Model-Based Construction of Wastewater Treatment Plant Influent Data for Simulation Studies
3.1. Verification of the Modelling Approach for Dry Weather Inflow Pattern
A second-order Fourier analysis was calculated to adjust the water flow rates. The analysed data sets only contain 2 h values for the water quantity; with this temporally not very high-resolution database, no significant improvement in mapping can be achieved with a higher-order Fourier series. The analysis calculates the parameters a0, a1, a2, b1 and b2.
The parameters and were optimized to adjust the to the measured values. This means that the time series of the concentration is completely determined by the mixture of infiltration water ( ≈ 0) and wastewater ( constant). A time shift to the time series of the water quantity and a further deformation of the pattern results from the transport through a storage volume.
The parameters , and were optimised to adjust the to the measured values. The time series of the concentration is characterised by a proportion of , representing organic bound nitrogen, in the grey water (constant concentration) and a very high concentration (approximately 400 gN/m3) in the urine. The time function of the urine flow rate is characterised by a constant proportion () and a nitrogen peak in the morning . This nitrogen peak is determined by the two shape parameters (time) and (the shape parameter of the Gumbel function and the width of the peak).
The parameter was optimised to adjust the concentration to the measured values. Similar to the course of the concentration, the course of the concentration is characterised by a proportional fraction ()) in the grey water (constant concentration) and a very high concentration in the urine. By adjusting the parameter , the pattern can be varied, with larger values of the factor in the direction of the temporal course of the concentration, with smaller values in the direction of the pattern of the concentration. Unfortunately, it was only possible to adjust the concentration for a small number of measured daily patterns due to the lack of measurement values in several data sets.
This analysis impressively demonstrates that the dry weather inflow of wastewater treatment plants can be plausibly described with regard to , and concentrations using the simple model approach presented. The results of the analysis of all 21 daily cycles considered can be found in the appendix. Based on the analysis carried out, the systematic dependencies of the adjusted shape parameters on the plant size can also be analysed. These correlations can help to estimate typical daily patterns even for locations for which no measurements are available.
3.2. Experiences with the Method to Generate Long-Term Dynamic Simulation Influent Data
The method described is not applicable in the following cases. A necessary precondition is that the dry weather inflow is mainly created from normal human activities in urbanizations (municipal wastewater). If the wastewater is completely produced by industry or a large fraction of the wastewater arises from industrial sources, the assumptions regarding cyclic production patterns of greywater and urine will not hold. The method cannot be applied. A failure of the method was also observed in one case in which municipal wastewater was pumped to the wastewater treatment plant in a widely branched pressurized pipe system with very long residence times (>4 h) (wastewater treatment plant in Berlin). In general, the method might fail in situations where large fractions of the wastewater will be managed and stored in the sewer system. This will lead to a non-predictable change in the resulting inflow patterns.
One major limitation of the method results from the assumed measurement situation (only routine data). In this situation, the influent concentration (24 h composite sample) are not measured every day but only on some days (e.g., 50 times a year). As a consequence, a constant load for every day must be assumed. Stochastic variations and typical weekly patterns cannot be reproduced. A limited quality of the influent data arises. Better influent data quality can be achieved only with an additional (non-routine) measurement effort.
But, if applicable, the method drastically reduces the requirements for influent data down to a continuous influent flow measurement and a few 24 h composite samples.
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