Modelling Micropollutant Removal Through Ozonation in Wastewater

Abstract: Proper management of water resources is a key to climate change adaptation and resilience in modern societies. Adequate treatment of wastewater is essential for ensuring the sustainability of the water cycle and the health of the environment and the ecosystems that inhabit it. It can also contribute to water supply needs in regions facing issues stemming from water stress. In this context, computer models can play an important role in assisting wastewater treatment systems facing growing populations and more stringent demands. This study proposes the combined use of an ozone decomposition model and a micropollutant model to simulate micropollutant removal through ozonation. The model is based on the use of second-order rate constants, denominated kinetic coefficients, and it solves a continuity equation that describes the dynamics of compounds over time. It is developed from the work of Audenaert et al. (2013), which includes the presence of Dissolved Organic Matter (DOM), and adds the effect of Total Suspended Solids (TSS). It also considers fractionation steps for the Chemical Oxyen Demand (COD) and the conjugated, particulate and soluble fractions of a sample of micropollutants, to enable it to be used in combination with other treatment configurations. The model is calibrated by using two sets of data, one from batch experiments carried out by Juárez et al. (2021) and the other from the operation of an ozonation pilot plant (Ekblad et al., 2021). The validation is then performed by comparing to experimental data from Lee et al. (2014), and achieved with an average coefficient of determination (r2) of 0.89. Overall, the model presents a good fit for simulations with values based on batch experimentation, and to a lesser extent in comparison to the data from the pilot plant. Moreover, when the model diverges from the source data it does so with an underestimation in most cases. It also shows lower removal performance with the addition of H2O2, and better removal capacity with higher Hydraulic Retention Time (HRT). It responds, however, with negligible sensitivity to variations in pH. This research offers many possibilities for future work, as it could be applied into risk impact studies, expanded to other micropollutant species, or improved by deepening the work on the effect of TSS, pH, nitrogen species, particulate organic matter, inorganic matter, or others. The model can also be tested in combination with other treatment configurations and hybrid systems, or together with Computational Fluid Dynamics (CFD). Lastly, there could be a way to put the emerging potential of Artificial Intelligence (AI) to the benefit of wastewater treatment modelling.