Aeration plays a major role in the activated-sludge wastewater treatment process. Microbes require an oxygen-enriched environment to digest organic materials and remove nutrients from the wastewater stream. For providing this oxygen, artificial aeration is required, which is responsible for the majority of energy use in treatment plants. Typically, this is accomplished with diffused aerators, where pressurized air is forced through a porous medium at the bottom of deep basins, and bubbles float up through the water column transferring oxygen in the process. This is a relatively energy-intensive process, which is prone to fouling at the porous media. In this study, a Confined Tube Aeration (CTA) system is proposed, where a pump forces water through a Venturi injector, where air is naturally drawn in. At the Venturi discharge, the flow is diverted to a coiled tube in which oxygen transfer occurs. This study analyzes the effect of parallelizing the CTA system with multiple injectors and at varying pump speeds (flow rates). Using experimental and analytical means, the maximum standard aeration efficiency was found to be with three injectors in parallel, at maximum pump speed, and utilizing a CTA with diameter 31.75 mm and length 3.048 m. This value was found to be 0.542 kg O2/kWh, and represents a 25% increase over the single-injector case. Although the analyses herein utilize a relatively small (0.746 kW) pump, these results indicate that CTA systems may scale well to larger pump sizes necessary for full-scale municipal wastewater treatment.
Keywords
wastewater
treatment
Venturi
injector
parallel
discrete bubble model (DBM)
SOTR
SAE
Author information
Introduction
In the United States, 1–2% of the total electrical energy demand is consumed by wastewater treatment plants [1]. A significant role is played by aeration, which operates largely by the activated-sludge process and consumes as much as 60% of overall electrical use in a facility [2].
Venturi-type bubble generators have gained much attention for wastewater treatment purposes for their simplistic design and relatively low energy demand [3]. Venturi injectors operate based on Bernoulli’s principle, which states that an increase in the speed of a fluid must cause a decrease in thermodynamic pressure if potential and viscous effects are ignored. As a result, a converging nozzle in the subsonic flow regime will cause velocity to increase and pressure to decrease. If pressure decreases below that of the ambient, air may be naturally drawn into the system and bubbles may break up in the diffuser due to rapid pressure recovery, facilitating mass transfer. Venturi injectors are capable of generating very small bubbles, down to 100 𝜇m or less [4]. They have several advantages over diffused aerators, including ease of installation, no internal moving parts, and lower maintenance requirements.
Most recent research efforts related to Venturi injectors are devoted to examining how the breakup of bubbles occurs, as influenced by various flow conditions and geometric parameters. For example, Martinez et al. [5] discuss the bubble breakup mechanism due to the presence of surrounding water jets. Here, the bubble diameter is shown to be correlated with the dissipation rate of the turbulent kinetic energy of the surrounding liquid. Similar findings were observed in [6], where the bubble breakup mechanism is also shown to correlate positively with an increase in divergent angle. Gabbard [7] generates a dimensionless correlation to predict the bubble diameter as a function of liquid surface tension and Reynolds number, similar to the work of Sun [8]. Gordiychuk [9] utilized different flow parameters, showing that bubble sizes reduce as the liquid flow rate increases and increases with increasing gaseous volume fraction. Suwartha and Syamzida [10] investigate three designs of Venturi bubble generators. Here, smaller bubbles have slower rising velocities and increased contact (mass transfer) time. Additionally, it was found that smaller bubbles could improve mass transfer efficiency and reduce energy consumption. The bubble size is also shown to be dependent on the structural parameters of the injector [11, 12]. In these studies, both divergent angles and throat diameter have shown to effectively decrease bubble size at a constant liquid flow rate; however, inlet pressure, and therefore energy consumption, increases.
More recently, Zeghloul et al. [13] investigate a Venturi injector for the purpose of aeration, where the multiphase flow ejects into a partially submerged vertical downcomer pipe, which showed promise in controlling water aeration as the bubble penetration depth is seen to increase as compared to jet ejection at the surface. Here, a correlation for entrained gas flow is proposed based on the Froude number and the ratio of perforation to throat area. Furthermore, bubble sizes are seen to decrease linearly with liquid flow rates and void fraction, and bubble penetration depth increases with increased liquid superficial velocity. This work builds on several other studies [14, 15] that focus on relating two-phase pressure drop through Venturi meters to flow velocity measurements with varying void fractions.
Much of the aforementioned works, e.g., [4–8, 11, 12, 16], focus on developing a better understanding of the fundamental phenomena responsible for bubble generation in Venturi injectors suited for general chemical processes. A relatively scarce amount, e.g, [3, 9, 10, 13], are concerned with wastewater treatment in general, with few discussing the inherent energy cost in producing bubbles as they relate to effective treatment of wastewater on a large scale. Most recent studies found in the literature focusing on oxygenation of wastewater or aquaculture systems involve experimental analysis of the effect of various system parameters on standard aeration efficiency (SAE); [17] analyze the effect of discharge level, [18] perform a dimensional analysis of the effects of various Venturi geometric properties, and [19] considers the effect of discharge rates on aeration efficiency.
This study focuses on an entirely different application of Venturi injectors for wastewater treatment. Here, mass transfer does not occur as a result of bubbles rising through a column due to buoyancy forces. Instead, oxygenation of the liquid phase occurs in a confined tube, where discharge pressures may be much lower than that required for injection into deep basins, reducing energy consumption. This technique, termed Confined Tube Aeration (CTA), was first introduced by the authors relatively recently [20, 21] with the most recent study focusing on the effects of varying Venturi and CTA diameter [22]. The CTA has many proposed advantages over traditional bubble diffusers, most notably that pumps are used in lieu of compressors or blowers, which require several orders of magnitude more specific energy input. Additionally, there is no requirement for complicated/expensive aeration basins, and membrane fouling is avoided (which may require costly downtime of treatment plants). The device is simplistic in design, requiring only a pump, Venturi injector, and coiled tube, and maintenance may be completed above the surface of the aeration basin. However, as this technology is quite novel, CTA efficiency is still lower than that of state-of-the-art diffused aeration, necessitating the progress outlined herein.
In this study, a CTA system is tested experimentally with multiple injectors arranged in parallel. Each injector discharges water into an aeration tank via a separate CTA, served by a single pump and common header. Performance is assessed while varying pump speeds and CTA diameter from an energy efficiency standpoint. This research is the first of its kind to elucidate the effects of parallelization in conjunction with pump speed on the performance of CTA systems.
Methodology
System setup
The general system setup under examination is shown in Figure 1. It is possible to test when one, two, or three injectors are in parallel and the outlet pipe diameter is easily changed to assess the effects of varying diameter on parallel CTA implementation. The CTA in Figure 1 does not discharge to a single header as this increases the outlet pressure of the injector when multiple injectors are running together, decreasing efficiency. Discharging in separate pipes is also advantageous as it increases the bubble residence time (and hence, mass transfer) when compared with a single discharge header of the same diameter.
Figure 1.
Schematic of the system.
The experimental component in this work is performed to help determine optimal system configuration in conjunction with a discrete bubble model (DBM) analysis, which predicts mass transfer between phases. Both experimental and analytical techniques are explained in detail after a brief treatment of standard performance parameters associated with wastewater aeration.
Performance metrics
There exist multiple theories that explain the process of mass transfer across gas–liquid interfaces. These include the two-film theory [23], the penetration theory [24], and the surface renewal theory [25]. The standard two-film theory assumes that there exist two hypothetical layers or films on either side of the gas–liquid interface, providing resistance to the mass transfer. Nitrogen and oxygen have lower solubility in the liquid phase, and mass transfer resistance is mainly from the liquid side film; gas film resistance is generally ignored. Therefore, oxygen mass flux across the air–water interface can be expressed by the following equation:
where KL is the liquid side mass transfer coefficient, and Cs and CL are the saturation concentration of the oxygen in aqueous phase and aqueous concentration of the oxygen in the bulk liquid, respectively.
For an aeration system, with a tank volume of V, the oxygen transfer rate becomes
where A is the total gas–liquid interfacial area available for mass transfer in the aeration tank. In general, in field testing, it is complex to measure the instantaneous interfacial area. Therefore, the overall volumetric mass transfer coefficient (KLa) is used, which is the composite of two terms, KL and (interfacial area per unit volume of water). Thus, Equation (2) becomes
The saturation concentration Cs is a function of water temperature, barometric pressure, and water salinity. Equation (3) can be integrated from time 0 to t and the concentration from initial concentration C0 to concentration CL at time t can be obtained as follows:
Equation (5) is a linearized form, the slope of which represents the oxygen transfer coefficient KLa at temperature T. In order to compare different aeration systems, aeration tests are conducted in clean water to avoid the complexity and variability of unclean or processed water conditions. The experimentally obtained KLa is usually adjusted to standardized conditions (1 atm, 20 °C) in the following way [26]:
where 𝜃 is the temperature correction factor (1.024 for pure water) and T is the water temperature (°C) during the experiment.
Another performance metric is the standard oxygen transfer rate (SOTR). It is the mass of oxygen transferred per unit time into fresh water at standard conditions and zero dissolved initial oxygen concentration:
The SOTR is usually expressed in terms of lbs O2/hr or kg O2/hr and here, Cs,20 is the saturation concentration of dissolved oxygen at 20 °C.
The oxygen transfer rate per unit of power consumption is known as standard aeration efficiency (SAE). The power input can be either delivered power (DP) or wired power (WP).
Manufacturers usually express SAE in lbs O2/hp⋅hr or kg O2/kWh.
Experimental procedure
Figure 1 shows a simple schematic of the experimental setup under investigation. The main parts of the system are a water tank, a centrifugal pump, and Venturi injectors. The tank’s maximum capacity is 350 gallons (1.325 m3) with a diameter and height of 0.90 m and 2.08 m, respectively. The Venturi injectors used for this study are Mazzei Model 1078, and are 25.4 mm (1 in) in diameter. There are flow meters attached before the injectors at each line, and one flow meter after the pump outlet to determine the total flow rate. All flow rate meters are Lumax LX-1371 accurate to ±0.5%. Each injector line has two pressure gauges (one before and one after the injector), and an additional two pressure gauges measure pump suction and discharge pressure. The pressure gauges utilized in this study are Mouser model MG1-500-A-9V-R with accuracy ±1%. The pump rated power is 0.746 kW (1 hp), and is controlled by a variable frequency drive (VFD). Before the injector, the pipe material used is either PEX or PVC. After the injector, flexible PVC clear vinyl tubing is used, with each line having its own ball valve to control water flow. A ball valve is also attached after the pump’s discharge right before the water flow meter in the main line, and is used to change the water flow rate in the system. An optical dissolved oxygen probe (Vernier ODO-BTA, accuracy ±2%) is used to measure the concentration of oxygen in the aeration tank. An infrared thermometer (Omega OSXL685, accuracy 2%) is used to measure the surface temperature of the water and the outer surface temperature of the aeration tank.
The aeration tank is initially filled with 285 gallons (1.08 m3) of tap water. Before each experiment, the tank is cleaned with tap water, and all valves are opened to prime the pump and remove any air in the piping system. The tank is then covered to prevent surface oxygenation from the ambient air. The dissolved oxygen in the tank is then measured and chemically scavenged using neutral-pH sodium sulfite (Na2SO3) and potassium sulfite (K2SO3) solutions to completely remove all dissolved oxygen. Cobalt chloride (CoCl2) is used as a catalyst to speed up the reaction process, which requires about 30 minutes for each experiment with the pump running in a closed loop, drawing from and discharging to the water tank. Afterwards, air injection ports on the Venturi nozzles are opened, and the reaeration process begins.
Dissolved oxygen probes are used to observe and measure dissolved oxygen concentration during deaeration and reaeration processes. During the reaeration process, the dissolved oxygen in the tank is recorded at 5-minute intervals until levels reach 98% saturation. The injector inlet and outlet pressure, the pump’s inlet and outlet pressure, and the water flow rate in main and parallel lines are monitored throughout each experiment. The temperature of the water is also measured before each test at approximately 18.4 °C, and did not change appreciably during the course of aeration experiments. At this temperature, the saturation level of dissolved oxygen in the tank is 9.4 mg/L [27] at standard atmospheric pressure and zero salinity. For the analysis of SOTR, the saturation concentration of oxygen at 20 °C is taken as 9.1 mg/L.
Three experimental configurations are considered, utilizing one, two, and three injector lines. For each configuration, two system parameters are changed to observe their effects by changing the pump speed or the CTA tube diameter. Thus, a total of 12 experiments are considered (Table 1). Note that the pipe length in each case is 10 ft (3.048 m). Given this constant pipe length, the number of injectors, pipe diameter, and measured flow rate, an equivalent bubble residence time tres can be calculated for each experiment as shown in Table 1. This helps in the discussion of results.
No. of inj.
Pump speed (%)
Pipe diameter (mm)
Injector inlet pressure (kPa g)
Injector outlet pressure (kPa g)
Pump differential pressure (kPa)
Water flow rate (m3/h)
tres(s)
1
100
31.8
146
32.4
144
2.41
3.62
1
100
25.4
148
43.4
147
2.41
2.31
1
80
31.8
112
24.1
112
1.95
4.47
1
80
25.4
110
29.6
112
1.91
2.91
2
100
31.8
89.6
22.1
93.8
3.45
5.05
2
100
25.4
91.0
29.6
93.8
3.36
3.31
2
80
31.8
62.0
17.9
69.6
2.91
5.99
2
80
25.4
62.0
23.4
69.6
2.86
3.89
3
100
31.8
55.2
15.9
65.5
3.93
6.65
3
100
25.4
56.5
22.1
65.5
4.04
4.13
3
80
31.8
46.2
14.5
53.8
3.57
7.32
3
80
25.4
46.9
19.3
51.0
3.57
4.67
Table 1
Experimental conditions.
Numerical methodology
This analysis makes use of the DBM. This method deals with each bubble in the system as a separate entity [28, 29]. Several assumptions are made for the development of the DBM, listed as follows:
Bubbles and water travel at the same velocity.
Bubbles are uniformly distributed in the pipe and are spherical in shape.
Bubble breakup and coalescence are neglected.
Buoyancy effects are ignored.
Mass transfer is calculated only in the CTA piping itself.
In this study, the Friedel [30] correlation is used for calculating frictional pressure drop in the pipe. Whalley [31] suggests the Friedel correlation performs best for analyzing two-phase frictional pressure drop in smooth tubes when . The relationships related to the Friedel correlation are given below:
Liquid properties are represented by the subscript l and gas properties by g. The properties involved are density (𝜌) and viscosity (𝜇) of the liquid and gas with geometric properties of the pipe (diameter, Dpipe and area Apipe). The terms g and 𝜎 stand for gravitational acceleration and surface tension of the liquid, respectively.
In the above equations, fL and fG represent friction factors for liquid and gas flow, respectively, individually in the pipe. The friction factors are dependent on the pipe roughness and Reynolds numbers of liquid and gas flow. For estimating friction factors, Colebrook developed a relationship based on experimental studies of turbulent flow systems. This relationship is valid for both smooth and rough pipes in the turbulent flow regime [32]:
Here, f′ is the friction factor for a single-phase flow, e is the roughness of the pipe, and Re is the Reynolds number.
Reynolds numbers for liquid and gas phases are determined as follows:
Two other parameters are required to determine the two-phase pressure drop: 𝜒 and 𝜖g are the mass quality of gas and the volume fraction of the gas in the liquid, respectively. They are represented by the following equations:
Here, and represent the mass flow rate and the volume flow rate, respectively. The mixture density (𝜌m) is calculated as follows:
The bubble diameter plays a significant role in predicting the mass transfer efficiency of the system. Interfacial mass transfer increases as the bubble size decreases due to the availability of more surface area to transfer mass per unit volume. Lately, the Sauter mean diameter has widely been accepted by researchers to present the average bubble distribution [33, 34]. In the present analysis, the Sauter mean diameter relationship for the Venturi bubble injector developed by Gordiychuk [9] is used. This relationship depends on several parameters, e.g., air inlet size, air-to-water ratio (𝛼), air flow rate, initial air velocity through the suction port, and water flow rate:
For the remainder of this discussion, d32 will be represented as the bubble diameter db. Here, Rew and Reair are the Reynolds numbers of water and air inside the injectors, respectively, noting that the two phases are assumed to have equivalent velocity (denoted by vm for mixture velocity):
In this analysis, the water flow rate is obtained from experimental measurement. However, the air flow rate is obtained from the manufacturer’s data based on the system’s operating conditions.
The oxygen transfer model adopted here is the DBM [29]. This model considers the progression of individual bubbles in the CTA and takes into consideration the change in bubble size due to the changes in pressure and also due to mass transfer (oxygen and nitrogen gas) between gas and liquid phases. Considering the one-dimensional movement of the spherical gas bubbles in the confined pipe system, the species mass transfer equation from the gas–liquid interfaces becomes
In the above equation, the negative sign indicates the species is leaving the gaseous phase. The term Mi stands for the total mass transfer of a species from all the bubbles in the 𝛥x segment. Subscript i represents the gas species (oxygen or nitrogen) in the above equation; Ab is the bubble area, which is for spherical bubbles; KL is the liquid side mass transfer coefficient (m∕s); Cs and Cb are the concentration of the gas species at equilibrium with water at a specific temperature and pressure and the bulk aqueous concentration of the species, respectively. For a bubble moving with the water having an immobile surface, the Sherwood number can be estimated by [35]
The corresponding terms of the above equation, the Schmidt number Sc and the Reynolds number Reb of the bubble in the liquid, are determined from the following equations:
The liquid mass transfer coefficient KL is obtained from Equation (26) using the following relationship, knowing the diffusion coefficient of the gas species:
The diffusion coefficients of oxygen and nitrogen in water at the current experimental temperature 18.4 °C are 1. 9 × 10−9 m2∕s and 1. 8 × 10−9 m2∕s.
The equilibrium concentrations of oxygen and nitrogen species of gas bubbles in the liquid are dependent on Henry’s constant (H, g ⋅ m−3 ⋅ bar−1) and partial pressure of the gas species in the liquid:
Henry’s constant is dependent on the temperature of the liquid and is calculated for oxygen and nitrogen separately using the following relationships [36]:
Since the gas suction rate in the system is constant, the number of bubbles per unit pipe length is fixed. For a segment of length 𝛥x, this number is calculated as follows:
The bubble flux is dependent on the initial bubble volume and the initial gas suction flow rate in the actual experimental condition. For a spherical bubble, the initial bubble volume is calculated as follows:
The manufacturer’s specification sheets provided data for the air suction flow rate at standard conditions, which are converted using ideal gas laws:
where std is the air suction rate at standard temperature (Tstd, 20 °C) and standard pressure (Pstd, 1.013 bar). The actual temperature (Ta) used in this simulation is 18.4 °C and the actual pressure (Pa) is the absolute pressure inside the pipe.
The partial pressure of the gas species is dependent on the pressure acting on the bubble surface in a confined tube system, which is
where PL is the liquid static pressure in the pipe at different points. The gas components’ partial pressure (pi) and mole fractions (yi) are obtained in the following way:
where N is the total number of moles in the gas bubble, and using the ideal gas law,
Here, R is the universal gas constant (8.3145 J ⋅ mol−1 ⋅ K−1). For simulation purposes, Equation (25) is converted to a molar formulation:
where Mw, i is the molecular weight of oxygen (32 g/mol) and nitrogen (28 g/mol) gas. Equation (40) is a first-order differential equation and can be numerically solved using a simple first-order forward Euler scheme. The air composition used for this simulation is diatomic nitrogen (79%) and oxygen (21%).
For the calculation of performance parameter SAE, the hydraulic power developed by the pump is considered for analysis. The fluidic power (DP) is determined as follows:
From the experimental data, 𝛥Ppump across the pump inlet and outlet and the water flow rate Qw are obtained and directly used for the purpose of simulation. A flow chart of the algorithm used for the calculation of mass transfer and performance parameters is presented in Figure 2.
Figure 2.
Flowchart of the simulation procedure.
Results and discussion
This section discusses the experimental results obtained from 1-, 2-, and 3-injector systems, with varying pump speed and CTA diameter. Three performance parameters, namely, the mass transfer coefficient (KLa), oxygen transfer rate (SOTR), and oxygen transfer efficiency (SAE), are used to compare the effectiveness of these systems. With two pump speeds and two pipe diameters considered, four subsequent sections follow, after which performance metrics are discussed. Additionally, while all performance metrics are discussed as presented, the reader may find use in referring to Table 2, where results from subsections may be more easily compared.
Larger diameter piping and full pump speed
The reoxygenation process for a pump running at full speed and with a CTA diameter of 31.8 mm is discussed. Figure 3 compares experimental values to that found using the DBM method. From experimental data, in all test configurations, the dissolved oxygen level monotonically increases with time and reaches an asymptotic value, which corresponds to the saturation level of dissolved oxygen in the aeration tank. The DBM method predicts dissolved oxygen level variations for the three systems effectively. Differences between predicted and experimental values for the 3-injector system are comparatively lesser than the other two configurations. The reason for this is likely due to the limiting assumption of the DBM method, which only accounts for oxygen transfer in the CTA tube and ignores the aeration tank. For the 3-injector system, more oxygen transfer occurs in the CTA tube as the air–water mixture moves more slowly. However, at high fluid flow rates, such as in the 1-injector system, the bubbles travel quickly through the CTA tube, and comparatively more mass transfer occurs in the aeration tank after the multiphase mixture is discharged. It can be observed from Figure 3 that the 3-injector system takes the longest to approach saturation, whereas the 2-injector system reaches saturation much faster.
Figure 3.
Dissolved oxygen concentration variation over time for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at full pump speed with larger CTA pipe diameter.
Figure 4.
Log deficit of oxygen concentration for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at full pump speed with larger CTA pipe diameter.
Figure 4 shows the log deficit of oxygen concentration for all systems as calculated with Equation (5). The green straight line and red circle represent the log-deficit concentration of numerical and experimental data, respectively. The log-deficit concentration is calculated based on the average saturation concentration of oxygen in the aeration tank and the instantaneous concentration of dissolved oxygen as time varies. The negative slope of the linear regression gives the overall mass transfer coefficient KLa (min−1). For 1-injector, 2-injector, and 3-injector systems, the KLa values are 0.069 min−1, 0.0759 min−1, and 0.0633 min−1, respectively, from experimentation. Using the DBM method, KLa values were calculated to be 0.0553 min−1, 0.0709 min−1, and 0.0572 min−1 for 1-, 2-, and 3-injector systems, respectively. Interestingly, the 2-injector system obtained the highest mass transfer coefficient corresponding to a reduced time to reach saturation concentration (Figure 3). This is likely an optimum condition for mass transfer among the systems evaluated as it provides sufficient time for bubbles to transfer oxygen without decreasing the water flow rate significantly. This condition also helps to maintain adequate air suction to occur in the system for the Venturi injectors considered. For the 3-injector system, a significant decrease in water flow rate causes the suction air flow to reduce, which negates the positive effect of the increase in bubble contact time.
Smaller diameter piping and full pump speed
Figure 5 depicts the reoxygenation process for the three systems in question when the pump is running at full speed and with a smaller CTA system diameter (25.4 mm). In this case, all three systems take more time to reach the saturation concentration compared to the larger diameter systems. This is likely due to the reduced time in which any individual bubble comes in contact with water (tres) due to the smaller pipe diameter and higher velocity (see Table 1 for bubble residence times). This is also the reason for the 1-injector system requiring the most time to reach saturation. Again, the difference between predicted and measured dissolved oxygen values is greatest for the 1-injector system for reasons described above. The DBM model again predicts dissolved oxygen levels for the 2- and 3-injector systems more accurately.
Figure 5.
Dissolved oxygen concentration variation over time for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at full pump speed with smaller CTA pipe diameter.
Log-deficit concentration graphs for this condition are shown in Figure 6. The maximum mass transfer coefficient is experimentally observed for the 2-injector system to be 0.0624 min−1. Generally, the mass transfer coefficients for the smaller pipe diameters are less than that observed for the larger pipe diameters (with full pump speed). This is due to a reduced air/water residence time for the smaller diameter pipes (tres; see Table 1).
Figure 6.
Log deficit of oxygen concentration for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at full pump speed with smaller CTA pipe diameter.
Larger diameter piping and reduced pump speed
Here, the VFD-controlled pump is set to 80% speed, and the 1-, 2-, and 3-injector systems are evaluated with the larger (31.8 mm) diameter piping, with results seen in Figure 7. All three systems take longer to reach saturation compared to full speed (see Figure 3). The 1-injector and 2-injector systems reach saturation concentration at a similar time, whereas the 3-injector system requires a slightly longer time. In this case, with pump speed reduced, the water flow rate decreases and therefore, when multiple injector lines are open, the natural suction of the airflow drops. This is why the 2- and 3-injector systems, in this case, are unable to take advantage of the increased bubble contact time with water. The mass transfer coefficient for the 1-injector system is the maximum (0.0482 min−1) between the three systems, seen in the log-deficit plots in Figure 8. This time, the 1-injector system has a higher air suction rate than the other two systems along with a slower water flow rate causing the bubble residence time (tres; see Table 1) to increase. The experimental mass transfer coefficients for the 2-injector and 3-injector systems are 0.0442 min−1 and 0.0399 min−1, respectively. The DBM method predicts mass transfer coefficients more accurately with reduced pump speed since more oxygenation occurs in the piping itself due to a reduced water/air flow rate.
Figure 7.
Dissolved oxygen concentration variation over time for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at reduced pump speed with larger CTA pipe diameter.
Figure 8.
Log deficit of oxygen concentration for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at reduced pump speed with larger CTA pipe diameter.
Smaller diameter piping and reduced pump speed
Lastly, the smaller outlet pipe diameter (25.4 mm) is considered with a reduced (80%) pump speed. Figure 9 shows all three system configurations more slowly approaching saturation compared to all the aforementioned scenarios. This is due to a reduction in air/water residence time, as well as a reduction in suction pressure in the Venturi, as the pipe inlet pressure must increase to force the multiphase flow through the piping at the desired flow rate (thereby reducing pressure differential across the Venturi, which is a main factor in the air suction rate). In this case, the 1-injector system is capable of dissolving oxygen more quickly than the other two systems, reflected in the mass transfer coefficient values depicted in the log-deficit plots of Figure 10. Experimentally, the 1-injector system experiences a KLa of 0.0388 min−1, whereas the 2-injector and 3-injector systems achieve lower values of 0.0367 min−1 and 0.0317 min−1, respectively. The DBM also performs reasonably well in these cases in predicting the dissolved oxygen content and mass transfer coefficients.
Figure 9.
Dissolved oxygen concentration variation over time for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at reduced pump speed with smaller CTA pipe diameter.
Figure 10.
Log deficit of oxygen concentration for (a) 1-injector, (b) 2-injector, and (c) 3-injector systems at reduced pump speed with smaller CTA pipe diameter.
Performance and efficiency metrics
Various performance parameters calculated for different test conditions are discussed. Values of KLa are calculated according to Equation (5) and are used to derive SOTR and SAE values using Equations (6)–(8). Figure 11 compares KLa, SOTR, and SAE for three different system configurations with the pump at full speed and with a CTA diameter of 31.8 mm. From Figure 11a, it is clear that the 3-injector parallel system has the greatest aeration efficiency. Experimentally and computationally, these values are 0.54 kg O2/kWh and 0.49 kg O2/kWh, respectively. The 2-injector system has the second highest SAE at 0.52 kg O2/kWh and the one-injector system achieves 0.43 kg O2/kWh experimentally. Computationally, the same pattern is observed, with values of 0.48 kg O2/kWh and 0.35 kg O2/kWh for the 2-injector and 1-injector systems, respectively.
Figure 11.
Performance (KLa, SOTR, and SAE) parameters for 1-, 2-, and 3-injector systems at full pump speed with (a) larger (31.8 mm) and (b) smaller (25.4 mm) pipe diameters.
When multiple injectors are running in parallel, each aeration line receives an equal amount of volumetric flow rate. Thus, the water velocity is lower than when only one injector is running. This provides more time for the bubbles to stay in the CTA system. Additionally, the total flow rate coming from the pump increases with a decreased total head developed. In this case, for three injectors, the total flow rate is 1.6 times greater than when one injector is running, whereas for two injectors, the total flow rate is increased by 43%. However, the total head developed by the pump drops by 35% and 55% for two and three injectors, respectively, compared to one injector. From this discussion, it can be inferred that without increasing the DP significantly, it is possible to reduce greatly the motive flow velocity by running multiple injectors and thereby provide greater residence time between the bubbles and water.
Figure 11b compares performance parameters with the smaller (25.4 mm) pipe diameter and full pump speed. According to experimental data, the 3-injector system demonstrates the highest SAE of 0.47 kg O2/kWh. The DBM method is also able to predict the trend of increasing SAE with increasing number of injectors. With a smaller outlet pipe diameter, the outlet pressure of the injector increases as seen in Table 1. This causes the suction flow rate to be reduced, and fewer bubbles are generated in the system. Moreover, with a small pipe diameter, the fluid flow velocity increases, resulting in less bubble residence time and hence, less mass transfer. Generally, the smaller pipe diameters cause a reduction in SAE as compared to that seen in Figure 11a.
It is evident from Figure 11 that the 2-injector system achieves the highest mass transfer coefficient both experimentally and computationally. However, this does not necessarily lead to higher aeration efficiency. Equation (23) (which was utilized to calculate bubble size) posits that an increase in water flow rate causes a drop in bubble diameter, which increases the available mass transfer area. Additionally, the liquid side mass transfer coefficient KL is also proportional to the flow velocity, which can be seen in Equations (26) and (29). This suggests that the 1-injector system has the minimum bubble size distribution due to the maximum water flow rate between the three systems. However, this is not enough to offset a reduction in bubble contact time, which is why the 1-injector system does not employ the maximal mass transfer coefficient or SAE (Figure 11a). Conversely, for a smaller diameter, the 3-injector system performs better in terms of mass transfer than the 1-injector system as the smaller diameter causes increased flow velocity and further decreases contact time. As a result, the 2-injector system performs better from a mass transfer efficiency standpoint since it takes advantage of longer bubble contact time without significantly lowering the natural air suction rate due to the reduced flow through each line. The 3-injector system performs relatively poorly due to lower air suction through the injectors (caused by lower water flow rate).
The red bar graph in Figure 11 represents the oxygen transfer rate in standard conditions for full pump speed. The SOTR is the derivative of the mass transfer coefficient KLa; therefore, behavior patterns are similar to that of KLa. The maximal SOTR is observed for the 2-injector system experimentally and numerically, with values tabulated in Table 2. The DBM again is able to predict patterns when comparing the SOTR across the number of injectors and pipe diameter.
Experimental
Analytical
No. of inj.
Pump speed (%)
Pipe diameter (mm)
KLa (hr−1)
SOTR (kg O2 /h)
SAE (kg O2 /kWh)
KLa (hr−1)
SOTR (kg O2 /h)
SAE (kg O2 /kWh)
1
100
31.8
4.28
0.042
0.43
3.43
0.034
0.35
1
100
25.4
2.96
0.029
0.30
2.47
0.024
0.25
1
80
31.8
2.99
0.029
0.48
2.77
0.027
0.44
1
80
25.4
2.41
0.024
0.39
2.18
0.021
0.36
2
100
31.8
4.71
0.046
0.52
4.34
0.043
0.48
2
100
25.4
3.87
0.038
0.43
3.69
0.036
0.41
2
80
31.8
2.74
0.027
0.48
2.61
0.025
0.46
2
80
25.4
2.28
0.022
0.40
2.09
0.020
0.36
3
100
31.8
3.93
0.039
0.54
3.55
0.035
0.49
3
100
25.4
3.50
0.034
0.47
3.24
0.032
0.44
3
80
31.8
2.48
0.025
0.47
2.42
0.024
0.44
3
80
25.4
1.97
0.020
0.38
1.82
0.018
0.35
Table 2
Aeration experimental results.
Figure 12 depicts the performance parameters for the three injector configurations with reduced (80%) pump speed. The 1-injector SAE sees improvement over the full pump speed case (11% and 31% improvement for larger and smaller diameter piping, respectively). This is due to a lower power requirement as well as increased bubble residence time from reduced velocity. At this reduced pump speed, the 1-injector system achieves SAE values close to the 2- and 3-injector systems. This is in contrast to the full pump speed situation, and can be attributed to lower water flow rates for each line, resulting in reduced air suction, interfacial area, and hence, mass transfer. At this reduced pump speed, the maximum experimentally measured SAE was found to be 0.48 kg O2/kWh for both the 1- and 2-injector systems with the larger pipe diameter. Smaller pipe diameter experiments generally resulted in lower SAE values (Table 2). The DBM method is able to better predict performance metrics for the larger pipe diameters for the same reasons as explained above regarding KLa.
Figure 12.
Performance (KLa, SOTR, and SAE) parameters for 1-, 2-, and 3-injector systems at reduced pump speed with (a) larger (31.8 mm) and (b) smaller (25.4 mm) pipe diameters.
The key findings in this study are that aeration efficiency in a CTA system can be increased by parallelizing the system. In this study, 1-, 2-, and 3-injector systems are studied with varying pipe diameter and pump speed, and the highest SAE was found with the larger pipe diameter utilizing three injectors and at full pump speed. It is both possible and likely that SAE could be increased further, utilizing more injectors or larger pipe diameters. However, this may require more pump power to avoid losses in air suction as flow rates decrease with increased parallelization.
Furthermore, while results indicate that SAE generally decreases with decreased pump speed, this may simply be due to the characteristics of the pump chosen for these experiments. Indeed, a larger pump more suitably sized for higher flow rates, running at reduced speed, may increase SAE beyond that seen in this study.
Finally, these results indicate that larger CTA pipe diameters lead to higher SAE values in general, both computationally and experimentally. This is primarily due to a increased bubble residence time in the pipes themselves as well as lower viscous (manifested in pressure) losses incurred in the pipes themselves. However, it is expected that sufficient increases in pipe diameter will incur losses in SAE due to bubble coalescence, which reduces interfacial area. The limiting assumptions inherent in the DBM method outlined in this study are currently unable to predict this phenomenon.
Conclusions
The effects of parallelizing CTA systems are studied in varying pump speed and CTA tube diameters. The following key results are obtained with respect to SAE, SOTR, and mass transfer coefficient (KLa):
Increasing the CTA pipe diameter generally increased SAE, SOTR, and KLa. This is mainly attributed to a higher residence time of bubbles within the system as flow rates decrease in each individual injector line.
Reducing the pump speed generally reduced SAE, SOTR, and KLa. This is primarily due to a reduction in water flow rates into the injectors themselves, which causes a reduction in air suction and hence, interfacial area available for mass transfer.
For full-speed pump operation, the 3-injector system achieved the highest SAE of 0.542 kg O2/kWh, representing a 25% increase over the single-injector case. However, the 2-injector system recorded the highest SOTR and KLa. For the reduced pump speed, the SAE remained relatively consistent during the parallelization of injectors, but SOTR and KLa reduced as parallelization increased.
The results suggest that there is a complex trade-off between a desired increase in water flow rate (which increases air suction and hence, interfacial mass transfer) and an undesired increase in viscous pressure losses through the system. Generally, higher parallelization of injectors and larger piping diameters increase the aeration efficiency in this instance, but future studies are required in order to determine the extent to which improvement may be made. This includes scaling up the experimental apparatus to incorporate a larger pump and piping diameter, utilizing larger Venturi injectors that may reduce viscous losses at the air injection site, and perhaps investigating the alignment of the Venturi injector to more efficiently produce bubbles for CTA oxygenation. A closer inspection and perhaps a redesign of Venturi injectors themselves may be useful to fully take advantage of the novel CTA system outlined in this study.
From a practical standpoint, these results indicate that the CTA concept may scale well with increased motive flow rate and utilizing multiple injectors. Although the SAE values found in this study are not competitive with state-of-the-art bubble diffuser technology utilized in activated-sludge treatment, the CTA design may become immediately useful in remote settings (for example, in lagoon systems) where bubble diffusers are not feasible. Future studies may determine whether CTA systems scaled appropriately may compete with existing technologies for municipal wastewater treatment.
Conflict of interest
The authors declare no conflict of interest.
References
1.
PabiS, AmarnathA, GoldsteinR, ReekieL. Electricity use and management in the municipal water supply and wastewater industries. Palo Alto: Electric Power Research Institute; 2013. 194 p.
2.
MasłońA, CzarnotaJ, SzajaA, Szulżyk-CieplakJ, ŁagódG. The enhancement of energy efficiency in a wastewater treatment plant through sustainable biogas use: Case study from Poland. Energies. 2020;13(22):6056.
3.
AnsariM, BokhariHH, TurneyDE. Energy efficiency and performance of bubble generating systems. Chem Eng Process-Process Intensification. 2018;125: 44–55.
4.
KawamuraT, FujiwaraA, TakahashiT, KatoH, MatsumotoY, KodamaY. The effects of the bubble size on the bubble dispersion and skin friction reduction. Japan: National Maritime Research Institute; 2004.
5.
Martınez-BazánC, MontañésJL, LasherasJC. Bubble size distribution resulting from the breakup of an air cavity injected into a turbulent water jet. Phys Fluids. 2000;12(1):145–148.
6.
HuangJ, SunL, DuM, MoZ, ZhaoL. A visualized study of interfacial behavior of air–water two-phase flow in a rectangular Venturi channel. Theoret Appl Mech Lett. 2018;8(5):334–344.
7.
GabbardC. Development of a Venturi type bubble generator for use in the molten-salt reactor xenon removal system. In: Technical Report. Oak Ridge, TN (United States): Oak Ridge National Laboratory (ORNL); 1972.
8.
SunL, MoZ, ZhaoL, LiuH, GuoX, JuX, Characteristics and mechanism of bubble breakup in a bubble generator developed for a small TMSR. Ann Nucl Energy. 2017;109: 69–81.
9.
GordiychukA, SvaneraM, BeniniS, PoesioP. Size distribution and Sauter mean diameter of micro bubbles for a Venturi type bubble generator. Exp Thermal Fluid Sci. 2016;70: 51–60.
10.
SuwarthaN, SyamzidaD, PriadiCR, MoersidikSS, AliF. Effect of size variation on microbubble mass transfer coefficient in flotation and aeration processes. Heliyon. 2020;6(4):e03748. doi:10.1016/j.heliyon.2020.e03748.
11.
SongY, WangD, YinJ, LiJ, CaiK. Experimental studies on bubble breakup mechanism in a Venturi bubble generator. Annal Nucl Energy. 2019;130: 259–270.
12.
ZhaoL, SunL, MoZ, DuM, HuangJ, BaoJ, Effects of the divergent angle on bubble transportation in a rectangular Venturi channel and its performance in producing fine bubbles. Int J Multiph Flow. 2019;114: 192–206.
13.
ZeghloulA, Al-SarkhiA, GhendourN. Improved aeration using perforated Venturi throat of a vertical liquid jet system with downcomer, Iranian Journal of Science and Technology. Trans Mech Eng. 2024;48(1):163–175.
14.
MessilemA, AzziA, ZeghloulA, SaidjF, BouyahiaouiH, AbdelsalamA-S. Single-and two-phase pressure drop through vertical Venturis. Proc Institution Mech Eng Part C: J Mech Eng Sci. 2020;234(12):2349–2359.
15.
ZeghloulA, MessilemA, GhendourN, Al-SarkhiA, AzziA, HasanA. Theoretical study and experimental measurement of the gas liquid two-phase flow through a vertical Venturi meter. Proc Inst Mech Eng C: J Mech Eng Sci. 2021;235(9):1567–1584.
16.
HuangJ, SunL, DuM, LiangZ, MoZ, TangJ, An investigation on the performance of a micro-scale Venturi bubble generator. Chem Eng J. 2020;386: 120980.
17.
DangeA, WarkhedkarR. An experimental study of Venturi aeration system. Mater Today: Proc. 2023;72: 615–621.
YadavA, KumarA, SarkarS. Effect of varying discharge rate on the performance of Venturi aeration system. Aquac Studies. 2022;22(4):AQUAST824.
20.
MacPheeDW. Confined tube aspiration aeration devices and systems. US Patent. May 3 2022;11: 318–432.
21.
MahmudR, ErguvanM, MacPheeDW. Performance of closed loop Venturi aspirated aeration system: experimental study and numerical analysis with discrete bubble model. Water. 2020;12(6):1637.
22.
MahmudR, CarpenterJ, MacPheeDW. Performance assessment of Venturi-assisted confined tube aerators with varying diameter. Energies. 2024;17(7):1733.
23.
LewisWK, WhitmanWG. Principles of gas absorption. Ind Eng Chem. 1924;16(12):1215–1220.
24.
HigbieR. The rate of absorption of a pure gas into a still liquid during short periods of exposure. Trans AIChE. 1935;31: 365–389.
25.
DanckwertsP. Significance of liquid-film coefficients in gas absorption. Ind Eng Chem. 1951;43(6):1460–1467.
26.
The American Society of Civil Engineers. Measurement of oxygen transfer in clean water. Virginia: American Society of Civil Engineers; 2007.
27.
KuttyM. Site selection for aquaculture: chemical features of water. Port Harcourt: African Regional Aquaculture Center; 1987.
28.
DarmanaD, DeenN, KuipersJ. Detailed modeling of hydrodynamics, mass transfer and chemical reactions in a bubble column using a discrete bubble model,. Chem Eng Sci. 2005;60(12):3383–3404.
29.
DelnoijE, LammersF, KuipersJ, van SwaaijWPM. Dynamic simulation of dispersed gas–liquid two-phase flow using a discrete bubble model. Chem Eng Sci. 1997;52(9):1429–1458.
30.
FriedelL. Improved friction pressure drop correlations for horizontal and vertical two-phase pipe flow. In: European Two-Phase Flow Group Meeting, Ispra, Italy. 1979.
31.
WhalleyPB. Boiling, condensation, and gas–liquid flow. Oxford University Press; 1990.
32.
ColebrookCF, BlenchT, ChatleyH, EssexE, FinniecomeJ, LaceyG, Correspondence. Turbulent flow in pipes, with particular reference to the transition region between the smooth and rough pipe laws (includes plates). J Inst Civil Eng. 1939;12(8):393–422.
33.
ChoY, LaskowskiJ. Effect of flotation frothers on bubble size and foam stability. Int J Miner Process. 2002;64(2):69–80. FROTHING IN FLOTATION III. doi:10.1016/S0301-7516(01)00064-3 URL: https://www.sciencedirect.com/science/article/pii/S0301751601000643.
34.
NajafabadiMM, TabriziHB, AhmadiG. Geometry effect on deposition and residence time of polydisperse fine drops in mini-risers under developing flow condition. Part Sci Technol. 2014;32(6):560–575. doi:10.1080/02726351.2014.933143.
