Generation of (synthetic) influent data for performing wastewater treatment modelling studies

The success of many modelling studies strongly depends on the availability of sufficiently long influent time series - the main disturbance of a typical wastewater treatment plant (WWTP) - representing the inherent natural variability at the plant inlet as accurately as possible. This is an important point since most modelling projects suffer from a lack of realistic data representing the influent wastewater dynamics. The objective of this paper is to show the advantages of creating synthetic data when performing modelling studies for WWTPs. This study reviews the different principles that influent generators can be based on, in order to create realistic influent time series. In addition, the paper summarizes the variables that those models can describe: influent flow rate, temperature and traditional/emerging pollution compounds, weather conditions (dry/wet) as well as their temporal resolution (from minutes to years). The importance of calibration/validation is addressed and the authors critically analyse the pros and cons of manual versus automatic and frequentistic vs Bayesian methods. The presentation will focus on potential engineering applications of influent generators, illustrating the different model concepts with case studies. The authors have significant experience using these types of tools and have worked on interesting case studies that they will share with the audience. Discussion with experts at the WWTmod seminar shall facilitate identifying critical knowledge gaps in current WWTP influent disturbance models. Finally, the outcome of these discussions will be used to define specific tasks that should be tackled in the near future to achieve more general acceptance and use of WWTP influent generators

The incorporation of variability and uncertainty evaluations in WWTP design by means of stochastic dynamic modeling: the case of the Eindhoven WWTP upgrade

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

Model-based optimisation and economic analysis to quantify the viability and profitability of an integrated nutrient and energy recovery treatment train

In order to hasten the implementation of optimal, cost-effective and sustainable treatment trains for resource recovery from biowaste, a new nutrient recovery model (NRM) library has been developed and validated at steady state. It includes physico-biochemical mathematical models for anaerobic digestion, struvite precipitation and ammonia stripping and absorption as ammonium sulfate. The present paper describes the use of the NRM library to establish the operational settings of a sustainable and cost-effective treatment scenario with maximal resource (nutrients and biogas) recovery and minimal energy and chemical requirements. Under the optimised conditions and assumptions made, potential financial benefits for a large-scale anaerobic digestion and nutrient recovery project treating 2700 m(3)/d of pig manure were estimated at US$2.8-6.5/m(3) based on net variable cost calculations, or an average of similar to$2/(m(3) year), equivalent to $40/(t total solids year), over 20 years in the best case when also taking into account capital costs. Hence, it is likely that in practice a full-scale zero-cost biorefinery for nutrient and energy recovery from manure can be constructed. As such, this paper demonstrates the potential of the NRM library to facilitate the implementation of sustainable nutrient and energy (biogas) recovery treatment trains for biowaste valorisation

The inclusion of variability and uncertainty evaluations in WWTP design by means of stochastic dynamic modelling: the case of Eindhoven

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