Activated sludge modelling in practice: an international survey

Modelling and prediction of the effect of operational parameters on the fate of contaminants of emerging concern in WWTPs

Wastewater treatment plants (WWTPs) provide a barrier against the discharge of contaminants of emerging concern (CECs) into the environment. The removal of CECs is highly WWTP-specific and the underlying mechanisms are still poorly understood, hampering the optimization of biological treatment steps for their removal. To fill this knowledge gap, we assessed the influence of four operational parameters of activated sludge biological treatment, namely total suspended solids, temperature, pH and redox conditions, on the sorption and biodegradation of four CECs under controlled laboratory conditions. Design of Experiments was used to better address the factors influencing CECs removal and interactions among operational parameters. The derived statistical models showed results in concordance with previous studies and indicated how sorption and biodegradation of the investigated CECs depend on most tested parameters and few of their interactions. The predictions of the developed models have been compared with literature values, indicating how the tested parameters are responsible for most of the variability of sorption, while they could not reliably generalize biodegradation rates. The developed models were also implemented as an extension of a mechanistic biological treatment model, successfully describing the dynamic behaviour of a large-scale WWTP, which was observed during a three-day continuous monitoring campaign. Compared to a traditional modelling approach, the one including the developed models showed on average almost a three-fold uncertainty reduction, favouring its use to aid WWTP managers and regulators for improved assessment of CEC fate and removal. Finally, the models highlighted that, while higher temperatures and solids concentrations generically favoured CECs removal, removal efficiency vary significantly due to operational parameters and no globally optimum conditions for CECs removal exist. The use of these models opens the door to the combined dynamic management of both traditional contaminants and CECs in WWTPs.

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.

Integrated supervisory and override control strategies for effective biological phosphorus removal and reduced operational costs in wastewater treatment processes

A novel control strategy is developed for a municipal wastewater treatment plant (WWTP) consisting of anaerobic-anoxic-aerobic reactors. The idea is to generate more organic matter with a reduction of nitrate concentration in the anoxic section so that more biological phosphorus (P) removal happens. For this, the Supervisory and Override Control Approach (SOPCA) is designed based on the benchmark simulation model (BSM1-P) and is evaluated by considering dynamic influent. In the supervisory layer, proportional integral (PI) and fuzzy controllers are designed. Additionally, dissolved oxygen (S_o) control loops in the aerobic reactors are designed. PI controller is designed for control of nitrate levels in the anoxic reactors and is integrated with override control and supervisory layer. It is found that the novel SOPCA approach gave better nutrient removal with slightly higher operating costs when S_o control is not put in place. With three S_o control loops in place, the WWTP showed better effluent quality and lower cost. Here, the improved removal efficiency of 28.5% and 20.5% are obtained when Fuzzy and PI control schemes respectively are used in the supervisory layer. Therefore, the application of SOPCA is recommended for a better P removal rate.

Design of control strategies for nutrient removal in a biological wastewater treatment process

Wastewater treatment plants (WWTP) are highly non-linear operations concerned with huge disturbances in flow rate and concentration of pollutants with uncertainties in the composition of influent wastewater. In this work, the activated sludge process model with seven reactor configuration in the ASM3bioP framework is used to achieve simultaneous removal of nitrogen and phosphorus. A total of 8 control approaches are designed and implemented in the advanced simulation framework for assessment of the performance. The performance of the WWTP (effluent quality index and global plant performance) and the operational costs are also evaluated to compare the control approaches. Additionally, this paper reports a comparison among proportional integral (PI) control, fuzzy logic control, and model-based predictive control (MPC) configurations framework. The simulation outcomes indicated that all three control approaches were able to enhance the performance of WWTP when compared with open loop operation.

Steady-State Methodology for Activated Sludge Model 1 (ASM1) State Variable Calculation in MBR

The complexity of Activated Sludge Model No. 1 (ASM1) is one of the main obstacles slowing its widespread use, particularly among wastewater treatment plant (WWTP) professionals. In this paper, a simplification procedure based on steady-state mass balances is proposed for the conventional activated sludge process (ASP) configuration, consisting of an aerated bioreactor and a perfect settler (without particular compounds in the outlet). The results do, in fact, show perfect suitability to a membrane bioreactor process (MBR). Both organic carbon and nitrogen removal were investigated. The proposed approach was applied to ASM1, and simple analytical expressions of the state variables were obtained. These analytical expressions were then validated by comparison to simulations given by the original ASM1 (implemented in GPS-X software). A strong match (less than 4% of error overall) was obtained between both results in the steady-state; consequently, these analytical expressions may be useful as tools for quickly estimating the main state variables, feeding the filtration models, or identifying the interaction between operating parameters. Moreover, this enables a sensitivity analysis, covering relevant factors such as kinetics or operating parameters. For instance, the sludge retention time (SRT) effect is lower on XBH and XS at high SRT (˃20 days), while it is more pronounced on XP and XI as their variations with SRT are linear.

A novel metabolic-ASM model for full-scale biological nutrient removal systems

This study demonstrates that META-ASM, a new integrated metabolic activated sludge model, provides an overall platform to describe the activity of the key organisms and processes relevant to biological nutrient removal (BNR) systems with a robust single-set of default parameters. This model overcomes various shortcomings of existing enhanced biological phosphorous removal (EBPR) models studied over the last twenty years. The model has been tested against 34 data sets from enriched lab polyphosphate accumulating organism (PAO)-glycogen accumulating organism (GAO) cultures and experiments with full-scale sludge from five water resource recovery facilities (WRRFs) with two different process configurations: three stage Phoredox (A2/O) and adapted Biodenitro™ combined with a return sludge sidestream hydrolysis tank (RSS). Special attention is given to the operational conditions affecting the competition between PAOs and GAOs, capability of PAOs and GAOs to denitrify, metabolic shifts as a function of storage polymer concentrations, as well as the role of these polymers in endogenous processes and fermentation. The overall good correlations obtained between the predicted versus measured EBPR profiles from different data sets support that this new model, which is based on in-depth understanding of EBPR, reduces calibration efforts. On the other hand, the performance comparison between META-ASM and literature models demonstrates that existing literature models require extensive parameter changes and have limited predictive power, especially in the prediction of long-term EBPR performance. The development of such a model able to describe in detail the microbial and chemical transformations of BNR systems with minimal adjustment to parameters suggests that the META-ASM model is a powerful tool to predict and mitigate EBPR upsets, optimise EBPR performance and to evaluate new process designs.

Challenges of biofilm control and utilization: lessons from mathematical modelling

This article reviews modern applications of mathematical descriptions of biofilm formation. The focus is on theoretically obtained results which have implications for areas including the medical sector, food industry and wastewater treatment. Examples are given as to how models have contributed to the overall knowledge on biofilms and how they are used to predict biofilm behaviour. We conclude that the use of mathematical models of biofilms has demonstrated over the years the ability to significantly contribute to the vast field of biofilm research. Among other things, they have been used to test various hypotheses on the nature of interspecies interactions, viability of biofilm treatment methods or forces behind observed biofilm pattern formations. Mathematical models can also play a key role in future biofilm research. Many models nowadays are analysed through computer simulations and continue to improve along with computational capabilities. We predict that models will keep on providing answers to important challenges involving biofilm formation. However, further strengthening of the ties between various disciplines is necessary to fully use the tools of collective knowledge in tackling the biofilm phenomenon.

Consistent microbial dynamics and functional community patterns derived from first principles

Microbial communities are key engines that drive earth's biogeochemical cycles. However, existing ecosystem models have only limited ability to predict microbial dynamics and require the calibration of multiple population-specific empirical equations. In contrast, we build on a new kinetic "Microbial Transition State" (MTS) theory of growth derived from first principles. We show how the theory coupled to simple mass and energy balance calculations provides a framework with intrinsically important qualitative properties to model microbial community dynamics. We first show how the theory can simultaneously account for the influence of all the resources needed for growth (electron donor, acceptor, and nutrients) while still producing consistent dynamics that fulfill the Liebig rule of a single limiting substrate. We also show consistent patterns of energy-dependent microbial successions in mixed culture without the need for calibration of population-specific parameters. We then show how this approach can be used to model a simplified activated sludge community. To this end, we compare MTS-derived dynamics with those of a widely used activated sludge model and show that similar growth yields and overall dynamics can be obtained using two parameters instead of twelve. This new kinetic theory of growth grounded by a set of generic physical principles parsimoniously gives rise to consistent microbial population and community dynamics, thereby paving the way for the development of a new class of more predictive microbial ecosystem models.

A simple respirogram-based approach for the management of effluent from an activated sludge system

Managing wastewater treatment plant (WWTP) based on respirometric analysis is a new and promising field. In this study, a multi-dimensional respirogram space was constructed, and an important index R_es/t (ratio of in-situ respiration rate to maximum respiration rate) was derived as an alarm signal for the effluent quality control. A smaller R_es/t value suggests better effluent. The critical R'_es/t value used for determining whether the effluent meets the regulation depends on operational conditions, which were characterized by temperature and biomass ratio of heterotrophs to autotrophs. With given operational conditions, the critical R'_es/t value can be calculated from the respirogram space and effluent conditions required by the discharge regulation, with no requirement for calibration of parameters or any additional measurements. Since it is simple, easy to use, and can be readily implemented online, this approach holds a great promise for applications.

Parameter estimation of activated sludge process based on an improved cuckoo search algorithm

It is essential to use appropriate values for kinetic parameters in activated sludge model when the model is applied for wastewater treatment processes under different environments. An improved cuckoo search (ICS) algorithm was proposed in this paper for the estimation of kinetic parameters used in Activated Sludge Model No. 1 (ASM1). ICS is tested for its speed and accuracy in reaching solution by searching global minima of six standard functions. Cyclical adjustment strategy was employed into the detected probability to increase searching ability. Meanwhile, the searching step was adaptively adjusted based on the optimal nest of the last generation and the current iteration numbers. Subsequently, ICS is used to estimate 7 sensitive parameters in ASM1 for practical applications. Field data are used to validate prediction accuracy of ASM1 with estimated parameters. Predicted results of the model are closer to the actual data with adjusted parameters.

A novel protocol for model calibration in biological wastewater treatment

Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models.

Modelling of micropollutant removal in biological wastewater treatments: a review

Modelling the fate of micropollutants through wastewater treatment plants is of present concern. Indeed, such a tool is useful to increase the removal of micropollutants and reduce their release to the environment. In this paper, 18 literature models describing micropollutant removal in activated sludge processes were reviewed. Investigated micropollutants were mainly volatile organic compounds, metals, surfactants, pesticides and pharmaceutical compounds. This work provides a detailed insight about the main mechanisms leading to the micropollutant removal (volatilisation, sorption, biodegradation, cometabolism), the associated mathematical equations and the parameter values found in the literature. A critical analysis was carried out to evaluate the conditions and the domain of validity for which each model was set-up. We also propose (i) an inventory of the experimental methodologies applied to determine the values of model parameters, (ii) a critical study of the main differences between models and (iii) suggestions for a standardisation of calibration methodologies. Finally, this review highlights the lack of explanation concerning the domain of validity of the models and proposes future developments to improve modelling of micropollutant removal in wastewater treatment plants.

Data reconciliation for wastewater treatment plant simulation studies-planning for high-quality data and typical sources of errors

Model results are only as good as the data fed as input or used for calibration. Data reconciliation for wastewater treatment modeling is a demanding task, and standardized approaches are lacking. This paper suggests a procedure to obtain high-quality data sets for model-based studies. The proposed approach starts with the collection of existing historical data, followed by the planning of additional measurements for reliability checks, a data reconciliation step, and it ends with an intensive measuring campaign. With the suggested method, it should be possible to detect, isolate, and finally identify systematic measurement errors leading to verified and qualitative data sets. To allow mass balances to be calculated or other reliability checks to be applied, few additional measurements must be introduced in addition to routine measurements. The intensive measurement campaign should be started only after all mass balances applied to the historical data are closed or the faults have been detected, isolated, and identified. In addition to the procedure itself, an overview of typical sources of errors is given.