Sources of Parameter Uncertainty in Predicting Treatment Performance: The Case of Preozonation in Drinking Water Engineering

This study investigates the factors that determine
parameter uncertainty when applying predefined, existing
models to predict the performance of a full scale treatment
system from environmental engineering. The analysis
is performed for ozonation of surface water, a technology
applied in drinking water treatment for disinfection and
oxidation of micropollutants. The pseudo first order rate
constant of ozone decay kO3 is characterized as a
time dependent parameter and estimated from data
obtained from three experimental setups representing
upscaling stages in engineering design. To obtain meaningful
uncertainty estimates, various factors need to be
acknowledged:  uncertainty about the model structure,
uncertainty of other model parameters, uncertainty due to non-representative sampling, and errors in chemical analysis.
It is concluded that an on-site automated sequencing batch
reactor is best suited for estimating kinetics during
operation of the full scale system. Furthermore, the
transferability of information in upscaling from laboratory
experiments to the full scale system is found to be critical.
Although uncertainty analysis enhances the understanding
of the system, it is also shown to be a subjective process
depending on the knowledge and assumptions of the modeler
and the availability and quality of data.