Development and application of a mechanistic model for advanced wastewater treatment by ozonation

This study presents a novel mechanistic model approach to predict the simultaneous oxidation of dissolved organic matter (DOM) and organic micropollutants (OMP) during ozonation in advanced wastewater treatment. The model captures the two-step dynamics of ozone decay through the fractionation of dissolved organic carbon (DOC) and includes four different OMP. An optimization procedure is implemented to estimate the DOM fractions and their stoichiometric relationship with ozone, thus enabling the prediction of ozone decay and ultraviolet adsorption coefficient at 254 nm (UVA₂₅₄) abatement across a range of ozone dosages, without relying on dose-dependent parameter fitting, thereby overcoming the key limitations of existing empirical or semi-empirical models. Hydroxyl radical (•OH) exposure was modeled using two approaches: the Rct-based approach and a chain reaction modeling method. Batch experiments to measure ozone decay were performed with two different wastewaters. Ozone concentrations were measured directly using a photometer and indirectly using the indigo method. Model predictions showed strong agreement between simulated and observed ozone decay (R² > 0.94) using the indigo method but showed weaker model performance for predicting kinetic UVA₂₅₄ reduction at low ozone dosages, with R² values ranging from 0.1 to 0.57, improving significantly at higher dosages (R² > 0.92). Full-scale model validation showed that the model can well predict removals of ozone-reactive compounds such as diclofenac and carbamazepine. However, the Rct-based approach underestimated the removal of benzotriazole and metoprolol as they are •OH-dependent. Better predictions for these OMP were reached using the chain reaction modeling approach. Simulations revealed that increasing DOM concentrations (5–20 mg/L) reduced OMP removal up to 35% due to competitive ozone consumption. Multistage ozone dosing simulations showed a 20% ozone savings without compromising treatment performance. These findings highlight the model's potential to aid in the planning and optimization of ozonation processes and enhance resource efficiency in advanced wastewater treatment.