Conductivity as alternative measurement for WWTP inflow dynamics –

Posted by 11 Apr, 2013

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Along with the development of  more and more complex integrated models for urban water systems the need of sufficient  data  bases grows as well. It is even complicated to measure relevant parameters,e.g. dissolved nitrogen or COD, for their use in Waste Water Treatment Plants and sewer models to describe the influence of catchments to the receiving water.

This poster presents a method regarding the possibility of substituting an online ammonia measurement by conductivity measurements in the inflow of a Waste Water Treatment Plant . The aim was the description of the dynamics in wet weather flow through storm water events for modelling purposes.

The conductivity of an aqueous solution is the measure of its ability to conduct electricity. Responsible for that phenomenon are ions of dissolved salts. In natural and drinking water these are mainly carbonates, chlorides and sulphates of calcium, magnesium, sodium and potassium. Conducted experiences and measurements in combined sewers showed a relation between conductivity in Waste Water Treatment Plant inflow and the concentration of dissolved components, e.g. ammonia, in case of rainfall events. The data for different 3 Waste Water Treatment Plant are shown in Figure 1. Rainwater has nearly no ions that cause conductivity to be measured. Therefore, diluted wastewater flowing into the Waste Water Treatment Plant can be detected by a conductivity probe. The measure and quality of linear regression between ammonia concentration and conductivity can be found in Table 1 for all data from Figure 1.

Material and Methods

With this knowledge a simple regression-based inflow model for use in activated sludge modelling of Waste Water Treatment Plant was defined to use conductivity beside available composite samples as a measure for dynamics in ammonia concentration as one of the most dynamic measure.

Results and Discussion

For one of the considered Waste Water Treatment Plants (WWTP) the resulting quality for the inflow model is shown in Figure 2 for a time series of a week.

Furthermore, the inflow model was used as a source for a retention tank model at the inlet of another Waste Water Treatment Plant to describe the impact of different management strategies (storage or flow through) on receiving water and Waste Water Treatment Plant (Figure 3).

A long-term modelling of 9 storm water events was used to show the predictive capacity of the model. The regression parameters were fitted by an optimisation routine to get best fit for all concentrations (also for COD, not presented here). Figure 3 shows the fit for all events. A good prediction of dynamics and absolute values for ammonia can be seen.

The results of different Goodness-of-fit measures are summarized in Table 2 for both presented WWTP inflows. Especially the values for the modified Coefficient of Efficiency, as a well-known and used measure for model quality in hydrological sciences, show the degree of predicting of the used method and the usability of conductivity for description of influent dynamics to Waste Water Treatment Plant in storm water cases.


This simple and easy-to-use method is well suited for implementation in Waste Water Treatment Plant models to describe the inflow dynamics regarding a more realistic behavior e.g. for optimization of process control.

by Markus Ahnert*, Norbert Günther*, Volker Kuehn*, University of Dresden 

Ahnert, M., Blumensaat, F., Langergraber, G., Alex, J., Woerner, D., Frehmann, T., Halft, N., Hobus, I., Plattes, M., Spering, V. und Winkler, S. (2007), Goodness-of-fit measures for numerical modelling in urban water management – a summary to support practical applications., paper presented at 10th IWA Specialised Conference on “Design, Operation and Economics of Large Wastewater Treatment Plants”, 9-13 September 2007, Vienna, Austria, 9-13 September 2007.

Nash, J. E. und Sutcliffe, J. V. (1970), River flow forecasting through conceptual models part I – A discussion of principles, Journal of Hydrology, 10, 282.

IWA Water Wiki ( / CC BY-SA 3.0

Figure 1

Table 1

Figure 2

Figure 3

Table 2


Categories : Case Studies & Application Stories, Science and Industry Updates
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