The Impact of data availability on the predictive accuracy of wastewater treatment works models

In order for wastewater treatment works (WwTW) models to be used with confidence for active control it is important to perform model calibration to accurately represent its performance. Data is usually a limitation in achieving high levels of calibration since it can be costly and time consuming. Thus it is important to carefully assess the minimum data requirement and determine the need for further data collection. This study assesses how far model performance can be improved by considering more frequent and perhaps costly monitoring .This is achieved by comparing simulated quality indicators with a measured dataset after performing sensitivity analysis to identify parameters to which the model is most sensitive. WwTW models are tested using three different datasets of increasing number of quality variables. Calibration accuracy, as measured using R2 and RMSE goodness-of-fit tests, increases for TSS and NH3-N concentrations as compared to the baseline for scenarios two and three, albeit still with low absolute values. In this case study, the results indicate the importance of characterising influent wastewater organic matter and nitrogen concentrations to reduce prediction uncertainty and help build confidence in the use of models for active control.<p></p>