Proceedings

The impact of data availability on predicative accuracy of wastewater treatment works models

Ashagre, B.1, Fu, G.1, Davidson, K.2, Butler, D.1, 1University of Exeter, 2Scottish Water, UK

(free)

Abstract

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.

Keywords: Active control, calibration, data reconciliation, wastewater treatment

Introduction

Future demand for model based control of wastewater treatment works is expected to increase. In Europe, including the UK, models are mostly a research subject whereas in other parts of the world like North America Wastewater Treatment Works (WwTWs) are predominantly used as an engineering tool in practice (Hauduc et al. 2009). This is now changing in the UK, and water utilities have started to incorporate WwTW models in decision making, process control and optimisation. According to UKWIR (2013) WwTW models are now being used for advanced process control and this practice is expected to increase significantly in the future due to tighter regulations and the potential of this approach to save energy, chemical usage and greenhouse gas emissions. One of the challenges is the availability of data and their quality.

High quality data is crucial for the effective use of WwTW models. The reliability of model results is strongly linked to the amount of the data used to set up and calibrate the model (Rieger et al. 2010). A carefully designed and collected dataset can reduce time for the subsequent modelling study and also can increase the confidence in using the model for practical application. In addition, data scarcity and low quality data can distort the simulation results and increase the chance of faulty conclusions, which might lead to very expensive decisions and/or could cause breaching of licenses.

Historical data can be used to understand the long-term behaviour of the treatment works. Dynamic modelling for control purposes requires high resolution spatial and temporal data, which includes subdaily monitoring of various parameters. Stoichiometric/kinetic data can also be monitored to accurately estimate the model parameters. However, this can be costly and demand experience to achieve all these datasets. Thus, it is important to determine what level is sufficient for the modelbased study.

The importance of monitoring wastewater within the WwTW has been suggested to be crucial both for design and modelling purposes (Gernaey et al. 2006, Melcer 2003, Metcalf & Eddy 2004, Rosén et al. 2003), but the question is what level of data is sufficient to have reasonable confidence in the model to be used for active control purpose. The aim of this paper is therefore to investigate the model performance using different levels of dataset. Hence, two scenarios were used in this study, in addition to a baseline scenario, each with a different level of data availability in order to set up and calibrate the model. The differences in model performance among the scenarios are used to assess the benefit of using the specific dataset considered in the corresponding scenario.

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