Selection of media for the design of ballasted flocculation processes
Graphical abstract
Introduction
Clarification processes require specific footprints that translate into important capital costs, especially in the Nordic climate where settling basins must be located inside heated buildings. Ballasted flocculation, consisting of injecting micron-sized granular media to increase the specific gravity (SG) and size of flocs, is being used increasingly in the water industry owing to its potential for achieving very high superficial design velocities (as high as 85 m/h; MDDEP (2009)). This advantage offers a more compact process (i.e., smaller footprint), stable suspended solids removal performance, faster start-up, and important savings compared to conventional flocculation (Desjardins et al., 2002; Guibelin et al., 1994). In earlier studies, we showed that the floc SG and size were two important factors for predicting settling performance (Lapointe et al., 2017). The final SG of flocs depends on the SG of the medium: a denser ballast medium (BM) leading to denser flocs. However, a denser medium requires a higher velocity gradient (i.e., G value in s−1) to maintain it in suspension. This leads to lower average floc diameters owing to floc break-up, and to higher fraction of un-ballasted flocs. Therefore, with reference to the design criteria (minimizing footprint or minimizing settled water turbidity); an optimal BM can be selected for a given application.
The concentration of BM inside the flocculation tank (typically referred to as the maturation tank) is very high, in the range of 4–10 g silica sand (SS)/L for industrial applications. The BM concentration depends on the particle concentration in the flocculated waters (which is linked to the coagulant dosage and the source-water particle concentration). For example, in jar testing, a higher BM concentration was necessary for a wastewater application compared to a drinking water application (2 g of SS/L for surface water of 12 NTU and 3 g of SS/L for municipal wastewater of 130 NTU (Lapointe and Barbeau, 2017; Lapointe et al., 2017)). Apart from the BM concentration, its particle size distribution is also expected to impact ballasted media performance. A medium of smaller particles translates into a higher number of available BM particles for a given mass concentration. Flocculation performance is known to be a function of G×t×No, where G is the mean velocity gradient, t the flocculation time, and No, the initial number of particles to flocculate. However, large BM media can produce larger flocs, which settle more rapidly. Consequently, similar to the selection of a BM SG, there is an optimum selection of the medium size to achieve the best overall flocculation/settling performance.
In summary, the selection of an appropriate BM medium is dictated by its SG, size, and the need to minimize un-ballasted flocs. This is because the latter are not well removed during settling at high superficial velocity. In addition, BM availability and cost, its resistance to abrasion, and the effectiveness of its recovery by hydrocyclone are important criteria to consider (Desjardins et al., 2002; Sibony, 1981; Young and Edwards, 2003). However, no systematic approach has yet been proposed to compare and select an appropriate BM with respect to its SG and size for a given application. In order to facilitate this procedure, this research project explored the hypothesis that flocculation performance is controlled by the surface area of BM available for ballasted flocculation, expressed as m2 of surface per L of water. We hypothesized that differing media density and particle size distributions impact ballast media performance by increasing/reducing the reaction surface. This hypothesis was tested at laboratory scale by evaluating five ballast media with differing SG, all of which were sieved to produce three different particle size distributions. Flocculation kinetics were monitored by measuring floc size by microscopy and with a camera installed directly on the jar-test beaker. Settling performance was monitored using turbidity measurements. Recommendations are made for guiding the selection of an adequate BM to achieve turbidity removal for a specific design superficial velocity.
Section snippets
Water characteristics
All experiments were conducted at laboratory scale at 21 ± 1 °C using surface water from the Sainte-Rose drinking water treatment plant (10 ± 2 NTU; pH 6.9), which is fed by the Mille-Îles River (Quebec, Canada). The tested surface water exhibits a relatively low alkalinity (30 mg CaCO3/L) and a significant dissolved organic carbon (DOC) concentration (6.9 mg C/L). The raw water was collected just past the 10 mm influent bar screens and refrigerated at 4 °C during the experiments (2 weeks).
Jar test procedure
The
Selection of the optimal ballast medium concentration
In past research efforts, BM optimal dosages were selected on the basis of mass base concentration (expressed in g/L). However, identical mass base concentration BM media with differing average diameters or densities will generate different particle concentration and surface area. Fig. 2 compares the impact of the BM media (expressed in g/L in Fig. 2A and m2/L in Fig. 2B) on settled water turbidities. To prepare this figure, the smallest/lightest GAC (80–125 μm) and largest/densest MS medium
Conclusions
This study demonstrated that ballasted flocculation is best described as contact between surfaces (microflocs and BM). Consequently, the BM concentrations should be normalized with respect to surface in order to select an optimal and appropriate BM. For very high settling rates, the BM surface concentration, SG, and size had important effects on turbidity removal. On the other hand, as the superficial velocity applied in the clarifier decreased, it was shown that the BM SG and size were less
Acknowledgments
These experiments were conducted as part of the Industrial-NSERC Chair in Drinking Water (Polytechnique Montreal) research program, which benefits from the financial support of Veolia Water Technologies Canada, Inc., the City of Montreal, the City of Laval, the City of Repentigny and the City of Longueuil. Experiments were conducted at the CREDEAU, a research infrastructure supported by the Canadian Foundation for Innovation and the Ministry of Education of Quebec.
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