Modeling NOM removal by softening in a surface water treatment plant

Abstract

Phenomenological models and hybrid phenomenological-chemometric models were developed to predict natural organic matter (NOM) removal based on the real water treatment data from the city of Minneapolis over a 3 year period. The analysis of the modeling results showed that the phenomenological model was able to capture the major variations of NOM removal but it tended to over predict the NOM removal in independent data sets. These results could be significantly improved by the hybrid model, which was less biased and much more accurate than the phenomenological model. The phenomenological model parameters showed low statistical confidence because the available data, collected in real water treatment conditions, was not sufficiently informative to identify the complex model structure. By comparison, the hybrid modeling method enabled a more reliable discrimination of the most important factors affecting NOM removal. The final hybrid model was implemented in an Excel spreadsheet and can be easily used for NOM removal prediction and the control of chemical dosing.