Generation of diurnal variation for influent data for dynamic simulation

Institute of Sanitary Engineering and Water Pollution Control, University of Natural Resources and Applied Life Sciences, Vienna BOKU, Muthgasse 18, A-1190 Vienna, Austria.
Water Science & Technology (Impact Factor: 1.11). 02/2008; 57(9):1483-6. DOI: 10.2166/wst.2008.228
Source: PubMed


When using dynamic simulation for fine tuning of the design of activated sludge (AS) plants diurnal variations of influent data are required. For this application usually only data from the design process and no measured data are available. In this paper a simple method to generate diurnal variations of wastewater flow and concentrations is described. The aim is to generate realistic influent data in terms of flow, concentrations and TKN/COD ratios and not to predict the influent of the AS plant in detail. The work has been prepared within the framework of HSG-Sim (Hochschulgruppe Simulation,, a group of researchers from Germany, Austria, Luxembourg, Poland, the Netherlands and Switzerland.

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Available from: Norbert Weissenbacher, Mar 20, 2014
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    • "To generate the influent time series during DWF conditions, taking into account the daily periodic variation, auto-correlation, and cross-correlation in time, a multivariate time series models was developed and its parameters were estimated using the methodology proposed by Neumaier and Schneider (2001). The proposed stochastic model is expected to be superior compared to previous attempts in the generation of influent, as in previous studies the diurnal variation of the influent under DWF conditions was modeled using only univariate time series models (Martin et al., 2007), or by multiplying the daily average influent values to a set of coefficients representing the normalized dynamics of the influent at different times of a day with or without addition of a noise term to the generated time series (Achleitner et al., 2007; Langergraber et al., 2008; Gernaey et al., 2011). The problem resulting from the application of univariate time series models is that the cross-correlation structure that exists among different wastewater constituents may not be respected, as the different constituents are generated independently from the others. "
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    ABSTRACT: 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.
    Full-text · Article · Mar 2016 · Environmental Modelling and Software
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    • ". (from Langergraber et al., 2008 "
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    ABSTRACT: This paper makes a critical review of the available techniques for analysing, completing and generating influent data for WWTP modelling. The solutions found in literature are classified according to three different situations from engineering practice: 1) completing an incomplete dataset about the quantity and quality of the influent wastewater; 2) translating the common quality measurements (COD, TSS, TKN, etc.) into the ASM family components (fractionation problem); 3) characterising the uncertainty in the quality and quantity of the influent wastewater. In the first case (Situation 1), generators based on Fourier models are very useful to describe the daily and weekly wastewater patterns. Another specially promising solution is related to the construction of phenomenological models that provide wastewater influent profiles in accordance with data about the catchment properties (number of inhabitant equivalents, sewer network, type of industries, rainfall and temperature profiles, etc.). This option has the advantage that using hypothetical catchment characteristics (other climate, sewer network, etc.) the modeller is able to extrapolate and generate influent data for WWTPs in other scenarios. With a much lower modelling effort, the generators based on the use of databases can provide realistic influent profiles based on the patterns observed. With regard to the influent characterisation (Situation 2), the WWTP modelling protocols summarise well established methodologies to translate the common measurements (COD, TSS, TKN, etc.) into ASM family components. Finally, some statistical models based on autoregressive functions are suitable to represent the uncertainty involved in influent data profiles (Situation 3). However, more fundamental research should be carried out to model the uncertainty involved in the underlying mechanisms related to the wastewater generation (rainfall profiles, household and industries pollutant discharges, assumed daily and weekly patterns, etc.). (C) 2014 Published by Elsevier Ltd.
    Full-text · Article · Oct 2014 · Environmental Modelling and Software
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    • "In this case, a dynamic model has limited informative power as information on load and temperature variation and the specific dynamics of the system are not available or can only be derived through estimations. Influent generators (Langergraber et al., 2008; Gernaey et al., 2006), taking connected population equivalents (PE) and sewer system specifics into account, may help to get a better understanding of the dynamics of the planned system. "
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    ABSTRACT: Building an aeration system requires a major investment, with blowers typically being the single most costly piece of equipment at a WWTP. Aeration is also responsible for about 50-60 % of the total energy demand for an activated sludge plant. Moreover the DO concentration controls which processes are active or kinetically limited. The investment and operational costs, together with the impact of aeration on the treatment performance, highlight the need for a careful design of this essential part of every activated sludge treatment plant. Because oxygen transfer and oxygen demand are highly dynamic and directly linked to variations in influent load, temperature, etc., dynamic models should be used in the design process. This paper describes current practice, discusses available models and links models to specific objectives.
    Full-text · Article · Jan 2012
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