Search

The main requirement for metabolic flux analysis (MFA) is that the cells are in a pseudo-steady state, that there is no accumulation or depletion of intracellular metabolites. In the past, the applications of MFA were limited to the analysis of continuous cultures. This contribution introduces the concept of dynamic MFA and extends MFA so that it is applicable to transient cultures. Time series of concentration measurements are transformed into flux values. This transformation involves differentiation, which typically increases the noisiness of the data. Therefore, a noise-reducing step is needed. In this work, polynomial smoothing was used. As a test case, dynamic MFA is applied on Escherichia coli cultivations shifting from carbon limitation to nitrogen limitation and vice versa. After switching the limiting substrate from N to C, a lag phase was observed accompanied with an increase in maintenance energy requirement. This lag phase did not occur in the C- to N-limitation case. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify the main variants in the samples: the presence of signature and marker mutations and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short-read and long-read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.

This article presents the authors' perspectives on modelling best practices for nature-based solutions (NBS). The authors led a workshop on NBS modelling as part of the 8th IWA Water Resource Recovery Modelling Seminar (WRRmod2022+) in January 2023, where the discussion centred around the design, use cases, and potential applications of NBS models. Four real-world case studies, encompassing an aerated lagoon, a biofilm-enhanced aerated lagoon, a stormwater basin, and a constructed wetland were reviewed to demonstrate practical applications and challenges in modelling NBS systems. The initial proposed modelling framework was derived from these case studies and encompassed eight sub-models used for these NBS types. The framework was subsequently extended to include eight additional NBS categories, requiring a total of 10 sub-models. In a subsequent step, with a different perspective, the framework was refined to focus on 13 primary use cases of NBS, identifying 10 sub-models needed or potentially required for these specific NBS applications. These frameworks help to identify the necessary sub-models for the NBS system at hand or the use case. This article also discusses the benefits and challenges of applying water resource recovery modelling best practices to NBS, along with recommendations for future research in this area ., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO 3 -N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO 3 -N/L)., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

The recent SARS-COV-2 pandemic has sparked the adoption of wastewater-based epidemiology (WBE) as a low-cost way to monitor the health of populations. In parallel, the pandemic has encouraged researchers to openly share their data to serve the public better and accelerate science. However, environmental surveillance data are highly dependent on context and are difficult to interpret meaningfully across sites. This paper presents the second iteration of the Public Health Environmental Surveillance Open Data Model (PHES-ODM), an open-source dictionary and set of data tools to enhance the interoperability of environmental surveillance data and enable the storage of contextual (meta)data. The data model describes how to store environmental surveillance program data, metadata about measurements taken on various specimens (water, air, surfaces, sites, populations) and data about measurement protocols. The model provides software tools that support the collection and use of PHES-ODM formatted data, including performing PCR calculations and data validation, recording data into input templates, generating wide tables for analysis, and producing SQL database definitions. Fully open-source and already adopted by institutions in Canada, the European Union, and other countries, the PHES-ODM provides a path forward for creating robust, interoperable, open datasets for environmental public health surveillance for SARS-CoV-2 and beyond.

Nitrous oxide (N 2 O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) total carbon footprint. Given the complexity of the pathways involved, estimating N 2 O emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting N 2 O emissions hold substantial potential. However, so far, a comprehensive approach is still overlooked, impeding the advancement of full-scale application. Therefore, this study develops a comprehensive approach for using machine learning to perform online process modeling of N 2 O emissions. The approach is tested on a long-term N 2 O emission dataset from a full-scale WWTP. Uniquely, the proposed approach emphasizes not just model accuracy, but it also considers model complexity, computational speed, and interpretability, equipping operators with the insights needed for informed corrective actions. Algorithms with varying levels of complexity and interpretability including k-Nearest Neighbors (kNN), decision trees, ensemble learning models, and deep neural networks (DNN) were considered. Furthermore, a parametric multivariate outlier removal method was adjusted to account for data statistical distributions, significantly reducing data loss. By employing an effective feature selection methodology, a trade-off between data acquisition, model performance, and complexity was found, reducing the number of features by 40 % and decreasing data collection cost, model complexity and computational burden without significant effect on modeling accuracy. The best performing models are kNN (R 2  = 0.88), AdaBoost (R 2  = 0.94), and DNN (R 2  = 0.90). Feature importance of models was analyzed and compared with process knowledge to test interpretability, guiding N 2 O mitigation decisions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

A wide diversity of regulatory practices for wastewater resource recovery plants exists throughout the world. This contribution aims to highlight the implications of choosing particular permitting structures and investigate the equivalence of effluent standards in terms of limit values and compliance assessment specifications. These factors heavily affect the true performance that a treatment plant has to attain and thus the required plant capacity and operation. The dynamic simulations executed in this work, based on a realistic case study and three selected permits from China, The Netherlands and the USA, show the impact of certain compliance specifications like sampling frequency, averaging and tolerable permit exceedances leading to differences in the required design capacity of more than 250% for the same wastewater to be treated. The results also reveal clear differences between permits in their capacity to handle excess variability. The latter is important to avoid overdesign, i.e., when further investment in treatment capacity would result only in marginal effluent quality gains, as well as to create a safe space for testing innovative technologies or ways of operation that might otherwise trigger compliance issues.

Background: Wastewater surveillance (WWS) of pathogens is a rapidly evolving field owing to the 2019 coronavirus disease pandemic, which brought about a paradigm shift in public health authorities for the management of pathogen outbreaks. However, the interpretation of WWS in terms of clinical cases remains a challenge, particularly in small communities where large variations in pathogen concentrations are routinely observed without a clear relation to clinical incident cases., Methods: Results are presented for WWS from six municipalities in the eastern part of Canada during the spring of 2021. We developed a numerical model based on viral kinetics reduction functions to consider both prevalent and incident cases to interpret the WWS data in light of the reported clinical cases in the six surveyed communities., Results: The use of the proposed numerical model with a viral kinetics reduction function drastically increased the interpretability of the WWS data in terms of the clinical cases reported for the surveyed community. In line with our working hypothesis, the effects of viral kinetics reduction modeling were more important in small communities than in larger communities. In all but one of the community cases (where it had no effect), the use of the proposed numerical model led to a change from a +1.5% (for the larger urban center, Quebec City) to a +48.8% increase in the case of a smaller community (Drummondville)., Conclusion: Consideration of prevalent and incident cases through the proposed numerical model increases the correlation between clinical cases and WWS data. This is particularly the case in small communities. Because the proposed model is based on a biological mechanism, we believe it is an inherent part of any wastewater system and, hence, that it should be used in any WWS analysis where the aim is to relate WWS measurement to clinical cases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rioux, Guillemette, Lemarchand, Doiron, Lemay, Maere, Dolcé, Quessy, Abonnenc, Vanrolleghem and Frigon.)

The integration of biological phosphorus removal (bio-P) and shortcut nitrogen removal (SNR) processes is challenging because of the conflicting demands on influent carbon: SNR allows for upstream carbon diversion, but this reduction of influent carbon (especially volatile fatty acids [VFAs]) prevents or limits bio-P. The objective of this study was to achieve SNR, either via partial nitritation/anammox (PNA) or partial denitrification/anammox (PdNA), simultaneously with biological phosphorus removal in a process with upstream carbon capture. This study took place in a pilot scale A/B process with a sidestream bio-P reactor and tertiary anammox polishing. Despite low influent rbCOD concentrations from the A-stage effluent, bio-P occurred in the B-stage thanks to the addition of A-stage WAS fermentate to the sidestream reactor. Nitrite accumulation occurred in the B-stage via partial denitrification and partial nitritation (NOB out-selection), depending on operational conditions, and was removed along with ammonia by the tertiary anammox MBBR, with the ability to achieve effluent TIN less than 2 mg/L. PRACTITIONER POINTS: A sidestream reactor with sufficient fermentate addition enables biological phosphorus removal in a B-stage system with little-to-no influent VFA. Enhanced biological phosphorus removal is not inhibited by intermittent aeration and is stable at a wide range of process SRTs. Partial nitritation and partial denitrification are viable routes to produce nitrite within an A/B process with sidestream bio-P, for downstream anammox in a polishing MBBR., (© 2023 The Authors. Water Environment Research published by Wiley Periodicals LLC on behalf of Water Environment Federation.)

The relatively poor settling characteristics of particles produced in moving bed biofilm reactor (MBBR) outline the importance of developing a fundamental understanding of the characterization and settleability of MBBR-produced solids. The influence of carrier geometric properties and different levels of biofilm thickness on biofilm characteristics, solids production, particle size distribution (PSD), and particle settling velocity distribution (PSVD) is evaluated in this study. The analytical ViCAs method is applied to the MBBR effluent to assess the distribution of particle settling velocities. This method is combined with microscopy imaging to relate particle size distribution to settling velocity. Three conventionally loaded MBBR systems are studied at a similar loading rate of 6.0 g/(m 2  •day) and with different carrier types. The AnoxK™ K5 carrier, a commonly used carrier, is compared to so-called thickness-restraint carriers, AnoxK™ Z-carriers that are newly designed carriers to limit the biofilm thickness. Moreover, two levels of biofilm thickness, 200 μm and 400 μm, are studied using AnoxK™ Z-200 and Z-400 carriers. Statistical analysis confirms that K5 carriers demonstrated a significantly different biofilm mass, thickness, and density, in addition to distinct trends in PSD and PSVD in comparison with Z-carriers. However, in comparison of thickness-restraint carriers, Z-200 carrier results did not vary significantly compared to the Z-400 carrier. The K5 carriers showed the lowest production of suspended solids (0.7 ± 0.3 g-TSS/day), thickest biofilm (281.1 ± 8.7 µm) and lowest biofilm density (65.0 ± 1.5 kg/m 3 ). The K5 effluent solids also showed enhanced settling behaviour, consisting of larger particles with faster settling velocities., (Copyright © 2021. Published by Elsevier B.V.)

An improved 1D reactive settler model is pursued in order to increase the understanding of reactive settling processes and obtain a better prediction of the nitrogen mass balance in wastewater treatment systems. The developed model is based on the 1D Bürger-Diehl settler model with compression function and the Activated Sludge Model No. 1 biological reactions. Specific attention was paid in the model development phase to optimal selection of settling velocity functions and integration of the correct clarifier geometry. A unique measurement campaign was carried out with different operational scenarios to quantify the denitrification in a secondary settling tank. A detailed step-wise calibration effort demonstrated that by choosing an appropriate settling velocity function (power-law structure) and considering the true clarifier geometry allows to accurately capture the biomass concentration profile, total sludge mass, sludge blanket height, and the reaction rates. The resulting model is able to accurately describe total suspended solids (TSS) and nitrate concentration profiles throughout a settling tank under different operational conditions. As such the model can be applied in further scenario analysis and system optimization. PRACTITIONER POINTS: A unique measurement campaign was carried out to obtain detailed data for a reactive settler model development. A 1-D reactive settler model is developed based on the Bürger-Diehl framework including ASM1 biokinetics and the clarifier geometry. An extensive calibration and model selection effort was performed. The model accurately predicts measured concentration profiles in the settling tank. The developed model can be integrated in a plant-wide model to properly calculate the nitrogen mass balance of a WRRF., (© 2022 Water Environment Federation.)

In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes., Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00835-w., Competing Interests: Conflicts of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© The Author(s), under exclusive licence to Tehran University of Medical Sciences 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Modelling, automation, and control are widely used for water resource recovery facility (WRRF) optimization. An influent generator (IG) is a model, aiming to provide the flowrate and pollutant concentration dynamics at the inlet of a WRRF for a range of modelling applications. In this study, a new data-driven IG model is proposed, only using routine data and weather information, and without need for any additional data collection. The model is constructed by an artificial neural network (ANN) and completed with a multivariate regression to generate time series for certain pollutants. The model is able to generate flowrate and quality data (TSS, COD, and nutrients) at different time scales and resolutions (daily or hourly), depending on various user objectives. The model performance is analyzed by a series of statistical criteria. It is shown that the model can generate a very reliable dataset for different model applications.

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.

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 N 2 O 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.

Nowadays, modelling, automation and control are widely used for Water Resource Recovery Facilities (WRRF) upgrading and optimization. Influent generator (IG) models are used to provide relevant input time series for dynamic WRRF simulations used in these applications. Current IG models found in literature are calibrated on the basis of a single performance criterion, such as the mean percentage error or the root mean square error. This results in the IG being adequate on average but with a lack of representativeness of, for instance, the observed temporal variability of the dataset. However, adequately capturing influent variability may be important for certain types of WRRF optimization, e.g., reaction to peak loads, control system performance evaluation, etc. Therefore, in this study, a data-driven IG model is developed based on the long short-term memory (LSTM) recurrent neural network and is optimized by a multi-objective genetic algorithm for both mean percentage error and variability. Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. To further increase the variability of the generated time series and in this way approximate the true variability better, the model is extended with a random walk process.

The standard 5-day biochemical oxygen demand (BOD 5 ) measurement of water quality is used widely as a design parameter for water resource recovery facilities (WRRFs). This measure usually includes a component of nitrogenous oxygen demand (NOD) that can cause oversizing of biological processes and under-evaluation of process capacity. Carbonaceous BOD (CBOD 5 ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD 5 and therefore should be used as a basis for sizing aerobic treatment processes. Nitrogenous oxygen demand or reduced nitrogen content should be used as a loading and process performance parameter for nitrogen removal processes. PRACTITIONER POINTS: Oxygen demand for aerobic biodegradation reactions typically is divided into two major categories-carbonaceous biochemical oxygen demand (CBOD) and nitrogenous oxygen demand (NOD). Use of BOD 5 as a design parameter and CBOD 5 as an effluent water quality parameter distorts the true performance and loading rate capacity of a treatment plant. Carbonaceous BOD (CBOD 5 ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD 5 and therefore should be used as a basis for sizing and evaluating the performance of aerobic treatment processes. Nitrogenous oxygen demand or reduced nitrogen content should be used as a loading and process performance parameter for nitrogen removal processes., (© 2021 Water Environment Federation.)

Five ferric-phosphate (Fe(III)Ps) with amorphous or crystalline structures were added to waste activated sludge (WAS) for anaerobic fermentation, aiming to investigate effects of Fe(III)Ps forms on phosphorus (P) release and the performance of WAS fermentation. The results revealed that the Fe(III) reduction rate of hexagonal-FePO 4 was faster than that of monoclinic-FePO 4 ·2H 2 O, thanks to its lower crystal field stabilization energy. FePO 4 ·nH 2 O was reduced to vivianite and part of the phosphate was released as orthophosphate (PO 4 -P). Giniite (Fe 5 (PO 4 ) 4 (OH) 3 ·2H 2 O) as an iron hydroxyphosphate was transformed to βFe(III)Fe(II)(PO 4 )O-like compounds without PO 4 -P release. In addition, Fe(III)Ps had an adverse effect on the anaerobic fermentation of WAS. The specific hydrolysis rate constant and volatile fatty acids (VFAs) yield decreased by 38.4% and 41.9%, respectively, for the sludge sample with amorphous-FePO 4 ·3H 2 O, which dropped the most. This study provides new insights into various forms of Fe(III)Ps performance during anaerobic fermentation and is beneficial to enhancing P recovery efficiency., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

A full-scale biofilm-enhanced aerated lagoon using fixed submerged media was monitored using automated water quality monitoring stations over the span of one year to quantify its nitrification performance. The system was operating at a high organic loading rate averaging 5.8 g total CBOD 5 /m 2 of media per day (23.9 g total CBOD 5 /m 3 of lagoon per day), a total ammonia nitrogen loading rate averaging 0.9 g NH 4 -N/m 2  day (3.7 g NH 4 -N/m 3  day), and temperatures ranging from 1.6 to 20.8°C. The system showed an extended seasonal nitrification period compared with a simulated aerated lagoon system of the same dimensions. This extension of complete nitrification with approximately 1 month was observed in the fall despite the decrease of operating temperature down to 4°C. During this maximum nitrification period, substantial denitrification occurred, and the effluent un-ionized ammonia ratio was reduced. A temporary loss of nitrification was also experienced in relation to an episode of elevated suspended solids concentration. Measured biofilm characteristics, namely the detachment dynamics and the biofilm thickness, were used to explain this temporary nitrification loss. During wintertime, a low nitrate production was still observed, suggesting year-long retention of nitrifying bacteria in the biofilm. PRACTITIONER POINTS: Nitrification in a highly loaded biofilm-enhanced aerated lagoon is mainly affected by operating temperature. Maximum nitrification is observed during the warmer months and occurs even at high organic loading rates (>5 g CBOD 5 /m 2  day). Compared with a simulated suspended growth system, the biofilm-enhanced lagoon shows a significantly extended nitrification period. The extension is observed at the end of the summertime maximum nitrification period. Low amounts of nitrate still produced during winter in the biofilm-enhanced aerated lagoon suggest year-long retention of autotrophic nitrifying biomass in the biofilm. Nitrification in the biofilm-enhanced aerated lagoon is negatively impacted by the presence of important quantities of accumulated solids that resuspend when their digestion starts as temperature increases., (© 2019 Water Environment Federation.)

Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology. This review paper examines the state-of-the-art in the transformation of raw data into actionable insight, specifically for water resource recovery facility (WRRF) operation. Sources of difficulties found along the way are pinpointed, while also exploring possible paths towards improving the value of collected data for all stakeholders, i.e., all personnel that have a stake in the good and efficient operation of a WRRF.

Grit chambers are installed at the headworks of a water resource recovery facility (WRRF) to reduce the impact of grit particles to the equipment and processes downstream. This settling process should thus be designed and operated in an efficient way. Despite the importance of knowing settling characteristics for design and operation of grit chambers, previous grit definitions have been based only on particle size characteristics, and not on settling velocities. Thus, this study presents an evaluation of the performance of two promising settling velocity characterization methods, ViCAs and elutriation, to characterize wastewater particles in view of the design and the optimization of the efficiency of the grit removal unit. PRACTITIONER POINTS: Settling characteristics are the governing parameter for grit chamber design. Since grit particles are vastly heterogeneous, it is preferred to measure these characteristics directly rather than to estimate them from particle size (and assumptions of density, form factor, …). More detailed settling information about grit particles can improve grit chamber design and estimation of removal performance. Adapted ViCAs and elutriation methods for faster settling particles allow studying the particle settling characteristics in a grit chamber. These methods are simple, fast, and cheap and only require small wastewater samples. A relationship was found between the influent TSS concentration and the location of the PSVD curve, with higher TSS concentrations corresponding to higher settling velocities. It was demonstrated that the dynamics of the wastewater characteristics under dry, wet, and snowmelt weather conditions influence grit settling characteristics., (© 2019 Water Environment Federation.)

Regulatory water quality limits are extended from the wastewater resource recovery facility (WRRF) to the sewer system. It is thus necessary to properly integrate those systems for the evaluation of the overall emissions to the receiving water. The integration of the sewer system and the WRRF, however, leaves us with multiple potential options to reduce these emissions. The proposed approach builds on previous research using global sensitivity analysis (GSA) as a screening method for available control handles. It considers parameter and input uncertainty to select control handles that generate large benefits even if the model differs from reality. Results from a real-life case study indicate that the three top-rated handles are comparably effective for all considered uncertainty and variability scenarios. But the results also showed that this does not apply to lower-rated handles.

Grit chambers are meant to reduce the impact of inorganic particles on equipment and processes downstream. Despite their important role, characterization and modelling studies of these process units are scarce, leading to a lack of knowledge and suboptimal operation. Thus, this study presents the first dynamic model, based on mass balances and particle settling velocity distributions, for use in a water resource recovery facility (WRRF) simulator for design and optimization of grit removal units.

Hydrolysis is considered the limiting step during solid waste anaerobic digestion (including co-digestion of sludge and biosolids). Mechanisms of hydrolysis are mechanistically not well understood with detrimental impact on model predictive capability. The common approach to multiple substrates is to consider simultaneous degradation of the substrates. This may not have the capacity to separate the different kinetics. Sequential degradation of substrates is theoretically supported by microbial capacity and the composite nature of substrates (bioaccessibility concept). However, this has not been experimentally assessed. Sequential chemical fractionation has been successfully used to define inputs for an anaerobic digestion model. In this paper, sequential extractions of organic substrates were evaluated in order to compare both models. By removing each fraction (from the most accessible to the least accessible fraction) from three different substrates, anaerobic incubation tests showed that for physically structured substrates, such as activated sludge and wheat straw, sequential approach could better describe experimental results, while this was less important for homogeneous materials such as pulped fruit. Following this, anaerobic incubation tests were performed on five substrates. Cumulative methane production was modelled by the simultaneous and sequential approaches. Results showed that the sequential model could fit the experimental data for all the substrates whereas simultaneous model did not work for some substrates., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation , where we will experience a data demand, i.e. a "pull" of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.

Phosphorus recovery has drawn much attention during recent years, due to estimated limited available quantities, and to the harmful environmental impact that it may have when freely released into aquatic environments. Struvite precipitation from wastewater or biological sludge is among the preferred approaches applied for phosphorus recovery, as it results in the availability of valuable fertilizer materials. This process is mostly affected by pH and presence of competitive ions in solution. Modeling and optimization of the precipitation process may help understanding the optimal conditions under which the most efficient recovery could be achieved. In this study, a combination of chemical equilibrium modeling and response surface methodology (RSM) was applied to this aim to aerobic sludge from a plant in Italy. The results identify optimum chemical parameters values for best phosphorus precipitation recovery and removal efficiencies, respectively. Identification of optimal conditions for process control is of great importance for implementing pilot scale struvite precipitation and achieve efficient phosphorus recovery., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The choice of the spatial submodel of a water resource recovery facility (WRRF) model should be one of the primary concerns in WRRF modelling. However, currently used mechanistic models are limited by an over-simplified representation of local conditions. This is illustrated by the general difficulties in calibrating the latest N 2 O models and the large variability in parameter values reported in the literature. The use of compartmental model (CM) developed on the basis of accurate hydrodynamic studies using computational fluid dynamics (CFD) can take into account local conditions and recirculation patterns in the activated sludge tanks that are important with respect to the modelling objective. The conventional tanks in series (TIS) configuration does not allow this. The aim of the present work is to compare the capabilities of two model layouts (CM and TIS) in defining a realistic domain of parameter values representing the same full-scale plant. A model performance evaluation method is proposed to identify the good operational domain of each parameter in the two layouts. Already when evaluating for steady state, the CM was found to provide better defined parameter ranges than TIS. Dynamic simulations further confirmed the CM's capability to work in a more realistic parameter domain, avoiding unnecessary calibration to compensate for flaws in the spatial submodel.

The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like 'black box' models, computational fluid dynamics techniques, etc.? Can new data sources - e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis - keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.

This paper describes the use of global sensitivity analysis (GSA) for factor prioritization in nutrient recovery model (NRM) applications. The aim was to select the most important factors influencing important NRM model outputs such as biogas production, digestate composition and pH, ammonium sulfate recovery, struvite production, product purity, particle size and density, air and chemical requirements, scaling potential, among others. Factors considered for GSA involve: 1) input waste stream characteristics, 2) process operational factors, and 3) kinetic parameters incorporated in the NRMs. Linear regression analyses on Monte Carlo simulation outputs were performed, and the impact of the standardized regression coefficients on major performance indicators was evaluated. Finally, based on the results, the paper describes the original use of GSA to obtain insight in complex nutrient recovery systems and to propose an optimal nutrient and energy recovery treatment train configuration that maximizes resource recovery and minimizes energy and chemical requirements., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Grit causes problems in water resource recovery facilities (WRRFs): clogging pipes, damaging pumps, and reducing the active volume of aeration tanks and anaerobic digesters by grit accumulation. Grit chambers are built to remove these particles. However, no standardized methodology exists to characterize grit particles for grit chamber design and operation despite the large observed variability in grit composition. Therefore, this paper proposes a combination and adaptation of existing methods to sample and characterize grit particles in view of proper grit chamber design and its modelling to ultimately optimize the efficiency of this important WRRF unit process. Characteristics evaluated included particle size distribution from sieving after different sample pretreatments, organic/inorganic fractions, and density.

This paper aims to develop a generic roadmap for setting up strategies for nutrient recovery from digested waste (digestate). First, a guideline-based decision-tree is presented for setting up an optimal bio-based fertilization strategy as function of local agronomic and regulatory criteria. Next, guidelines and evaluation criteria are provided to determine the feasibility of bio-based fertilizer production as function of the input digestate characteristics. Finally, a conceptual decision making algorithm is developed aiming at the configuration and optimization of nutrient recovery treatment trains. Important input digestate characteristics to measure, and essential factors for monitoring and control are identified. As such, this paper provides a useful decision-support guide for wastewater and residuals processing utilities aiming to implement nutrient recovery strategies. This, in turn, may stimulate and hasten the global transition from wastewater treatment plants to water resource recovery facilities. On top of that, the proposed roadmap may help adjusting the choice of nutrient recovery strategies to local fertilizer markets, thereby speeding up the transition from a fossil-reserve based to a bio-based circular nutrient economy., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Cellulose, mostly in the form of toilet paper, forms a major component of the particulates in raw municipal wastewater, which could lead to significant consequences due to the potential accumulation of cellulosic fibers and slow biodegradability. Despite the sparse reports on cellulose content and degradation in wastewater and sludge, an accurate and validated method for its quantification in such matrices does not exist. In this paper, four different methods were compared including dilute acid hydrolysis, concentrated acid hydrolysis, enzymatic hydrolysis, and the Schweitzer reagent method. The Schweitzer reagent method, applied to municipal wastewater and sludge, was found to be a very robust and reliable quantification method in light of its reproducibility, accuracy, and ideal (100%) recovery. The determination of cellulose content is critical to understand its fate in wastewater treatment plants as well as improve sludge management and enhance resource recovery.

A researcher or practitioner can employ a biofilm model to gain insight into what controls the performance of a biofilm process and for optimizing its performance. While a wide range of biofilm-modeling platforms is available, a good strategy is to choose the simplest model that includes sufficient components and processes to address the modeling goal. In most cases, a one-dimensional biofilm model provides the best balance, and good choices can range from hand-calculation analytical solutions, simple spreadsheets, and numerical-method platforms. What is missing today is clear guidance on how to apply a biofilm model to obtain accurate and meaningful results. Here, we present a five-step framework for good biofilm reactor modeling practice (GBRMP). The first four steps are (1) obtain information on the biofilm reactor system, (2) characterize the influent, (3) choose the plant and biofilm model, and (4) define the conversion processes. Each step demands that the model user understands the important components and processes in the system, one of the main benefits of doing biofilm modeling. The fifth step is to calibrate and validate the model: System-specific model parameters are adjusted within reasonable ranges so that model outputs match actual system performance. Calibration is not a simple 'by the numbers' process, and it requires that the modeler follows a logical hierarchy of steps. Calibration requires that the adjusted parameters remain within realistic ranges and that the calibration process be carried out in an iterative manner. Once each of steps 1 through 5 is completed satisfactorily, the calibrated model can be used for its intended purpose, such as optimizing performance, trouble-shooting poor performance, or gaining deeper understanding of what controls process performance.

The presence of micropollutants in the environment and their toxic impacts on the aquatic environment have raised concern about their inefficient removal in wastewater treatment plants. In this study, the fate of micropollutants of four different classes was simulated in a conventional activated sludge plant using a bioreactor micropollutant fate model coupled to a settler model. The latter was based on the Bürger-Diehl model extended for the first time to include micropollutant fate processes. Calibration of model parameters was completed by matching modelling results with full-scale measurements (i.e. including aqueous and particulate phase concentrations of micropollutants) obtained from a 4-day sampling campaign. Modelling results showed that further biodegradation takes place in the sludge blanket of the settler for the highly biodegradable caffeine, underlining the need for a reactive settler model. The adopted Monte Carlo based calibration approach also provided an overview of the model's global sensitivity to the parameters. This analysis showed that for each micropollutant and according to the dominant fate process, a different set of one or more parameters had a significant impact on the model fit, justifying the selection of parameter subsets for model calibration. A dynamic local sensitivity analysis was also performed with the calibrated parameters. This analysis supported the conclusions from the global sensitivity and provided guidance for future sampling campaigns. This study expands the understanding of micropollutant fate models when applied to different micropollutants, in terms of global and local sensitivity to model parameters, as well as the identifiability of the parameters., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The objective of this paper is to report the effects that control/operational strategies may have on plant-wide phosphorus (P) transformations in wastewater treatment plants (WWTP). The development of a new set of biological (activated sludge, anaerobic digestion), physico-chemical (aqueous phase, precipitation, mass transfer) process models and model interfaces (between water and sludge line) were required to describe the required tri-phasic (gas, liquid, solid) compound transformations and the close interlinks between the P and the sulfur (S) and iron (Fe) cycles. A modified version of the Benchmark Simulation Model No. 2 (BSM2) (open loop) is used as test platform upon which three different operational alternatives (A 1 , A 2 , A 3 ) are evaluated. Rigorous sensor and actuator models are also included in order to reproduce realistic control actions. Model-based analysis shows that the combination of an ammonium ( [Formula: see text] ) and total suspended solids (X TSS ) control strategy (A 1 ) better adapts the system to influent dynamics, improves phosphate [Formula: see text] accumulation by phosphorus accumulating organisms (X PAO ) (41%), increases nitrification/denitrification efficiency (18%) and reduces aeration energy (E aeration ) (21%). The addition of iron ( [Formula: see text] ) for chemical P removal (A 2 ) promotes the formation of ferric oxides (X HFO-H , X HFO-L ), phosphate adsorption (X HFO-H,P , X HFO-L,P ), co-precipitation (X HFO-H,P,old , X HFO-L,P,old ) and consequently reduces the P levels in the effluent (from 2.8 to 0.9 g P.m -3 ). This also has an impact on the sludge line, with hydrogen sulfide production ( [Formula: see text] ) reduced (36%) due to iron sulfide (X FeS ) precipitation. As a consequence, there is also a slightly higher energy production (E production ) from biogas. Lastly, the inclusion of a stripping and crystallization unit (A 3 ) for P recovery reduces the quantity of P in the anaerobic digester supernatant returning to the water line and allows potential struvite ( [Formula: see text] ) recovery ranging from 69 to 227 kg.day -1 depending on: (1) airflow (Q stripping ); and, (2) magnesium ( [Formula: see text] ) addition. All the proposed alternatives are evaluated from an environmental and economical point of view using appropriate performance indices. Finally, some deficiencies and opportunities of the proposed approach when performing (plant-wide) wastewater treatment modelling/engineering projects are discussed., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The presence of micropollutants in the environment has triggered research on quantifying and predicting their fate in wastewater treatment plants (WWTPs). Since the removal of micropollutants is highly related to conventional pollutant removal and affected by hydraulics, aeration, biomass composition and solids concentration, the fate of these conventional pollutants and characteristics must be well predicted before tackling models to predict the fate of micropollutants. In light of this, the current paper presents the dynamic modelling of conventional pollutants undergoing activated sludge treatment using a limited set of additional daily composite data besides the routine data collected at a WWTP over one year. Results showed that as a basis for modelling, the removal of micropollutants, the Bürger-Diehl settler model was found to capture the actual effluent total suspended solids (TSS) concentrations more efficiently than the Takács model by explicitly modelling the overflow boundary. Results also demonstrated that particular attention must be given to characterizing incoming TSS to obtain a representative solids balance in the presence of a chemically enhanced primary treatment, which is key to predict the fate of micropollutants.

Chemically enhanced primary treatment (CEPT) can be used to mitigate the adverse effect of wet weather flow on wastewater treatment processes. In particular, it can reduce the particulate pollution load to subsequent secondary unit processes, such as biofiltration, which may suffer from clogging by an overload of particulate matter. In this paper, a simple primary clarifier model able to take into account the effect of the addition of chemicals on particle settling is presented. Control strategies that optimize the treatment process by chemical addition were designed and tested by running simulations with this CEPT model. The most adequate control strategy in terms of treatment performance, chemicals saving, and maintenance effort was selected. Full-scale implementation of the controller was performed during the autumn of 2015, and the results obtained confirmed the behaviour of the controlled system. Practical issues related to the implementation are presented.

A new perspective on the modelling of settling behaviour in water resource recovery facilities is introduced. The ultimate goal is to describe in a unified way the processes taking place both in primary settling tanks (PSTs) and secondary settling tanks (SSTs) for a more detailed operation and control. First, experimental evidence is provided, pointing out distributed particle properties (such as size, shape, density, porosity, and flocculation state) as an important common source of distributed settling behaviour in different settling unit processes and throughout different settling regimes (discrete, hindered and compression settling). Subsequently, a unified model framework that considers several particle classes is proposed in order to describe distributions in settling behaviour as well as the effect of variations in particle properties on the settling process. The result is a set of partial differential equations (PDEs) that are valid from dilute concentrations, where they correspond to discrete settling, to concentrated suspensions, where they correspond to compression settling. Consequently, these PDEs model both PSTs and SSTs.