The performance of flocculation tanks either in full‐ or pilot‐scale systems cannot be fully quantified using average velocity gradient values (G values) alone. The computational fluid dynamics (CFD) process was used to investigate the turbulent flow characteristics of a three‐stage hydraulic flocculation facility at the J.D. Kline Water Supply Plant in Halifax, Nova Scotia. Calculations of the local velocity gradient were achieved using predicted energy dissipation rates. CFD analysis showed unbalanced mixing conditions in the hydraulic flocculation tanks resulting from short‐circuiting and from recirculation zones. Inconsistent mixing energy, interrupted with severe spikes in the localized G values at the weir columns, led to little or no mixing energy in most of the regions in the flocculation tanks. Practical outcomes of operating at such varying mixing gradients can be detrimental to the flocculation process and overall organic removal in the treatment plant.

The operational significance of energy-intensive rapid mixing processes remains unaddressed in coagulation and flocculation of insoluble precipitates (flocs), which play an important role in the removal of impurities from drinking water supplies. In this study, the influence of rapid mixing and associated mixing energy on floc aggregation was examined for a surface water source characterized by a high fraction of aquatic humic matter. Infrared spectral analyses showed that the colloidal complexes resulting from ligand exchange between iron and dissolved natural organic matter (DOM) were not substantially influenced by the mixing energy input. This signified that DOM removal by coagulation can be achieved at lower mixing intensity, thereby reducing energy consumption. In contrast, macroscopic investigations showed the coagulation mixing energy affected floc size distributions during the slow mixing stage in flocculation and, to some extent, their settling characteristics. The results from analysis of floc properties clearly showed that more mixing energy was expended than necessary in coagulation, which is typically designed at a high mixing intensity range of 600-1000 s in treatment plants. The key findings from this study have practical implications to water utilities to strategically meet water quality goals while reducing energy demands.

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In-line measurements of floc size distributions at different locations in a hydraulic flocculation tank using a holographic microscopy.

Achieving uniform mixing conditions are essential for the fioccuiation process to optimize floe size and avoid floc-breakup. Limited literature is available on establishing consistent operational conditions and procedures for pilot-scale fioccuiation systems, which have tank sizes smaller than full-scale and larger than jar-test equipment, in this study, the influence of mixing speeds on the determination of the conventional design parameter, the average velocity gradient (G), was investigated for pilot-scale paddle flocculators. The pilot-scale plant for this paper was hosted at the J.D. Kline water Supply Plant (JDKWSP) in Halifax, Canada. Computational fluid dynamics (CFD) was evaluated as an alternative design technique and compared against traditionally used empirical-based calculations. Comparison of both approaches showed that the G-values of empirical method were substantially higher than the predicted values for rotational speeds greater than 5 rpm. In contrast, CFD predictions found that G-values used for tapered paddle fioccuiation process (up to 60s"^) could be achieved at lower rotational speed (around 15 rpm), which minimizes the power input required for mixing. The practical implications of operating at higher than required G-values relates to potential negative consequences such as floe break up, and the reliance of chemical additives to avoid floe break-up. These very practical outcomes could impact the interpretation of findings from pilot-scale treatment systems. NOMENCLATURE Ap C CD CFD FE FTC FV FD G projected area of paddle (m^) mean tracer concentration (kgm^) drag coefficient on paddle for turbulent flow (dimensionless number) computational fluid dynamics finite element flow through curve finite volume drag force on paddles global root mean square velocity gradient or energy input rate (s~^)