Data assimilation for prediction of ammonium in wastewater treatment plant: From physical to data driven models

Abstract

This study compares various modeling approaches to predict ammonium concentration in wastewater treatment plants (WWTPs), with a focus on integrating data assimilation techniques. It explores white-box, grey-box, and black-box models, evaluating their ability to capture the complex dynamics of WWTPs and manage uncertainties associated with limited data and sensor noise. The article highlights the importance of data assimilation for simultaneously calibrating model parameters, latent variables (such as unmeasured species concentrations), and quantifying prediction uncertainty. Simulation results demonstrate that the non-parametric black box model outperforms all other models in terms of predictive accuracy and uncertainty estimation. This finding underscores the effectiveness of machine learning when integrated with data assimilation techniques to extract insights from training datasets, even in the presence of limited data. Interestingly, the addition of an extra sensor, such as an oxygen sensor, did not enhance model performance. Experiments conducted in a real system showed that the non-parametric black box model could effectively capture the general dynamics of ammonium concentration in an actual wastewater treatment plant. However, its performance was somewhat diminished compared to simulation results, likely due to variability in input concentrations that were not accounted for in the model.