Particle-bubble collisions in dissolved air flotation (DAF) systems play a crucial role in the removal of total suspended solids (TSS). DAF particle-bubble collision models incorporate factors such as particle diameters, charge and density, bubble diameters, and collision factors
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Particle-bubble collisions in dissolved air flotation (DAF) systems play a crucial role in the removal of total suspended solids (TSS). DAF particle-bubble collision models incorporate factors such as particle diameters, charge and density, bubble diameters, and collision factors. The challenge lies in accounting for the wide range of particle and bubble sizes and obtaining complex model inputs. To address this, a simplified model for TSS removal in DAF units was established using low-cost laboratory measurements, including particle size distribution and density. Additionally, microbubble diameter profiles were derived from bubble velocities using particle image velocimetry software (PIV). Six independent variables, encompassing influent particle characteristics (such as particle size distribution and density) and DAF running characteristics (temperature, contact zone detention time, inflow and recycle flows), were employed in the simplified model. The model's accuracy was evaluated using a laboratory-scale DAF system with two different influents: Delft canal water and anaerobic sludge. The predicted TSS removal from the simplified model aligned well with the laboratory-scale DAF results, yielding removal efficiencies of 68 ± 1 % and 77 ± 3 % for Delft canal water and anaerobic sludge, respectively. Furthermore, when the simplified model was applied to two full-scale DAF systems, it successfully identified an underperforming system (DAF2) with a TSS removal efficiency of 91 %, contrasting with the theoretical removal model-predicted efficiency of 98 %. This study highlights the utility of combining bubble size distribution measured by PIVlab and particle size distribution obtained using FIJI-ImageJ as an economical and efficient approach to acquiring the necessary inputs for predicting TSS removal in DAF systems.
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