Ozonation of pharmaceutical residues in a wastewater treatment plant : Modeling the ozone demand based on a multivariate analysis of influential parameters

Abstract: Most pharmaceutical residues in wastewater treatment plants (WWTPs) end up in the hydrosphere where they cause negative effects on the aquatic life and might disrupt ecosystems. By implementing an ozonation step (treatment with ozone) in the wastewater treatment process, these pharmaceutical residues can be reduced.  The purpose of this project was to verify that the ozonation process works in full-scale, thereby verifying a pilot study conducted in 2014 at Tekniska Verken i Linköping AB (TVAB). Additionally, the purpose was to investigate which parameters influence the ozone demand in order to formulate a model for the ozone demand. The initial phases during this thesis were a pre-study and a literature study. This was followed by the multivariate analysis and model construction based on different data from the pilot study. Measurements were performed on the wastewater in the full-scale facility in order to verify the results from the pilot study. Moreover, measurements were performed to find new ozone consuming parameters. The reduction of pharmaceutical residues was similar to the pilot study, although slightly lower. Several parameters and factors that were different between pilot study and new measurements affected the reduction of pharmaceutical residues. For example, DOC and nitrate concentrations have increased since the pilot study in 2014. Also, factors such as the growth in population in Linköping and the differences in design between the pilot plant and the full-scale facility have influenced the reduction of pharmaceutical residues. A control strategy based on a linear relationship between ozone sensitive Ultra Violet Absorption (UVA) left and remaining pharmaceutical residues after ozonation could potentially be used. Moreover, three models were constructed and the Multivariate Analysis 1 (MVA1)-model was deemed as the best, this model includes ozone residual, nitrite, turbidity, simulated Chemical Oxygen Demand (COD(sim)) and ozone dose. The variations in the dose compared to the input parameters for the validation data show that the model predict the ozone dose well. However, in future other interesting parameters can be included in the model to further improve the accuracy in the ozone dose predicted by the model.