Wastewater treatment modelling: dealing with uncertainties

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.

Modelling the impacts of operational conditions on the performance of a full-scale membrane aerated biofilm reactor

Membrane Aerated Biofilm Reactors (MABR) are gaining more and more acceptance in the plethora of wastewater process intensification technologies. Mathematical modelling has contributed to show their feasibility in terms of reduced energy consumption and footprint. Nevertheless, most simulation studies published until now are still focused on analyzing MABR as single units and not fully integrated within the flow diagram of the water treatment plant (WWTP). In this paper, the prediction capabilities of an integrated modelling approach is tested using full-scale data from Ejby Mølle WWTP+MABR site (Odense, Denmark). Mass balances, data reconciliation methods, process simulation and the different evaluation criteria were used to adjust influent, effluent and process indicators. Results show 10 % mismatch between flow, COD, N and P predictions and measurements in different plant locations. Using the adopted hydraulic retention time (HRT), nitrogen load (NL), membrane surface area (MA) and oxygen transfer rate (OTR), it was possible to predict nitrification rates (NR) within the interquartile range. This has been done under two different MABR operational conditions: with (#S2) and without (#S1) external aeration (EA) in the bulk liquid. The model provides additional process insights about biofilm structure, substrate gradients, weak acid base chemistry and precipitation potential. More specifically, simulations suggest the potential undesirable effects of sulfate (SRB) and iron reducing bacteria (IRB) on both microbial activity and composition of the biofilm. The latter may have a strong impact on ammonium (NH_x), sulfate (SO_x) and ferrous ion (Fe⁺²) conversion processes. The change of operational strategy in the scenario analysis highlights that the denitrifying activity of phosphorus accumulating organisms (PAOs) can enhance nutrient removal in MABR tanks. In addition, it was possible to assess the chance of success (in terms of energetic cost of nitrogen removal) of adding several MABR units in one tank of the WWTP under study before full-scale implementation.

Modelling the performance of an integrated fixed-film activated sludge (IFAS) system: a systematic approach to automated calibration

IFAS systems are inherently complex due to the hybrid use of both suspended and attached bacterial colonies for the purpose of pollutant degradation as part of wastewater treatment. This poses challenges when attempting to represent these systems mathematically due to the vast number of parameters involved. Besides becoming convoluted, large effort will be incurred during model calibration. This paper demonstrates a systematic approach to calibration of an IFAS process model that incorporates two sensitivity analyses to identify influential parameters and detect collinearity from a subset of 68 kinetic and stoichiometric parameters, and the use of the Nelder–Mead optimization algorithm to estimate the required values of these parameters. The model considers the removal of three critical pollutants including biochemical oxygen demand (BOD), total nitrogen (TN) and total suspended solids (TSS). Results from the sensitivity analyses identified four parameters that were the primary influence on the model. The model was found to be most sensitive to the two stoichiometric parameters including aerobic heterotrophic yield on soluble substrate whose total effects were responsible for 92.4% of the model’s BOD output sensitivity and 92.8% of the model’s TSS output sensitivity. The anoxic heterotrophic yield on soluble substrate was observed to be responsible for 54.3% of the model’s TN output sensitivity. To a lesser extent the two kinetic parameters, aerobic heterotrophic decay rate and reduction factor for denitrification on nitrite, were responsible for only 8.0% and 13.1% of the model’s BOD and TN output sensitivities respectively. Parameter estimation identified the need for only minor adjustments to default values in order to achieve sufficient accuracy of simulation with deviation from observed data to be only ± 3.6 mg/L, ± 1.3 mg/L, and ± 9.5 mg/L for BOD, TN and TSS respectively. Validation showed the model was limited in its capacity to predict system behaviour under extreme dissolved oxygen stress.

Plant-wide assessment of alternative activated sludge configurations for biological nutrient removal under uncertain influent characteristics

This study presents an extensive plant-wide model-based assessment of four alternative activated sludge (AS) configurations for biological nitrogen (N) and phosphorus (P) removal under uncertain influent loads and characteristics. Zeekoegat wastewater treatment plant (WWTP) in South Africa was chosen as case study due to its flexible design that enables operation in four different AS configurations: 3-stage Bardenpho (A2O), University of Cape Town (UCT), UCT modified (UCTM), and Johannesburg (JHB). A metamodeling based global sensitivity analysis was performed on a steady-state plant-wide simulation model using Activated Sludge Model No. 2d with the latest extension of physico-chemical processes describing the plant-wide P transformations. The simulation results showed that the predictions of effluent chemical oxygen demand (COD), N and P using the proposed approach fall within the interquartile range of measured data. The study also revealed that process configuration can affect: 1) how influent uncertainty is reflected in model predictions for effluent quality and cost related performances, and 2) the parameter rankings based on variance decomposition, particularly for effluent phosphate, sludge disposal and methane production. The results identified UCT and UCTM as more robust configurations for P removal (less propagated uncertainty and less sensitivity to N load) in the expense of incomplete denitrification. Moreover, based on the results of Monte-Carlo based scenario analysis, the balanced SRT for N and P removal is more sensitive to influent load variation/uncertainty for the A2O and JHB configurations. This gives a more operational flexibility to UCT and UCTM, where a narrow SRT range can ensure both N and P removal.

A framework for deriving dispatching rules of integrated urban drainage systems

The urban drainage system (UDS) with the integration of new and old, green and gray infrastructures has become an underlying trend, but its sewage dispatch is getting more and more difficult owing to complex network topology, much uncertainty introduced by the lack of empirical data or monitoring system, and consideration of multiple objectives. We propose a novel framework for deriving dispatching rules that allocate the wastewater inflow according to the comprehensive capacity of treatment facilities. The main innovative point of the framework is using the Prophet algorithm to handle sewage volume uncertainty introduced by rainfall. Moreover, we construct a multi-objective optimization model of operating costs and surface water quality and apply it in multiple scenarios. Lastly, we build a decision tree to mine dispatching rules and threshold identification. Taking the Pingshan River Basin, we plot dispatching diagrams and discover the threshold of creating the risk to surface water quality. When the sewage volume reached 18.0 × 10⁵ m³ in winter, water quality exceeded limits. Other than that, the wastewater spatiotemporal allocation placed receiving water quality and operating cost in a competitive relationship. An application of dispatching rules shows that they fill a gap in the scientific and reasonable dispatch of the UDS.

A framework for model-based assessment of resilience in water resource recovery facilities against power outage

Current practice to enhance resilience in Water Resource Recovery Facilities (WRRFs) is to ensure redundancy or back-up for most critical equipment (e.g. pumps or blowers). Model-based assessment allows evaluation of different strategies for quantitatively and efficiently enhancing resilience and justifying the allocation of resources. The goal of this study is to provide guidance for the development of tailored deterministic models of full-scale WRRFs. A framework for model-based resilience assessment is proposed that provides guidance on data collection, model selection, model calibration and scenario analysis. The framework is embedded into the Good Modeling Practice (GMP) Unified Protocol, providing a new application for resilience assessment and an initial set of stressors for WRRFs. The usefulness of the framework is illustrated through a resilience assessment of the WRRF of Girona against power outage. Results show that, for the Girona facility, limited energy back-up can cause non-compliance of WRRF discharge limits in the case of a blower power shut-down of 6 h, and around 12 h when the blower shut-down is also combined with a shut-down of the recirculation pumps. The best option to enhance resilience would be increasing the power back-up by 218%, which allows the plant to run with recirculation pumps and blowers at minimum capacity. In such a case, resilience can be further enhanced by manipulating the air supply valves to optimise the air distribution, to balance oxygen needs in each reactor with the overall system pressure. We conclude that, with industry consensus on what is considered an acceptable level of resilience, a framework for resilience assessment would be a useful tool to enhance the resilience of our current water infrastructure. Further research is needed to establish if the permit structure should accommodate levels sof functionality to account for stress events.

Effect of sewage sampling frequency on determination of design parameters for municipal wastewater treatment plants

The uncertainty associated with the determination of load parameters, which is a key step in the design of wastewater treatment plants (WWTPs), was investigated on the basis of data sets from 58 WWTPs. A further analysed aspect was the organic load variations associated with variable sewage temperatures. Data from 26 WWTPs with a high inflow sampling frequency was used to simulate scenarios to investigate the effect of lower sampling frequencies through a Monte Carlo approach. The calculation of 85-percentile values for chemical oxygen demand (COD) loadings based on only 26 samples per year is associated with a variability of up to ±18%. Approximately 90 samples per year will be necessary to reduce this uncertainty for estimation of COD loadings below 10%. Hence, a low sampling frequency can potentially lead to under- or overestimation of design parameters. Through an analogous approach, it was possible to identify uncertainties of ±11% in COD loading when weekly average data was used with four samples per week. Finally, a tendency to lower COD input loads with increasing temperatures was identified, with a reduction of about 1% of the average loading per degree Celsius.

Identification of behavioural model input data sets for WWTP uncertainty analysis

Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertainty analysis. This article presents an approach to verify uncertainty distributions of input data (otherwise often assumed) by using historical observations and actual plant data.

Quantifying the sources of uncertainty when calculating the limiting flux in secondary settling tanks using iCFD

Solids-flux theory (SFT) and state-point analysis (SPA) are used for the design, operation and control of secondary settling tanks (SSTs). The objectives of this study were to assess uncertainties, propagating from flow and solids loading boundary conditions as well as compression settling behaviour to the calculation of the limiting flux (J_L) and the limiting solids concentration (X_L). The interpreted computational fluid dynamics (iCFD) simulation model was used to predict one-dimensional local concentrations and limiting solids fluxes as a function of loading and design boundary conditions. A two-level fractional factorial design of experiments was used to infer the relative significance of factors unaccounted for in conventional SPA. To move away from using semi-arbitrary safety factors, a systematic approach was proposed to calculate the maximum SST capacity by employing a factor of 23% and a regression meta-model to correct values of J_L and X_L, respectively - critical for abating hydraulic effects under wet-weather flow conditions.

Position paper - progress towards standards in integrated (aerobic) MBR modelling

Membrane bioreactor (MBR) models are useful tools for both design and management. The system complexity is high due to the involved number of processes which can be clustered in biological and physical ones. Literature studies are present and need to be harmonized in order to gain insights from the different studies and allow system optimization by applying a control. This position paper aims at defining the current state of the art of the main integrated MBR models reported in the literature. On the basis of a modelling review, a standardized terminology is proposed to facilitate the further development and comparison of integrated membrane fouling models for aerobic MBRs.

Modelling gas-liquid mass transfer in wastewater treatment: when current knowledge needs to encounter engineering practice and vice versa

Gas-liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas-liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid-base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas-liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas-liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas-liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas-liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners.

Uncertainty analysis in a large-scale water quality integrated catchment modelling study

Receiving water quality simulation in highly urbanised areas requires the integration of several processes occurring at different space-time scales. These integrated catchment models deliver results with a significant uncertainty level associated. Still, uncertainty analysis is seldom applied in practice and the relative contribution of the individual model elements is poorly understood. Often the available methods are applied to relatively small systems or individual sub-systems, due to limitations in organisational and computational resources. Consequently this work presents an uncertainty propagation and decomposition scheme of an integrated water quality modelling study for the evaluation of dissolved oxygen dynamics in a large-scale urbanised river catchment in the Netherlands. Forward propagation of the measured and elicited uncertainty input-parametric distributions was proposed and contrasted with monitoring data series. Prior ranges for river water quality-quantity parameters lead to high uncertainty in dissolved oxygen predictions, thus the need for formal calibration to adapt to the local dynamics is highlighted. After inferring the river process parameters with system measurements of flow and dissolved oxygen, combined sewer overflow pollution loads became the dominant uncertainty source along with rainfall variability. As a result, insights gained in this paper can help in planning and directing further monitoring and modelling efforts in the system. When comparing these modelling results to existing national guidelines it is shown that the commonly used concentration-duration-frequency tables should not be the only metric used to select mitigation alternatives and may need to be adapted in order to cope with uncertainties.

Evaluation of anaerobic digestion post-treatment options using an integrated model-based approach

The objective of this paper is to present the main results of an engineering-research project dealing with model-based evaluation of waste streams treatment from a biotech company. This has been extensively done in domestic treatment systems, but is equally important, and with different challenges in industrial wastewater treatment. A new set of biological (activated sludge, anaerobic digestion), physicochemical (aqueous phase, precipitation, mass transfer) process models and model interfaces are required to describe removal of organics in an upflow anaerobic sludge blanket (UASB) reactor plus either traditional nitrification/denitrification (A₁) or partial nitritation (PN)/anammox (ANX) (A₂) processes. Model-based analysis shows that option A₁ requires a decrease in digestion energy recovery (E_recovery) in order to have enough organic substrate for subsequent post NO₃ reduction treatment (95 kWh.kg N^-1). In contrast, A₂ in an aerobic granular sludge reactor allows for higher UASB conversion since N removal is carried out autotrophically. The study also reveals that the addition of an aerated pre-treatment unit prior to the PN/ANX (A₂) reactor promotes COD and H₂S oxidation, CO₂ and CH₄ stripping, a pH increase (up to 8.5) and a reduction of the risk of intra-granular precipitation as well as sulfide inhibition. Simulations indicate clear differences regarding the microbial distribution/abundance within the biofilm in A₂ when comparing the two operational modes. Final results show the effects of different loading and operational conditions; dissolved oxygen (DO), Total Suspended Solids (TSS_op), energy recovery (E_recovery); on the overall process performance; N removal, aeration energy (E_aeration), net energy production (E_recovery); using response surfaces, highlighting the need of integrated approaches to avoid sub-optimal outcomes. The study shows the benefits of virtual plant simulation and demonstrates the potential of model-based evaluation when process engineers in industry have to decide between competing options.

Decision Support Concept to Selection of Wastewater Treatment Plant Location—the Case Study of Town of Kutina, Croatia

In environmental projects, decision-making can be a complex and challenging task due to the in-built existence of compromises between environmental, socio-political, and economic factors. This paper explores a systematic approach to developing a decision support concept that includes the analysis of wastewater treatment problems, knowledge acquisition, and the identification and evaluation of criteria that bring forth an optimal solution to the location selection of wastewater treatment plants (WWTPs). The objective of this research is to develop a decision support concept (DSC) to aid in the planning phases of complex engineering projects, such as the construction of WWTP. The development of the concept starts with an assessment of the issue and an identification of relevant stakeholders accepting their different views and attitudes in an attempt to resolve this issue. The DSC was tested on a real case project—WWTP location selection within the town of Kutina, Croatia. Results indicate that it is possible to develop such a concept based on multicriteria methods on which decision-makers can rely.

Recent insights on uncertainties present in integrated catchment water quality modelling

This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.

Short-sludge age EBPR process - Microbial and biochemical process characterisation during reactor start-up and operation

The new paradigm for used water treatment suggests the use of short solid retention times (SRT) to minimize organic substrate mineralization and to maximize resource recovery. However, little is known about the microbes and the underlying biogeochemical mechanisms driving these short-SRT systems. In this paper, we report the start-up and operation of a short-SRT enhanced biological phosphorus removal (EBPR) system operated as a sequencing batch reactor (SBR) fed with preclarified municipal wastewater, which is supplemented with propionate. The microbial community was analysed via 16S rRNA amplicon sequencing. During start-up (SRT = 8 d), the EBPR was removing up to 99% of the influent phosphate and completely oxidized the incoming ammonia. Furthermore, the sludge showed excellent settling properties. However, once the SRT was shifted to 3.5 days nitrification was inhibited and bacteria of the Thiothrix taxon proliferated in the reactor, thereby leading to filamentous bulking (sludge volume index up to SVI = 1100 mL/g). Phosphorus removal deteriorated during this period, likely due to the out-competition of polyphosphate accumulating organisms (PAO) by sulphate reducing bacteria (SRB). Subsequently, SRB activity was suppressed by reducing the anaerobic SRT from 1.2 day to 0.68 day, with a consequent rapid SVI decrease to ∼200 ml/g. The short-SRT EBPR effectively removed phosphate and nitrification was mitigated at SRT = 3 days and oxygen levels ranging from 2 to 3 mg/L.

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2-4h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The 'traditional' variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.

Adaptation in hindsight: dynamics and drivers shaping urban wastewater systems

Well-planned urban infrastructure should meet critical loads during its design lifetime. In order to proceed with design, engineers are forced to make numerous assumptions with very little supporting information about the development of various drivers. For the wastewater sector, these drivers include the future amount and composition of the generated wastewater, effluent requirements, technologies, prices of inputs such as energy or chemicals, and the value of outputs produced such as nutrients for fertilizer use. When planning wastewater systems, there is a lack of methods to address discrepancies between the timescales at which fundamental changes in these drivers can occur, and the long physical life expectancy of infrastructure (on the order of 25-80 years). To explore these discrepancies, we take a hindsight perspective of the long-term development of wastewater infrastructure and assess the stability of assumptions made during previous designs. Repeatedly we find that the drivers influencing wastewater loads, environmental requirements or technological innovation can change at smaller timescales than the infrastructure design lifetime, often in less than a decade. Our analysis shows that i) built infrastructure is continuously confronted with challenges it was not conceived for, ii) significant adaptation occurs during a structure's lifetime, and iii) "muddling-through" is the pre-dominant strategy for adaptive management. As a consequence, we argue, there is a need to explore robust design strategies which require the systematic use of scenario planning methods and instruments to increase operational, structural, managerial, institutional and financial flexibility. Hindsight studies, such as this one, may inform the development of robust design strategies and assist in the transition to more explicit forms of adaptive management for urban infrastructures.

Flexible design in water and wastewater engineering--definitions, literature and decision guide

Urban water and wastewater systems face uncertain developments including technological progress, climate change and urban development. To ensure the sustainability of these systems under dynamic conditions it has been proposed that technologies and infrastructure should be flexible, adaptive and robust. However, in literature it is often unclear what these technologies and infrastructure are. Furthermore, the terms flexible, adaptive and robust are often used interchangeably, despite important differences. In this paper we will i) define the terminology, ii) provide an overview of the status of flexible infrastructure design alternatives for water and wastewater networks and treatment, and iii) develop guidelines for the selection of flexible design alternatives. Results indicate that, with the exception of Net Present Valuation methods, there is little research available on the design and evaluation of technologies that can enable flexibility. Flexible design alternatives reviewed include robust design, phased design, modular design, modular/component platform design and design for remanufacturing. As developments in the water sector are driven by slow variables (climate change, urban development), rather than market forces, it is suggested that phased design or component platform designs are suitable for responding to change, while robust design is an option when operations face highly dynamic variability.

Calibration and validation of a phenomenological influent pollutant disturbance scenario generator using full-scale data

The objective of this paper is to demonstrate the full-scale feasibility of the phenomenological dynamic influent pollutant disturbance scenario generator (DIPDSG) that was originally used to create the influent data of the International Water Association (IWA) Benchmark Simulation Model No. 2 (BSM2). In this study, the influent characteristics of two large Scandinavian treatment facilities are studied for a period of two years. A step-wise procedure based on adjusting the most sensitive parameters at different time scales is followed to calibrate/validate the DIPDSG model blocks for: 1) flow rate; 2) pollutants (carbon, nitrogen); 3) temperature; and, 4) transport. Simulation results show that the model successfully describes daily/weekly and seasonal variations and the effect of rainfall and snow melting on the influent flow rate, pollutant concentrations and temperature profiles. Furthermore, additional phenomena such as size and accumulation/flush of particulates of/in the upstream catchment and sewer system are incorporated in the simulated time series. Finally, this study is complemented with: 1) the generation of additional future scenarios showing the effects of different rainfall patterns (climate change) or influent biodegradability (process uncertainty) on the generated time series; 2) a demonstration of how to reduce the cost/workload of measuring campaigns by filling the gaps due to missing data in the influent profiles; and, 3) a critical discussion of the presented results balancing model structure/calibration procedure complexity and prediction capabilities.

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

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

Sequential chemical-biological processes for the treatment of industrial wastewaters: review of recent progresses and critical assessment

When direct wastewater biological treatment is unfeasible, a cost- and resource-efficient alternative to direct chemical treatment consists of combining biological treatment with a chemical pre-treatment aiming to convert the hazardous pollutants into more biodegradable compounds. Whereas the principles and advantages of sequential treatment have been demonstrated for a broad range of pollutants and process configurations, recent progresses (2011-present) in the field provide the basis for refining assessment of feasibility, costs, and environmental impacts. This paper thus reviews recent real wastewater demonstrations at pilot and full scale as well as new process configurations. It also discusses new insights on the potential impacts of microbial community dynamics on process feasibility, design and operation. Finally, it sheds light on a critical issue that has not yet been properly addressed in the field: integration requires complex and tailored optimization and, of paramount importance to full-scale application, is sensitive to uncertainty and variability in the inputs used for process design and operation. Future research is therefore critically needed to improve process control and better assess the real potential of sequential chemical-biological processes for industrial wastewater treatment.

Activated sludge rheology: a critical review on data collection and modelling

Rheological behaviour is an important fluid property that severely impacts its flow behaviour and many aspects related to this. In the case of activated sludge, the apparent viscosity has an influence on e.g. pumping, hydrodynamics, mass transfer rates, sludge-water separation (settling and filtration). It therefore is an important property related to process performance, including process economics. To account for this, rheological behaviour is being included in process design, necessitating its measurement. However, measurements and corresponding protocols in literature are quite diverse, leading to varying results and conclusions. In this paper, a vast amount of papers are critically reviewed with respect to this and important flaws are highlighted with respect to rheometer choice, rheometer settings and measurement protocol. The obtained rheograms from experimental efforts have frequently been used to build viscosity models. However, this is not that straightforward and a lot of errors can be detected with respect to good modelling practice, including fair model selection criteria, qualitative parameter estimations and proper model validation. These important steps are however recurrently violated, severely affecting the model reliability and predictive power. This is illustrated with several examples. In conclusion, dedicated research is required to improve the rheological measurements and the models derived from them. At this moment, there is no guidance with respect to proper rheological measurements. Moreover, the rheological models are not very trustworthy and remain very "black box". More insight in the physical background needs to be gained. A model-based approach with dedicated experimental data collection is the key to address this.

ICA and me--a subjective review

ICA - instrumentation, control and automation - is a hidden technology. It is ubiquitous in all industrial processes, including water and wastewater treatment systems. Still, as long as everything works fine, it is not noted but when things go wrong it will be observed. ICA has now about forty years of history in water and wastewater systems and is well recognized. One early attitude was that ICA will be a necessary burden to be added to a plant in order to correct for a poor design. However, the key reason for ICA is the fact that all processes are subject to disturbances, externally via the wastewater influent, from the customers in a water supply system, or from operations in one unit process that will propagate as a disturbance to another unit within a plant. This paper is an attempt to describe the development of ICA in water and wastewater systems. Most of it is based on personal experiences with all their limitations. No single paper can fairly describe the development that is documented in thousands of research papers, practiced by so many operators and process engineers and implemented in so many treatment systems. Still, the hope is that the paper can give a flavour of the most important ingredients of this fascinating development.

Membrane-aerated biofilm proton and oxygen flux during chemical toxin exposure

Bioreactors containing sessile bacteria (biofilms) grown on hollow fiber membranes have been used for treatment of many wastestreams. Real time operational control of bioreactor performance requires detailed knowledge of the relationship between bulk liquid water quality and physiological transport at the biofilm-liquid interface. Although large data sets exist describing membrane-aerated bioreactor effluent quality, very little real time data is available characterizing boundary layer transport under physiological conditions. A noninvasive, microsensor technique was used to quantify real time (≈1.5 s) changes in oxygen and proton flux for mature Nitrosomonas europaea and Pseudomonas aeruginosa biofilms in membrane-aerated bioreactors following exposure to environmental toxins. Stress response was characterized during exposure to toxins with known mode of action (chlorocarbonyl cyanide phenyl-hydrazone and potassium cyanide), and four environmental toxins (rotenone, 2,4-dinitrophenol, cadmium chloride, and pentachlorophenol). Exposure to sublethal concentrations of all environmental toxins caused significant increases in O(2) and/or H(+) flux (depending on the mode of action). These real time microscale signatures (i.e., fingerprints) of O(2) and H(+) flux can be coupled with bulk liquid analysis to improve our understanding of physiology in counter-diffusion biofilms found within membrane aerated bioreactors; leading to enhanced monitoring/modeling strategies for bioreactor control.