Using plant data to estimate biodegradable COD fractions – Case Study KwaMashu WWTP

The kwaMashu WWTW in eThekwini, South Africa, is a conventional wastewater treatment plant with a nominal capacity of 50 ML/d. It has primary settlers and anaerobic digesters, but is not configured for P removal. A modelling study is under way in preparation for a planned upgrade to 80ML/d. The upgrade is planned to improve nutrient removal, and tertiary treatment to recover potable water is also being considered.

When the configuration of an existing plant is to be changed, the most critical part of the model calibration is the influent wastewater fractionation. Furthermore, due to the complexity of the systems involved, model calibration protocols typically involve the sequential calibration of the various subsystems starting with the influent characterization. As a result, errors in the influent characterization are propagated through the other calibration steps (Grau et al., 2007).

In general, raw sewage COD and TSS measurements are available from routine monitoring data. However, treatment models require the fractionation of raw COD and TSS into, at minimum, soluble biodegradeable and unbiodegradable organic components, particulate biodegradable and unbiodegradable organic components and an inorganic particulate component. The constantly varying characteristics of wastewater make experimental determination of an adequately representative set of components using protocols such as those recommended by the IWA Guidelines (Reiger et al., 2012) difficult, time-consuming and expensive, which constitutes a significant barrier to the adoption of modelling by many municipalities, including eThekwini. Biodegradable organic fractions in raw and settled sewage are typically determined via BOD measurements (Hulsbeek et al., 2002) or respirometric methods (Vanrolleghen, 2002). Both of these methods take days to get results for a single sample and most  municipalities simply do not have the equipment or experienced personnel to undertake these type of characterization studies. Furthermore, translating laboratory results to full scale WWTP plants can be quite challenging due to important differences between the two types of systems (Sin et al., 2005).

Compliance and process operation monitoring generate large sets of measurements of COD, TSS, FSA etc, but these are insufficient for determining the wastewater  characteristics required by models. Furthermore, they tend to include many errors and inconsistencies, as they are seldom evaluated critically. Nevertheless, a probabilistic fractionator tool that we have developed (Brouckaert et al., 2016) has proved effective for certain modelling purposes. This combines routine measurements with estimates based on literature and plant experience to determine a probable composition expressed in terms of model components. The probabilistic fractionator, which is included in the PWM_SA model implemented in WEST (MikebyDHI), is similar inconcept to the influent characterization methodology developed by Grau et al. (2007) but includesonly the components required for the PWM_SA model as well as a simpler fitting procedure.However, routine measurements on the influent wastewater contain no information on importantparameters, such as biodegradability, so for this the probabilistic fractionator must rely entirely onestimates.In this study, we explore the possibility of extending the probabilistic fractionator by includingroutine process measurements with the routine influent measurements, and coupling the fractionator with a simplified plant-wide steady state model, so as to obtain a more accuratefractionation of the influent. This technique could make wastewater treatment modellingaccessible to a wider range of municipalities.