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Benedetti L.,WaterWays | Belia E.,Primodal Inc. | Cierkens K.,Ghent University | Flameling T.,Waterschap de Dommel | And 3 more authors.
Water Science and Technology | Year: 2013

This paper illustrates how a dynamic model can be used to evaluate a plant upgrade on the basis of post-upgrade performance data. The case study is that of the Eindhoven wastewater treatment plant upgrade completed in 2006. As a first step, the design process based on a static model was thoroughly analyzed and the choices regarding variability and uncertainty (i.e. safety factors) were made explicit. This involved the interpretation of the design guidelines and other assumptions made by the engineers. As a second step, a (calibrated) dynamic model of the plant was set up, able to reproduce the anticipated variability (duration and frequency). The third step was to define probability density functions for the parameters assumed to be uncertain, and propagate that uncertainty with the dynamic model by means of Monte Carlo simulations. The last step was the statistical evaluation and interpretation of the simulation results. This work should be regarded as a 'learning exercise' increasing the understanding of how and to what extent variability and uncertainty are currently incorporated in design guidelines used in practice and how model-based post-project appraisals could be performed. Copyright © IWA Publishing 2013 Water Science and Technology. Source


Choubert J.-M.,IRSTEA | Rieger L.,EnviroSim Associates Ltd. | Shaw A.,Black and Veatch Corporation | Copp J.,Primodal Inc. | And 7 more authors.
Water Science and Technology | Year: 2013

Increasingly stringent effluent limits and an expanding scope of model system boundaries beyond activated sludge has led to new modelling objectives and consequently to new and often more detailed modelling concepts. Nearly three decades after the publication of Activated Sludge Model No1 (ASM1), the authors believe it is time to re-evaluate wastewater characterisation procedures and targets. The present position paper gives a brief overview of state-of-the-art methods and discusses newly developed measurement techniques on a conceptual level. Potential future paths are presented including on-line instrumentation, promising measuring techniques, and mathematical solutions to fractionation problems. This is accompanied by a discussion on standardisation needs to increase modelling efficiency in our industry. © IWA Publishing 2013. Source


Copp J.B.,Primodal Inc. | Belia E.,Primodal U.S. Inc. | Hubner C.,Institute fur Automation und Kommunikation E.V. Magdeburg Ifak | Vanrolleghem P.,Laval University | Rieger L.,Laval University
2010 IEEE International Conference on Automation Science and Engineering, CASE 2010 | Year: 2010

The implementations of water quality monitoring networks have a number of inherent engineering challenges and the automation of the data collection and analysis only adds to that complexity. This paper has been written to discuss the challenges and solutions that have been developed within the framework of an industrial/academic partnership. Water quality monitoring stations are important tools in the area of environmental water science; however, traditional monitoring station installations and their maintenance tend to require more effort than desirable. Common sensors are not easily integrated into fieldbus systems and the lack of storable meta data (status, calibration information, location,...) available from sensor devices in this field, requires additional effort on the part of the owner if a fully utilizable database of meaningful values is to be constructed. An approach is proposed to automate this effort by providing an electronic catalog of predefined devices that can be input by the user during setup or read from the sensor in real-time. Automated data evaluation, alarm triggering and real-time data 'correction' are all being developed with an aim to create fully documented long-term databases of usable and meaningful water quality data. And finally, to initiate improvements in the area of monitoring automation, some thoughts on the future of advanced fieldbus systems are presented. © 2010 IEEE. Source


Talebizadeh M.,Laval University | Belia E.,Primodal Inc. | Vanrolleghem P.A.,Laval University
Environmental Modelling and Software | Year: 2016

The availability of influent wastewater time series is crucial when using models to assess the performance of a wastewater treatment plant (WWTP) under dynamic flow and loading conditions. Given the difficulty of collecting sufficient data, synthetic generation could be the only option. In this paper a hybrid of statistical (a Markov chain-gamma model for stochastic generation of rainfall and two different multivariate autoregressive models for stochastic generation of air temperature and influent time series in dry conditions) and conceptual modeling techniques is proposed for synthetic generation of influent time series. The time series of rainfall and influent in dry weather conditions are generated using two types of statistical models. These two time series serve as inputs to a conceptual sewer model for generation of influent time series. The application of the proposed influent generator to the Eindhoven WWTP shows that it is a powerful tool for realistic generation of influent time series and is well-suited for probabilistic design of WWTPs as it considers both the effect of input variability and total model uncertainty. © 2015 Elsevier Ltd. Source


Alferes J.,Laval University | Tik S.,Laval University | Copp J.,Primodal Inc. | Vanrolleghem P.A.,Laval University
Water Science and Technology | Year: 2013

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. © 2013 IWA Publishing. Source

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