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. 2014 Sep 1:60:118-129.
doi: 10.1016/j.watres.2014.04.037. Epub 2014 May 2.

Predicting the disinfection efficiency range in chlorine contact tanks through a CFD-based approach

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Predicting the disinfection efficiency range in chlorine contact tanks through a CFD-based approach

Athanasios Angeloudis et al. Water Res. .

Abstract

In this study three-dimensional computational fluid dynamics (CFD) models, incorporating appropriately selected kinetic models, were developed to simulate the processes of chlorine decay, pathogen inactivation and the formation of potentially carcinogenic by-products in disinfection contact tanks (CTs). Currently, the performance of CT facilities largely relies on Hydraulic Efficiency Indicators (HEIs), extracted from experimentally derived Residence Time Distribution (RTD) curves. This approach has more recently been aided with the application of CFD models, which can be calibrated to predict accurately RTDs, enabling the assessment of disinfection facilities prior to their construction. However, as long as it depends on HEIs, the CT design process does not directly take into consideration the disinfection biochemistry which needs to be optimized. The main objective of this study is to address this issue by refining the modelling practices to simulate some reactive processes of interest, while acknowledging the uneven contact time stemming from the RTD curves. Initially, the hydraulic performances of seven CT design variations were reviewed through available experimental and computational data. In turn, the same design configurations were tested using numerical modelling techniques, featuring kinetic models that enable the quantification of disinfection operational parameters. Results highlight that the optimization of the hydrodynamic conditions facilitates a more uniform disinfectant contact time, which correspond to greater levels of pathogen inactivation and a more controlled by-product accumulation.

Keywords: Disinfection by-products; Disinfection tanks; Pathogen inactivation; RANS modeling; Residence time distribution.

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