mechanisms can, to some extent, compensate. This model provides a theoretical framework to analyse the different relevant mechanisms for aerobic granular sludge formation and can form the basis for a comprehensive model that includes detailed nutrient removal aspects.

Microalgal-bacterial granular sludge for municipal wastewater treatment: From concept to practice

Recently, increasing interest has been placed in microalgal-bacterial granular sludge (MBGS) in the journey towards the energy and carbon neutrality of municipal wastewater treatment. Different from aerobic granular sludge, the performance of MBGS is mainly determined by the mutualism and symbiosis between coexisting microalgae and bacteria. It appears from the literature that most of studies on MBGS were conducted at small benchtop scales under controlled conditions with synthetic wastewater. Therefore, this article attempts to look into the major engineering gaps between the knowledge generated from numerous laboratory research works and the large-scale application of MBGS, including massive production of MBGS, type of bioreactor, effect of alternate photo and dark metabolisms on effluent quality, resource recovery from waste MBGS, etc. It is clearly demonstrated that MBGS is still at its infant stage, and more effort is strongly needed to identify the technological bottlenecks of full-scale applications, while providing corresponding engineering solutions.

Successful startup of the single-stage PN-A (partial nitrification-anammox) process by controlling the oxygen supply

The single PN-A (partial nitrification-anammox) reactor offers a cost-effective solution for nitrogen removal. However, optimal control of the PN-A reactor is challenging due to the interactive mechanisms among the oxygen supply, bulk liquid DO (dissolved oxygen) concentration, and the balance of various functional bacterial species. In this study, a mathematical model was used to derive the optimal control variable for the maximum nitrogen removal, and an experimental PN-A reactor was operated to verify the model simulation results. The model simulation results indicate that the oxygen supply to the ammonium load ratio is the key factor to control the single-stage PN-A reactor for optimal TN removal. For optimal TN removal, the oxygen supply to the ammonium load ratio should be 1.9 mg O₂/mg N. The DO concentration is not the key control parameter to get the maximum TN removal as the optimal TN removal could be achieved under a wide range of DO concentration. The model simulation results were verified in the experimental PN-A reactor under oxygen transfer rate ([Formula: see text]) at 52 day^-1, HRT at 24 h, and ammonium load ratio of 0.55 kg N/(m³∙day).

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.

Performance evaluation and model-based optimization of the mainstream deammonification in an integrated fixed-film activated sludge reactor

This study aimed to model and optimize mainstream deammonification in an integrated fixed-film activated sludge (IFAS) pilot plant under natural seasonal temperature variations. The effect of gradually decreasing temperature on the performance was evaluated during a winter season and a transition period to summer conditions, and the correlation of the performance parameters was investigated using principal component analysis (PCA). The optimization of intermittent aeration in the long-term (30 days) dynamic conditions with on/off ratio and dissolved oxygen (DO) set-point control was used to maximize the N-removal rate (NRR) and N-removal efficiency (NRE). Optimization results (DO set-point of 0.2-0.25 mgO₂/L, and on/off ratio of 0.05) increased the NRE and NRR of total inorganic N (daily average) from 30% to > 50% and 15 gN/m³d to 25 gN/m³d, respectively. This novel long-term optimization strategy is a powerful tool for enhancing the efficiency in mainstream deammonification.

Hybrid modelling of water resource recovery facilities: status and opportunities

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors

In this study, data-driven deep learning methods were applied in order to model and predict the treatment of real municipal wastewater using anaerobic membrane bioreactors (AnMBRs). Based on the one-year operating data of two AnMBRs, six parameters related to the experimental conditions (temperature of reactor, temperature of environment, temperature of influent, influent pH, influent COD, and flux) and eight parameters for wastewater treatment evaluation (effluent pH, effluent COD, COD removal efficiency, biogas composition (CH₄, N₂, and CO₂), biogas production rate, and oxidation-reduction potential) were selected to establish the data sets. Three deep learning network structures were proposed to analyze and reproduce the relationship between the input parameters and output evaluation parameters. The statistical analysis showed that deep learning closely agrees with the AnMBR experimental results. The prediction accuracy rate of the proposed densely connected convolutional network (DenseNet) can reach up to 97.44%, and the single calculation time can be reduced to within 1 s, suggesting the high performance of AnMBR treatment prediction with deep learning methods.

Effect of low salinity on nitrogen removal from municipal wastewater via a double-anammox process coupled with nitritation and denitratation: Performance and microbial structure

Saline wastewater present in municipal pipe networks poses challenges to biological nitrogen removal due to its inhibition on microorganisms. This study focuses on the effects of low salinity (0.0%, 0.4%, 0.7% and 1.0%) on a system featuring a combination of nitritation/anammox in oxic stage and denitratation/anammox in anoxic stage (double-anammox) in a step-feed SBR for municipal wastewater over a period of 130 days. The results showed that a maximum nitrogen removal efficiency of 81.2% was achieved at a salinity of 1.0% with anammox contribution of 76.5%. Analysis of anammox contribution and sludge activities discovered that low salinity promoted both nitritation and denitratation, further enhancing the coupling with anammox. Further, microbial analysis confirmed that Ca. Brocadia was enriched on biofilms from 0.21% to 0.51% and Nitrosomonas was enriched in flocs from 0.50% to 1.04%. Overall, the double-anammox process appears to be a promising method for the treatment of saline wastewater.

Integrated anaerobic and algal bioreactors: A promising conceptual alternative approach for conventional sewage treatment

Conventional sewage treatment applying activated sludge processes is energy-intensive and requires great financial input, hampering widespread implementation. The introduction of anaerobic membrane bioreactors (AnMBR) followed by an algal reactor growing species of commercial interest, may present an alternative, contributing to the envisaged resource recovery at sewage treatment plants. AnMBRs can be applied for organic matter removal with energy self-sufficiency, provided that effective membrane fouling management is applied. Haematococcus pluvialis, an algal species with commercial value, can be selected for ammonium and phosphate removal. Theoretical analysis showed that good pollutant removal, positive financial output, as well as a significant reduction in the amount of hazardous activated sludge can be achieved by applying the proposed process, showing interesting advantages over current sewage treatment processes. Microbial contamination to H. pluvialis is a challenge, and technologies for preventing the contamination during continuous sewage treatment need to be applied.

Evaluation of the joint effects of Cu2+, Zn2+ and Mn2+ on completely autotrophic nitrogen-removal over nitrite (CANON) process

The completely autotrophic nitrogen-removal over nitrite (CANON) process has merits in energy saving and consumption reducing, thus being considered as an attractive alternative over the common denitrification technology. In this study, the effects of three common heavy metals (Cu²⁺, Zn²⁺ and Mn²⁺) in wastewater to the CANON process were evaluated comprehensively. A central composite design with response surface methodology was utilized to investigate the joint effect of these three metal ions on the nitrogen removal performance of CANON process. In accordance with the determined optimal dosage in batch tests, four bioreactors were established with different amounts of heavy metal dosage in long-term operation, which determined the optimal concentrations for Cu²⁺, Zn²⁺ and Mn²⁺ to be 0.25, 0.81 and 1.00 mg/L, respectively. However, the optimal dosing level determined in batch tests showed no promotion during long-term experiment. This indicated that the actual concentration of heavy metals in bioreactors during long-term operation could be higher than expectation, leading to the difference between short-term tests and long-term experiment. The distribution of metal ions revealed that Mn²⁺ was mainly absorbed in anammox bacteria cells while Cu²⁺ and Zn²⁺ were mostly identified inside AOB cells. Moreover, the addition of heavy metals consistently showed positive effects for the relative abundance of AOB, while only a low level of dosage could promote the abundance of anammox bacteria. Furthermore, a mathematical model was established to simulate the CANON system considering the impacts of heavy metals, which was calibrated and validated using independent dataset in this study.

Low strength wastewater anammox start-up by stepwise decrement in influent nitrogen: Biofilm formation mechanism and mathematical modelling

The application of mainstream anammox process is hampered by its overlong start-up and instability under disturbance. A lab-scale mainstream anammox moving bed biofilm reactor (MBBR) was successfully started in 120 days with stepwise decrement in influent nitrogen concentration from sidestream to mainstream condition. The initial colonization by Candidatus Jettenia and filamentous fermenter Anaerolineaceae were potentially mediated by hydrophobic interaction and type IV pilus. Ca. Kuenenia with higher substrate affinity outcompeted Ca. Jettenia, and the predominant fermenters shifted to fermentative Ignavibacteriaceae in the mature biofilm. A novel mainstream anammox biofilm development (MABD) model was constructed to describe biofilm growth, population dynamics, and nitrogen removal performance. The simulation results suggested that higher inocula biomass (460-690 mgVSS·L^-1), relative abundance of low-affinity AnAOB in the inocula (e.g., Ca. Jettenia, 1.3-2%), and the early-stage solids retention time (45-68 days) were desired to form thicker biofilm and improve effluent quality during 120-day mainstream anammox MBBR start-up. The mechanistic insights into biofilm formation and predictive power of the newly developed MABD model are of importance to the design and operation of mainstream anammox processes towards more biofilm biomass and higher nitrogen removal efficiency.

Multiscale Modelling of De Novo Anaerobic Granulation

A multiscale mathematical model is presented to describe de novo granulation, and the evolution of multispecies granular biofilms, in a continuously fed bioreactor. The granule is modelled as a spherical free boundary domain with radial symmetry. The equation governing the free boundary is derived from global mass balance considerations and takes into account the growth of sessile biomass as well as exchange fluxes with the bulk liquid. Starting from a vanishing initial value, the expansion of the free boundary is initiated by the attachment process, which depends on the microbial species concentrations within the bulk liquid and their specific attachment velocity. Nonlinear hyperbolic PDEs model the growth of the sessile microbial species, while quasi-linear parabolic PDEs govern the dynamics of substrates and invading species within the granular biofilm. Nonlinear ODEs govern the evolution of soluble substrates and planktonic biomass within the bulk liquid. The model is applied to an anaerobic, granular-based bioreactor system, and solved numerically to test its qualitative behaviour and explore the main aspects of de novo anaerobic granulation: ecology, biomass distribution, relative abundance, dimensional evolution of the granules and soluble substrates, and planktonic biomass dynamics within the bioreactor. The numerical results confirm that the model accurately describes the ecology and the concentrically layered structure of anaerobic granules observed experimentally, and that it can predict the effects on the process of significant factors, such as influent wastewater composition; granulation properties of planktonic biomass; biomass density; detachment intensity; and number of granules.

Unaerated feeding alters the fate of dissolved methane during aerobic wastewater treatment

In municipal wastewater treatment plants, some dissolved methane can enter the aerobic bioreactors. This greenhouse gas originates from sewers and return flows from anaerobic sludge treatment. In well-mixed conventional activated sludge reactors, methane emissions are largely avoided because methane oxidizing bacteria consume a large fraction, even without optimizing for this purpose. In this work, the fate of dissolved methane is studied in aerobic granular sludge reactors, as they become increasingly popular. The influence of the characteristic design and operating conditions of these reactors are studied with a mathematical model with apparent conversion kinetics and stripping: the separation of feeding and aeration in time, a higher substrate transport resistance, a high retention time of granular biomass and a taller water column. Even for a best-case scenario combining an unrealistically low intragranule substrate transport resistance, a high retention time, a tall reactor, an extremely high influent methane concentration and no oxygen limitation, the methane conversion efficiency was only 12% when feeding and aeration were separated in time, which is lower than for continuous activated sludge reactors under typical conditions. A more rigorous model was used to confirm the limited conversion, considering the multi-species and multi-substrate biofilm kinetics, anoxic methane consumers and the high substrate concentration at the bottom during upward plug flow feeding. The observed limited methane conversion is mainly due to the high concentration that accumulates during unaerated feeding phases, which favours stripping more than conversion in the subsequent aeration phase. Based on these findings, strategies were proposed to mitigate methane emissions from wastewater treatment plants with sequentially operated reactors.

Hydrodynamic analysis of full-scale in-situ biogas upgrading in manure digesters

The addition of hydrogen to anaerobic digesters is an emerging technique for the sustainable upgrading of biogas to biomethane with renewable electricity. However, it is critically dependent on the effective gas-liquid transfer of hydrogen, which is a sparingly soluble gas. Very little is known about the impact of liquid and gas flow and bubble size on gas-liquid transfer during H₂ injection in full-scale anaerobic digesters. A computational fluid dynamic model was developed using a two-fluid approach for non-Newtonian liquid in the open-source computational fluid dynamics (CFD) platform, OpenFOAM. The newly developed model was validated against published experimental data-sets of a gas-mixed, laboratory-scale anaerobic digester, with good agreement between the numerical and experimental velocity fields. The hydrodynamics of the full-scale in-situ biomethanation system using venturi ejectors for H₂ injection was then simulated to investigate gas-liquid dynamics, including gas-liquid mass transfer, at different operational conditions. Gas-liquid mixing is mainly controlled by the gas-plumes interaction, which promotes turbulence at the interaction zone, resulting in increasing gas bubbles mixing with the liquid and the gas-liquid interfacial area. However, beyond the plume interaction zone, the digester had flow short-circuiting and inactive zones. It was found that, due to this short-circuiting behaviour, an increase in gas flow-rate may not be an effective option in reducing inactive zones, although it can increase the gas-liquid interfacial area. Comparative analysis of the impact of gas flow and bubble size indicated that gas flow had a linear effect on both kLa and gas holdup, but that bubble size had a non-linear impact, with higher kLa values achieved at bubble sizes less than 2 mm. Comparison against measured data in the same system indicated the predicted kLa values were at the same level as measured kLa, at a bubble size of 2 mm.

Pseudo-analytical solutions for multi-species biofilm model of aerobic granular sludge

This paper demonstrates modelling of the aerobic granular sludge (AGS) process with the pseudo-analytical solutions (PAS) of a biofilm model. A MATLAB programmed graphical user interface platform was developed to facilitate the model calculation and access. Model calibration and validation were carried out through using experimental data collected from a granular sludge sequencing batch reactor operation. The experimental and modelling results identified the distribution of heterotrophs and nitrifiers on the AGS and its contribution to the performance of wastewater treatment. The model could describe multi-species biofilms according to the distinguishing features among the three levels of PAS models. The models demonstrated increasing degrees of interaction (no interaction, competition for nitrogen and layering and protection) between heterotrophs and nitrifiers. Modelling the AGS process using PAS increases the accessibility of the simulation of multiple species in both biofilm and suspended biomass.

Anammox-Based Processes for Mature Leachate Treatment in SBR: A Modelling Study

Mature landfill leachates are characterized by high levels of ammoniacal nitrogen which must be reduced for discharge in the sewer system and further treatment in municipal wastewater treatment plants. The use of anammox-based processes can allow for an efficient treatment of ammonium-rich leachates. In this work, two real scale sequencing batch reactors (SBRs), designed to initially perform partial nitritation/anammox (PN/A) and simultaneous partial nitrification and denitrification (SPND) for the treatment of ammonium-rich urban landfill leachate, were modelled using BioWin 6.0 in order to enable plant-wide modelling and optimizing. The constructed models were calibrated and validated using data from long- and short-term (one cycle) SBR operation and fit well to the main physical-chemical parameters (i.e., ammonium, nitrite and nitrate concentrations) measured during short-term (one cycle) operations. Despite the different strategies in terms of dissolved oxygen (DO) concentrations and aeration and mixing patterns applied for SBR operation, the models allowed for understanding that in both reactors the PN/A process was shown as the main contributor to nitrogen removal when the availability of organic carbon was low. Indeed, in both SBRs, the activity of nitrite oxidizing bacteria was inhibited due to high levels of free ammonia, whereas anammox bacteria were active due to the simultaneous presence of ammonium and nitrite and their ability to recover from DO inhibition. Increasing the external carbon addition, a prompt decrease of the anammox biomass was observed, with SPND becoming the main nitrogen removal mechanism. Models were also applied to estimate the production rates of nitrous oxide by aerobic ammonia oxidizing bacteria and heterotrophic denitrifiers. The models were found to be a robust tool for understanding the effects of different operating conditions (i.e, temperature, cycle phases, DO concentration, external carbon addition) on the nitrogen removal performances of the two reactors, assessing the contribution of the different bacterial groups involved.

Enhancement of biological nutrient removal process with advanced process control tools in full-scale wastewater treatment plant

One of the major challenges in existing WasteWater Treatment Plants (WWTPs) is to comply with the increasingly stringent nutrient discharge limits established by the competent authorities to enhance environmental protection, while keeping operation costs as low as possible. This paper describes the results obtained from upgrading a full-scale WWTP during seven years (2013-2020) applying five different Advanced Process Control (APC) strategies. Results show that implementation of APC and the development of ammonia-based aeration control, aeration/non-aeration cycles, improved internal/external recirculation and chemical dosage controls resulted in an improvement in nutrients removal rates (+25.48% and +9.25%, for nitrogen and phosphorus, respectively) and in a reduction (-21.79%) of the specific energy ratio. In addition, the promotion of an Enhanced Biological Phosphorous Removal (EBPR) process by APC resulted in an improvement in biological phosphorous removal (+43.90%), chemical savings (-20.00%) and nutrient recovery in the dewatered sludge. Molecular biology tools and laboratory batch tests confirmed the Polyphosphate Accumulating Organisms (PAOs) activity, specifically Tetrasphaera genera. Finally, an economic analysis was conducted, showing a rate of return for the incurred capital expenses with the implemented APC systems of about five years, being an affordable alternative to the upgrading existing WWTPs.

Microbial biofilms in biorefinery - Towards a sustainable production of low-value bulk chemicals and fuels

Harnessing the potential of biocatalytic conversion of renewable biomass into value-added products is still hampered by unfavorable process economics. This has promoted the use of biofilms as an alternative to overcome the limitations of traditional planktonic systems. In this paper, the benefits and challenges of biofilm fermentations are reviewed with a focus on the production of low-value bulk chemicals and fuels from waste biomass. Our study demonstrates that biofilm fermentations can potentially improve productivities and product yields by increasing biomass retention and allowing for continuous operation at high dilution rates. Furthermore, we show that biofilms can tolerate hazardous environments, which improve the conversion of crude biomass under substrate and product inhibitory conditions. Additionally, we present examples for the improved conversion of pure and crude substrates into bulk chemicals by mixed microbial biofilms, which can benefit from microenvironments in biofilms for synergistic multi-species reactions, and improved resistance to contaminants. Finally, we suggest the use of mathematical models as useful tools to supplement experimental insights related to the effects of physico-chemical and biological phenomena on the process. Major challenges for biofilm fermentations arise from inconsistent fermentation performance, slow reactor start-up, biofilm carrier costs and carrier clogging, insufficient biofilm monitoring and process control, challenges in reactor sterilization and scale-up, and issues in recovering dilute products. The key to a successful commercialization of the technology is likely going to be an interdisciplinary approach. Crucial research areas might include genetic engineering combined with the development of specialized biofilm reactors, biofilm carrier development, in-situ biofilm monitoring, model-based process control, mixed microbial biofilm technology, development of suitable biofilm reactor scale-up criteria, and in-situ product recovery.

Application of principal component analysis (PCA) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge

For the first time, principal component analysis (PCA) was used to extract relevant information hidden in the partial-nitrification process using aerobic granular sludge. The objectives of this research are (a) to determine total ammonia nitrogen (TAN), total nitrite nitrogen (NO₂-N), nitrate nitrogen (NO₃-N), and other water quality parameters; (b) to identify the diversity of nitrification and denitrification bacterial community of wastewater samples during the partial-nitrification process using aerobic granular sludge and; (c) to analyze the correlation of available parameters using PCA. The nitrite accumulation ratio was determined from TAN, NO₂-N, and NO₃-N. Other water quality parameters were mixed liquor volatile suspended solids (MLVSS), alkalinity, total nitrogen (TN) and sludge volume index (SVI), pH, and dissolved oxygen (DO). The identification of bacterial community was conducted using 16S rRNA gene-based pyrosequencing by GS Junior Sequencing system. The water quality parameters were computed for PCA using software MATLAB. A nitrite accumulation ratio (NAR) between 0.55 and 0.85 was determined while maintaining the aerobic granular sludge's compact and dense structure. The PCA was used to reduce the data dimensionality from the original 8 variables to 2 principal components explaining 75% of the total data variance. Applying PCA to the data analysis in biological wastewater treatment can support detecting data anomalies and separating useful information from unwanted interferences.

Development of a kinetic model to evaluate thiosulfate-driven denitrification and anammox (TDDA) process

Recently, the integration of sulfur-driven denitrification and anammox process has been extensively studied as a promising alternative nitrogen removal technology. Most of these studies investigated the process feasibility and monitored the community dynamics. However, an in-depth understanding of this new sulfur-nitrogen cycle bioprocess based on mathematical modeling and elucidation of complex interactions among different microorganisms has not yet been achieved. To fill this gap, we developed a kinetic model (with 7 bioprocesses, 12 variables, and 19 parameters) to assess the sulfur(thiosulfate)-driven denitrification and anammox (TDDA) process in a single reactor. The parameters used in this process were separately estimated by fitting the data obtained from the experiments. Then, the model was further validated under different conditions, and the results demonstrated that the developed model could describe the dynamic behaviors of nitrogen and sulfur conversions in the TDDA system. The newly developed branched thiosulfate oxidation model was also verified by conducting a metagenomics analysis. Using the developed model, we i) examined the interactions between sulfur-oxidizing bacteria and anammox bacteria at steady-state conditions with varying substrates to demonstrate the reliability of TDDA, and ii) evaluated the feasibility and operation of the TDDA process in terms of practical implementation. Our results will benefit further exploration of the significance of this novel S-N cycle bioprocess and guide its future applications.

Modeling interspecific competition of the microbial community during anaerobic digestion based on cellular automata and ADM1

Interspecific competition for substrate and space gives rise to considerable variation in biomass distribution within the microbial community. To study microbial community in depth, we used several research methods as sampling and analytical measurements, and developed a cellular automata (CA) model that would facilitate a description of the microbial growth process based on Anaerobic Digestion Model No. 1 (ADM1) of the International Water Association (IWA). Using the CA model, we aimed to determine whether interspecific competition occurs among acidogens, acetogens and methanogens, and to examine the influence of interspecific competition on the spatial structure of microbial communities. We found that acetogens and methanogens competed for core space, resulting in a multi-layer structure. Butyrate-degrading acetogens increased in number, resulting in inhibition of propionate-degrading acetogens. Hydrogenotrophic methanogens showed stronger competitive advantage than acetotrophic methanogens. The simulation showed that the multi-layer structure of the microbial community was formed by interspecific competition.

Explore the sludge stabilization process in sludge drying bed by modeling study from mesocosm experiments

Sludge drying bed (SDB) treatment is a valuable alternative to conventional sludge treatment methods. However, changes in sludge hydrotexture during dewatering present a barrier for direct modeling of the SDB process. This study proposes a modeling strategy to simulate the sludge stabilization process in SDB treatment by separating sludge dewatering and sludge solids stabilization into independent processes. Two cell decay theories widely used by activated sludge models (ASM), death-regeneration concept and endogenous respiration theory, are compared to describe the biokinetic processes of sludge digestion. Both cell decay theories are found to adequately describe effluent total COD, NH₄-N, NO₃-N, and sludge layer composition, but have pronounced differences in describing effluent COD compositions. Results show that natural aeration does not maintain adequate aerobic/anoxic sludge digestion within the sludge layer to fully nitrify NH₄-N released by cell decay. Results also indicate that the kinetics of sludge digestion are adaptable over time, indicating the need to adopt lumped values for biokinetic simulations. While lowered sludge dewatering rates (outflow) can increase biodegradable COD for cell metabolism, increased sludge loading rates (inflow) lead to higher effluent COD and NH₄-N concentrations. Contrary to conventional judgement, this study demonstrates the merit of sludge layer formation to reduce leaching loss of biodegradable COD. Overall, the proposed modeling strategy is proven capable of simulating deposited sludge digestion processes in an SDB.

Bifurcation Analysis of an Impulsive System Describing Partial Nitritation and Anammox in a Hybrid Reactor

Low-energy nitrogen removal under mainstream conditions is a technology that has received significant attention in recent years as the water industry drives toward long-term sustainability goals. Simultaneous partial nitritation-Anammox (PN/A) is one process that can provide substantial energy reduction and lower sludge yields. Mathematical modeling of the PN/A process offers engineers insights into the operating conditions necessary to maximize its potential. Laureni et al. (Laureni et al. Water Res. 2019, 14) have recently published a simplified mechanistic model of the process operated as a sequencing batch reactor that investigated the effect of three key operating parameters on performance (Anammox biofilm activity, dissolved oxygen concentration and fraction of solids wasted). The analysis of the model was limited, however, to simulation with relatively few discrete parameter sets. Here, we demonstrate through the use of bifurcation theory applied to an impulsive dynamical system that the parameter space can be partitioned into regions in which the system converges to different fixed points that represent different outcomes: either the washout of nitrite-oxidizing bacteria or their survival. Mapping process performance data onto these spaces allows engineers to target suitable operating regimes for specific objectives. Here, for example, we note that the nitrogen removal efficiency is maximized close to the curve that separates the regions in parameter space where nitrite-oxidizing bacteria washout from the region in which they survive. Further, control of solids washout and Anammox biofilm activity can also reduce oxygen requirements while maintaining an appropriate hydraulic retention time. The approach taken is significant given the possibility for using such a methodology for models of increasing complexity. This will enable engineers to probe the entire parameter space of systems of higher dimension and realism in a consistent manner.

Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors

Machine learning models provide an adaptive tool to predict the performance of treatment reactors under varying operational and influent conditions. Aerobic granular sludge (AGS) is still an emerging technology and does not have a long history of full-scale application. There is, therefore, a scarcity of long-term data in this field, which impacted the development of data-driven models. In this study, a machine learning model was developed for simulating the AGS process using 475 days of data collected from three lab-based reactors. Inputs were selected based on RReliefF ranking after multicollinearity reduction. A five-stage model structure was adopted in which each parameter was predicted using separate models for the preceding parameters as inputs. An ensemble of artificial neural networks, support vector regression and adaptive neuro-fuzzy inference systems was used to improve the models' performance. The developed model was able to predict the MLSS, MLVSS, SVI₅, SVI₃₀, granule size, and effluent COD, NH₄-N, and PO₄^3- with average R², nRMSE and sMAPE of 95.7%, 0.032 and 3.7% respectively.

Uncover the secret of granule calcification and deactivation in up-flow anaerobic sludge bed (UASB) reactor with long-term exposure to high calcium

Calcification of anaerobic granular sludge causing the decrease of microbial activity has been focused for several decades, but the mechanism of calcification and deactivation of calcified granule are still lacking. In this study, the calcification process of anaerobic granular sludge was analyzed in the UASB reactor with long-term exposure to a high calcium level of 2 gCa²⁺L^-1. Greyish-white calcified granules with larger size and density first appeared at the bottom of sludge bed. Characterization of granular surface demonstrated that calcified granules had smaller specific surface area and pore volume with the disappearing channels in granules visualized over calcification. The coverage of calcite on granular surface with a thickness of 500-600 µm was observed by a microscope, which is responsible for the blockage of channels for substrate diffusion and transport. The evaluation of mass transfer showed that the 'effectiveness factor' η of calcified granules was higher than 1.23, proving the existence of mass transfer resistance. Furthermore, the interactions between calcium and extracellular polymeric substances (EPS) revealed the possible mechanism of calcite location at the outer layer of anaerobic granular sludge: The bulk calcite directly binds the EPS such as humic acids via charging functional groups, and bulk CO₃^2- could react easily with Ca²⁺ bound to EPS according to thermodynamic analysis.