Stefan Weijers

Modelling and monitoring of integrated urban wastewater systems: Review on status and perspectives

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.

Impact-based integrated real-time control for improvement of the Dommel River water quality

The KALLISTO project aims at finding cost-efficient sets of measures to meet the Water Framework Directive (WFD) derived goals for the river Dommel. Within the project, both acute and long term impacts of the urban wastewater system on the chemical and ecological quality of the river are studied with an integral monitoring campaign in the urban wastewater system (WWTP and sewers) and in the river. Based on this monitoring campaign, detailed models were calibrated. These models are partly simplified and integrated in a single model, which is validated using the detailed submodels. The integrated model was used to study the potential for impact-based real-time control (RTC). Impact based RTC proved to be able to improve the quality of the receiving waters significantly, although additional measures remain necessary to be able to meet the WFD requirements.

Impact of influent data frequency and model structure on the quality of WWTP model calibration and uncertainty

Application of activated sludge models (ASMs) to full-scale wastewater treatment plants (WWTPs) is still hampered by the problem of model calibration of these over-parameterised models. This either requires expert knowledge or global methods that explore a large parameter space. However, a better balance in structure between the submodels (ASM, hydraulic, aeration, etc.) and improved quality of influent data result in much smaller calibration efforts. In this contribution, a methodology is proposed that links data frequency and model structure to calibration quality and output uncertainty. It is composed of defining the model structure, the input data, an automated calibration, confidence interval computation and uncertainty propagation to the model output. Apart from the last step, the methodology is applied to an existing WWTP using three models differing only in the aeration submodel. A sensitivity analysis was performed on all models, allowing the ranking of the most important parameters to select in the subsequent calibration step. The aeration submodel proved very important to get good NH(4) predictions. Finally, the impact of data frequency was explored. Lowering the frequency resulted in larger deviations of parameter estimates from their default values and larger confidence intervals. Autocorrelation due to high frequency calibration data has an opposite effect on the confidence intervals. The proposed methodology opens doors to facilitate and improve calibration efforts and to design measurement campaigns.

Cost-effective solutions for water quality improvement in the Dommel River supported by sewer-WWTP-river integrated modelling.

This project aims at finding cost-efficient sets of measures to meet the Water Framework Directive (WFD) derived goals for the Dommel River (The Netherlands). Within the project, both acute and long-term impacts of the urban wastewater system on the chemical and ecological quality of the river are studied with a monitoring campaign in the urban wastewater system (wastewater treatment plant and sewers) and in the receiving surface water system. An integrated model, which proved to be a powerful tool to analyse the interactions within the integrated urban wastewater system, was first used to evaluate measures in the urban wastewater system using the existing infrastructure and new real-time control strategies. As the latter resulted to be beneficial but not sufficient, this paper investigated the use of additional infrastructural measures to improve the system cost-effectively and have it meet the Directives goals. Finally, an uncertainty analysis was conducted to investigate the impact of uncertainty in the main model assumptions and model parameters on the performance robustness of the selected set of measures. Apart from some extreme worst-case scenarios, the proposed set of measures turned out to be sufficiently robust. Due to the substantial savings obtained with the results of this project, the pay-back time of the whole monitoring and modelling work proved to be less than 5 months. This illustrates the power of mathematical modelling for decision support in the context of complex urban water systems.

On data requirements for calibration of integrated models for urban water systems

Modeling of integrated urban water systems (IUWS) has seen a rapid development in recent years. Models and software are available that describe the process dynamics in sewers, wastewater treatment plants (WWTPs), receiving water systems as well as at the interfaces between the submodels. Successful applications of integrated modeling are, however, relatively scarce. One of the reasons for this is the lack of high-quality monitoring data with the required spatial and temporal resolution and accuracy to calibrate and validate the integrated models, even though the state of the art of monitoring itself is no longer the limiting factor. This paper discusses the efforts to be able to meet the data requirements associated with integrated modeling and describes the methods applied to validate the monitoring data and to use submodels as software sensor to provide the necessary input for other submodels. The main conclusion of the paper is that state of the art monitoring is in principle sufficient to provide the data necessary to calibrate integrated models, but practical limitations resulting in incomplete data-sets hamper widespread application. In order to overcome these difficulties, redundancy of future monitoring networks should be increased and, at the same time, data handling (including data validation, mining and assimilation) should receive much more attention.

Detailed off-gas measurements for improved modelling of the aeration performance at the WWTP of Eindhoven.

At wastewater treatment plants (WWTPs), the aerobic conversion processes in the bioreactor are driven by the presence of dissolved oxygen (DO). Within these conversion processes, the oxygen transfer is a rate limiting step as well as being the largest energy consumer. Despite this high importance, WWTP models often lack detail on the aeration part. An extensive measurement campaign with off-gas tests was performed at the WWTP of Eindhoven to provide more information on the performance and behaviour of the aeration system. A high spatial and temporal variability in the oxygen transfer efficiency was observed. Applying this gathered system knowledge in the aeration model resulted in an improved prediction of the DO concentrations. Moreover, an important consequence of this was that ammonium predictions could be improved by resetting the ammonium half-saturation index for autotrophs to its default value. This again proves the importance of balancing sub-models with respect to the need for model calibration as well as model predictive power.

Developing a framework for continuous use of models in daily management and operation of WWTPs: a life cycle approach.

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 plants 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.

Practical application of dynamic process models for wastewater treatment plant optimization: work in progress

BIOMATH, Ghent University, Department of Mathematic l Modelling, Statistics and Bioinformatics, Coupure Links 653, 9000 Gent, Belgi um 2 Waterschap De Dommel, 5280 Boxtel, The Netherlands Introduction In the European Union, the Water Framework Directiv e (WFD) enforces a good ecological and chemical status of all surface waters, which is to be accomplished before 2015 (2000/60/EC). Exceptions are only allowed after pro per justification, e.g. when it is technically infeasible or disproportionately costly to restore the water body to good status by 2015. Many surface waters throughout Europe still d o not meet the WFD requirements due to discharges of combined sewer overflows (CSO) and ef fluents of wastewater treatment plants (WWTP). The extent of non-compliance and the need f or measures are to be decided in 2012, based on the results of the monitoring programs, es tabli hed since 2009 (Commission reports, COM(2009) 156 final and COM(2012) 670 final ). Mathematical models provide a valuable tool for guiding these decisions.

An open software package for data reconciliation and gap filling in preparation of Water and Resource Recovery Facility Modeling

Abstract High quality data is of crucial importance for model development: it provides a model input and is a prerequisite for model calibration and validation. Data reconciliation is often a very time-consuming task, so even when on-line data is available, the option is often chosen to synthetically generate data, losing a lot of information contained in the available data. This contribution showcases a Python™ package that allows a streamlined work-flow and provides possibilities for data analysis, validation and gap filling, with as main goals to use as much of the data as possible and to fill gaps in the data with a known reliability. This provides a means towards more data use and a more sound calibration and validation, while significantly reducing time spent on data reconciliation. The package is published and made openly available on GitHub. This avoids multiple implementations while being accessible to the community for suggested improvements.

Multi-point monitoring of nitrous oxide emissions in three full-scale conventional activated sludge tanks in Europe

The large global warming potential of nitrous oxide (N2O) is currently of general concern for the water industry, especially in view of a new regulatory framework concerning the carbon footprint of water resource recovery facilities (WRRFs). N2O can be generated through different biological pathways and from different treatment steps of a WRRF. The use of generic emission factors (EF) for quantifying the emissions of WRRFs is discouraged. This is due to the number of different factors that can affect how much, when and where N2O is emitted from WRRFs. The spatial and temporal variability of three WRRFs in Europe using comparable technologies is presented. An economically feasible and user-friendly method for accounting for the contribution of anoxic zones via direct gas emission measurements was proven. The investigation provided new insights into the contribution from the anoxic zones versus the aerobic zones of biological WRRF tanks and proved the unsuitability of the use of a single EF for the three WRRFs. Dedicated campaigns for N2O emissions assessment are to be advised. However, similarities in the EF magnitude can be found considering treatment strategy and influent water composition.