Towards Optimal Bubble Generation for Biological Wastewater Treatment

Gas bubble dispersion determines the efficiency of the aeration process in biological wastewater treatment plants (WWTP). The purpose of aeration is to provide an aerobic environment for microbial degradation of organic matters. This is an expensive procedure, which is responsible for the largest share of energy bill in the whole WWTP in the range from 45% to 75% [1]. The state of the art of aerators, which are currently in use at the activated sludge facilities, is the rubber membrane diffusers. These diffusers offer relatively low standard oxygen transfer efficiency (SOTE) in the range of 40% to 60% [2]. Several factors affect the SOTE, e.g. the gas holdup, bubble size, bubble residence time, and the apparent viscosity [3]. Among these parameters, the bubble size is of a great importance, since it directly influences the gas holdup and the bubble residence time. Moreover, the bubble size determines the surface area to volume ratio, which affects the volumetric oxygen transfer coefficient k_L a and the oxygen transfer rate OTR. To specify the oxygen transfer, one needs to know the mass transfer coefficient from a gas bubble as a function of its diameter and accurate information on the terminal bubble rising velocity. Accordingly, Motarjemi and Jameson have measured the initial bubble size required to achieve 95% transfer of available oxygen from an air bubble as a function of the depth of the basin [3].
To achieve the optimal bubble size, it is important to know the relation between the initial bubble volume and other influential parameters, e.g. the gas flow rate, orifice diameter, gas reservoir volume, and physical properties of both phases. Since 1960, many authors have tried to calculate the initial bubble volume. The majority of these models divide the bubble formation into two stages, namely the growing stage and the elongation stage through a neck. Each stage can be solved either by its corresponding force balance, or by empirical assumptions related to the moment of bubble detachment. Although these models are quite reliable in low flow rates, by increasing the gas flow rate, they diverge. Latter is due to the fact that, the assumptions, which are used to close the equations in each stage, do not take into account the variation in the detachment condition at different bubbling regimes.By increasing the gas flow rate, the bubble surface moves more dynamic and the influence of the gas momentum force is more pronounced. In this case, the final bubble is a product of multiple coalescence of smaller bubbles right above the orifice. Moreover, the three-phase contact of the gas phase, the liquid phase, and the solid phase during the bubble formation is generally a dynamic procedure. However, in most of the models this measure is assumed to be a constant value.

In the current study, we investigate the bubble formation from a submerged orifice at different bubbling regimes. To track the three-contact phase point inside and above the orifice, we use an optical setup with a matched refractive index of the solid and the liquid phase. Consequently, we are able to follow the three-phase contact point even inside of the orifice. To mimic the bubble formation in water, we keep the dimensionless Reynolds number constant. The bubble formation is recorded with a high-speed camera with a maximum spatial resolution of 2 μm and a temporal resolution of up to 25 μs.
The gas flow rate is set via a mass flow controller. We cover the full range of bubbling regimes, from the quasi-static to the chaotic regime. Similar to Badam et al., the change in the map of the bubbling regime is reported according to the dimensionless Froude and Bond number [4]. By increasing the gas flow rate, we track the progressive bubble volume and the trajectory of the bubble’s center of mass using an in-house bubble tracking algorithm. Latter enables us to report the change in the distance of the bubble’s center of mass to the orifice surface, until one instant before the bubble pinch-off, and correlate it to its corresponding bubbling regime. By implementing these detachment conditions, we develop a model to estimate the final bubble volume. Finally, using this model we are able to estimate the appropriate operating parameters, e.g. the gas flow rate, and the orifice diameter, in order to achieve the optimal bubble size for enhanced aeration efficiency.

References
1. Zimmerman, W.B., V. Tesař, and H.H. Bandulasena, Towards energy efficient nanobubble generation with fluidic oscillation. Current Opinion in Colloid & Interface Science, 2011. 16(4): p. 350-356.
2. Wang, L.K., N.K. Shammas, and Y.-T. Hung, Advanced biological treatment processes. Vol. 9. 2010: Springer Science & Business Media.
3. Motarjemi, M. and G. Jameson, Mass transfer from very small bubbles—the optimum bubble size for aeration. Chemical Engineering Science, 1978. 33(11): p. 1415-1423.
4. Badam, V., V. Buwa, and F. Durst, Experimental investigations of regimes of bubble formation on submerged orifices under constant flow condition. The Canadian Journal of Chemical Engineering, 2007. 85(3): p. 257-267.