A comprehensive, open-source data model for wastewater-based epidemiology

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.

Machine learning for modeling N2O emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretability

Nitrous oxide (N2O) 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 N2O emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting N2O 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 N2O emissions. The approach is tested on a long-term N2O 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 (R2 = 0.88), AdaBoost (R2 = 0.94), and DNN (R2 = 0.90). Feature importance of models was analyzed and compared with process knowledge to test interpretability, guiding N2O mitigation decisions.

Assessing the equivalence of WRRF regulations using dynamic model simulations

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.

Wastewater-based epidemiology: the crucial role of viral shedding dynamics in small communities

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.

Sidestream bio-P and mainstream anammox in a BNR process with upstream carbon capture

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.

The ASM2d model with two-step nitrification can better simulate biological nutrient removal systems enriched with complete ammonia oxidizing bacteria (comammox Nitrospira)

The discovery of comammox Nitrospira, a complete ammonia-oxidizing microorganism belonging to the genus Nitrospira, has brought new insights into the nitrification process in wastewater treatment plants (WWTPs). The applicability of Activated Sludge Model No. 2 d with one-step nitrification (ASM2d-OSN) or two-step nitrification (ASM2d-TSN) for the simulation of the biological nutrient removal (BNR) processes of a full-scale WWTP in the presence of comammox Nitrospira was studied. Microbial analysis and kinetic parameter measurements showed comammox Nitrospira was enriched in the BNR system operated under low dissolved oxygen (DO) and long sludge retention time (SRT). The relative abundance of Nitrospira under the conditions of stage I (DO = 0.5 mg/L, SRT = 60 d) was about twice of that under stage II conditions (DO = 4.0 mg/L, SRT = 26 d), and the copy number of the comammox amoA gene for stage I was 33 times higher than that for stage II. Compared to the ASM2d-OSN model, the ASM2d-TSN model simulated the performance of the WWTP under stage I conditions better, and the Theil inequality coefficient values of all the tested water quality parameters were lower than using ASM2d-OSN. These results indicate that an ASM2d model with two-step nitrification is a better choice for the simulation of WWTPs with the presence of comammox.

Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review

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.

An improved 1D reactive Bürger-Diehl settler model for secondary settling tank denitrification

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.

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.

The transition of WRRF models to digital twin 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.

An essential tool for WRRF modelling: a realistic and complete influent generator for flow rate and water quality based on data-driven methods

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.

An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithm

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.

Carbonaceous vs. total biochemical oxygen demand as a basis for WRRF design and performance monitoring

The standard 5-day biochemical oxygen demand (BOD₅ ) 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 (CBOD₅ ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD₅ 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 BOD₅ as a design parameter and CBOD₅ as an effluent water quality parameter distorts the true performance and loading rate capacity of a treatment plant. Carbonaceous BOD (CBOD₅ ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD₅ 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.

Effects of ferric-phosphate forms on phosphorus release and the performance of anaerobic fermentation of waste activated sludge

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-FePO₄ was faster than that of monoclinic-FePO₄·2H₂O, thanks to its lower crystal field stabilization energy. FePO₄·nH₂O was reduced to vivianite and part of the phosphate was released as orthophosphate (PO₄-P). Giniite (Fe₅(PO₄)₄(OH)₃·2H₂O) as an iron hydroxyphosphate was transformed to βFe(III)Fe(II)(PO₄)O-like compounds without PO₄-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-FePO₄·3H₂O, 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.

Nitrification in a biofilm-enhanced highly loaded aerated lagoon

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 CBOD₅ /m² of media per day (23.9 g total CBOD₅ /m³ of lagoon per day), a total ammonia nitrogen loading rate averaging 0.9 g NH₄ -N/m²  day (3.7 g NH₄ -N/m³  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 CBOD₅ /m²  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.

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

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.

Characterizing the settleability of grit particles

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.

No-regret selection of effective control handles for integrated urban wastewater systems management under parameter and input uncertainty

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.

Dynamic grit chamber modelling: dealing with particle settling velocity distributions

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.

Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions

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.

How Urban Storm- and Wastewater Management Prepares for Emerging Opportunities and Threats: Digital Transformation, Ubiquitous Sensing, New Data Sources, and Beyond - A Horizon Scan

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.

Optimization of P compounds recovery from aerobic sludge by chemical modeling and response surface methodology combination

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.

Tanks in series versus compartmental model configuration: considering hydrodynamics helps in parameter estimation for an N2O model

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 N₂O 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 future of WRRF modelling - outlook and challenges

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.

Optimizing the configuration of integrated nutrient and energy recovery treatment trains: A new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library

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.

Grit particle characterization: influence of sample pretreatment and sieving method

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.

Nutrient recovery from digested waste: Towards a generic roadmap for setting up an optimal treatment train

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.

Experimental assessment and validation of quantification methods for cellulose content in municipal wastewater and sludge

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.

Predicting the fate of micropollutants during wastewater treatment: Calibration and sensitivity analysis

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.

Plant-wide modelling of phosphorus transformations in wastewater treatment systems: Impacts of control and operational strategies

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 (A₁, A₂, A₃) 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 (A₁) 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 (A₂) 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 (A₃) 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.

Chemically enhancing primary clarifiers: model-based development of a dosing controller and full-scale implementation

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.

Concentration-driven models revisited: towards a unified framework to model settling tanks in water resource recovery facilities

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.

Influence of Different Sewer Biofilms on Transformation Rates of Drugs

To estimate drug consumption more reliably, wastewater-based epidemiology would benefit from a better understanding of drug residue stability during in-sewer transport. We conducted batch experiments with real, fresh wastewater and sewer biofilms. Experimental conditions mimic small to medium-sized gravity sewers with a relevant ratio of biofilm surface area to wastewater volume (33 m² m^-3). The influences of biological, chemical, and physical processes on the transformation of 30 illicit drug and pharmaceutical residues were quantified. Rates varied among locations and over time. Three substances were not stable-that is, >20% transformation, mainly due to biological processes-at least for one type of tested biofilm for a residence time ≤2 h: amphetamine, 6-acetylcodeine, and 6-monoacetylmorphine. Cocaine, ecgonine methyl ester, norcocaine, cocaethylene, and mephedrone were mainly transformed by chemical hydrolysis and, hence, also unstable in sewers. In contrast, ketamine, norketamine, O-desmethyltramadol, diclofenac, carbamazepine, and methoxetamine were not substantially affected by in-sewer processes under all tested conditions and residence times up to 12 h. Our transformation rates include careful quantification of uncertainty and can be used to identify situations in which specific compounds are not stable. This will improve accuracy and uncertainty estimates of drug consumption when applied to the back-calculation.

Fate and mass balance of contaminants of emerging concern during wastewater treatment determined using the fractionated approach

Contaminants of emerging concern (CECs) are often poorly removed from wastewater using conventional treatment technologies and there is limited understanding of their fate during treatment. Inappropriate sampling strategies lead to inaccuracies in estimating removals of CECs. In this study, we used the "fractionated approach" that accounts for the residence time distribution (RTD) in treatment units to investigate the fate of 26 target CECs in a municipal wastewater treatment plant (WWTP) that includes primary, secondary and tertiary treatment steps. Prior hydraulic calibration of each treatment unit was performed. Wastewater and sludge samples were collected at different locations along the treatment train and the concentrations of target CECs were measured by liquid chromatography mass spectrometry. The most substantial aqueous removal occurred during activated sludge treatment (up to 99%). Removals were <50% in the primary clarifier and tertiary rotating biological contactors (RBCs) and up to 70% by sand filtration. Mass balance calculations demonstrated that (bio)degradation accounted for up to 50% of the removal in the primary clarifier and 100% in activated sludge. Removal by sorption to primary and secondary sludge was minimal for most CECs. Analysis of the selected metabolites demonstrated that negative removals obtained could be explained by transformations between the parent compound and their metabolites. This study contributes to the growing literature by applying the fractionated approach to calculate removal of different types of CECs across each wastewater treatment step. An additional level of understanding of the fate of CECs was provided by mass balance calculations in primary and secondary treatments.

Optimal flow sensor placement on wastewater treatment plants

Obtaining high quality data collected on wastewater treatment plants is gaining increasing attention in the wastewater engineering literature. Typical studies focus on recognition of faulty data with a given set of installed sensors on a wastewater treatment plant. Little attention is however given to how one can install sensors in such a way that fault detection and identification can be improved. In this work, we develop a method to obtain Pareto optimal sensor layouts in terms of cost, observability, and redundancy. Most importantly, the resulting method allows reducing the large set of possibilities to a minimal set of sensor layouts efficiently for any wastewater treatment plant on the basis of structural criteria only, with limited sensor information, and without prior data collection. In addition, the developed optimization scheme is fast. Practically important is that the number of sensors needed for both observability of all flows and redundancy of all flow sensors is only one more compared to the number of sensors needed for observability of all flows in the studied wastewater treatment plant configurations.

Modelling and characterization of primary settlers in view of whole plant and resource recovery modelling

Characterization and modelling of primary settlers have been neglected pretty much to date. However, whole plant and resource recovery modelling requires primary settler model development, as current models lack detail in describing the dynamics and the diversity of the removal process for different particulate fractions. This paper focuses on the improved modelling and experimental characterization of primary settlers. First, a new modelling concept based on particle settling velocity distribution is proposed which is then applied for the development of an improved primary settler model as well as for its characterization under addition of chemicals (chemically enhanced primary treatment, CEPT). This model is compared to two existing simple primary settler models (Otterpohl and Freund; Lessard and Beck), showing to be better than the first one and statistically comparable to the second one, but with easier calibration thanks to the ease with which wastewater characteristics can be translated into model parameters. Second, the changes in the activated sludge model (ASM)-based chemical oxygen demand fractionation between inlet and outlet induced by primary settling is investigated, showing that typical wastewater fractions are modified by primary treatment. As they clearly impact the downstream processes, both model improvements demonstrate the need for more detailed primary settler models in view of whole plant modelling.

Effect of sensor location on controller performance in a wastewater treatment plant

Complete mixing is hard to achieve in large bioreactors in wastewater treatment plants. This often leads to a non-uniform distribution of components such as dissolved oxygen and, hence, the process rates depend on them. Furthermore, when these components are used as input for a controller, the location of the sensor can potentially affect the control action. In this contribution, the effect of sensor location and the choice of setpoint on the controller performance were examined for a non-homogeneously mixed pilot bioreactor described by a compartmental model. The impacts on effluent quality and aeration cost were evaluated. It was shown that a dissolved oxygen controller with a fixed setpoint performs differently as a function of the location of the sensor. When placed in a poorly mixed location, the controller increases the aeration intensity to its maximum capacity leading to higher aeration costs. When placed just above the aerated zone, the controller decreases the aeration rate resulting in lower dissolved oxygen concentrations in the remainder of the system, compromising effluent quality. In addition to the location of the sensor, the selection of an appropriate setpoint also impacts controller behavior. This suggests that mixing behavior of bioreactors should be better quantified for proper sensor location and controller design.

Population balance models: a useful complementary modelling framework for future WWTP modelling

Population balance models (PBMs) represent a powerful modelling framework for the description of the dynamics of properties that are characterised by distributions. This distribution of properties under transient conditions has been demonstrated in many chemical engineering applications. Modelling efforts of several current and future unit processes in wastewater treatment plants could potentially benefit from this framework, especially when distributed dynamics have a significant impact on the overall unit process performance. In these cases, current models that rely on average properties cannot sufficiently capture the true behaviour and even lead to completely wrong conclusions. Examples of distributed properties are bubble size, floc size, crystal size or granule size. In these cases, PBMs can be used to develop new knowledge that can be embedded in our current models to improve their predictive capability. Hence, PBMs should be regarded as a complementary modelling framework to biokinetic models. This paper provides an overview of current applications, future potential and limitations of PBMs in the field of wastewater treatment modelling, thereby looking over the fence to other scientific disciplines.

Removal of selected pharmaceuticals, personal care products and artificial sweetener in an aerated sewage lagoon

A sewage lagoon serving the small municipality of Lakefield in Ontario, Canada was monitored in the summer, fall and winter to determine removals of carbamazepine, trimethoprim, sulfamethoxazole, ibuprofen, gemfibrozil, triclosan, sucralose, HHCB and AHTN. Concentrations of these compounds in untreated and treated wastewater were estimated by deploying POCIS and SPMD passive samplers in the sewage lagoon. Passive samplers were also deployed at several points upstream and downstream of the point of discharge from the lagoon into the Otonabee River. LC-MS/MS and GC-MS were utilized to determine the concentrations of pharmaceuticals and personal care products (PPCPs) and sucralose, an artificial sweetener. Among PPCPs sampled by POCIS, the highest estimated concentration in untreated wastewater was ibuprofen sampled during the fall, at an estimated concentration of 60.3 ng/L. The estimated average concentration of sucralose was 13.6 ng/L in the untreated wastewaters. Triclosan, HHCB and AHTN in SPMDs were highest during fall season, at 30, 1677 and 109 ng/L, respectively. For all compounds except gemfibrozil, carbamazepine and sucralose, removals were highest in the summer (83.0 to 98.8%) relative to removals in the fall (48.4 to 91.4%) and winter (14.0 to 78.3%). Finally, the estimated concentrations of carbamazepine, sulfamethoxazole, triclosan and HHCB were compared with predicted values obtained through application of the WEST® modeling tool, with a new model based on the River Water Quality Model No. 1 and extended with dynamic mass balances describing the fate of chemicals of emerging concern subject to a variety of removal pathways. The model was able to adequately predict the fate of these four compounds in the lagoon in summer and winter, but the model overestimated removals of three of the four test compounds in the fall sampling period. This lagoon was as effective at removing PPCPs as many conventional WWTPs, but removals were better during the summer.

Variance-based sensitivity analysis for wastewater treatment plant modelling

Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models that characterise technical or natural systems. In the field of wastewater modelling, most of the recent applications of GSA use either regression-based methods, which require close to linear relationships between the model outputs and model factors, or screening methods, which only yield qualitative results. However, due to the characteristics of membrane bioreactors (MBR) (non-linear kinetics, complexity, etc.) there is an interest to adequately quantify the effects of non-linearity and interactions. This can be achieved with variance-based sensitivity analysis methods. In this paper, the Extended Fourier Amplitude Sensitivity Testing (Extended-FAST) method is applied to an integrated activated sludge model (ASM2d) for an MBR system including microbial product formation and physical separation processes. Twenty-one model outputs located throughout the different sections of the bioreactor and 79 model factors are considered. Significant interactions among the model factors are found. Contrary to previous GSA studies for ASM models, we find the relationship between variables and factors to be non-linear and non-additive. By analysing the pattern of the variance decomposition along the plant, the model factors having the highest variance contributions were identified. This study demonstrates the usefulness of variance-based methods in membrane bioreactor modelling where, due to the presence of membranes and different operating conditions than those typically found in conventional activated sludge systems, several highly non-linear effects are present. Further, the obtained results highlight the relevant role played by the modelling approach for MBR taking into account simultaneously biological and physical processes.

Instrumentation, control and automation in wastewater--from London 1973 to Narbonne 2013

Key developments of instrumentation, control and automation (ICA) applications in wastewater systems during the past 40 years are highlighted in this paper. From the first ICA conference in 1973 through to today there has been a tremendous increase in the understanding of the processes, instrumentation, computer systems and control theory. However, many developments have not been addressed here, such as sewer control, drinking water treatment and water distribution control. It is hoped that this review can stimulate new attempts to more effectively apply control and automation in water systems in the coming years.

Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs

The objective of this paper was to show the potential additional insight that result from adding greenhouse gas (GHG) emissions to plant performance evaluation criteria, such as effluent quality (EQI) and operational cost (OCI) indices, when evaluating (plant-wide) control/operational strategies in wastewater treatment plants (WWTPs). The proposed GHG evaluation is based on a set of comprehensive dynamic models that estimate the most significant potential on-site and off-site sources of CO₂, CH₄ and N₂O. The study calculates and discusses the changes in EQI, OCI and the emission of GHGs as a consequence of varying the following four process variables: (i) the set point of aeration control in the activated sludge section; (ii) the removal efficiency of total suspended solids (TSS) in the primary clarifier; (iii) the temperature in the anaerobic digester; and (iv) the control of the flow of anaerobic digester supernatants coming from sludge treatment. Based upon the assumptions built into the model structures, simulation results highlight the potential undesirable effects of increased GHG production when carrying out local energy optimization of the aeration system in the activated sludge section and energy recovery from the AD. Although off-site CO₂ emissions may decrease, the effect is counterbalanced by increased N₂O emissions, especially since N₂O has a 300-fold stronger greenhouse effect than CO₂. The reported results emphasize the importance and usefulness of using multiple evaluation criteria to compare and evaluate (plant-wide) control strategies in a WWTP for more informed operational decision making.

Including Life Cycle Assessment for decision-making in controlling wastewater nutrient removal systems

This paper focuses on the use of Life Cycle Assessment (LCA) to evaluate the performance of seventeen control strategies in wastewater treatment plants (WWTPs). It tackles the importance of using site-specific factors for nutrient enrichment when decision-makers have to select best operating strategies. Therefore, the LCA evaluation is repeated for three different scenarios depending on the limitation of nitrogen (N), phosphorus (P), or both, when evaluating the nutrient enrichment impact in water bodies. The LCA results indicate that for treated effluent discharged into N-deficient aquatic systems (e.g. open coastal areas) the most eco-friendly strategies differ from the ones dealing with discharging into P-deficient (e.g. lakes and rivers) and N&P-deficient systems (e.g. coastal zones). More particularly, the results suggest that strategies that promote increased nutrient removal and/or energy savings present an environmental benefit for N&P and P-deficient systems. This is not the case when addressing N-deficient systems for which the use of chemicals (even for improving N removal efficiencies) is not always beneficial for the environment. A sensitivity analysis on using weighting of the impact categories is conducted to assess how value choices (policy decisions) may affect the management of WWTPs. For the scenarios with only N-limitation, the LCA-based ranking of the control strategies is sensitive to the choice of weighting factors, whereas this is not the case for N&P or P-deficient aquatic systems.