Though the case study site has been used by several studies, the modelling strategies applied may not be the same due to the different simulation platforms used (SYNOPSIS in Lau et al., (2002), Schütze et al. (2002) and Zacharof et al. (2004), SIMBA5 in Astaraie-Imani et al. (2012) and Fu et al. (2008), and SIMBA6 in Casal-Campos et al. (2015) and this work) and diverse modelling techniques provided even by the same simulation platform. The model employed for this work (modelling method for each component summarised in Table 4.2) is similar to the one used in Fu et al. (2008), however a few changes were made to suit the purpose of this study. The major modifications are listed below.
In previous studies, the sludge treatment unit was not modelled. However, sludge treatment and disposal are one of the most important cost factors. For example, the operational cost for sludge treatment can amount to more than half of the total operational cost for wastewater treatment (Nowak, 2006). Therefore, a mechanical dewatering unit was added to have a more complete representation of the cost consequences of different operational strategies.
The modelling of the primary clarifier was modified to adapt to the introduction of the sludge treatment unit. Firstly, settled sludge in the primary clarifier was drawn at a constant rate (about 15% of DWF by referring to Schütze et al. (2002), comparable to that reported by Tardy (2011)) to the
sludge dewatering unit. Secondly, the supernatant flow from the sludge treatment unit was added to the front of the primary clarifier for treatment. For simplicity, ammonia is the single pollutant investigated in this work. The average river flow rate was increased to three times the previous value
1.5 m3/s, as a preliminary optimisation run suggested that no operational strategy could meet the environmental standards on total ammonia with the original designated dilution capacity.
A one-year simulation was set up so that long-term performance of the system can be evaluated. In the previously established models, the evaluation of system performance was rather short-term (e.g. one week) so wastewater temperature and upstream river flow rate and water quality were assumed to be constant. To accommodate long-term simulations, a pattern of seasonal wastewater temperature was defined and one-year input data sets (rainfall and corresponding river data) were incorporated into the model. As no monitoring data on temperature of the Norwich WWTP were available, a seasonal pattern (18 °C, 23 °C, 19 °C and 15 °C from spring to winter) was assumed by adjusting a WWTP wastewater temperature pattern reported in the literature (Shatat and Al-najar, 2011) to data on the local climate of Norwich (Hughes, 2006). Detailed river water quality data is commonly scarce, thus a hypothetical dynamic river data set was generated by adding an agricultural runoff pollution source to the upstream of the hypothetical river. After finishing the research work for Chapters 4 and 5, however, a detailed data set of river water quality from another area along with the corresponding river flow and rainfall records were acquired from the Environment Agency. Due to time constraints, however, results produced using the semi-hypothetical input data are used to illustrate the proposed method, while the newly acquired real-life data are employed to examine the reliability of the methodology. Details on the two data sets are provided as follows.
Data set ‘A’:
One year (01/10/2012 to 30/09/2013) 15-mininute increment time series of total ammonia concentration and flow rate of the runoff from North Wyke
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Farm (Okehampton, UK) were used to generate a dynamic upstream river water quality (annual average about 0.09 NH3-N mg/L). To reflect the
dynamic dilution capacity of the river at different times of the year, monthly river base flow rates were defined (annual average 4.5 m3/s) according to the rainfall data. The one-year rainfall 15-minute increment time series (687.60 mm/year) (Figure 4.3a) corresponding to the agricultural runoff was used in the model to generate urban runoff and stormwater flowing to the sewer system.
Data set ‘B’:
This data set is from one-year (24/05/2013 to 23/05/2014) records of an online analyser placed downstream of a WWTP in an English Midlands river. The automatic sampler monitors in-river total ammonia concentration every 30 minutes and flow rate every 15 minutes. The flow scale of the river (annual average about 5.6 m3/s) is similar to that of data set ‘A’, but the river water quality (annual mean: 0.67 NH3-N mg/L, 90%ile: 0.95 NH3-N mg/L,
99%ile: 1.84 NH3-N mg/L) is much worse (for data set ‘A’, annual mean:
0.09 NH3-N mg/L, 90%ile: 0.09 NH3-N mg/L, 99%ile: 0.11 NH3-N mg/L).
Despite the natural decay of ammonia occurring in the river flow, it is impossible, according to a preliminary optimisation run, to achieve at reach 11 (after all wastewater discharges) the environmental standards applied to the first data set (second row of Table 4.3, corresponding to the fourth row of Table B.4) or the less stringent set of standards (third row of Table 4.3, corresponding to the fifth row of Table B.4). The two sets of environmental limits are the standards in England and Wales for different types of rivers (Defra, 2010; Foundation for Water Research, 2012). To demonstrate the benefits of optimal operational design, the monitoring data on total ammonia concentration were downscaled to a level where the environmental standards are still violated but can be met through optimisation of operational strategies. In this study, the downscaling factor is defined to be 0.6 and the more relaxed set of environmental standards was employed. The downscaling factor was deliberately designed to be not too small so as to simulate a very different scenario with a more polluted river. The corresponding one-year rainfall data (868.79 mm/year) are shown in Figure 4.3b at an hourly time step.
Table 4.2 Dimensions of the case study UWWS and the modelling methods Process unit Dimension Hydraulic/pollutant transport model Models for sedimentation Models for biochemical reaction processes
Catchment Total area of 7 sub-
catchments: 725.8 ha Nash cascade
Not modelled Not modelled Sewer -- Translation Storage tank Tank 2: 2800 m3; Tank 4: 1400 m3; Tank 6: 2000 m3; and Tank 7: 7000 m3 Completely mixed reactors Simplified model by a coefficient of settling efficiency Storm tank 6750 m3 Primary clarifier 6785 m 3 Empirical equation as a function of hydraulic retention time (HRT) Aerator 10,400 m3 -- An extension of Activated Sludge Model No. 1 Secondary clarifier 6600 m 3 3-layer model, using exponential function to simulate settling velocity Not modelled Mechanical dewatering -- -- Idealised solid separation
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Table 4.3 Environmental standards on total ammonia concentration in England and Wales applied to data sets ‘A’ and ‘B’
Data set 90 percentile (mg/L) [1] 99 percentile (mg/L) [2]
A 0.3 0.7
B 0.6 1.5
Note:
[1] Requirements from the UK regulation transposing the WFD requirement (Defra, 2010); [2] Requirements from the Urban Pollution Management Manual (Foundation for Water Research, 2012);
Figure 4.3 Rainfall time series of a) data set ‘A’ (Oct 2012 to Oct 2013) and b) data set ‘B’ (May 2013 to May 2014)
Discrepancies are expected between predictions by the established model and real world data. For one reason, it is impractical, as mentioned in section 3.1, to make extensive simulation of all (possibly known) processes in each treatment unit in the context of integrated modelling. For another, no model exists that can accurately represent a real life system due to limitations in our knowledge and sources of uncertainty that cannot be reduced by more data/studies (see more in section 4.5.2). Hence, some processes are simulated in a simple manner (e.g. mixing, sedimentation in storm/storage tanks) or not accounted in modelling (e.g. biochemical reactions in the sewer) in this case study as no sufficient data is available to identify and calibrate parameters of more sophisticated models. Nevertheless, processes critical for wastewater treatment and its environmental
impacts, namely sedimentation in the secondary clarifier and biochemical reactions in the aeration tank and the receiving river, are modelled in a relatively detailed manner.