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Wastewater surveillance of pathogens may be a useful tool to help determine whether clinical surveillance of disease is effective or inadequate due to under-reporting and under-detection. In addition, tracking of pathogen concentrations over time could potentially provide a measure of the effectiveness of public health control measures and the impact of the gradual relaxation of these controls. Analysis of wastewater using quantitative molecular methods offers a real-time measure of infections in the community, and thus is expected to provide a more sensitive and rapid indication of changes in infection rates before such effects become detectable by clinical health surveillance. Models may help to back-calculate wastewater prevalence to population prevalence or to correct pathogen counts for wastewater catchment-specific and temporal effects. They may also help to design the wastewater sampling strategy. This article provides a brief summary of the history of pathogen wastewater surveillance to help set the context for the SARS-CoV-2 wastewater-based epidemiology (WBE) programmes currently being undertaken globally. HIGHLIGHTS A brief history of pathogen wastewater surveillance.; Context for current SARS-CoV-2 wastewater-based epidemiology (WBE) programmes.; Recommendations on research and investigation priorities.; Wide potential of modelling is presented together with remaining knowledge gaps.

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.

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.

This paper proposes a new conceptual modelling approach and calibration method for sewer systems, driven by the interest in using the sewer as a bioreactor and long-term biochemical modelling of the integrated system consisting of the sewage collection system and the wastewater treatment plant (WWTP). The new approach consists of a method for simplifying a complex sewer network into a simpler, yet accurate, computationally efficient (conceptual) model able to capture the relevant sewer dynamics, both in terms of water quantity and water quality entering the wastewater treatment plant. Water quality predicted by the conceptual model was assessed using a detailed biochemical process model and compared against a whole-network biochemical model that covers all hydraulic segments and pipes of the sewer system. The conceptual biochemical model achieves very similar results as the whole-network model in terms of water quality, while providing much faster simulation speed (minutes rather than hours) and easy calibration. Given these advantages, the conceptual biochemical model of a collection system can be used in an integrated model with which sewer and WWTP can be simulated simultaneously for long-term evaluation of optimization scenarios. The model was applied to the prediction of the biochemical components, e.g. sulfide, organics and nutrients, which are essential for the control and management of sewer corrosion and pollutant removal.

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 reported research developed a generic nutrient recovery model (NRM) library based on detailed chemical solution speciation and reaction kinetics, with focus on fertilizer quality and quantity as model outputs. Dynamic physicochemical three-phase process models for precipitation/crystallization, stripping and acidic air scrubbing as key unit processes were developed. In addition, a compatible biological-physicochemical anaerobic digester model was built. The latter includes sulfurgenesis, biological N/P/K/S release/uptake, interactions with organics, among other relevant processes, such as precipitation, ion pairing and liquid-gas transfer. Using a systematic database reduction procedure, a 3- to 5-fold improvement of model simulation speeds was obtained as compared to using full standard thermodynamic databases. Missing components and reactions in existing standard databases were discovered. Hence, a generic nutrient recovery database was created for future applications. The models were verified and validated against a range of experimental results. Their functionality in terms of increased process understanding and optimization was demonstrated.

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.

Denitrification during the sedimentation process in secondary settlers in waste-water treatment plants has been reported to be still a significant problem. The modelling of such a process with the conservation of mass leads to a nonlinear convection–diffusion–reaction partial differential equation, which needs non-standard numerical methods to obtain reliable simulations. The purpose of this study is to provide a first extension of the Burger–Diehl settler model, which models sedimentation without reactions, to include biological reactions. This is done with a reduced biokinetic model that contains only the relevant particulate and soluble components. Furthermore, the development is limited to batch settling. The final model describes the last settling stage of a sequencing batch reactor with denitrification. The main result of the paper is a numerical scheme that is tested for simulations with several initial conditions.

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.

Nutrient recovery from digested biodegradable waste as marketable products has become an important task for anaerobic digestion plants to meet both regulatory drivers and market demands, while producing an internal revenue source. As such, the present waste problem could be turned into an economic opportunity. The aim of this study was to provide a comprehensive overview and critical comparison of the available/emerging technologies for nutrient recovery from digestate, and a classification of the resulting end-products according to their fertilizer characteristics. Based on the stage of implementation, the technical performance, as well as financial aspects, struvite precipitation/crystallization, ammonia stripping and (subsequent) absorption using an acidic air scrubber were selected as best available technologies to be applied at full-scale for nutrient recovery as marketable fertilizer commodities. The resulting end-products can and should be classified as renewable nitrogen–phosphorus (N/P) precipitates and nitrogen–sulfur (N/S) solutions, respectively, in fertilizer and environmental legislations. This would stimulate their use and foster nutrient recovery technology implementation.

In this paper, a new conceptual modelling approach and calibration method is proposed for integrating sewage collection system and wastewater treatment plant (WWTP) operations. The method consists of simplifying a complex sewer network into a computationally efficient (conceptual) model able to capture the sewer dynamics, both in terms of flow rate and water quality, entering the wastewater treatment plant. Biochemical process modelling was carried out on this conceptual model and compared with a whole-network biochemical model which covered all pipes of a sewer system. The conceptual biochemical model can achieve similar results as the whole-network model in terms of water qualities, but provides fast simulation speed and easy calibration. Those advantages mean that the conceptual biochemical model of a collection system can be used in an integrated model with which sewer and WWTP can be simulated simultaneously for long-term evaluation of optimization scenarios.

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.

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.

The increasing efforts in reducing the emission of micropollutants (MP) into the natural aquatic environment require the development of modelling tools to support the decision making process. This article presents a library of dynamic modelling tools for estimating MP fluxes within Integrated Urban Wastewater and Stormwater system (IUWS - including drainage network, stormwater treatment units, wastewater treatment plants, sludge treatment, and the receiving water body). The models are developed by considering the high temporal variability of the processes taking place in the IUWS, providing a basis for the elaboration of pollution control strategies (including both source control and treatment options) at the small spatial scale of urban areas. Existing and well-established water quality models for the different parts of the IUWS (e.g. ASM models) are extended by adding MP fate processes. These are modelled by using substance inherent properties, following an approach commonly used in large-scale MP multimedia fate and transport models. The chosen level of complexity ensures a low data requirement and minimizes the need for field measurements. Next to a synthesis of model applications, a didactic example is presented to illustrate the potential of the use of the developed model library for developing, evaluating and comparing strategies for reduction of MP emissions from urban areas. Display Omitted We created a model library for dynamic modelling of micropollutant fluxes in cities.The IUWS_MP model library includes sewer, treatment options and receiving waters.IUWS_MP combines existing models for evaluating pollution control strategies.Dynamic modelling allows estimation of both MP fluxes and concentrations.A didactic example showing the potential of the developed tool is presented.

International audience; 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.

When Ron Droste, my colleague Editor-in-Chief, announced last year he felt it was time for him to step down and give a chance to somebody else to team up with me as Editor-in-Chief, a little panic attack followed. How do you replace a devoted, experienced and meticulous person as Ron? Well, we found a person that comes with an extraordinary skill set: Dr Arash Zamyadi, recently appointed assistant professor at Polytechnique Montreal. He is young and eager and has shown his devotion to the International Water Association in many ways, as recognized with the IWA Fellowship he obtained in 2016. For instance, in 2010 he initiated the North American chapter of IWA's Young Water Professionals. People that know him, can certainly vouch for his optimism, his incredible energy and high emotional intelligence quotient. However, in order to take up the challenging task of being Editor-in-Chief of a peer-reviewed journal that seeks to increase its impact, an excellent scientific reputation is key and the wide scope …

Retention tanks (RTs) are commonly used to reduce combined sewer overflows, management of which is an important way of reducing the impacts of urban development on receiving waters. However, overflow characteristics and the processes affecting them are not yet fully understood. In a context of integrated urban wastewater systems, the management of RTs is mainly done to satisfy hydraulic constraints even if the idea behind such structures is to limit the discharge of pollutants to the environment. This study reports new insights in the settling processes and the pollutant behaviour occurring in an off-line RT. The authors first focus on the total suspended solids (TSS) and the total chemical oxygen demand (CODt) dynamics at the inlet and the outlet of a RT. Secondly, they focus on the possible relationship between the variation of the particle settling velocity distribution of particles and the TSS concentration dynamics. Finally, analyses of the TSS and CODt concentration evolution during tank emptying give information on the interaction between wastewater retention time and the settling performance.

Three global sensitivity analysis (GSA) methods are applied and compared to assess the most relevant processes occurring in wastewater treatment systems. In particular, the Standardised Regression Coefficients, Morris Screening and Extended-FAST methods are applied to a complex integrated membrane bioreactor (MBR) model considering 21 model outputs and 79 model factors. The three methods are applied with numerical settings as suggested in literature. The main objective considered is to classify important factors (factors prioritisation) as well as non-influential factors (factors fixing). The performance is assessed by comparing the most reliable method (Extended-FAST), by means of proposed criteria, with the two other methods. In particular, similarity to results obtained from Extended-FAST is assessed for sensitivity indices, for the ranking of sensitivity indices, for the classification into important/non-influential factors and for the method's ability to detect interaction among factors and to provide results in a reasonable time. It was found that the computationally less expensive SRC method was applied outside its range of applicability (R^2) = (0.3-0.6) < 0.7. Still, the SRC produced a ranking of important factors similar to Extended-FAST. For some variables significant interactions among the factors were revealed by computing the total effect indices S"T"i using Extended-FAST. This means that to obtain reliable variance decomposition and to detect and quantify interactions among the factors, the use of the Extended-FAST is recommended. Regarding the comparison between Morris screening and Extended-FAST a poor agreement was found. In particular, the Morris screening overestimated the number of both important and non-influential factors compared to Extended-FAST for the analysed case study.

Real Time Control (RTC) has become an accepted technique for improving the performance of Urban Drainage Systems (UDS) due to its flexibility and sustainability. Numerous implementations of RTC have been reported during the last decades. At the same time, guideline documents and state-of-the-art reports have been published. Whereas the general aspects and challenges of planning and installation of RTC systems are well covered, there is a lack of information about the adequate equipment for RTC of UDS. After identifying and briefly discussing the basic components of RTC systems for UDS, this paper describes the specific components in detail. This comprises the introduction of available technologies for sensors, actuators, controllers and telemetry systems in the context of RTC and the discussion of their potential and limitations. Lessons learned from the field operational experiences and future trends and challenges are identified.

In situ continuous monitoring at high frequency is used to collect water quality information about water bodies. However, it is crucial that the collected data be evaluated and validated for the appropriate interpretation of the data so as to ensure that the monitoring programme is effective. Software tools for data quality assessment with a practical orientation are proposed. As water quality data often contain redundant information, multivariate methods can be used to detect correlations, pertinent information among variables and to identify multiple sensor faults. While principal component analysis can be used to reduce the dimensionality of the original variable data set, monitoring of some statistical metrics and their violation of confidence limits can be used to detect faulty or abnormal data and can help the user apply corrective action(s). The developed algorithms are illustrated with automated monitoring systems installed in an urban river and at the inlet of a wastewater treatment plant.

While the general principles and modelling approaches for integrated management/modelling of urban water systems already present a decade ago still hold, in recent years aspects like model interfacing and wastewater treatment plant (WWTP) influent generation as complements to sewer modelling have been investigated and several new or improved systems analysis methods have become available. New/improved software tools coupled with the current high computational capacity have enabled the application of integrated modelling to several practical cases, and advancements in monitoring water quantity and quality have been substantial and now allow the collecting of data in sufficient quality and quantity to permit using integrated models for real-time applications too. Further developments are warranted in the field of data quality assurance and efficient maintenance.

Design inputs (wastewater characteristics, operational settings, effluent requirements or safety factors,...) need to be supplied when using activated sludge process design guidelines (ASPDG) to determine the design outputs (biological reactor volume, the dissolved oxygen demand or the different internal/external recycle flow-rates). The values of the design inputs might have strong effects on the future characteristics of the plant under study. For this reason, there is a need to determine how both design inputs and outputs are linked and how they affect wastewater treatment plant (WWTP) designs. In this paper we assess ASPDG with a methodology based on Monte Carlo (MC) simulations and Global Sensitivity Analysis (GSA). The novelty of this approach relies on working with design input and output ranges instead of single values, identifying the most influential design inputs on the different design outputs and improving the interpretation of the generated results with a set of visualization tools. The variation in these design inputs is attributed to epistemic uncertainty, natural variability as well as operator, owner and regulator decision ranges. Design outputs are calculated by sampling the previously defined input ranges and propagating this variation through the design guideline. Standard regression coefficients (SRC), cluster analysis (CA) and response surfaces (RS) are used to identify/interpret the design inputs that influence the variation on the design outputs the most. The illustrative case study uses the widely recognized Metcalf & Eddy guidelines and presents a didactic design example for an organic carbon (C) and nitrogen (N) removal pre-denitrifying activated sludge plant. Results show that the proposed GSA can satisfactorily decompose the variance of the design outputs (R^2 > 0.7): aerobic (V"A"E"R) and anoxic (V"A"N"O"X) volume, air demand (Q"A"I"R) and internal recycle flow rate (Q"I"N"T"R). Response surfaces are proposed to facilitate the visualization of how, when and why the design outputs may change when the most influential design inputs are modified. Finally, it is demonstrated that the proposed method is useful for process engineers providing a regional instead of a local picture of a design problem.

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 Burger-Diehl settler model was found to capture the actual effluent total suspended solids (TSS) concentrations more efficiently than the Takacs 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.

Life cycle assessment (LCA) allows evaluating the potential environmental impacts of a product or a service in relation to its function and over its life cycle. In past LCAs applied to wastewater treatment plants (WWTPs), the system function definition has received little attention despite its great importance. This has led to some limitations in LCA results interpretation. A new methodology to perform LCA on WWTPs is proposed to avoid those limitations. It is based on net environmental benefit (NEB) evaluation and requires assessing the potential impact of releasing wastewater without and with treatment besides assessing the impact of the WWTP's life cycle. The NEB allows showing the environmental trade-offs between avoided impact due to wastewater treatment and induced impact by the WWTP's life cycle. NEB is compared with a standard LCA through the case study of a small municipal WWTP consisting of facultative aerated lagoons. The NEB and standard LCA show similar results for impact categories solely related to the WWTP's life cycle but differ in categories where wastewater treatment environmental benefit is accounted for as NEB considers influent wastewater quality whereas standard LCA does not.

To obtain greater precision in modelling small agricultural watersheds, a shorter simulation time step is beneficial. A daily time step better represents the dynamics of pollutants in the river and provides more realistic simulation results. However, with a daily evaluation performance, good fits are rarely obtained. With the Shuffled Complex Evolution (SCE) method embedded in the Soil and Water Assessment Tool (SWAT), two calibration approaches are available, single-objective or multi-objective optimization. The goal of the present study is to evaluate which approach can improve the daily performance with SWAT, in modelling flow (Q), total suspended solids (TSS) and total phosphorus (TP). The influence of weights assigned to the different variables included in the objective function has also been tested. The results showed that: (i) the model performance depends not only on the choice of calibration approach, but essentially on the influential parameters; (ii) the multi-objective calibration estimating at once all parameters related to all measured variables is the best approach to model Q, TSS and TP; (iii) changing weights does not improve model performance; and (iv) with a single-objective optimization, an excellent water quality modelling performance may hide a loss of performance of predicting flows and unbalanced internal model components.

The control of wastewater treatment plants can help to achieve good effluent quality, in a complex, highly non-linear environment. A key but time-demanding component of such modelling studies is uncertainty analysis (UA). The general aims of this paper are (a) to evaluate methods for reduction of the time necessary to conduct an UA, and (b) to evaluate the sensitivity of parameters and model subsystems. Two UA studies on the Benchmark Simulation Model no. 2 (BSM2) are used to illustrate how the above mentioned aims can be achieved: (1) robustness of performance evaluations against changing operation and design conditions; and (2) uncertainty of performance evaluations for a given plant layout and operation. The main conclusions are: (1) solver settings have a large impact on simulation speed and require proper attention; (2) to reach convergence in Monte Carlo simulations with Latin Hypercube Sampling, the number of simulations should be at least 50 times the number of sampled parameters, which is more than what is reported in similar studies; and (3) the number of uncertain parameters that needs to be considered to make a proper uncertainty assessment of a model can be reduced significantly by omitting parameters that have little influence.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]TED [Axe_IRSTEA]TED-EPURE; International audience; L’étude a pour objet de mieux comprendre les causes du colmatage des membranes dans les bioréacteurs à membrane traitant des eaux résiduaires urbaines. Une installation grandeur réelle (60 000 E.H.) a été suivie pendant plus d’un an. Une analyse statistique a confronté six variables de fonctionnement à trois indicateurs du colmatage. Le flux, la température, la charge organique et l’âge de boue semblent tous avoir un effet négatif marqué sur l’évolution à long terme de la perméabilité des membranes. Le chlorure ferrique et les MES ont un faible effet positif. Les liens entre ces variables et deux indicateurs court-terme du colmatage (colmatage et décolmatage au cours des cycles filtration/rétrolavages) révèlent des différences de comportement inexpliquées entre les quatre bassins membranaires. Une prédiction statistique sur quelques semaines des évolutions de la perméabilité des membranes sur la base de ces six variables semble cohérente pour l’aide à la conduite d’installations.

The problem of model discrimination arises when several models are proposed to describe one and the same process, a situation encountered in many research fields. To identify the best model from the set of rival models, it may be necessary to collect new information about the process, and thus additional experiments have to be performed. Several approaches have been described in literature to design optimal discriminatory experiments. The anticipatory approach is one of them and is very appealing from a conceptual point of view because the expected information content of the newly designed experiment is considered, even before the experiment is performed (anticipatory design). In this paper, the performance of this approach is evaluated by comparing it with the performance of other, established approaches to optimal experimental design for model discrimination. To conduct this comparison four performance measures were defined: (1) whether the most appropriate model could be identified, (2) the number of additional experiments that have to be designed and performed to achieve model discrimination, (3) the quality of the parameter estimates of the model that is eventually identified as the most appropriate one, and (4) the rate at which the inadequate models are identified. The results clearly indicate that the anticipatory approach has its benefits and may be the preferred approach in many applications in (bio)chemical engineering and in-silico biology.

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.

In WWTP models, the accurate assessment of solids inventory in bioreactors equipped with solid-liquid separators, mostly described using one-dimensional (1-D) secondary settling tank (SST) models, is the most fundamental requirement of any calibration procedure. Scientific knowledge on characterising particulate organics in wastewater and on bacteria growth is well-established, whereas 1-D SST models and their impact on biomass concentration predictions are still poorly understood. A rigorous assessment of two 1-DSST models is thus presented: one based on hyperbolic (the widely used Takács-model) and one based on parabolic (the more recently presented Plósz-model) partial differential equations. The former model, using numerical approximation to yield realistic behaviour, is currently the most widely used by wastewater treatment process modellers. The latter is a convection-dispersion model that is solved in a numerically sound way. First, the explicit dispersion in the convection-dispersion model and the numerical dispersion for both SST models are calculated. Second, simulation results of effluent suspended solids concentration (XTSS,Eff), sludge recirculation stream (XTSS,RAS) and sludge blanket height (SBH) are used to demonstrate the distinct behaviour of the models. A thorough scenario analysis is carried out using SST feed flow rate, solids concentration, and overflow rate as degrees of freedom, spanning a broad loading spectrum. A comparison between the measurements and the simulation results demonstrates a considerably improved 1-D model realism using the convection-dispersion model in terms of SBH, XTSS,RAS and XTSS,Eff. Third, to assess the propagation of uncertainty derived from settler model structure to the biokinetic model, the impact of the SST model as sub-model in a plant-wide model on the general model performance is evaluated. A long-term simulation of a bulking event is conducted that spans temperature evolution throughout a summer/winter sequence. The model prediction in terms of nitrogen removal, solids inventory in the bioreactors and solids retention time as a function of the solids settling behaviour is investigated. It is found that the settler behaviour, simulated by the hyperbolic model, can introduce significant errors into the approximation of the solids retention time and thus solids inventory of the system. We demonstrate that these impacts can potentially cause deterioration of the predictive power of the biokinetic model, evidenced by an evaluation of the system's nitrogen removal efficiency. The convection-dispersion model exhibits superior behaviour, and the use ofthis type of model thus is highly recommended, especially bearing in mind future challenges, e.g., the explicit representation of uncertainty in WWTP models.

Activated sludge models can be very useful for designing and managing wastewater treatment plants (WWTPs). However, as with every model, they need to be calibrated for correct and reliable application. Activated sludge model calibration is still a crucial point that needs appropriate guidance. Indeed, although calibration protocols have been developed, the model calibration still represents the main bottleneck to modelling. This paper presents a procedure for the calibration of an activated sludge model based on a comprehensive sensitivity analysis and a novel step-wise Monte Carlo-based calibration of the subset of influential parameters. In the proposed procedure the complex calibration issue is tackled both by making a prior screening of the most influential model parameters and by simplifying the problem of finding the optimal parameter set by splitting the estimation task into steps. The key point of the proposed step-wise procedure is that calibration is undertaken for sub-groups of variables instead of solving a complex multi-objective function. Moreover, even with this step-wise approach parameter identifiability issues may occur, but this is dealt with by using the general likelihood uncertainty estimation (GLUE) method, that so far has rarely been used in the field of wastewater modelling. An example from a real case study illustrates the effectiveness of the proposed methodology. Particularly, a model was built for the simulation of the nutrient removal in a Bardenpho scheme plant. The model was successfully and efficiently calibrated to a large WWTP in Sicily.

Wastewater treatment plant control and monitoring can help to achieve good effluent quality, in a complex, highly non-linear process. The Benchmark Simulation Model no. 2 (BSM2) is a useful tool to competitively evaluate plant-wide control on a long-term basis. A method to conduct scenario analysis of process designs by means of Monte Carlo (MC) simulations and multi-criteria evaluation is presented. It is applied to the open loop version of BSM2 and to two closed loop versions, one with a simple oxygen controller and the other one with an ammonium controller regulating the set-point of the oxygen controller (cascade controller). The results show a much greater benefit of the cascade controller compared to the simple controller, both in environmental and economic terms. From an optimal process design point of view, the results show that the volume of the primary clarifier and the anoxic fraction of the reactor volume have an important impact on process performance. The uncertainty analysis of the optimal designs, also performed with MC simulations, highlights the improved and more stable effluent under closed loop control.

Process choice and dimensioning of wastewater treatment plants (WWTPs) is difficult while ensuring regulatory standards are met and cost-efficiency is maintained. This step only accounts for a small fraction of the upfront costs, but can lead to substantial savings. This paper illustrates the results of a systematic methodology to evaluate system upgrade options by means of dynamic modelling. In contrast to conventional practice, the presented approach allows the most appropriate trade-off between cost of measures and effluent quality to be chosen and the reliability of a process layout to be assessed by means of uncertainty analysis. In a hypothetical case study, thirteen WWTP upgrade options are compared in terms of their effluent quality and economic performance. A further comparison of two options with regard to the resulting receiving water quality reveals the paramount importance of this aspect, and highlights the inadequacy of evaluation frameworks limited to the performance relative to a sub-system (WWTP effluent) when a wider perspective (as induced by the EU Water Framework Directive) has to be adopted.

When several rival mathematical models are proposed for one and the same process, experimental design techniques are available to design optimal discriminatory experiments. Because these techniques are model-based, it is important that the model predictions are not too uncertain. Therefore, model discrimination may become more efficient and effective if this uncertainty is reduced first. This can be achieved by performing experiments designed to increase the accuracy of the parameter estimates and, thus, the model predictions. However, performing such an additional experiment for each rival model may undermine the overall goal of optimal experimental design, which is to minimize the experimental effort. This paper deals with the design of a so-called compromise experiment, which is an experiment that is not optimal for each of the rival models, but sufficiently informative to improve the overall accuracy of the parameters of all rival models. For this purpose, the problem is approached as a multi-objective optimization problem and the ideal point method is proposed to design the compromise experiment. This method searches for the experiment that is as close as possible to the optimal experiments of the individual rival models. The method is applied to a case study where nine rival models are competing to describe the kinetics of an enzymatic reaction, and the obtained results show that the ideal method is capable of designing a compromise experiment.

Observed engineering challenges of developing water quality monitoring networks are presented in this paper and appropriate solution approaches are proposed. Water quality monitoring stations are important tools in the area of environmental water science that are used to collect measurement values. Because of missing plug-and-play support in prevalent field bus systems and lack of semantic information available in sensor devices, the installation and maintenance of monitoring stations requires much more effort than desirable form the perspective of engineers and users. An approach is proposed to reduce this effort by providing a catalog of predefined devices that contains all necessary semantic information. Finally, requirements for advanced field bus systems in the domain of monitoring automation are derived in order to encourage future improvements that minimize engineering effort.

Whole plant modeling and control has received increasing attention in recent years and as a result the Benchmark Simulation Model No 2 (BSM2) platform was developed to compare control strategies. The objective of this paper consists of evaluating whether MLSS concentration control (with changing setpoints in summer and winter time to maintain nitrification capacity) could lead to effluent ammonia violations due to return liquor overloads and how such violations could be minimised by introducing alternative control systems. In this study three different control strategies have been implemented in the BSM2 platform, simulated and evaluated from a whole plant perspective. The results show that with the implementation of the MLSS controller a large overload is applied to the digester at the end of the winter period, but the large retention time of the anaerobic digester sufficiently dampens this overload. On the other hand, the flows can be treated more efficiently when storing the nitrogen-rich return flows during day time and releasing them at night using a simple timer-based controller.

As mathematical modeling of wastewater treatment plants has become more common in research and consultancy, a mismatch between education and requirements for model-related jobs has developed. There seems to be a shortage of skilled people, both in terms of quantity and in quality. In order to address this problem, this paper provides a framework to outline different types of model-related jobs, assess the required skills for these jobs and characterize different types of education that modelers obtain “in school” as well as “on the job”. It is important to consider that education of modelers does not mainly happen in university courses and that the variety of model related jobs goes far beyond use for process design by consulting companies. To resolve the mismatch, the current connection between requirements for different jobs and the various types of education has to be assessed for different geographical regions and professional environments. This allows the evaluation and improvement of important educational paths, considering quality assurance and future developments. Moreover, conclusions from a workshop involving practitioners and academics from North America and Europe are presented. The participants stressed the importance of non-technical skills and recommended strengthening the role of realistic modeling experience in university training. However, this paper suggests that all providers of modeling education and support, not only universities, but also software suppliers, professional associations and companies performing modeling tasks are called to assess and strengthen their role in training and support of professional modelers.

The problem of model discrimination arises when several models are proposed to describe one and the same process. To identify the best model from the set of rival models, it may be necessary to collect new information about the process, and thus additional experiments have to be performed. This paper deals with the experimental design methodologies that are used to find the experimental conditions that allow to discriminate among rival models with the least experimental effort. For this, the expected experimental results should be predicted differently by the rival models, and the uncertainty on the measurements and on the model predictions should not be too large. These aspects were included in the approach developed by Buzzi-Ferraris and co-workers [G. Buzzi-Ferraris, P. Forzatti, G. Emig, H. Hofmann, Sequential experimental design procedure for model discrimination in the case of multiple responses. Chemical Engineering Science (1) (1984) 81–85], but in their approach the uncertainties are estimated from the information content of the already performed experiments. This work presents a modification of the Buzzi-Ferraris approach in which the expected information content of the newly designed experiment is considered, even before the experiment is performed (anticipatory design). In this way, a better estimate of the uncertainties is achieved, and an experiment with an increased discriminatory potential is obtained. The approaches were illustrated and compared by applying them to a case study in which two rival models are proposed to describe the in vitro kinetics of an enzyme.

In this paper a comparative analysis of the most important approaches for integrated WWTP modelling is presented. After an introductory presentation of the most important drawbacks and challenges for plant wide modelling, the fundamentals of three different approaches to construct integrated models are presented: “Interfaces” “Standard Supermodel” and “Tailored Supermodel”. Afterwards, a comparative analysis of these approaches from different points of view (difficulties for the model end user, characterization of the process in the plant, flexibility or adaptability for each case of study, simulation platform requirements and computational costs) is carried out. From this comparison, some important conclusions about the suitability of each alternative depending on the simulation case study are extracted.

Pesticides are useful for agriculture because of their ability to protect crops against pests. At the same time, excessive loading of pesticides in water bodies can produce toxic conditions that harm sensitive aquatic species, and render the water unfit for human consumption. Therefore, measures need to be designed, evaluated and undertaken in order to reduce pesticide pollution. In this study we focus on the Nil catchment, a small basin situated in the centre of Belgium. The necessary database and a watershed model (Soil and Water Assessment Tool - SWAT) were available to simulate different agricultural management scenarios. In order to make the model accurately predict pesticide loading to the river and instream transport, it was necessary to make several modifications to the source code. Special attention was given to implement an estimator for point losses (e.g. cleaning of spray equipment) and droplet drift, and improve the representation of physical processes in filter strips. The closing of mass balances is also described. Once the model was modified and calibrated, it could be used to simulate the pesticide mitigation strategies and evaluate their effectiveness. The simulation results revealed that strip-cropping seems to be more efficient than the sowing of cover crops, contour farming, the construction of filter strips, a 40% reduction of point losses and finally conservation agriculture. Several recommendations are given for further improvement of SWAT for management use.

In order to comply with the Water Framework Directive's requirement to reveal the major pressures and impacts on the receiving water at river basin level, the merits of a methodology that combines substance flow analysis and mass balances were evaluated with the aid of a case study. The river basin analysis consisted of the analysis of all individual municipal sewer catchments constituting the basin on a yearly time scale, and included the description of the main sewers and waste water treatment plants and their performance in environmental and economical terms. A wide set of indicators was evaluated. Uncertainties and information gaps arising from the study are described. The choice of the geographic scale seems a key factor in the evaluation. The case study indicates that such an evaluation is of great value for decision-makers in the perspective of the Water Framework Directive implementation, to highlight situations of weak or strong performance and to pinpoint information gaps requiring further research in order to take more informed decisions, to identify the main pressures on the environment and to plan more cost-effective measures.

This paper presents a set of tools developed to support an innovative methodology to design and upgrade wastewater treatment systems in a probabilistic way. For the first step, data reconstruction, two different tools were developed, one for situations where data are available and another one where no data are available. The second step, modelling and simulation, implied the development of a new simulation platform and of distributed computation software to deal with the simulation load generated by the third step, uncertainty analysis, with Monte Carlo simulations of the system over one year, important dynamics and stiff behaviour. For the fourth step, evaluation of alternatives, the evaluator tool processes the results of the simulations and plots the relevant information regarding the robustness of the process against input and parameters uncertainties, as well as concentration-duration curves for the risk of non-compliance with effluent and receiving water quality limits. This paper illustrates the merits of these tools to make the innovative methodology of practical interest. The design practice should move from conventional procedures suited for the relatively fixed context of emission limits, to more advanced, transparent and cost-effective procedures appropriate to cope with the flexibility and complexity introduced by integrated water management approaches.

We developed and evaluated a framework for the continuous use of dynamic models in daily management and operation of WWTPs. The overall aim is to generate knowledge and build in-house capacity for the reliable use of dynamic models in practice (within a regional water authority in The Netherlands). To this end, we have adopted a life cycle approach, where the plant model follows the different stages that make up the typical lifespan of a plant. Since this approach creates a framework in which models are continuously reused, it is more efficient in terms of resources and investment than the traditional approach where one always makes a new model for the plant whenever it is needed. The methodology was evaluated successfully at a 50,000 PE domestic EBPR plant (Haaren, The Netherlands). It is shown that the continuous use and update of models in a cyclic manner creates a learning cycle, which results in experience and knowledge generation about the plant's modelling that accumulates and translates into improvements into the modelling quality and efficiency. Moreover, a model is now always on-the-shelf for process optimization.

REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) is the new European chemical legislation which aims to assess risk or safety of tens of thousands of chemicals to improve the protection of human health and the environment. The chemical safety assessment process is of an iterative nature. First, an initial, worst-case assessment is conducted after which refinements are made until no risk has been estimated or the risk is adequately controlled. Wasting time and resources on additional testing and implementing risk management measures with low effect on risk conclusions should be avoided as much as possible. This paper demonstrates the usefulness of an intelligent data collection strategy based on a sensitivity (and uncertainty) analysis on the risk assessment model EUSES to identify and order the most important "within-EU-TGD-reducible" input parameters influencing the local and regional risk characterisation ratios. The ordering can be adjusted for the costs involved in additional testing (e.g. ecotoxicity, physico-chemical properties, emission estimates, etc.). The risk refinement tool therefore reduces the resources needed to obtain a realistic risk estimate (both less conservative and less uncertain) as efficient as possible.

This paper presents a new plant-wide modelling methodology for describing the dynamic behaviour of water and sludge lines in WWTPs. The methodology is based on selecting the set of process transformations needed for each specific WWTP to model all unit-process elements in the entire plant. This "transformation-based" approach, in comparison with the conventional "process-based" approach, does not require the development of specific transformers to interface the resulting unit-process models, facilitates the mass and charge continuity throughout the whole plant and is flexible enough to construct models tailored for each plant under study. As an illustrative example, a plant-wide model for a WWTP that includes carbon removal and anaerobic digestion has been constructed, and the main advantages of the proposed methodology for integrated modelling have been demonstrated. As a final consequence, this paper proposes a rewriting of the existing unit-process models according to the new standard transformation-based approach for integrated modelling purposes.

Virtual experimentation is a collective term that includes various model evaluation procedures such as simulation, optimization and scenario analysis. Given the complexity of the models used in these procedures, and the number of evaluations that is required to complete them, highly efficient model implementations are desired. Although water quality management is a domain in which complex virtual experimentation is often adopted, only relatively little attention has thus far been devoted to the automated generation of efficient executable models. This article reports on a number of promising results regarding executable model generation that were obtained in the scope of the Tornado kernel, using techniques such as equiv substitution and equation lifting.

The objective of the present study was to evaluate the patulin exposure of children consuming organic, handcrafted or conventional apple juice through a probabilistic approach and to evaluate the effectiveness of several risk management options aiming to reduce the risk for children due to patulin exposure. However, a large part of the data on patulin contamination of apple juice fell under the limit of detection (LOD). Different methods were tested to deal with these so-called left censored data and a uniform distribution with uncertain bounds was selected to handle this censorship. Variability and uncertainty assessment of patulin exposure showed that 0.9% [90% confidence interval (CI): 0.3–1.8%] of the children consuming only organic apple juice exceed the tolerable daily intake (TDI). For consumers of conventional and handcrafted apple juice this was respectively 0.1% [90% CI: 0–0.3%] and 0% [90% CI: 0–0.2%]. Reduction of the patulin contamination in apple juice to concentrations below 25 μg/kg reduced the percentage of the children exceeding the TDI to 0% [90%CI: 0–0.2%] for organic apple juice. Reduction of the apple juice consumption was less effective than a reduction of the patulin concentration in apple juice and is only useful when the patulin concentration of apple juice is below 25 μg/kg.