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concentration together with the known biofilm area and inflow rate of reject water could then be used to estimate the ASL. In order to verify this hypothesis the available data was reviewed. The one-minute average conductivity data (see section 2.1.2, page 4) was purged from a number of outliers. These most probably originated from measurements recorded during calibrations of the instruments. The remaining conductivity data turned out to be nearly constant for long periods of time. This was unfortunate since the lack of variation meant that the data contained less useful information for modelling. Data on the reject water’s NH +4 -concentration was only available from lab analyses. The reject water had been sampled approximately once a week so only a relatively small amount of data was available for modelling. In order to calculate the correlation between the NH +4 -concentration and the conductivity, it was necessary to find the conductivity values that matched those of the NH +4 -concentration in time. Only the date of the laboratory analyses had been recorded and the exact time of the day when the samples had been collected had varied. This made it impossible to pick out only the correct oneminute average conductivity values that exactly matched the NH +4 -concentration data in time. Instead the available conductivity data was averaged over the office-hours for the dates the NH +4 concentration had been analysed. Reviewing the conductivity data showed that the conductivity had been nearly constant during these periods so the averaging should not have removed too much significant variation from the conductivity data. The correlation between the two datasets was calculated, but found to be very low. The Pearson correlation-coefficient was only approximately 0.3. This was surprising since Trela et al. (2009) had shown a correlation coefficient of 0.74 for reject water of a similar origin. The reason for the low correlation was assumed to be the lack of variation of the operating conditions. Since the process had been operated with a nearly constant ASL, other factors, such as temperature fluctuations, could dominate the variation in the conductivity. Thus, the hypothesis was not considered to be either proved nor refuted. However, since the correlation was so low for the available data, the linear least squares fit was not considered well suited for use as a predictor. Instead, it was decided that a constant value for the NH +4 -concentration had to be used. Due to the constant nature of the NH +4 -concentration in the reject water such a value could be motivated. By assuming a constant NH +4 -concentration, the ASL-estimate would be directly proportional to the inflow of reject water since the third parameter, the biofilm area, also was constant. The inflow was to be controlled by a remote controlled pump, thus the estimation of the ASL was considered good enough. 3.2.2

N2 Estimator

An attempt to get an estimator of the N 2-production was made using linear least squares. However, no direct measurements of the system’s N 2-production was available. This made it impossible to make an estimator explicitly for the N 2-production directly from the measured parameters. To work around this problem a number of assumptions were made. It was assumed that all nitrogen entering the system entered as NH +4 and that it exited the system in four different forms, namely, N 2, NO -2, + NO 3 and NH 4 . A mass balance would then reveal the production of N 2 if the outflow rates of the other nitrogen-forms were known together with the assumption that no build-up of nitrogen occurred in the reactor. Data on the concentrations of the different nitrogen-forms in the influent and effluent was available from the laboratory-analyses. Since the inflow was only rarely sampled on the same date as the 19

/Olle_Trollberg  

http://www.sjostadsverket.se/download/18.50a499dd132037d524e80007759/Olle_Trollberg.pdf

/Olle_Trollberg  

http://www.sjostadsverket.se/download/18.50a499dd132037d524e80007759/Olle_Trollberg.pdf

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