Correlation coefficients among various operating parameters associated with pressure cycling and the E. coli inactivation efficiency.
\r\n\t
\r\n\tContamination with biomedical waste and its impact on the environment are global concerns. Biomedical waste that has not been collected and disposed in accordance with the regulations can become a total environmental hazard and cause negative impact on human health and the environment. Medical centers including hospitals, clinics, and places where diagnosis and treatment are conducted generate waste that is highly hazardous and put people under risk of fatal diseases. On the other hand, food waste is commonly produced in all the steps of food life cycle, such as during agricultural production, industrial manufacturing, processing and distribution, and is even consumer-generated within private households. Food waste mostly contains high-value components such as phytochemicals, proteins, flavor compounds, polysaccharides, and fibers, which can be reused as nutraceuticals and functional ingredients. Adsorption is a practicable separation method for purification, along with bulk separation where surface characteristics and pore structures are the main properties in determining equilibrium rate. Managing waste materials on the whole is often unsatisfactory, especially in developing countries, and the unreasonable disposal of waste is a major issue worldwide. The following issues will be of particular interest for this book: effects of waste on environment and health, biomedical waste - storage, management, treatment, and disposal, biomedical waste contamination, food waste, potential applications of low-cost sorbents in agricultural and food sectors, biosorbents and bioadsorbents, adsorption of modified agricultural and biological wastes (biosorption), compounds recovered from food waste, and agricultural and food waste-derived sorbents.
For more than a century, chlorination has been the most common method used worldwide for drinking water disinfection. Chlorine and chlorine-based compounds are widely used for the control of waterborne pathogens because of their high oxidizing potential, low cost, and residual disinfectant properties that prevent microbial recontamination. Unfortunately, the chemical reaction between chlorine and organic compounds in water generates carcinogenic agents such as trihalomethanes and halogenic acetic acids [1, 2]. Furthermore, some resistant microorganisms may only be inactivated with very high chlorine doses, which can exacerbate the formation of disinfection by-products (DBPs) [3]. Presently, growing concerns about the potential hazards associated with DBPs have boosted efforts to develop chlorination alternatives. Ozonation is effective at inhibiting a variety of pathogens; however, its disadvantages include the high cost and the potential formation of DBPs such as bromate in seawater [4, 5]. Other water treatment methods such as ultraviolet (UV) radiation, ultrasound, cavitation, or heat application can be used for the inactivation of organisms. Although these methods do not produce DBPs or other problematic chemical residues, they require substantial energy consumption and have high operational costs [5]. Besides, the efficiency of UV disinfection is greatly dependent on water quality because the activity of UV light is substantially decreased by turbidity or organic matter present in water [5].
Sterilization by using pressurized CO2 has been an active research field for decades [6, 7]. CO2 has been used extensively to sterilize dried food and liquid products via a nonthermal sterilization method [8] because of its effectiveness in inactivating microbes, nontoxicity, and low cost [9]. Prior research on high-pressure CO2 treatments has investigated the effects of several factors such as pressure, temperature, type of microorganisms, agitation speed, decompression rate, and pressure cycling on the inactivation capacity of this method [6, 8, 10–15]. Most studies have reported that high-pressure operating conditions (4–50 MPa) are required to inactivate significant numbers of pathogens [7, 9]. Subsequently, certain concerns involving high-pressure operations (i.e., the need for heavy-duty pressure equipment, high initial investment costs, energy consumption concerns, and pressure control and management issues) have hampered the implementation of high pressurized CO2 preservation technology at a large scale within the food industry.
In recent years, pressurized CO2 has shown great potential as a sustainable disinfection technology in water and wastewater treatment applications [16–22] largely because this method does not generate DBPs [9, 22]. Kobayashi et al. [16, 17] employed CO2 microbubbles in the treatment of drinking water and succeeded in inhibiting Escherichia coli within 13.3 min. However, the pressure (10 MPa) and temperature (35–55°C) requirements for effective inactivation [16, 17] are still high from a practical standpoint. Our research group has developed a novel method that uses low-pressure CO2 treatments (0.2–1.0 MPa) based on technology that produces high amounts of dissolved gas in water to inactive bacteria and bacteriophages in freshwater [19–21] and seawater [23, 24]. Cheng et al. [19] suggested that the sudden discharge and resulting reduction of pressure could cause cells to rupture via a mechanical mechanism, and further, that this would be lethal to cells at high levels of dissolved CO2 at 0.3–0.6 MPa and room temperature. Vo et al. [20, 21] demonstrated that acidified water and cellular lipid extraction caused by pressurized CO2 at 0.7 MPa and room temperature were major factors for efficient disinfection within a treatment time of 25 min.
Previous research has shown that pressure cycling is a potential means to improve bacterial inactivation during pressurized CO2 treatments [8–10, 13, 15]; nevertheless, the inactivation mechanism is still unknown for this process. Pressure cycling is defined as a repetitive procedure that involves the decompression and compression of CO2 [9, 10]. Evidence so far suggests that the decompression process may lead to mechanically induced explosive cell ruptures [14], while the compression process may intensify the mass transfer of CO2 across cell membranes [11–13]. In previous works, the pressure cycling procedure has been conducted with high-pressure operations (8–550 MPa) and with CO2 discharges between each cycle of decompression and compression [8, 10, 13, 15]. Despite the good bactericidal performance of pressurized CO2 technology enhanced by pressure cycling [11–13, 15], the high pressure and CO2 release requirements are drawbacks owing to the costly and complex operating procedures. Presently, it is not clear whether pressure cycling with low-pressure CO2 treatments (<1.0 MPa) will enhance the bactericidal activity. Therefore, in this study, we examined the effect of pressure cycling on the bactericidal performance of CO2 at low pressures and with no release of CO2 between each cycle of raised/lowered pressure.
This study investigated the use of pressurized CO2 at less than 1.0 MPa for seawater disinfection applications such as ballast water treatment. Comparisons of E. coli inactivation caused by pressurized CO2 and pressurized air were evaluated in both natural seawater and artificial seawater. The inactivation performance of pressurized CO2 against E. coli was examined for various conditions of pressure, temperature, flow rates, and working volume ratios (WVRs). In particular, the influence of pressure cycling on E. coli inactivation was evaluated. Changes in cell morphology after pressurized CO2 treatment were assessed by scanning electron microscopy (SEM). The research objective was to evaluate the bactericidal effectiveness of pressurized CO2 for disinfecting water, with the goal of addressing the abovementioned emerging problems associated with water disinfection technology.
For pressurized CO2 methods in the field of food preservation, the interaction efficiency between CO2 and pathogens in the foodstuffs is probably limited at low pressures and ambient temperatures, and consequently, high-pressure (4–50 MPa) or ultra-high-pressure (200–700 MPa) conditions are vital for sufficient inactivation. However, to be more attractive in terms of its economic feasibility, pressurized CO2 technology needs to be implemented at lower pressures. In this study, we employed the use of a liquid-film-forming apparatus, which enabled improvements in the interaction efficiency but with lower pressures (<1 MPa) for the water disinfection purposes.
The experimental apparatus for disinfection was a stainless steel chamber with an internal volume of 10 L and pressure tolerance up to 1.0 MPa. The device was designed with a solid stream nozzle and shield to enable vigorous agitation of the influent in such a way that produced liquid films along with fine bubbles (Figures 1–3). The device was supplemented with CO2 pressure prior to the treatments. Sample water was then pumped into the device at high speed through a small nozzle and directed onto the shield. The highly pressurized fluid stream thus collided with the bubble-generating shield. Subsequently, numerous gas bubbles, which were generated from inside the shield, were entrained by the ascending bubbles and overcame the shield; these bubbles then floated into the main chamber (outside the shield). Hence, CO2 transfers took place both within the interior and exterior sides of the thin liquid films. The presence of numerous small bubbles also enhanced the contact area between gas and water and facilitated CO2 dissolution into water. We hypothesized that the available interfacial contact area between CO2 and the cell suspension was greatly multiplied in this setup and that the CO2 transfer efficiency was high. Despite the lower pressures used, the high contact efficiency promoted by this apparatus enabled ample penetration of CO2 into the cell membranes of E. coli.
Apparatus for forming highly dissolved gas in water.
Representative pictures of liquid film formation with various nozzle diameters at a normal pressure in the pipeline.
Pictures of an untreated sample and a CO2-treated sample (the latter contains many small bubbles).
Stock cultures of E. coli (ATCC 11303) were propagated in Luria-Bertani (LB) broth (Wako Chemical Co., Ltd., Osaka, Japan) containing 30 g L−1 sodium chloride and incubated for 24 h at 37°C by using a reciprocal shaker set to rotate at 150 rpm. The initial enumeration was approximately 109–1010 CFU mL−1. The permanent stock was maintained in 20% glycerol at −80°C.
The E. coli inoculum for each disinfection experiment was prepared by inoculating 100 μL of bacterial glycerol stock into 100 mL of LB broth containing 30 g L−1 sodium chloride. The culture was then incubated for 20 h at 37°C with continuous shaking at 150 rpm. Cells were harvested and washed three times with a 0.9% (w/v) saline solution followed by centrifugation (10 min at 8000 g at room temperature) in a CF15D2 centrifuge (Hitachi, Japan). The pellet was re-suspended in 100 mL saline solution.
E. coli were enumerated by using the plate count technique. Briefly, the samples were diluted into a series of 10-fold dilutions by using autoclaved artificial seawater at 3.4% salinity, and 100 μL of either a diluted or an undiluted sample was plated on LB agar (Wako). For samples with a low number of viable cells, 1 mL of the undiluted sample was poured into agar maintained at 45°C. Colonies growing on each plate were counted after incubating the plates overnight at 37°C. Each sample was analyzed in triplicate.
The artificial seawater was prepared by adding artificial sea salt (GEX Inc., Osaka, Japan) to distilled water to obtain a final salinity of 3.4%, as measured with a salinity meter (YK-31SA, Lutron Electronic Enterprice Co., Ltd., Taiwan). As for the preparation of filtered natural seawater, natural seawater (pH = 8.3, salinity 3.3%) was first filtered through a glass fiber filter (GA-100, Advantec, Toyo); then, the seawater was filtered through a membrane filter with a pore size of 0.45 μm (Millipore, Ireland). For all experiments, prepared E. coli cultures were added into the artificial/filtered seawater to obtain a bacterial concentration of 5–6 log10 CFU mL−1. The solution was stirred for 30 min to acclimatize the bacteria before starting the experiments. For each batch mode operation, 12 L of samples were prepared, of which 4–5 L were used to restart the system. The pH and temperature of samples were measured with a pH meter (Horiba D-51, Japan).
Disinfection experiments were conducted in batch mode (Figure 4). Sample water, as the influent, was pumped in one shot into the device. Following the first influx of water, pressurized CO2 was also injected into the main chamber. System pressure was adjusted by a gas pressure regulator and gas exhaust valve. The fluid was then circulated by pumping inside the system for 25 min. A pump was used to apply a higher pressure than that inside the main chamber to accelerate gas solubilization in water. During the treatment period, the outer wall of the device was kept in contact with cool water by using a water jacket to maintain the initial temperature of the sample at ±1.0°C. The treated water was then collected from a bottom valve of the device.
Setup of the water treatment apparatus.
To investigate the effects of pressure and temperature, 7 L of sample were pumped into the main chamber by using a 0.2 kW pump (Iwaya-WPT-202), and the fluid was circulated inside the system at a flow rate of 14 L min−1 (hydraulic retention time, HRT = 0.5 min). The pump was used to apply 0.12 MPa higher pressure than that inside the main chamber. The sensitivity of bacteria to pressurized CO2 treatments under different conditions was determined by varying the CO2 pressure (0.2–0.9 MPa) and seawater temperature (11–28°C) for a 25-min treatment period [23]. Each experiment was conducted in triplicate.
In previous works, the pressure cycling procedure was conducted with high-pressure operations (8–550 MPa) and with CO2 discharges between each cycle of decompression and compression [8, 10, 13, 15]. However, such high pressure and CO2 release are undesirable from an economic standpoint. In order to overcome the above disadvantages, in the present study, we employed a process involving pressure cycling for E. coli inactivation but used lower pressures (<1 MPa) and no discharge of CO2 between each cycle of raised and lowered pressure.
To investigate the effect of pressure cycling, two pumps (0.20 kW, Iwaya-WPT-202, Japan; 0.75 kW, 32 mm × 32 mm SUP-324 M, Toshiba, Japan) and nozzles with various sizes (15 mm height × 4–8 mm diameter) were used to change the flow rate and pressure power of the input (a treatment without a nozzle was also used, whereby the diameter of the pipeline inlet was 15 mm). Pumping pressure and system pressure were measured by pressure gages. The pressure difference ΔP = pumping pressure (MPa) − pressure inside the main chamber (MPa). The water flow rate was measured by a flow meter (GPI, Nippon Flow Cell Co., Ltd., Japan). The recycle number was calculated in relation to the treatment time and HRT, wherein HRT = sample volume/flow rate.
The WVR is defined as the ratio between the sample volume and apparatus volume. To examine the effect of WVR, different sample volumes (5, 6, 7, and 8 L) were used to vary the sample volume ratios (50, 60, 70, and 80%). The experiment was conducted with the following two flow rate levels: 14 and 25 L min−1. The water level was measured by using a gauge to evaluate the effect of WVR on the bubble-generating shield inside the main chamber. The HRT and recycle number were calculated as described in Section 3.4.2.
Changes in cell morphology after pressurized CO2 treatment were assessed by using SEM. The pellets of E. coli were immobilized with 2.5% glutaraldehyde in phosphate buffered saline (PBS) for 3 h at 4°C and then rinsed with PBS three times. Next, the samples were soaked in 1.0% osmium tetroxide in cacodylate buffer for 90 min and then washed three times with cacodylate buffer for removal of the fixative. After fixation, the cells were dehydrated by consecutive soaking in increasing concentrations of ethanol solutions (50, 70, 80, 90, 95, and 100%), and this was followed by an ethanol/t-butyl alcohol (v/v = 1:1) treatment for 30 min. The prepared cells were then soaked in t-butyl alcohol two times for 1 h, freeze-dried for 2 h, and sputter coated with gold-palladium. Finally, the cells were examined by using a scanning electron microscope (QuantaTM 3D, FEI Co., USA) at 20 kV [23].
The statistical analysis was done by using the statistical computer program R (version 3.2.2, available at
where yi represents the predicted responses, xi is a parameter, β0 is the model intercept, and βi is the linear coefficient.
Bactericidal effects of pressurized CO2 in comparison with pressurized air against E. coli in seawater were investigated at three pressure conditions (0.3, 0.7, and 0.9 MPa) and at 20 ± 1°C (Figure 5). In general, the disinfection efficiency of the pressurized CO2 treatment was not different between filtered seawater and artificial seawater. At every operating pressure, the E. coli inactivation efficiency of pressurized CO2 was always higher than that of pressurized air. Approximately 5.4–5.7 log reductions of the E. coli load were achieved within 10–25 min by the pressurized CO2 treatment (this involved complete inactivation of bacterial cells), whereas only 0.4–0.9 log reductions were achieved after 25 min by the pressurized air treatment; these tests involved pressures of 0.3–0.9 MPa (Figure 5a).
Effect of pressurized CO2 and pressurized air on (a) E. coli inactivation and (b) the pH of seawater (SW). Operating conditions: 0.3–0.9 MPa, 20 ± 1°C, and a working volume ratio (WVR) of 70%. Asterisks (*) and (**) indicate that the E. coli load was completely inactivated after 25 and 10 min, respectively.
Pressurized CO2 reduced the pH of both filtered seawater and artificial seawater to around 5.0 after the first few minutes of exposure time, whereas the pH of pressurized air-treated seawater remained around 8.3 during the treatment period (Figure 5b). It has been hypothesized that the decrease in pH caused by pressurized CO2 is probably a major factor driving the bacterial inactivation process [12, 20, 21, 25]. However, Dang et al. [24] demonstrated that the low pH alone is not the main cause of the bactericidal activity. Perhaps with the concomitant presence of pressure and dissolved CO2, the low pH prompted the E. coli cells to become more permeable, thereby stimulating the process of CO2 penetration into the cells [24].
E. coli was disinfected in various pressure conditions (0.2–0.9 MPa) at 20°C (Figure 6). In general, E. coli inactivation significantly increased with increasing pressure, and higher pressures required shorter exposure times to achieve the same log reduction. For example, a treatment application period of 25 min was required to reduce the E. coli load by approximately 5.0 log with pressure applications of 0.2–0.4 MPa, whereas pressure applications of 0.5 and 0.6 MPa resulted in a reduction of the treatment period to 20 and 15 min, respectively. The treatment period was further reduced to 10 min with pressure applications of 0.7–0.9 MPa. However, the increased pressure application from 0.7 to 0.9 MPa did not result in significant increase in the rate of bacterial inactivation. These data indicated that the optimal CO2 pressure for inactivating E. coli was in the range of 0.7–0.9 MPa, and hence, 0.7 MPa was chosen as the optimal pressure condition for effective bactericidal activity [23].
Effect of pressure on E. coli inactivation during the pressurized CO2 treatment at 20 ± 1.0°C and a working volume ratio (WVR) of 70% [23]. Asterisks (*) indicate that no colonies were detected.
The disinfection efficiency of pressurized CO2 substantially increased with increasing temperatures (11–28°C) at 0.7 MPa (Figure 7). The E. coli load was reduced by more than 5.0 log within 25 min of treatment at 11°C, whereas only 20, 12, and 10 min of pressurized CO2 treatment at 15, 18, and 20–28°C, respectively, were required to reduce the E. coli load to a similar extent [23]. Taken together, these findings suggest that E. coli inactivation by pressurized CO2 could be efficiently conducted at low-pressure (0.7 MPa) and ambient temperature conditions. On the other hand, after disinfection and decompression, the pressurized CO2-treated samples were placed at normal conditions to assess the viability of the remaining bacteria. After the 5-d holding period, the number of E. coli in the treated samples had not increased, i.e., no regrowth of bacteria was observed.
Inactivation of E. coli in seawater at various temperatures by using the pressurized CO2 treatment at 0.7 MPa and a working volume ratio (WVR) of 70% [23]. Asterisks (*) indicate that no colonies were detected.
CO2 is lipo-hydrophilic in nature, and it can easily penetrate into the phospholipid bilayer of cell membranes [26]. Thus, the increase in CO2 pressure and temperature may stimulate the diffusion of CO2 into cells and may increase the fluidity of cell membranes [11, 27]. In the present study, the solubility of CO2 into seawater was considerably improved by using the liquid-film-forming apparatus. Hence, we speculate that simultaneous effects of pressure, temperature, and high efficiency of contact with this apparatus may have stimulated the process of CO2 penetration into E. coli cells, thereby accelerating the efficiency of the pressurized CO2 treatment [23].
The effect of pressure cycling on E. coli inactivation was investigated by using various nozzle diameters (4–8 mm) (a treatment without a nozzle was also tested, where the diameter of the pipeline inlet was 15 mm) and two pump powers (0.20 and 0.75 kW) to change both the flow rate and ∆P of the input. The disinfection experiments were conducted under 0.7 MPa of pressurized CO2 at 20 ± 1°C with a WVR of 70% for a duration of 25 min (Figure 8). In general, larger nozzle diameters led to higher flow rates (Figure 8c) and faster fluid recycling in the treatment system (Figure 8d). In contrast, increases in the nozzle diameter reduced the pressure difference ΔP (Figure 8c). Furthermore, at the same nozzle diameter, stronger pumping powers improved not only the flow rate but also the pressure difference ΔP of the input (Figure 8c). At every nozzle diameter, operation of the pump with 0.75 kW of power (Figure 8b) yielded greater inactivation efficiencies than those with 0.20 kW of power (Figure 8a).
Effect of pressure cycling on the inactivation of E. coli in seawater. Effect of (a) 0.20 kW pump power and (b) 0.75 kW pump power along with various nozzle diameters on the inactivation with pressurized CO2. Influence of different pump powers and nozzle diameters on the (c) flow rate and pressure difference ΔP, and (d) the circulation number. Operating conditions: 0.7 MPa, 20 ± 1°C, and a working volume ratio (WVR) of 70% within a duration of 25 min. Asterisks (*) indicate that no colonies were detected.
It is hypothesized that pressure cycling enhances the inactivation efficiency by facilitating the mass transfer of CO2 into bacterial cell membranes [9, 10]. Thus, an increase in water flow rate can be expected to improve the E. coli inactivation. However, our results show that the E. coli inactivation efficiency did not increase with higher flow rates or faster recirculation. When 0.20 kW of pumping power was used (Figure 8a), the length of treatment periods required for complete inactivation of the E. coli load by more than 5.0 log increased with the greater nozzle sizes (i.e., 10 min with the 4-mm nozzle, 15 min with the 5–6-mm nozzles, and 20 min with the 7-mm nozzle, which corresponded to flow rates of 14, 17–19, and 19 L min−1, respectively). Furthermore, the reduction in E. coli load was only 3.0 log after 25 min when the device was operated without a nozzle (flow rate = 20 L min−1). A similar finding was found when the pump was operated at 0.75 kW of power (Figure 8b); at the higher power, more than a 5.0 log reduction was achieved within 5 min with the 5-mm nozzle (flow rate = 21 L min−1), whereas only a 4.0 log reduction was obtained after 25 min in the treatment lacking a nozzle (flow rate = 26 L min−1). These results indicate that the bactericidal performance of pressurized CO2 associated with pressure cycling can probably not be attributed to the flow rate alone.
On the other hand, the disinfection efficiency substantially increased with the higher ΔP (Figure 8). A 5.4 log reduction in E. coli load was achieved within 5 min by the treatment with a ΔP of 0.25 MPa, whereas only a 3.0 log reduction was attained after 25 min by the treatment with a ΔP of 0.05 MPa. When operating the device with the same pump power, as noted above, a larger nozzle diameter resulted in higher water flow rates but weaker ∆P values. Hence, the reduction of ΔP may be considered as a key reason for the phenomenon of low inactivation efficiency at high flow rates. This suggests that the disinfection effect of pressure cycling might be influenced by not only by the frequency of circulation but also by the ΔP.
Noticeably, at the same ΔP value, a faster frequency of circulation substantially augmented the E. coli inactivation efficiency (Figure 8). For instance, at the same ΔP of 0.12 MPa (generated by a 5-mm nozzle and 0.20 kW pump, and a 7-mm nozzle and 0.75 kW pump), the periods required for complete inactivation of E. coli were reduced from 15 to 5 min when the frequency of pressure cycling was raised from 67 cycles/25 min to 92 cycles/25 min, respectively. A similar association between the disinfection efficiency and frequency of pressure cycling was found at ΔP = 0.10 MPa (generated by a 6-mm nozzle and 0.20 kW pump and a 8-mm nozzle and 0.75 kW pump); the associated treatment periods were 15 and 10 min for the recycle numbers corresponding to 71 cycles/25 min and 95 cycles/25 min, respectively. These results affirm the effect of pressure cycling on E. coli inactivation during pressurized CO2 treatment.
Table 1 summarizes the coefficients of correlation for the inactivation efficiency and parameters associated with pressure cycling, including the nozzle diameter (x1), pressure difference ΔP (x2), flow rate (x3), and recycle number (x4). Based on the Pearson matrix correlation results, E. coli inactivation efficiencies were correlated with ΔP values (r = 0.63, p < 0.0001) and recycle numbers (r = 0.66, p < 0.0001). The flow rate showed a weak correlation with the inactivation efficiency (r = 0.09, p = 0.3). Meanwhile, an inverse correlation (r = −0.35, p = 0.0004) was found between the nozzle diameter and disinfection efficiency. These data indicate that operations with a high flow rate, high ∆P value, large recycle number, and small nozzle diameter will yield greater inactivation efficiencies.
Factor | Symbol code | Unit | r | t-statistic | p-value |
---|---|---|---|---|---|
Nozzle diameter | x1 | mm | −0.35 | −3.64 | 0.0004* |
Pressure difference ∆P | x2 | Pa | 0.63 | 8.08 | 1.69e-12* |
Flow rate | x3 | L min−1 | 0.09 | 1.05 | 0.30 |
Recycle number | x4 | cycles | 0.66 | 8.73 | 6.928e-14* |
Correlation coefficients among various operating parameters associated with pressure cycling and the E. coli inactivation efficiency.
*p < 0.05 (significant at the 95% confidence level); df = 98.
Regression coefficients, t-values, and p-values were analyzed for the four factors as shown in Table 2. The outcome of the multicollinearity regression model analysis (R2 = 0.77, p < 0.001) suggests that the model can explain 77% of the inactivation efficiency of E. coli. With bootstrap analysis, the results of multivariate regression analyses were validated. The variables of x1, x2, x3, and x4 that were found to be associated with pressure cycling in the original analyses were significantly associated with pressure cycling in approximately 8, 28, 3, and 37%, respectively, of the 1000 iterations of the multivariate analyses. Taken together, these findings suggest that the frequency of recirculation (x4) and the ∆P magnitude of the input (x2) were key factors that drove the effectiveness pressure cycling.
Source | Coefficient | t-statistic | p-value |
---|---|---|---|
Intercept | −0.63 | −0.99 | 0.33 |
x1 | −0.13 | −3.59 | 0.0005* |
x2 | 0.01 | 7.32 | 7.8e-11* |
x3 | 0.10 | 3.40 | 0.001* |
x4 | 0.05 | 11.29 | <2e-16* |
Regression results showing the influence of operating parameters associated with pressure cycling on the inactivation efficiency (at 20 ± 1°C, system pressure = 0.7 MPa, and working volume ratio (WVR) = 70%).
*Significant at the 95% confidence level; multiple R2 = 0.77; adjusted R2 = 0.76.
F-statistic = 78.77 with 4 and 95 degrees of freedom, p < 2.2e-16.
Although the use of small nozzle diameters was associated with effective inactivation, operating conditions at high ΔP values and low flow rates may be more complex and of lesser economical interest. The highest inactivation efficiency was observed when 5–7 mm nozzle diameters and the 0.75 kW pump were used (Figure 8b). Since a large processing capacity is of great commercial interest, the 7 mm nozzle and 0.75 kW pump were used for subsequent experiments.
The effect of WVR was investigated at four ratios (50, 60, 70, and 80%) by applying a pressure of 0.7 MPa at a temperature of 20 ± 1°C and two flow rates (14 and 25 L min−1) for 25 min (Figure 9). As shown in Figure 9c, decreasing WVR from 80 to 50% resulted in a decrease in the water level (22–11 cm) and a faster frequency of pressure cycling. In regard to pressure cycling, the circulation number increased from 44 to 72 cycles with the flow rate of 14 L min−1, and from 78 to 125 cycles with the flow rate of 25 L min−1.
Effect of the working volume ratio (WVR) on the inactivation of E. coli in seawater by pressurized CO2 at 0.7 MPa and 20 ± 1°C with (a) a flow rate of 14 L min−1 [23] and (b) a flow rate of 25 L min−1. (c) Influence of the WVR on the circulation number and water level in the main chamber. Asterisks (*) indicate that no colonies were detected.
E. coli inactivation efficacy of pressurized CO2 significantly increased with decreases in the WVR (Figure 9). Besides, at every WVR, operations with a high flow rate greatly enhanced the disinfection efficiency. When operating the device with a flow rate of 14 L min−1, an approximate 5.7 log reduction of E. coli was achieved within 15 min at 80% WVR, whereas only 5 min was required at 50% WVR to reduce the E. coli load to a similar extent (Figure 9a; [23]). A similar tendency was found in the case of the 25 L min−1 flow rate (Figure 9b). The durations required for complete inactivation of E. coli were 10 min at 80%, 5 min at 60–70%, and 3 min at 50%.
Pressure cycling boosts the inactivation efficiency by providing a driving force for CO2 transfer efficiency [9–13]. Recall that at the same flow rate and ΔP, a decrease in WVR increased the frequency of pressure cycling. Hence, it is hypothesized that a smaller WVR may have stimulated the CO2 transfer across cell membranes and thus improved the bactericidal performance of pressurized CO2 [11, 28, 29]. In this study, the low inactivation efficiency with a large WVR (i.e., 80%) may be related to the high water level (20–22 cm; Figure 9c), which led to submergence of the shield inside the device; this may have in turn decreased bubble formation via shield interactions [23, 24]. In contrast, the operations with smaller WVRs helped not only to promote a greater efficiency for CO2 bubble generation but also increased the speed of the pressure cycling. Consequently, CO2 supported by the high pressure and high efficiency of interactions in the apparatus easily penetrated into the cell membranes, thereby accelerating the E. coli inactivation efficiency.
Regarding the effect of WVR in pressure cycling treatments, Pearson regression tests showed that E. coli inactivation efficiency was strongly correlated with the recycle number (r = 0.95, p < 0.001). The regression coefficient, t-value, and p-value were analyzed with regard to the recycle number at various WVRs and flow rates (Table 3). According to the regression analysis, the experimental results fit the linear model shown in the following equation:
Coefficients | Estimate | Standard error | t-statistic | p-value | R2 |
---|---|---|---|---|---|
Intercept | 0.736 | 0.195 | 3.77 | 0.0009* | |
x4 | 0.285 | 0.019 | 15.30 | 7.2e-14* | 0.91 |
Regression results showing the influence of pressure cycling on the inactivation efficiency (at 20 ± 1°C, system pressure = 0.7 MPa, ΔP = 0.12 MPa, flow rate = 14 to 25 L min−1, and initial bacterial concentration = 5–6 log10 CFU mL−1).
*95% confidence level.
Here, x4 is the recycle number (cycles), and Y is reduction ratio (−log N/N0) of E. coli caused by pressurized CO2.
As shown in Table 3, the t values of the regression model were positive and significant (p < 0.05), thus indicating that the model result was significant. The outcome of the linear regression model analysis (R2 = 0.91, p < 0.001) suggests that 91% of the variation in the E. coli inactivation efficiency was explained by the frequency of pressure cycling (ΔP = 0.12 MPa, flow rate = 14–25 L min−1). Predicted values of E. coli reduction ratios were calculated based on Eq. (2), and the data are summarized in Table 4 along with the experimental results. The predicted values were fairly similar to the experimental results, thus suggesting that the model could adequately describe the strong relationship between pressure cycling and bactericidal activity (p < 0.05). Taken together, these findings affirm that at the same ΔP, faster pressure cycling can achieve a greater E. coli inactivation efficiency.
Flow rate, L min−1 | HRT, min | Variables | Responses Y: Reduction ratio, −log(Nt/N0) | ||
---|---|---|---|---|---|
WVR, % | x4, cycles | Experimental | Predicted | ||
25a | 0.20 | 50 | 15c | 5.2 ± 0.2 | 5.0* |
25a | 0.24 | 60 | 21d | 5.5 ± 0.0 | 6.4* |
25a | 0.28 | 70 | 18d | 5.3 ± 0.2 | 5.8* |
14b | 0.36 | 50 | 14d | 5.7 ± 0.1 | 4.7* |
14b | 0.43 | 60 | 19e | 5.7 ± 0.0 | 6.1* |
14b | 0.50 | 70 | 20f | 5.7 ± 0.2 | 6.5* |
Validation of model regression for the inactivation efficiency responses to pressure cycling as a function of various working volume ratios (WVRs) and flow rates (at 20 ± 1°C, system pressure = 0.7 MPa, ΔP = 0.12 MPa, and initial bacterial concentration = 5–6 log10 CFU mL−1).
*Predicted values calculated based on Eq. (2).
a, bGenerated by a 7-mm nozzle and 0.75 kW pump, and a 5-mm nozzle and 0.20 kW pump, respectively.
c, d, e, fExposure times were 3, 5, 8, and 10 min, respectively, when bacteria were completely inactivated.
HRT, hydraulic retention time
Dillow et al. [13] reported that an increase of pressure cycling from 3 to 6 cycles using supercritical CO2 (at 20.5 MPa and 34°C) within 0.6 h increased the inactivation from 3 to 9 log reductions. Silva et al. [10] found that an 8.0 log reduction could be achieved with pressure cycling (5 cycles/140 min) and supercritical CO2 at 8 MPa, whereas a 5.0 log reduction was observed with 1 cycle/28 min and 8 MPa. However, high pressure and CO2 discharge are not interesting from both economic and practical viewpoints. As demonstrated in the present study where CO2 discharge was eliminated during the treatment process, pressure cycling at a low pressure (0.7 MPa) is a promising method to enhance the bactericidal activity of pressurized CO2.
Comparative SEM images of untreated samples and samples treated with pressurized CO2 (0.7 MPa and 20°C for a duration of 25 min) revealed changes in the morphology of E. coli cells (Figure 10). The E. coli cells treated with pressurized CO2 presented several small vesicles on the cell surface, and some treated cells appeared to be lysed (Figure 10b); in contrast, the untreated E. coli cells did not have such structures on the surface (Figure 10a) [23]. These results suggest that the pressurized CO2-treated E. coli cells may have been disrupted [19, 20, 23], and that intracellular substance may have leaked out, possibly because of the alterations in cell permeability [20, 23, 30]. The findings also affirm the excellent bactericidal performance of the pressurized CO2 treatment.
Representative scanning electron microscopy (SEM) images of E. coli cells that were (a) untreated and (b) treated by pressurized CO2 at 0.7 MPa and 20°C for a duration of 25 min [23].
Pressurized CO2 treatments can be used to eliminate E. coli from seawater. In this study, the inactivation efficiency was substantially enhanced by pressure cycling, which was conducted at a low pressure (0.7 MPa) and without CO2 release during the treatment period. Bactericidal performance of pressure cycling was concomitantly influenced by two key factors involving the frequency of recirculation and ΔP (p < 0.001). At the same ΔP, an increase in the frequency of pressure cycling significantly improved the E. coli inactivation efficiency (p < 0.001). Additionally, the sensitivity of E. coli to pressurized CO2 treatments substantially increased with increased pressures (0.2–0.9 MPa) and temperatures (11–28°C). Under identical treatment conditions (0.7 MPa, 20°C, 25 L min−1, and 50% WVR), more than 5.0 log reductions in the load of E. coli were achieved after treatments for 3 min by using pressure cycling (ΔP = 0.12 MPa, 15 cycles). Overall, these findings suggest that pressurized CO2 technology would be feasible for water disinfection applications such as those used in ballast water treatment.
This study was supported by the Ministry of Education and Training of Vietnam under the Ph.D. Program No. 911, Yamaguchi University (Japan), and the Takahashi Industrial and Economic Research Foundation.
Over the past several decades, there has been a large research interest in the problem of single-channel sound source separation. Such work focuses on the task of separating a single mixture recording into its respective sources and is motivated by the fact that real-world sounds are inherently constructed by many individual sounds (e.g., human speakers, musical instruments, background noise, etc.). While source separation is difficult, the topic is highly motivated by many outstanding problems in audio signal processing and machine learning, including the following:
Speech denoising and enhancement—the task of removing background noise (e.g., wind, babble, etc.) from recorded speech and improving speech intelligibility for human listeners and/or automatic speech recognizers
Content-based analysis and processing—the task of extracting and/or processing audio based on semantic properties of the recording such as tempo, rhythm, and/or pitch
Music transcription—the task of notating an audio recording into a musical representation such as a musical score, guitar tablature, or other symbolic notations
Audio-based forensics—the task of examining, comparing, and evaluating audio recordings for scientific and/or legal matters
Audio restoration—the task of removing imperfections such as noise, hiss, pops, and crackles from (typically old) audio recordings
Music remixing and content creation—the task of creating a new musical work by manipulating the content of one or more previously existing recordings
Nonnegative matrix factorization is a process that approximates a single nonnegative matrix as the product of two nonnegative matrices. It is defined by
\n\n\n
\n\n
Each column is an \n
Each row represents a data feature.
\n\n
A column represents a basis vector, basis function, or dictionary element.
Each column is not orthonormal, but commonly normalized to one.
\n\n
A row represents the gain of a corresponding basis vector.
Each row is not orthonormal, but sometimes normalized to one.
When used for audio applications, NMF is typically used to model spectrogram data or the magnitude of STFT data [2]. That is, we take a single-channel recording, transform it into the time-frequency domain using the STFT, take the magnitude or power V, and then approximate the result as \n
This process can be seen in Figure 1 [3], where a two-measure piano passage of “Mary Had a Little Lamb” is shown alongside a spectrogram and an NMF factorization. Notice how W captures the harmonic content of the three pitches of the passage and H captures the time onsets and gains of the individual notes. Also note that \n
NMF of a piano performing “Mary had a little lamb” for two measures with \n\n\nN\nz\n\n\n = 3. Notice how matrix W captures the harmonic content of the three pitches of the passage and matrix H captures the time onsets and gains of the individual notes [3].
This leads to two related interpretations of how NMF models spectrogram data. The first interpretation is that the columns of V (i.e., short-time segments of the mixture signal) are approximated as a weighted sum of basis vectors as shown in Figure 2 and Eq. (2):
\nNMF interpretation I. the columns of V (i.e., short-time segments of the mixture signal) are approximated as a weighted sum or mixture of basis vectors W [3].
The second interpretation is that the entire matrix V is approximated as a sum of matrix “layers,” as shown in Figure 3 and Eq. (3).
NMF interpretation II. The matrix V (i.e., the mixture signal) is approximated as a sum of matrix “layers” [3].
The application of NMF on noisy speech can be seen in Figure 4.
\nApplying NMF on noisy speech.
To estimate the basis matrix W and the activation matrix H for a given input data matrix V, NMF algorithm is formulated as an optimization problem. This is written as:
\nwhere \n
where \n
Popular cost functions include the Euclidean distance metric, Kullback-Liebler (KL) divergence, and Itakura-Saito (IS) divergence. Both the KL and IS divergences have been found to be well suited for audio purposes. In this work, we focus on the case where \n
where \n
This results in the following optimization formulation:
\nSubject to
\nGiven this formulation, we notice that the problem is not convex in W and H, limiting our ability to find a globally optimal solution to Eq. (7). It is, however, biconvex or independently convex in W for a fixed value of H and convex in H for a fixed value of W, motivating the use of iterative numerical methods to estimate locally optimal values of W and H.
\nTo solve Eq. (7), we must use an iterative numerical optimization technique and hope to find a locally optimal solution. Gradient descent methods are the most common and straightforward for this purpose but typically are slow to converge. Other methods such as Newton’s method, interior-point methods, conjugate gradient methods, and similar [4] can converge faster but are typically much more complicated to implement, motivating alternative approaches.
\nThe most popular alternative that has been proposed is by Lee and Seung [1, 5] and consists of a fast, simple, and efficient multiplicative gradient descent-based optimization procedure. The method works by breaking down the larger optimization problem into two subproblems and iteratively optimizes over W and then H, back and forth, given an initial feasible solution. The approach monotonically decreases the optimization objective for both the KL divergence and Euclidean cost functions and converges to a local stationary point.
\nThe approach is justified using the machinery of majorization-minimization (MM) algorithms [6]. MM algorithms are closely related to expectation maximization (EM) algorithms. In general, MM algorithms operate by approximating an optimization objective with a lower bound auxiliary function. The lower bound is then maximized instead of the original function, which is usually more difficult to optimize.
\nAlgorithm 1 shows the complete iterative numerical optimization procedure applied to Eq. (7) with the KL divergence, where the division is element-wise,\n
Algorithm 1 KL-NMF parameter estimation
\nProcedure KL-NMF (\n
\n\n
Initialize:\n
repeat
\nOptimize over W
\nOptimize over H
\nuntil convergence
\nreturn: W and H
\nNMF is an optimization technique using EM algorithm in terms of matrix, whereas probabilistic latent component analysis (PLCA) is also an optimization technique using EM algorithm in terms of probability. In PLCA, we are going to incorporate probabilities of time and frequency. In the next section, the development of PLCA-based algorithm to incorporate time-frequency constraints is discussed.
\nConsidering this approach, we now develop a new PLCA-based algorithm to incorporate the time-frequency user-annotations. For clarity, we restate the form of the symmetric two-dimensional PLCA model we use:
\nCompared to a modified NMF formulation, incorporating optimization constraints as a function of time, frequency, and sound source into the factorized PLCA model is particularly interesting and motivating to our focus.
\nIncorporating prior information into this model, and PLCA in general, can be done in several ways. The most commonly used methods are by direct observations (i.e., setting probabilities to zero, one, etc.) or by incorporating Bayesian prior probabilities on model parameters. Direct observations do not give us enough control, so we consider incorporating Bayesian prior probabilities. For the case of Eq. (10), this would result in independently modifying the factor terms \n
Given that we would like to incorporate the user-annotations as a function of time, frequency, and sound source, however, we notice that this is not easily accomplished using standard priors. This is because the model is factorized, and each factor is only a function of one variable and (possibly) conditioned by another, making it difficult to construct a set of prior probabilities that, when jointly applied to \n
where \n
Given this model, if we wish to incorporate additional information, we could independently modify:
\n\n
\n\n
\n\n
This way of manipulation allows us to maintain our factorized form and can be thought of as prior-based regularization. If we would like to incorporate additional information/regularization that is a function of all three variables z, f, and t, then we must do something else. The first option would be to try to simultaneously modify all factors together to impose regularization that is a function of all three variables. This is unfortunately very difficult—both conceptually difficult to construct and practically difficult to algorithmically solve.
\nThis motivates the use of posterior regularization (PR). PR provides us with an algorithmic mechanism via EM to incorporate constraints that are complementary to prior-based regularization. Instead of modifying the individual factors of our model as we saw before, we directly modify the posterior distribution of our model. The posterior distribution of our model, very loosely speaking, is a function of all random variables of our model. It is natively computed within each E step of EM and is required to iteratively improve the estimates of our model parameters. In this example, the posterior distribution would be akin to \n
The framework of posterior regularization, first introduced by Graca, Ganchev, and Taskar [9, 10], is a relatively new mechanism for injecting rich, typically data-dependent constraints into latent variable models using the EM algorithm. In contrast to standard Bayesian prior-based regularization, which applies constraints to the model parameters of a latent variable model in the maximization step of EM, posterior regularization applies constraints to the posterior distribution (distribution over the latent variables, conditioned on everything else) computation in the expectation step of EM. The method has found success in many natural language processing tasks, such as statistical word alignment, part-of-speech tagging, and similar tasks that involve latent variable models.
\nIn this case, what we do is constrain the distribution q in some way when we maximize the auxiliary bound \n
where \n
Note, the only difference between Eq. (12) and our past discussion on EM is the added term \n
This method of regularization is in contrast to prior-based regularization, where the modified maximization step would be
\nwhere \n
Given the general framework of posterior regularization, we need to define a meaningful penalty \n
where q and \n
We then apply our linear grouping constraints independently for each time-frequency point:
\nwhere we define \n
To solve the above optimization problem for a given time-frequency point, we form the Lagrangian
\nWith \n
set Eqs. (17) and (18) equal to zero, and solve for \n
where exp{} is an element-wise exponential function.
\nNotice the result is computed in closed form and does not require any iterative optimization scheme as may be required in the general posterior regularization framework [9], minimizing the computational cost when incorporating the constraints. Also note, however, that this optimization must be solved for each time-frequency point of our spectrogram data for each E step iteration of our final EM parameter estimation algorithm.
\nNow knowing the posterior-regularized expectation step optimization, we can derive a complete EM algorithm for a posterior-regularized two-dimensional PLCA model (PR-PLCA):
\nwhere \n
Algorithm 2 PR-PLCA with linear grouping expectation constraints
\nProcedure PLCA (
\n\n\n
\n\n
\n\n
\n\n
)
\nInitialize: feasible \n
repeat
\nExpectation step
\nfor allz, f, tdo
\nend for
\nMaximization step
\nfor allz, f, tdo
\nend for
\nuntil convergence
\nreturn:\n
Multiplicative Update Equations
We can rearrange the expressions in Algorithm 2 and convert to a multiplicative form following similar methodology to Smaragdis and Raj [12].
\nRearranging the expectation and maximization steps, in conjunction with Bayes’ rule, and
\nwe get
\nRearranging further, we get
\nwhich fully specifies the iterative updates. By putting Eqs. (30) and (31) in matrix notation, we specify the multiplicative form of the proposed method in Algorithm 3.
\nAlgorithm 3. PR-PLCA with linear grouping expectation constraints in matrix notation
\nProcedure PLCA (
\n\n\n
\n\n
\n\n
\n\n
)
\nInitialize:\n
Precompute:
\nFor all s do
\nEnd for
\nRepeat
\nFor all s do
\nEnd for
\nuntil convergence
\nreturn: W and H
\nOver the past several years, research has been carried out in single-channel sound source separation methods. This problem is motivated by speech denoising, speech enhancement [13], music transcription [14], audio-based forensic, and music remixing. One of the most effective approach is nonnegative matrix factorization (NMF) [5]. The user-annotations can be used to obtain the PR terms [15]. If the number of sources is more, then it is difficult to identify sources in the spectrogram. In such cases, the user interaction-based constraint approaches are inefficient.
\nIn order to avoid the previous problem, in the proposed method, an automatic iterative procedure is introduced. The spectral components of speech and noise are modeled as Gamma and Rayleigh, respectively [16].
\nLet noisy speech signal x[n] be the sum of clean speech s[n] and noise d[n] and their corresponding magnitude spectrogram be represented as
\nwhere f represents the frequency bin and t the frame number. The observed magnitudes in time-frequency are arranged in a matrix X\n
where R denotes the number of latent components.
\nThere are several ways to incorporate the user-annotations into latent variable models, for instance, by using the suitable regularization functions. For expectation maximization (EM) algorithms, posterior regularization was introduced by [9, 11]. This method is data dependent. This method gives richness and also gives the constraints on the posterior distributions of latent variable models. The applications of this method is used in many natural language processing tasks like statistical word alignment, part-of-speech tagging. The main idea is to constrain on the distribution of posterior, when computing expectation step in EM algorithm.
\nThe prior distributions for the magnitude of the noise spectral components are modeled as Rayleigh probability density function (PDF) with scale parameter\n
The above equation can be written as
\nBy applying negative logarithm on both sides of (41), we will get
\nThen, the regularization term for the noise is defined as
\nThe spectral components of speech modeled as Gamma probability density function [16, 18]
\nwith shape parameter \n
where the auxiliary variable s is defined as \n
The regularization term for the speech samples is defined as (by applying negative logarithm in both sides of (44))
\nSpecial case: When we fix k = 1, the Gamma density simplifies to the exponential density and
\nThe proposed multiplicative nonnegative matrix factorization method is summarized in Algorithm 4 [16]. In general, like in the specific case of Algorithm 4, one can only guarantee the monotonous descent of the iteration through a majorization-minimization approach [19] or the convergence to a stationary point [20].
\nThe subscript(s) with parenthesis represents corresponding columns or rows of the matrix assigned to a given source. 1 is an approximately sized matrices of ones, and \n
Algorithm 4: Gamma-Rayleigh regularized PLCA method (GR-NMF)
\nProcedure
\n\n\n
\n\n
Repeat
\nFor all s do
\nEnd for
\nFor all s do
\nEnd for
\nReconstruction
\nFor all s do
\nif update k % Gamma model
\nelse % Exponential model
\nk = 1,
\nend
\nEnd for
\nUntil Convergence
\nReturn: Time domain signals \n
The speech and noise audio samples were taken from NOIZEUS [21]. Sampling frequency is 8 KHz. The algorithm is iterated until convergence [16]. The proposed method was compared with Euclidean NMF (EUC-NMF) [5], Itakura-Saito NMF (IS-NMF) [22], posterior regularization NMF (PR-NMF) [15], Wiener filtering [23], and constrained version of NMF (CNMF)[24]. These methods are implemented by considering nonstationary noise, babble noise and street noise. The performance of proposed method was evaluated by using perceptual evaluation of speech quality (PESQ) [25] and source-to-distortion ratio (SDR) [26]. SDR gives the average quality of separation on dB scale and considers signal distortion as well as noise distortion. For PESQ and SDR, the higher value indicates the better performance. Tables 1 and 2 show the PESQ and SDR values of different NMF algorithms evaluated. The experimental results show that proposed method performs better than other existing methods in terms of the PESQ and SDR indices.
\nPESQ and SDR for babble noise.
PESQ and SDR for street noise.
A novel speech enhancement method based on an iterative and regularized NMF algorithm for single-channel source separation is proposed. The clean speech and noise magnitude spectra are modeled as Gamma and Rayleigh distributions, respectively. The corresponding log-likelihood functions are used as penalties to regularize the cost function of the NMF. The estimation of basis matrices and excitation matrices are calculated by using proposed regularization of multiplicative update rules. The experiments reveal that the proposed speech enhancement method outperforms other existing benchmark methods in terms of SDR and PESQ values.
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\n\nPolicy last updated: 2018-09-11
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