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Archive for April, 2013
Ultrapen PT1 – Product Overview Video
TweetLearn about the Myron L Company Ultrapen PT1. In this video, we will give an overview of the Ultrapen PT1 instrument and its functions. The Ultrapen PT1 package includes: – the PT1 Pocket Tester Pen with battery installed – Measurement Scoop – Pocket Clip – Holster – Lanyard – and Operating Instructions The Ultrapen PT1 […]
Learn about the Myron L Company Ultrapen PT1.
In this video, we will give an overview of the Ultrapen PT1 instrument and its functions.
The Ultrapen PT1 package includes:
– the PT1 Pocket Tester Pen with battery installed
– Measurement Scoop
– Pocket Clip
– Holster
– Lanyard
– and Operating Instructions
The Ultrapen PT1 has 5 different modes of measurement. These include:
– Cond KCl mode, which measures Conductivity for potassium chloride solutions, the measurement is displayed as microsiemens (μS). This is the standard mode for most conductivity applications.
– TDS 442 mode, which measures Total Dissolved Solids for Natural Water solutions, the measurement is displayed as parts per million (ppm).
– TDS NaCl mode, which measures TDS for sodium chloride solutions, the measurement is displayed as ppm.
– SALT 442 mode, which measures Salinity for Natural Water solutions, the measurement is displayed as parts per thousand (ppt).
– SALT NaCl mode, which measures Salinity for sodium chloride solutions, the measurement is displayed as parts per thousand (ppt)
A few other specifications we'd like to highlight are the Durable, Fully Encapsulated waterproof Electronics with Automatic Temperature Compensation and Autoranging measurements. The instrument casing is made out of anodized aluminum, so it can withstand harsh environments.
– The PT1 has an Accuracy of ±1% of READING designed to give Reliable Repeatable Results.
-The PT1 also has a measuremante range of 1 – 9,999 for Conductivity, TDS, and Salinity. with a temperature range of 32 – 160 degrees F
With all of the different functions and durable design, this small pen can handle a variety of applications. Now that you are a bit more familiar with the PT1, we will demonstrate how to use its various functions.
All the functions of the PT1 are activated by pressing the button on the end.
If you press the button and release, it will turn on and prepare to take a reading in the last mode that you used.
The screen will display the software version, then the Measurement mode, then the red light will begin blinking rapidly, this is when you will need to submerge the pen to get the measurement. Wehn the light blinking slows, this is when the pen is measuring the solution. When the red light stays red, the measurement is complete and will display on the screen.
To switch modes, press and release the button to turn it on, then simply press and hold the button to cycle through the different menu options. As the options change, just release the button to select that menu option. The screen will ask you to 'Push and Hold' the button to confirm your choice. If there are sub-menu options (such as choosing the Solution Selection menu), then as you hold the button, it will continue to cycle through those sub-menu options. Again, you simply release the button to choose one. Then push and hold to confirm. the PT1 will show 'SAVED' on the display.
These are the different menu options:
– Calibration mode (shown as CAL)
– Solution selection mode (shown as SOL SEL)
– Factory calibration mode (shown as FAC CAL)
– Temperature selection mode (shown as C* F* temp)
– Escape mode for exiting the menu (shown as ESC)
To calibrate the Ultrapen PT1, attach the measurement scoop to the end of teh pen. Then rinse it three times with your calibtaion solution.
Start the calibration by pressing and releasing the button to turn it on, then simply press and hold the button to cycle through the different menu options. Release the button when 'CAL' is shown. When the red light starts blinking rapidly, pour the calibration soution into the scoop and wait. The pen will read teh solution and automatically adjust the calibration settings based on teh reading. This assumes you are using calibration solution that is not contaminted or expired and that your scoop and measuremetn cell is clean.
A quick tip when using theinstrument:
– When submerging the pen for measurements, make sure there are not bubbles trapped on the cell epectrodes by tapping it to knock them free.
If you have more questions about this instrument, please submit them in the reviews section on the product details page at MyronLMeters.com.
Peat Water Treatment Using Combination of Cationic Surfactant Modified Zeolite, Granular Activated Carbon, and Limestone
Tweet MyronLMeters.com attempts to provide its customers with the latest in water quality research and industry updates. Find more at https://www.myronlmeters.com/. Abstract This research was conducted essentially to treat fresh peat water using a series of adsorbents. Initially, the characterization of peat water was determined and five parameters, including pH, colour, COD, turbidity, and iron ion […]
MyronLMeters.com attempts to provide its customers with the latest in water quality research and industry updates. Find more at https://www.myronlmeters.com/.
This research was conducted essentially to treat fresh peat water using a series of adsorbents. Initially, the characterization of peat water was determined and five parameters, including pH, colour, COD, turbidity, and iron ion exhibited values that exceeded the water standard limit. There were two factors influencing the adsorption capacity such as pH, and adsorbent dosages that were observed in the batch study. The results obtained indicated that the majority of the adsorbents were very efficient in removing colour, COD, turbidity at pH range 2-4 and Fe at pH range 6-8. The optimum dosage of cationic surfactant modified zeolite (CSMZ) was found around 2 g while granular activated carbon (GAC) was exhibited at 2.5 g. In column study, serial sequence of CSMZ, GAC, and limestone showed that the optimal reduction on the 48 hours treatment were found pH = 7.78, colour = 12 TCU, turbidity = 0.23 NTU, COD = 0 mg/L, and Fe= 0.11 mg/L. Freundlich isotherm model was obtained for the best description on the adsorption mechanisms of all adsorbents.
Keywords: cationic surfactant modified zeolite, granular activated carbon, limestone, peat water
1. Introduction
Water is essential and fundamental to all living forms and is spread over 70.9% of the earth's surface. However, only 3% of the earth's water is found as freshwater, of which 97% is in ice caps, glaciers and ground water (Bhatmagar & Minocha, 2006). In Malaysia, more than 90% of fresh water supply comes from rivers and streams. The demand for residential and industrial water supply has grown rapidly coupled with an increase in population and urban growth (WWF Malaysia, 2004). Water demand in affected populations such as rural areas also demands that attention is paid to providing more sustainable solutions rather than transporting bottled water (Loo et al., 2012). For this reason, it is essential to ensure availability of local sources of water supply and even develop new potential sources of water such as from peat swamp forest to overcome future water shortages.
River water surrounded by peat swamp forest is defined as peat water and is commonly available as freshwater since it has a low concentration of salinity. The previous study shows that peat swamp forest has high levels of acidity and organic material depending on its region and vegetation types (Huling et al., 2001). Under natural conditions, tropical peat lands serve as reservoirs of fresh water, moderate water levels, reduce storm-flow and maintain river flows, even in the dry season, and they buffer against saltwater intrusion (Wosten et al., 2008).
Due to the acidity and high concentration of organic material, selective treatment of peat water must be conducted prior to its use as water supply. Recently, many methods have been designed and have proven their effectiveness in treating raw water such as coagulation and flocculation (Franceschi et al., 2002; Liu et al., 2011; Syafalni et al., 2012a), absorption (Ćurković et al., 1997), filtration (Paune et al., 1998) and combining (Hidaka et al., 2003). Careful consideration of the most suitable method is important to ensure that the adsorption process is the most beneficial, economically feasible method as well as easy to operate for producing high quality of water in a particular location.
Many researchers have shown that activated carbon is an effective adsorbent for treating water with high concentrations of organic compounds (Eltekova et al., 2000; Syafalni et al., 2012b). Its usefulness derives mainly from its large micropore and mesopore volumes and the resulting high surface area (Fu & Wang, 2011). However, its high initial cost makes it less economically viable as an adsorbent. Low cost adsorbent such as zeolite nowadays has been explored for its ability in many fields especially in water treatment. Natural zeolite has negative surface charge which gives advantages in absorbing unwanted positive ions in water such heavy metal. These ions and water molecules can move within the large cavities allowing ionic exchange and reversible rehydration (Jamil et al., 2010). The effectiveness of zeolite has been improvised by modified zeolite with surfactant in order to achieve higher performance in removing organic matter (Li & Bowman, 2001). Among tested cationic surfactants, hexa-decyl-tri-methyl ammonium (HDTMA) ions adsorbed onto adsorbent surfaces are particularly useful for altering the surface charge from negative to positive (Chao & Chen, 2012). Surfactant modified zeolite has been shown to be an effective adsorbent for multiple types of contaminants (Zhaohu et al., 1999).
Zeolite is modified to improve its capability of exchanging the anion by cationic surfactants, called CSMZ. CSMZ adsorbs all major classes of water contaminants (anions, cations, organics and pathogens), thus making it reliable for a variety of water treatment applications (Bowman, 2003). Nowadays, interest in the adsorption of anions and neutral molecules by surfactant modified zeolite has increased (Zhang et al., 2002). Modification of zeolite by surfactant is commonly done by cationic or amphoteric surfactants. By introducing surfactant to the zeolite, an organic layer is developed on the external surfaces and the charge is reversed to positive (Li et al., 1998). However, the present study used zeolite that had been modified using Uniquat (QAC-50) as cationic surfactant (CSMZ) and their performance towards the removal of color, COD, turbidity and iron ion from peat water were investigated.
2. Materials
Four adsorbents were used in these experiments which are natural zeolite, zeolite modified by cationic surfactant, activated carbon and limestone. All adsorbents were prepared with equivalent sizes of 1.18 mm – 2.00 mm. Hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used for polishing zeolite during the preparation phase and for pH adjustment of the sample. Furthermore, potassium dichromate (K2CrO7), silver sulphate (Ag2SO4), sulphuric acid (H2SO4) and mercury (II) sulphate (HgSO4) were used as digestion solution reagents and acid reagents for COD analysis. Lastly, Uniquat (QAC-50) was used as cationic surfactant to modify the zeolite.
2.1 Preparation of Surfactant Modified Zeolite
In these studies, 100 g of prewashed natural zeolite was contacted with 5.6 ml/l Uniquat (QAC-50) as cationic surfactant (CSMZ). The mixture was then stirred at room temperature for 4 hours at 300 rpm (Karadag et al., 2007). The zeolite then was filtered and washed with distilled water several times. After that, the absorbent was dried in an oven at a temperature of 105 °C for 15 hours.
2.2 est Procedures
2.2.1 Batch Studies
Serial batch studies were conducted at room temperature (28 ± 1 °C) to investigate the influence of pH and dosage for removing colour, COD, turbidity and iron ion from peat water. Shaking speed of 200 rpm for 20 minutes were fixed and operated respectively. A working volume of 150ml peat water sample was set up in 250 ml conical flasks. Preceding the batch studies, initial concentration for those parameters was determined. The optimum pH and dosage of absorbent were determined. Subsequently, the percentage of removal was finally determined, plotted, and compared.
2.2.2 Batch Column Studies
Column studies were carried out using a plastic column with dimensions: 5.4 cm diameter and 48 cm length. Three adsorbents were filled inside the column at a specific depth with the supporting layers of marbles, cotton wool, and perforated net. Total volume of 2000 ml peat water was pumped in the up flow mode from the vessel into the column by using a Masterflex peristaltic pump at a minimum flow rate of (30, 60, 90) ml/min. In this study, however, column studies were performed un-continuously (batch) due to limitations of time. All parameters related to the column design are summarized in the following Table 1.
Table 1. Column studies parameters
|Horizontal Surface Area, A
|Column volume, V
|Flowrate, Q
|30, 60, 90
|Surface Loading Rate, SLR= Q/A
|1.31, 2.62, 3.93
The serial sequence arrangements of adsorbents were conducted as shown in Figure 1 below. Effluent samples were collected at various time intervals, whilst maintaining room temperature, and analysed.
Figure 1. Schematic diagrams of lab-scale column studies
3. Results and Discussion
3.1 eat Water Characterization
Surface water originating from the peat swamp forest was taken from Beriah peat swamp river along the Kerian River on several occasions as the main sample. The characterization of peat water was carried out at the sampling point (in-situ measurement) using a multi-parameter probe as well as in the environmental laboratory of civil engineering, USM. Fundamentally, the characterization procedures were based on the Standard Methods for the Examination of Water and Wastewater (APHA, 1992). Table 2 represents the peat water characteristics in average value and the comparison to the standard drinking water quality in Malaysia.
Table 2. The characteristics of peat water sample from Beriah Peat Swamp Forest
|Average Value
|4.67 – 4.98
|Iron, (Fe)
Thirteen parameters were successfully determined where the first six parameters, including pH, temperature, TDS, DO, conductivity, and salinity were measured at the sampling point, whilst the rest of the parameters, including colour, turbidity, COD, iron ion, Ammoniacal Nitrogen, NH3-N, Ammonia (NH3), and Ammonium (NH4+) were examined from the sample brought to the environmental laboratory on the same day.
Acidic pH of the peat water was predicted due to the composition of the surrounding peat soil itself which had been formed by decaying material possessing humic substances (Rieley, 1992). Besides that, humic substances also lead to the high organic content as humic substances are comprised of numerous oxygen containing functional group and fractions (humic acid, fulvic acids and humin) with different molecular weights which mean yielding high concentration of turbidity and COD as well as coloured water (Torresday et al., 1996). Moreover, composition of peat soil may also have an impact on the iron ion concentration of peat water (Botero et al., 2010).
From the thirteen parameters, five parameters were indicated exceeding the standard limit. These parameters were pH, colour, turbidity, COD, and iron ion that showed values of 4.67 – 4.98, 224.7 TCU, 20.8 NTU, 33.3 mg/l, and
1.24 mg/l respectively while the standard limit of these parameters are 6.5 – 9.0, 15 TCU, 5 NTU, 10 mg/l, and 0.3 mg/l accordingly.
3.2 Effect of Initial pH on the Efficiency of Colour, COD, Turbidity, and Iron Ion (Fe) Removal
Influence of initial pH on the adsorption capacity for removing colour, COD, turbidity, and iron ion were investigated.
Figure 2(a) to Figure 2(d) below, displayed the percentage removal of colour, COD, turbidity, and iron ion against pH of adsorbents respectively.
Figure 2(a) shows the maximum removal percentage of colour that was removed by natural zeolite, CSMZ, and granular activated carbon (GAC) which were 79%, 90%, 82% respectively. This adsorption is depended on the characteristic of adsorbents itself. For zeolite and CSMZ were related to the amount of cationic ions (Al3+) increased, resulting in high reaction activity and GAC was related to the adsorption capacity. It was observed that the adsorption capacity was highly dependent on the pH of the solution, and indicated that the colour removal efficiencies decreased with the increase of solution pH.
The pH of the system exerts profound influence on the adsorptive uptake of adsorbate molecules presumably due to its influence on the surface properties of the adsorbent and ionization or dissociation of the adsorbate molecule. Figure 2(b) represents the percentage removal of natural zeolite and CSMZ where they reach optimum efficiency in removing organic compound (COD) at pH 2 with efficiency of 53% and 60% respectively. Meanwhile, the highest percentage removal of COD for GAC was achieved at pH 4 with efficiency obtained about 61%. Identical trends in colour removal were exhibited in percentage removal of COD for natural zeolite, CSMZ and GAC. In fact, this result also reveals that GAC has the highest percentage removal among natural zeolite and CSMZ yet optimum in difference pH solution. Neutralization mechanism occurs in low pH makes color removal, COD removal and Turbidity removals at pH 2 are higher for most of absorbents in this process.
In Figure 2(c), percentage turbidity removal against pH for each adsorbent revealed that optimal reduction of turbidity was obtained in an acidic environment with efficiency removal of 96%, 98%, 95% for natural zeolite, CSMZ, and GAC respectively. When the pH of the solution was adjusted above pH 6 to pH 12, the tendencies of all adsorption performances were gradually decreased. Moreover, it also showed that the lowest efficiency for the three adsorbents were identified at pH 12 with percentage values removal 55%, 61%, and 59% for natural zeolite, CSMZ, and GAC respectively.
Figure 2(d) demonstrates the removal efficiencies of iron ion as a function of the influent pH. The maximum removal of iron ion was observed at pH 8 for both natural zeolite and CSMZ whereas GAC had its optimum removal at pH 6. Natural zeolite and CSMZ only yielded 73% and 62% removal efficiency while GAC had more significant removal with removal efficiency of 80% to the iron ion concentration. Further, it is evident from the graph that gradual increment of removal efficiency for natural zeolite, CSMZ, and GAC occurred when the initial pH of the solution was increased to higher values. Somehow, at pH values greater than 6 the removal efficiency of GAC reduced slightly while for natural zeolite and CSMZ the reduction occurred from pH values above 8.
3.3 Effect of Adsorbent Dosage on the Efficiency of Colour, COD, Turbidity, and Iron Ion (Fe) Removal
The effect of adsorbent dosage was studied for all adsorbents employed on colour, COD, turbidity, and iron ion removal by varying the dosage of adsorbent and keeping all other experimental conditions constant. The pH was set to acidic conditions which were most favourable in obtaining the highest removal efficiency. In this study, to find optimal adsorbent dosage of natural zeolite and CSMZ, the appropriate experiments were carried out at adsorbent dosages in the range of 0.5 g to 5.0 g while for GAC, the adsorbent dosage was varied from 0.01 g to 4.0
- The experimental results for all the adsorbents are represented by Figure 3(a) to Figure 4(d).
Figure 3. Percentage of color (a), COD (b), turbidity (c), and Fe (d) removal against pH for NZ, and CSMZ
Figure 3(a) displays the relationship between the amount of adsorbent mass (dosage) and adsorption efficiency for natural zeolite and CSMZ in terms of removing colour. The colour removal of peat water increased from about 25% to 52% with increasing adsorbent dosage of natural zeolite from 0.5 g to 3.5 g whereas for CSMZ, removal percentage increased from 41% to 53% with increasing adsorbent dosage from 0.5 g to 2.0 g. However, further increase in adsorbent dosage to 5.0 g only led to slight degradation of removal efficiency to 50% and 41% for natural zeolite and CSMZ respectively. This degradation with further increases in adsorbent dosage was due to the unsaturated adsorption active sites during the adsorption process since the adsorbates in the vessel were only shaken for 20 minutes (insufficient time). Besides, modification of zeolite by cationic surfactant had proven to have better colour removal as presented in the graph.
Percentage removal of COD against the adsorbent dosage is shown in Figure 3(b). It was observed that the highest percentage removal for both natural zeolite and CSMZ to remove COD were 51% and 59%, achieved at adsorbent dosage 3.5 g and 2.0 g respectively.
The variations in removal of turbidity of peat water at various system pH are shown in Figure 3(c). The removal rate of turbidity was highest at the adsorbent dosage of 0.5 g with 70% and 93% removal efficiency for respective natural zeolite and CSMZ. The removal rate showed a smooth downward trend with the increase in adsorbent dosage. Concurrently, the adsorption capacity gradually decreased with the increasing adsorbent dosage. The least efficient removal of turbidity was noted at dosage 5.0 g with percentage removal recorded for natural zeolite and CSMZ only 57% and 70% respectively.
Figure 3(d) demonstrates the percentage iron ion removal of natural zeolite and CSMZ with respect to their dosage. The result shows that there was a significant difference trend in iron ion adsorption efficiencies between natural zeolite and CSMZ. For natural zeolite, it was shown that the removal percentage of iron ion had increased until it reached 1.0g of dosage with 72% of removal efficiency. On the other hands, CSMZ was only able to remove about 63% of iron ion when its dosage was increased to 2.5 g. The lowest percentage removals were 47% and 57% recognized at the adsorbent dosage 5.0 g for respective natural zeolite and CSMZ.
Figure 4. Percentage of color (a), COD (b), turbidity (c), and Fe (d) removal against dosage for GAC
The result illustrated in Figure 4(a) shows the maximum removal percentage of colour for GAC at 2.5 g dosage was 62%. Moderate increment in colour removal was identified along with the addition dosage of 2.5 g whilst abatement of removal efficiency began subsequently at adsorbent dosage of 3.0 g to 4.0 g.
The results from Figure 4(b) indicated that increasing the GAC dosage would increase the efficiency in removing COD respectively. The optimum dosage was recorded at 3.0 g with 72% of removal efficiency. Meanwhile, increasing the dosage above 3.0 g exhibited a slight decrease in removal efficiency with 67% to 61% for COD removal. A better result in removing COD was also shown by GAC compared to the natural zeolite and CSMZ.
The percentage of turbidity removed by GAC in different dosages is described in Figure 4(c). The highest removal was indicated at adsorbent dosage 2.5 g with removal efficiency of 70% while the minimum removal was 52% recorded at the adsorbent dosage 0.01 g. However, starting from adsorbent dosage of 3.0 to 4.0 g, removal efficiency began to decrease to 68%, 67%, and 69% respectively.
The result of percentage removal of iron ion by GAC in peat water is presented in Figure 4(d). It was found that the rate of removal was rapid in the initial dosage between 0.01 g to 3.0 g at which the removal efficiency increased from 28% to 71% accordingly. Subsequently, a few significant changes in the rate of removal were observed. Possibly, at the beginning, the solute molecules were absorbed by the exterior surface of adsorbent particles, so the adsorption rate was rapid. However, after the optimum dose was reached, the adsorption of the exterior surface becomes saturated and thereby the molecules will need to diffuse through the pores of the adsorbent into the interior surface of the particle (Ahmad & Hameed, 2009).
3.4 Batch Column Experiment
On the first running, the column was packed with natural zeolite (1st layer), limestone (2nd layer), and GAC (3rd layer) as shown in Figure 5(a). Removal efficiency for colour, COD, turbidity, and iron ion was recognized to be increased when the contact time was increased. At the time interval 1 hour to 6 hours, however, the increment was not so significant. The removal efficiency at 1 hour treatment was 39%, 21%, 54%, 36% while at 6 hours treatment was 77%, 65%, 73%, 60% recorded for respective colour, COD, turbidity, and iron ion. Poor removal efficiency at 1 hour treatment indicated that the required time to remove all parameters were insufficient. It is evident that if the adsorption process is allowed to run for 24 hours on the column, the removal efficiency shows notable removal. Percentage removals of colour, COD, turbidity, and iron ion at 24 hours were 83%, 72%, 76%, 65% respectively. Furthermore, the highest removal for respective colour, COD, turbidity, and iron ion were obtained at 48 hours treatment with 87%, 81%, 86%, and 79% of removal efficiency.
Figure 5. Percentage removal of color, COD, turbidity, and Fe for 1st run(a), 2nd run(b), and 3rd run (c) at flowrate 30 ml/min
On the second running, the column was packed with CSMZ (1st layer), limestone (2nd layer), and GAC (3rd layer) as presented in Figure 5(b). The removal percentages of colour, COD, turbidity, and iron ion were noticed after 1 hour to be 52%, 49%, 71%, and 30% respectively. The time of contact between adsorbate and adsorbent is proven to play an important role during the uptake of pollutants from peat water samples by adsorption process. In addition, the development of charge on the adsorbent surface was governed by contact time and hence the efficiency and feasibility of an adsorbent for its use in water pollution control can also be predicted by the time taken to attain its equilibrium (Sharma, 2003). Removal efficiency of 90% for colour, 81% for COD, 91% for turbidity, and 57% for iron ion were obtained at 24 hours of contact time.
On the third running, the column was packed with a difference sequence of CSMZ (1st layer), GAC (2nd layer), and limestone (3rd layer) demonstrated in Figure 5(c). It can be seen that the adsorption of these four parameters were slightly rapid at time interval 1 hour to 6 hours treatment. Further gradual increment with the prolongation of contact time form 24 hours to 48 hours has also occurred. Observation at 1 hour treatment recorded the removal efficiency of 62%, 58%, 87%, and 48% for respective colour, COD, turbidity, and iron ion. Whereby, 6 hours treatment had yielded higher removal percentage removal of 75%, 77%, 93%, and 58% respectively for colour, COD, turbidity, and iron ion. Further removal of colour, COD, turbidity, and iron ion was recorded when the treatment was run for 24 hours which exhibited 92%, 91%, 98%, 77% of removal efficiency respectively. Prolonged time to 48 hours indeed showed better removal of colour, COD, turbidity, iron ion with percentage removal of 95%, 100%, 99%, and 89% respectively. It can be seen that the arrangement of CSMZ, GAC, and limestone has the highest removal efficiency for all parameters at the flow rate influent of 30 ml/min.
Figure 6. Percentage removal of color, COD, turbidity, and Fe against contact time for 2nd run(a) at flow rate 60 mL/min and at flowrate 90 mL/min (b)
The experimental adsorption behaviour was further seen for its adsorption capacity during 60 ml/min and 90 ml/min flow rate. In addition, the flow rate adjustment had also resulted in differences in surface loading rate in which the sample going through the surface area of adsorbent bed (horizontal surface area, A= 22.9 cm2) for 30 ml/min equals to 1.31 cm/min while the flow rate of 60ml/min equals to 2.62 cm/min, and the flow rate of 90 ml/min equals to 3.93 cm/min. The percentage removal for both flow rate adjustments of CSMZ, GAC, and limestone arrangement were exhibited in Figure 6 (a) and Figure 6 (b). Based on these Figures, lower removal efficiencies were indicated at 1 hour time interval of 6 hours of contact time. The percentage removals for both 60 ml/min and 90 ml/min flow rate at 1 hour were 57%, 56%, 80%, 38% and 49%, 58%, 61%, 35% for colour, COD, turbidity, and iron ion respectively. Subsequently, when the contact time was at 6 hours, the removal percentage were 70%, 79%, 88%, 56%, and 60%, 77%, 70%, 47%. However, the maximum removal efficiency at 48 hours for both flow rates was not much different from the 30ml/min flow rate.
3.5 Adsorption Isotherm
In the present investigation, the experimental data were tested with respect to both Freundlich and Langmuir isotherms. Based on the linearized Freundlich isotherm models for natural zeolite, CSMZ, GAC in terms of adsorptive capacity to remove colour, COD, turbidity, and iron ion, the majority of them exhibited fits for all adsorbate with regression value (R2) above 0.6, except for iron ion and turbidity for respective CSMZ, and GAC. On the other hand, the linearized Langmuir isotherm models for natural zeolite, CSMZ, GAC in terms of adsorptive capacity to remove colour, COD, turbidity, and iron ion, had exhibited fits for all adsorbate with regression value (R2) was at range of 0.242 to 0.912. The Langmuir isotherm model for all adsorption mechanisms were identified to have smaller R2 values compared to the Freundlich isotherm model. Thereby, it can be concluded that the Freundlich isotherm model was more applicable in determining the adsorption mechanisms for this study.
3.6 Peat Water Quality Post Column Treatment
Peat water treatment in column with serial sequence of natural zeolite, CSMZ, and limestone had exhibited the highest removal with percentage removal at 48 hours at 95%, 100%, 99%, and 89% for colour, COD, turbidity, and iron ion respectively. Final readings at 48 hours treatment on pH, TDS, DO, conductivity, salinity, colour, turbidity, COD, and iron ion were 7.78, 74 mg/l, 4.03 mg/l, 137 uS/cm, 0.05 ppt, 12 TCU, 0.23 NTU, 0 mg/l, and 0.11 mg/l respectively (see Table 3). These findings, on the other hand, have indicated that peat water treatment had successfully produced water which satisfied the standard drinking water quality.
Table 3. The characteristics of results of peat water treatment from Beriah Peat Swamp Forest
Note: 1. *)Malaysian standard for drinking water quality;2. NA = Not analyzed.
4. Conclusions
From the results presented in this paper, the following conclusions can be drawn:
1) The optimum removal of colour, COD, and turbidity for all adsorbents were observed to occur during acidic conditions at pH range 2 – 4 whereas for iron ion, the maximum removal was noted at pH range 6 – 8.
2) At pH 2, CSMZ yielded the highest removal for colour and turbidity with removal efficiency of 90% and 98% respectively. Meanwhile, GAC has the highest percentage removal of COD at pH 4 with removal efficiency obtained about 61% while at pH 6, GAC exhibited the best removal of iron ion with percentage removal around 80%.
3) CSMZ revealed stronger adsorptive capacity for colour, COD, and turbidity compared to natural zeolite.
4) The optimal removal was achieved for the serial sequence of CSMZ (1st layer), GAC (2nd layer), and Limestone (3rd layer) with the adsorbent media at 30 ml/min of flow rate.
5) Freundlich isotherm was more reliable to describe the adsorption mechanisms of colour, COD, turbidity, and iron ion for natural zeolite, CSMZ, and GAC.
The authors wish to acknowledge the financial support from the School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia and Universiti Sains Malaysia (Short Term Grant No. 304/PAWAM/60312015).
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Modern Applied Science; Vol. 7, No. 2; 2013
Published by Canadian Center of Science and Education
S. Syafalni1, Ismail Abustan1, Aderiza Brahmana1, Siti Nor Farhana Zakaria1 & Rohana Abdullah1
1 School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia. Correspondence: S. Syafalni, School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia,
Nibong Tebal 14300, Penang, Malaysia. E-mail: email@example.com
Received: December 3, 2012 Accepted: January 14, 2013 Online Published: January 22, 2013 doi:10.5539/mas.v7n2p39 URL: http://dx.doi.org/10.5539/mas.v7n2p39
Shared via Creative Commons Attribution 3.0 Unported license
Conductivity as alternative measurement for WWTP inflow dynamics – MyronLMeters.com
Tweet Myron L Meters Myron L Meters sells the most accurate, reliable conductivity instruments in the water treatment industry. You can find some of our most popular meters here: http://www.myronlmeters.com/SearchResults.asp?Search=conductivity&x=-1345&y=-145 Introduction Along with the development of more and more complex integrated models for urban water systems the need of sufficient data bases grows as well. […]
Myron L Meters
Myron L Meters sells the most accurate, reliable conductivity instruments in the water treatment industry. You can find some of our most popular meters here:
Along with the development of more and more complex integrated models for urban water systems the need of sufficient data bases grows as well. It is even complicated to measure relevant parameters,e.g. dissolved nitrogen or COD, for their use in Waste Water Treatment Plants and sewer models to describe the influence of catchments to the receiving water.
This poster presents a method regarding the possibility of substituting an online ammonia measurement by conductivity measurements in the inflow of a Waste Water Treatment Plant . The aim was the description of the dynamics in wet weather flow through storm water events for modelling purposes.
The conductivity of an aqueous solution is the measure of its ability to conduct electricity. Responsible for that phenomenon are ions of dissolved salts. In natural and drinking water these are mainly carbonates, chlorides and sulphates of calcium, magnesium, sodium and potassium. Conducted experiences and measurements in combined sewers showed a relation between conductivity in Waste Water Treatment Plant inflow and the concentration of dissolved components, e.g. ammonia, in case of rainfall events. The data for different 3 Waste Water Treatment Plant are shown in Figure 1. Rainwater has nearly no ions that cause conductivity to be measured. Therefore, diluted wastewater flowing into the Waste Water Treatment Plant can be detected by a conductivity probe. The measure and quality of linear regression between ammonia concentration and conductivity can be found in Table 1 for all data from Figure 1.
Material and Methods
With this knowledge a simple regression-based inflow model for use in activated sludge modelling of Waste Water Treatment Plant was defined to use conductivity beside available composite samples as a measure for dynamics in ammonia concentration as one of the most dynamic measure.
Results and Discussion
For one of the considered Waste Water Treatment Plants (WWTP) the resulting quality for the inflow model is shown in Figure 2 for a time series of a week.
Furthermore, the inflow model was used as a source for a retention tank model at the inlet of another Waste Water Treatment Plant to describe the impact of different management strategies (storage or flow through) on receiving water and Waste Water Treatment Plant (Figure 3).
A long-term modelling of 9 storm water events was used to show the predictive capacity of the model. The regression parameters were fitted by an optimisation routine to get best fit for all concentrations (also for COD, not presented here). Figure 3 shows the fit for all events. A good prediction of dynamics and absolute values for ammonia can be seen.
The results of different Goodness-of-fit measures are summarized in Table 2 for both presented WWTP inflows. Especially the values for the modified Coefficient of Efficiency, as a well-known and used measure for model quality in hydrological sciences, show the degree of predicting of the used method and the usability of conductivity for description of influent dynamics to Waste Water Treatment Plant in storm water cases.
This simple and easy-to-use method is well suited for implementation in Waste Water Treatment Plant models to describe the inflow dynamics regarding a more realistic behavior e.g. for optimization of process control.
by Markus Ahnert*, Norbert Günther*, Volker Kuehn*, University of Dresden
Ahnert, M., Blumensaat, F., Langergraber, G., Alex, J., Woerner, D., Frehmann, T., Halft, N., Hobus, I., Plattes, M., Spering, V. und Winkler, S. (2007), Goodness-of-fit measures for numerical modelling in urban water management – a summary to support practical applications., paper presented at 10th IWA Specialised Conference on "Design, Operation and Economics of Large Wastewater Treatment Plants", 9-13 September 2007, Vienna, Austria, 9-13 September 2007.
Nash, J. E. und Sutcliffe, J. V. (1970), River flow forecasting through conceptual models part I – A discussion of principles, Journal of Hydrology, 10, 282.
The thermal conductivity enhancement of nanofluids – MyronLMeters.com
TweetThe thermal conductivity enhancement of nanofluids Abstract Increasing interests have been paid to nanofluids because of the intriguing heat transfer enhancement performances presented by this kind of promising heat transfer media. We produced a series of nanofluids and measured their thermal conductivities. In this article, we discussed the measurements and the enhancements of the thermal […]
The thermal conductivity enhancement of nanofluids
Increasing interests have been paid to nanofluids because of the intriguing heat transfer enhancement performances presented by this kind of promising heat transfer media. We produced a series of nanofluids and measured their thermal conductivities. In this article, we discussed the measurements and the enhancements of the thermal conductivity of a variety of nanofluids. The base fluids used included those that are most employed heat transfer fluids, such as deionized water (DW), ethylene glycol (EG), glycerol, silicone oil, and the binary mixture of DW and EG. Various nanoparticles (NPs) involving Al2O3 NPs with different sizes, SiC NPs with different shapes, MgO NPs, ZnO NPs, SiO2 NPs, Fe3O4 NPs, TiO2 NPs, diamond NPs, and carbon nanotubes with different pretreatments were used as additives. Our findings demonstrated that the thermal conductivity enhancements of nanofluids could be influenced by multi-faceted factors including the volume fraction of the dispersed NPs, the tested temperature, the thermal conductivity of the base fluid, the size of the dispersed NPs, the pretreatment process, and the additives of the fluids. The thermal transport mechanisms in nanofluids were further discussed, and the promising approaches for optimizing the thermal conductivity of nanofluids have been proposed.
More efficient heat transfer systems are increasingly preferred because of the accelerating miniaturization, on the one hand, and the ever-increasing heat flux, on the other. In many industrial processes, including power generation, chemical processes, heating or cooling processes, and microelectronics, heat transfer fluids such as water, mineral oil, and ethylene glycol always play vital roles. The poor heat transfer properties of these common fluids compared to most solids is a primary obstacle to the high compactness and effectiveness of heat exchangers[1]. An innovative way of improving the thermal conductivities of working media is to suspend ultrafine metallic or nonmetallic solid powders in traditional fluids since the thermal conductivities of most solid materials are higher than those of liquids. A novel kind of heat transfer enhancement fluid, the so-called nanofluid, has been proposed to meet the demands [2].
"Nanofluid" is an eye-catching word in the heat transfer community nowadays. The thermal properties, including thermal conductivity, viscosity, specific heat, convective heat transfer coefficient, and critical heat flux have been studied extensively. Several elaborate and comprehensive review articles and books have addressed thermal transport properties of nanofluids [1,3–6]. Among all these properties, thermal conductivity is the first referred one, and it is believed to be the most important parameter responsible for the enhanced heat transfer. Investigations on the thermal conductivity of nanofluids have been drawing the greatest attention of the researchers. A variety of physical and chemical factors, including the volume fraction, the size, the shape, and the species of the nanoparticles (NPs), pH value and temperature of the fluids, the Brownian motion of the NPs, and the aggregation of the NPs, have been proposed to play their respective roles on the heat transfer characteristics of nanofluids [7–19]. Extensive efforts have been made to improve the thermal conductivity of nanofluids [7–19] and to elucidate the thermal transport mechanisms in nanofluids [20–23].
The authors have carried out a series of studies on the heat transfer enhancement performance of nanofluids. A variety of nanofluids have been produced by the one- or two-step method. The base fluids used include deionized water (DW), ethylene glycol (EG), glycerol, silicone oil, and the binary mixture of DW and EG (DW-EG). Al2O3 NPs with different sizes, SiC NPs with different shapes, MgO NPs, ZnO NPs, SiO2 NPs, Fe3O4 NPs, TiO2 NPs, diamond NPs (DNPs), and carbon nanotubes (CNTs) with different pretreatments have been used as additives. The thermal conductivities of these nanofluids have been measured by transient hot wire (THW) method or short hot wire (SHW) technique. In this article, the experimental results that elucidate the influencing factors for thermal conductivity enhancement of nanofluids are presented. The thermal transport mechanisms in nanofluids and promising approaches for optimizing the thermal conductivity of nanofluids are further presented.
Preparation of nanofluids
Two techniques have been applied to prepare nanofluids in our studies: two- and one-step techniques. Most of the studied nanofluids were prepared by the two-step technique. During the procedure of two-step technique, the dispersed NPs were prepared by chemical or physical methods first, and then the NPs were added into a specified base fluid, with or without pretreatment and surfactant based on the need. In the preparation of nanofluids containing metallic NPs, one-step technique was employed.
The process was quite simple in the preparation of nanofluids containing oxide NPs like Al2O3, ZnO, MgO, TiO2, and SiO2 NPs. The NPs were obtained commercially and were dispersed into a base fluid in a mixing container. The NPs were deagglomerated by intensive ultrasonication after being mixed with the base fluid, and then the suspensions were homogenized by magnetic force agitation.
Two-step method was used to prepare graphene nanofluids. The first step was to prepare graphene nanosheets. Functionalized graphene was gained through a modified Hummers method as described elsewhere [24]. Graphene nanosheets were obtained by exfoliation of graphite in anhydrous ethanol. The product was a loose brown powder, and it had good hydrophilic nature. The graphene nanosheets could be dispersed well in polar solvents, like DW and EG, without the use of surfactant. For liquid paraffin (LP)-based nanofluid, oleylamine was used as the surfactant. The fixed quality of graphene nanosheets with different volume fractions was dispersed in the base fluids.
Severe aggregation always takes place in the as-prepared CNTs (pristine CNTs: PCNTs) because of the non-reactive surfaces, intrinsic Von der Waals forces, and very large specific surface areas, and aspect ratios [25]. In CNT nanofluid preparations, surfactant addition is an effective way to enhance the dispersibility of CNTs [26–28]. However, surfactant molecules attaching on the surfaces of CNTs may enlarge the thermal resistance between the CNTs and the base fluid [29], which limits the enhancement of the effective thermal conductivity. The steps involved in the preparation of surfactant-free CNT nanofluids include (1) disentangling the nanotube entanglement and introducing hydrophilic functional groups on the surfaces of the nanotubes by chemical treatments; (2) cutting the treated CNTs (TCNTs) to optimal length by ball milling; and (3) dispersing the treated and cut CNTs into base fluids. CNTs including single-walled CNTs (SWNTs), double-walled CNTs (DWNTs), and multi-walled CNTs (MWNTs) were obtained commercially. Two chemical routes for treating CNTs were used for this study. One is oxidation with concentrated acid, and the other is mechanochemical reaction with potassium hydroxide (KOH). The detailed treatment processes have been described elsewhere [8,30].
Phase transfer method was used to prepare stable kerosene-based Fe3O4 magnetic nanofluid. The first step is to synthesize Fe3O4 NPs in water by coprecipitation. Oleic acid was added to modify the NPs. When kerosene is added to the mixture with slow stirring, the phase transfer process took place spontaneously. There was a distinct phase interface between the aqueous and kerosene. After the removal of the aqueous phase using a pipette, the kerosene-based Fe3O4 nanofluid was obtained [31].
Nanofluids containing copper NPs were prepared using direct chemical reduction method. Stable nanofluids were obtained with the addition of poly(vinylpyrrolidone) (PVP). The diameters of copper NPs prepared by chemical reduction procedure are in the range of 5-10 nm, and copper NPs disperse well with no clear aggregation [32].
Surface modification is always used to enhance the dispersibility of NPs in the preparation of nanofluids. For example, diamond NPs (DNPs) were purified and surface modified by acid mixtures of perchloric acid, nitric acid and hydrochloric acid according to the literature [33] before being dispersed into the base fluids. SiC NPs were heated in air to remove the excess free carbon and their surfaces modified to enhance their dispersibility.
Consideration on the thermal conductivity measurement
Inconsistent experimental results and controversial arguments arise unceasingly from different groups conducting research on nanofluids, indicating the complexity of the thermal transport in nanofluids. Through an investigation, a large degree of randomness and scatter have been observed in the experimental data published in the open literature. Given the inconsistency in the data, it is impossible to develop a convincing and comprehensive physical-based model that can predict all the trends. To clarify the suspicion on the scattered and wide-ranging experimental results of the thermal conductivity obtained by different groups, it is preferred to screen the measurement technique and procedure to guarantee the accuracy of the obtained results.
Several researchers observed the "time-dependent characteristic" of thermal conductivity [34–36], that is to say, thermal conductivity was the highest right after nanofluid preparation, and then it decreased considerably with elapsed time. We believe that the "time-dependent characteristic" does not represent the essence of thermal conduction capability of nanofluids. The following two factors may account for this phenomenon. The first one is the motion of the remained particle caused by the agitation during the nanofluid preparation. To make a nanofluid homogeneous and long-term stable, it is always subjected to intensive agitation including magnetic stirring and sonication to destroy the aggregation of the suspended NPs. In very short time after nanofluid preparation, the NPs still keep moving in the base fluid (different from Brownian motion). The motion of the remained particle would cause convection and enhance the energy transport in the nanofluids. Second, when a nanofluid is subjected to long-time sonication, its temperature would be increased. The temperature goes down gradually to the surrounding temperature (thermal conductivity measurement temperature). In very short time after the sonication stops, the process has been remaining. Although the temperature decrease is not severe, the thermal conductivity obtained is very sensitive to the temperature decrease when the transient hot-wire technique is used to measured the thermal conductivity. In our measurements, this phenomenon would be observed. When measuring the thermal conductivity at an unequilibrium state, it was found that the measured data might be very different for a nanofluid even at a specific temperature (see 25°C) if the process to reach this temperature is different. If the temperature is increasing, then the datum obtained of the thermal conductivity would be lower than the true value. While the temperature is decreasing, the datum obtained of the thermal conductivity would be higher than the true value. Therefore, keeping a nanofluid stable and initial equilibrium is very important to obtain accurate thermal conductivity data in measurements.
A transient short hot-wire method was used to measure the thermal conductivities of the base fluids (k0) and the nanofluids (k). The detailed measurement principle, procedure, and error analysis have been described in [37]. In our measurements, a platinum wire with a diameter of 50 μm was used for the hot wire, and it served both as a heating unit and as an electrical resistance thermometer. The platinum wire was coated with an insulation layer of 7-μm thickness. Initially the platinum wire immersed in media was kept at equilibrium with the surroundings. When a regulation voltage was supplied to initiate the measurement, the electrical resistance of the wire changed proportionally with the rise in temperature. The thermal conductivity was calculated from the slope of the rise in the wire's temperature against the logarithmic time interval. The uncertainty of this measurement is estimated to be within ± 1.0%. A temperature-controlled bath was used to maintain different temperatures of the nanofluids. Instead of monitoring the temperature of the bath, a thermocouple was positioned inside the sample to monitor the temperature on the spot. When the temperature of the sample reached a steady value, the authors waited for further 20 min to make sure that the initial state is at equilibrium. At every tested temperature, measurements were made three times and the average values were taken as the final results. A 20-min interval was needed between two successive measurements. After the above-mentioned careful check on the measurement condition and procedure, the authors could gain confidence on the experimental results.
Influencing factors of thermal conductivity enhancement
In the experiment of the study, it was found that the thermal conductivity enhancements of nanofluids might be influenced by multi-faceted factors including the volume fraction of the dispersed NPs, the tested temperature, the thermal conductivity of the base fluid, the size of the dispersed NPs, the pretreatment process, and the additives of the fluids. The effects of these factors are presented in this section.
Particle loading
The idea of nanofluid application originated from the fact that the thermal conductivity of a solid is much higher than that of a liquid. For example, the thermal conductivity of the most used conventional heat transfer fluid, water, is about 0.6 W/m · K at room temperature, while that of copper is higher than 400 W/m · K. Therefore, particle loading would be the chief factor that influences the thermal transport in nanofluids. As expected, the thermal conductivities of the nanofluids have been increased over that of the base fluid with the addition of a small amount of NPs. Figure 1 shows the enhanced thermal conductivity ratios of the nanofluids with NPs at different volume fractions [7,8,38–42]. (k – k0)/k0 and φ refer to the thermal conductivity enhancement ratio of nanofluids and the volume fraction of NPs, respectively, in this article. Figure1a presents oxide nanofluids, while Figure 1b presents nonoxide nanofluids. The results show that all the nanofluids have noticeable higher thermal conductivities than the base fluid without NPs. In general, the thermal conductivity enhancement increases monotonously with the volume fraction. For the graphene nanofluid with a volume fraction of 0.05, the thermal conductivity can be enhanced by more than 60.0%. There is an approximate linear relationship between the thermal conductivity enhancement ratios and the volume fraction of graphene nanosheets. The nanofluids containing graphene nanosheets show larger thermal conductivity enhancement than those containing oxide NPs. It demonstrates that graphene nanosheet is a good additive to enhance the thermal conductivity of base fluid. However, the enhancement ratios of nanofluids containing graphene nanosheets are less than those of CNTs with the same loading. Many factors have direct influence on the thermal conductivity of the nanofluid. One of the important factors is the crystal structure of the inclusion in the nanofluid. Graphene is a one-atom-thick planar sheet of sp2-bonded carbon atoms that are densely packed in a honeycomb crystal lattice. The perfect structure of graphene is damaged when graphite is chemically oxidized by treatment with strong oxidants. There is no doubt that the high thermal conductivity is diminished by defects, and the defects have direct influence on the heat transport along the 2-D structure.
Figure 1. Thermal conductivity enhancement ratios of the nanofluids as a function of nanoparticle loading. (a) Oxide nanofluids: MgO-EG [38]; Al2O3-EG [7]; ZnO-EG [39]; (b) Nonoxide nanofluids: CNT-EG [8]; DNP-EG [40]; Graphene-EG [41]; Cu-EG [42].
Some studies have demonstrated that the temperature has a great effect on the enhancement of the thermal conductivity for nanofluids. However, there is considerable disagreement in the literature with respect to the temperature dependence of their thermal conductivity. For example, Das et al. reported strong temperature-depended thermal conductivity for water-based Al2O3 and CuO nanofluids [43]. The thermal conductivity enhancements of nanofluids containing Bi2Te3nanorods in FC72 and in oil had been experimentally found to decrease with increasing temperature [44]. Micael et al. measured the thermal conductivities of EG-based Al2O3 nanofluids at temperatures ranging from 298 to 411 K. A maximum in the thermal conductivity was observed at all mass fractions of NPs [45].
Figure 2 shows our measured temperature-depended thermal conductivity enhancements of nanofluids [8,38–42]. For EG-based nanofluids containing MgO, ZnO, SiO2, and graphene NPs, the thermal conductivity enhancements almost remain constant when the tested temperature changes (see Figure 2a), which means that the thermal conductivity of the nanofluid tracks the thermal conductivities of the base liquid in the experimented temperature range of this study. The thermal conductivity enhancements of DW-EG-based nanofluids containing MgO, ZnO, SiO2, Al2O3, Fe2O3, TiO2, and graphene NPs also appear to have the same behavior. It was further found that kerosene-based Fe3O4 nanofluids presented temperature-independent thermal conductivity enhancements. Patel et al. [46] reported that the thermal conductivity enhancement ratios of Cu nanofluids are enhanced considerably when the temperature increases. The experimental results of this study shown in Figure 2b demonstrated similar tendency. At 10°C, the thermal conductivity enhancement of EG based Cu nanofluid with 0.5% nanoparticle loading is less than 15.0%. When the temperature is increased to 60°C, the enhancement reaches as large as 46.0%. Brownian motion of the NPs has been proposed as the dominant factor for this phenomenon. For the EG-based CNT nanofluids, cylindrical nanotubes with large aspect ratios were used as additions. The effect of Brownian motion will be negligible. Typical conduction-based models will give (k – k0)/k0, independent of the temperature. However, results shown in Figure 2b illustrate that (k – k0)/k0increases, though not drastically, with the temperature. CNT aggregation kinetics may contribute to the observed differences [21]. It is worthy of bearing in mind that the temperatures of the base fluid and the nanofluid should be the same when compared with the thermal conductivities between them. Comparison of the thermal conductivities between the nanofluid at one temperature and the base at another one is meaningless.
Figure 2. Thermal conductivity enhancement varying with the tested temperatures. (a) Oxide nanofluids: MgO-EG [38]; ZnO-EG[39]; Graphene-EG [41]; (b) Nonoxide nanofluids: Cu-EG [42]; CNT-EG[8]; DNP-EG [40].
Base fluid
Figure 3 shows the relation between the enhanced thermal conductivity ratios of the nanofluids and the thermal conductivities of the base fluids [7,8,40,41]. It is clearly seen that no matter what kind of nanoparticle was used, the thermal conductivity enhancement decreases with an increase in the thermal conductivity of the base fluid. For pump oil (PO)-based Al2O3 nanofluid with 5.0% nanoparticle loading, the thermal conductivity can be enhanced by more than 38% compared to that of PO. When the base fluid is substituted with water, the thermal conductivity enhancement achieved is only about 22.0% [7]. A greater dramatic improvement in thermal conductivity of CNT nanofluid is seen for a base fluid with lower thermal conductivity. At 1.0% nanoparticle loading, the thermal conductivity enhancements are 19.6, 12.7, and 7.0% for CNT nanofluids in decene, EG, and DW, respectively. No matter what kind of base fluid is used, the thermal conductivity enhancement of CNT nanofluids is much higher than that for Al2O3 nanoparticle suspensions [8] at the same volume fraction. The reason would lie in the substantial difference in thermal conductivity and morphology between alumina nanoparticle and carbon nanotube.
Figure 3. Thermal conductivity enhancement ratios as a function of the thermal conductivities of the base fluids: Al2O3 NFs [7]; CNT NFs [8]; Graphene NFs [41]; DNP NFs [40].
Particle size
Figure 4 presents the thermal conductivity enhancement of the nanofluids as a function of the specific surface area (SSA) of the suspended particles [7]. It is seen that the thermal conductivity enhancement increases first, and then decreases with an increase in the SSA, with the largest thermal conductivity at a particle SSA of 25 m2 · g-1. We ascribe the thermal conductivity change behavior to twofold factors. First, as particle size decreases, the SSA of the particle increases proportionally. Heat transfer between the particle and the fluid takes place at the particle-fluid interface. Therefore, a dramatic enhancement in thermal conductivity is expected because a reduction in particle size can result in large interfacial area. Second, the mean free path in polycrystalline Al2O3 is estimated to be around 35 nm, which is comparable to the size of the particle that was used. The intrinsic thermal conductivity of nanosized Al2O3 particle may be reduced compared to that of bulk Al2O3 due to the scattering of the primary carriers of energy (phonon) at the particle boundary. It is expected that the suspension's thermal conductivity is reduced with an increase in the SSA. Therefore, for a suspension containing NPs at a particle size much different from the mean free path, the thermal conductivity increases when the particle size decreases because the first factor is dominant. However, when the size of the dispersed NPs is close to or smaller than the mean free path, the second factor will govern the mechanism of the thermal conductivity behavior of the suspension.
Figure 4. Enhanced thermal conductivity ratios as a function of the SSAs: Al2O3-EG [7]; Al2O3-PO [7].
Figure 5 depicts the thermal conductivity enhancements of nanofluids containing CNTs with different sizes [47]. The base fluid is DW, and the volume fraction of the CNTs is 0.0054. It is observed from Figure 5 that the thermal conductivity enhancements show differences among these three kinds of nanofluids containing SWNTs, DWNTs, and MWNTs as the volume fraction of CNTs is the same. Two influencing factors may be addressed. The first one is the intrinsic heat transfer performance of the CNTs. It is reported that the thermal conductivity of CNTs decreases with an increase in the number of the nanotube layer. The tendency of the thermal conductivity enhancement of the obtained CNT nanofluids accords with that of the heat transfer performance of the three kinds of CNTs. The second one is the alignment of the liquid molecules on the surface of CNTs. There are greater number of water molecules close to the surfaces of CNTs with smaller diameter due to the larger SSA if the volume fractions of CNTs are the same. These water molecules can form an interfacial layer structure on the CNT surfaces, increasing the thermal conductivity of the nanofluid [47].
Figure 5. Thermal conductivity enhancements of nanofluids containing CNTs with different sizes: SWNT-DW [47]; DWNT-DW[47]; MWNT-DW [47].
In the preparation of nanofluids, solid additives are always subjected to various pretreatment procedures. The initial incentive is to tailor the surfaces of the NPs to enhance their dispersibility, thereby to enhance the stability of the nanofluids. The morphologies would be significantly changed when CNTs were subjected to chemical or mechanical treatments. Theoretical research into the thermal conductivity of composites containing cylindrical inclusions has demonstrated that the morphologies, including the aspect ratio, have influence on the effective thermal conductivity of the composites. Therefore, it can be expected that the thermal conductivity of CNT contained nanofluids would be affected by the pretreatment process.
Figure 6 shows the dependence of the thermal conductivity enhancement on the ball milling time of CNTs suspended in the nanofluids [48]. From theoretical prediction, the thermal conductivity of a composite increases with the aspect ratio of the included solid particles [49–51]. Intuition suggests that increasing the milling time should therefore decrease (k – k0)/k0 because of the reduced aspect ratio. Figure 6, however, shows clear peak and valley values in the thermal conductivity enhancement with respect to the milling time for all the studied CNT loadings. For nanofluid at a volume fraction of 0.01, the thermal conductivity enhancements present a peak value of 27.5% and a valley value of 10.4% when the milling times are 10 and 28 h, respectively. The maximal enhancement is intriguingly more than two and half times as the minimal one. Interestingly, when further increased the milling time from 28 to 38 h, (k – k0)/k0 increases from the valley value of 10.4 to 12.8%. Though the increment is not pronounced, it illustrates a difference in tendency from that in the milling time range from 10 to 28 h. Temperature-dependent thermal conductivity enhancement data further indicate that, at all the measured temperatures, nanofluid with CNTs milled for 10 h has the largest increment in thermal conductivity. Glory et al. [52] reported that the enhancement of the thermal conductivity noticeably increases when the nanotube aspect ratio increases. However, the thermal conductivity enhancement behavior of our CNT nanofluid is very different and cannot be explained only by the effect of the aspect ratio.
Figure 6. Dependence of the thermal conductivity enhancement on the ball milling time of CNTs suspended in the nanofluids [48].
The above results suggest other dominant factors that have the influence over the thermal conductivity of the CNT nanofluids. The authors proposed that the nonstraightness and the aggregation would play significantly roles. As is known, the walls of CNTs have similar structure of graphene sheet, and the thermal conductivity of CNTs shows greatly anisotropic behavior. Heat transports substantially quicker through axial direction than through radial direction [53]. For a nonstraight CNT, the high thermal anisotropy of CNTs induces a unique property that individual CNTs are nearly perfect one-dimensional thermal passages with negligibly small heat flux losses during long distance heat conductions [54]. For a nonstraight CNT with length L under a two-end temperature difference, the heat flux q goes through a curled passage. This CNT can be regarded as an equivalent straight thermal passage with a distance of Le. The same heat flux q is conducted between the two ends of this straight passage. Obviously, the equivalent length Le depends on the curvature of the actual nanotube in the nanofluid. A concept, straightness ratio η (η = Le/L), can be adopted to describe the straightness of a curled CNT. The lowest straightness ratio arises when a suspended nanotube forms ring closure [55].
When subjected to ball milling, CNTs were broken and cut short with appropriate average length. The straightness ratio was significantly increased and heat transports more effectively through the CNTs and across the interfaces between the CNT tips and the base fluid, resulting in the highest thermal conductivity enhancement in a nanofluid containing CNTs milled for 10 h. For nanofluids containing relatively straight nanotubes, the influence of the aspect ratio will surpass that of straightness ratio. Therefore, by further treatment on nanotubes with relatively high straightness ratio, the excessive deterioration of the aspect ratio would decrease the thermal conductivity of nanofluids, causing (k – k0)/k0 decrease from 10 to 28 h. Recent theoretical analysis has revealed that the aggregation of nanoparticle plays a significant role in deciding (k – k0)/k0 [21]. Percolation effects in the aggregates, as highly conducting nanotubes touch each other in the aggregate, help in increasing the thermal conductivity. Our experiments demonstrate that aggregates are the dominant appearance of CNTs when the ball-milling time is increased to 38 h. The aggregation accounts for the increment of thermal conductivity enhancement when the ball-milling time is increased from 28 to 38 h. This result implies that the positive influence of the aggregation surpasses the negative influence of the aspect ratio deterioration.
pH value
For some nanofluids, the pH values of the suspensions have direct effects on the thermal conductivity enhancement. Figure 7 presents the thermal conductivity enhancement ratios at different pH values [7,40]. The results show that the enhanced thermal conductivity increases with an increase in the difference between the pH value of aqueous suspension and the isoelectric point of Al2O3 particle [7]. When the NPs are dispersed into a base fluid, the overall behavior of the particle-fluid interaction depends on the properties of the particle surface. For Al2O3 particles, the isoelectric point (pHiep) is determined to be 9.2, i.e., the repulsive forces among Al2O3 particles is zero, and Al2O3 particles will coagulate together under this pH value. Therefore, when pH value is equal or close to 9.2, Al2O3 particle suspension is unstable according to DLVO theory [56]. The hydration forces among particles increase with the increasing difference of the pH value of a suspension from the pHiep, which results in the enhanced mobility of NPs in the suspension. The microscopic motions of the particles cause micro-convection that enhances the heat transport process. Wensel's study showed that the thermal conductivity of nanofluids containing oxide NPs and CNTs with very low percentage loading decreased when the pH value is shifted from 7 to 11.45 under the influence of a strong outside magnetic field [14].
Figure 7. Thermal conductivity enhancement ratios at different pH values: Al2O3-DW [7]; DNP-EG [40].
For DNP-EG nanofluids, it is observed from Figure 7 that the thermal conductivity enhancement increases with pH values in the range of 7.0-8.0. When pH value is above 8.0, there is no obvious relationship between pH value and the thermal conductivity enhancement. In our opinion, the influence of pH value on thermal conductivity is that pH value has a direct effect on the stability of nanofluids. When pH value is below 8.5, the suspension is not very stable, and DNPs are easy to form aggregations. The alkalinity of the solution is helpful to the dispersion and the stability of the nanofluids. In order to verify the above statement, the influence of settlement time on the thermal conductivity enhancement was further investigated. It is found that the thermal conductivity enhancement decreases with elapsed time for DNP-EG nanofluid when pH is 7.0. However, for the stable DNP-EG nanofluids with pH of 8.5, there is no obvious thermal conductivity decrease for 6 months [40].
Surfactant addition
Surfactant addition is an effective way to enhance the stability of nanofluids. Kim's study revealed that the thermal conductivity decreased rapidly for the instable nanofluids without surfactants after preparation. However, no obvious changes in the thermal conductivity of the nanofluids with sodium dodecyl sulfate (SDS) as surfactant were observed even after 5-h settlement [57]. Assael et al. investigated the thermal conductivities of the aqueous suspension of CNTs. When Sodium dodecyl sulfate (SDS) was employed as the dispersant, the maximum thermal conductivity enhancement obtained was 38.0% for a nanofluid with 0.6 vol% CNT loadings [58]. When the surfactant is substituted with hexadecyltrimethyl ammonium bromide (CTAB), the maximum thermal conductivity enhancement obtained was 34.0% for same fraction of CNT loading [26]. Liu et al. reported that the thermal conductivity of carbon nanotube-synthetic engine oil suspensions is higher compared with that of same suspensions without the addition of surfactant. The presence of surfactant as stabilizer has positive effect on the carbon nanotube-synthetic engine oil suspensions[59].
We used cationic gemini surfactants (12-3(4,6)-12,2Br-1) to stabilize water-based MWNT nanofluids. These surfactants were prepared following the process described in [60]. Figure 8presents the thermal conductivity enhancement ratios of the CNT-contained nanofluids with different surfactant concentrations. The volume fraction of the dispersed CNTs is 0.1%. The critical micelle concentration of 12-3-12, 2Br-1 is reported as 9.6 ± 0.3 × 10-4 mol/l [61]. Ten times critical micelle concentration of 12-3-12, 2Br-1 is 0.6 wt%. Solutions of 12-3-12, 2Br-1 with different concentrations (0.6, 1.8, and 3.6 wt% at room temperature) were selected to prepare CNT nanofluids. It is observed that at all the measured temperatures the thermal conductivity enhancement decreases with the surfactant addition. The surfactant added in the nanofluids acts as stabilizer which improves the stability of the CNT nanofluids. However, excess surfactant addition might hinder the improvement of the thermal conductivity enhancement of the nanofluids.
Figure 8. Thermal conductivity enhancement ratios with different surfactant concentrations.
The effect of the structures of cationic gemini surfactant molecules on the thermal conductivity enhancement is shown in Figure 9. The fractions of the dispersed CNTs and the cationic gemini surfactants is 0.1 vol% and 0.6 wt%, respectively. The spacer chain length of the cationic gemini surfactant increase from 3 methylenes to 6 methylenes. It is seen that the thermal conductivity enhancement ratio increases with the decrease of spacer chain length of cationic gemini surfactant. Zeta potential analysis indicates that the CNT nanofluids stabilized by gemini surfactant with short spacer chain length have better stabilities. Increase of spacer chain length of surfactant might give rise to sediments of CNTs in the nanofluids, resulting in the decrease of thermal conductivity enhancement of the nanofluids.
Figure 9. Effect of surfactant structures on the thermal conductivity enhancement ratio.
Nanofluids have great potential for heat transfer enhancement and are highly suited to application in practical heat transfer processes. This provides promising ways for engineers to develop highly compact and effective heat transfer equipments. More and more researchers have paid their attention to this exciting field. When addressing the thermal conductivity of nanofluids, it is foremost important to guarantee the accuracy in the measurement of the thermal conductivity of nanofluids. Two aspects should be considered. The first one is to prepare homogeneous and long-term stable nanofluids. The second one is to keep the initial equilibrium before measuring the thermal conductivity. In general, the thermal conductivity enhancement increases monotonously with the particle loading. The effect of temperature on the thermal conductivity enhancement ratio is somewhat different for different nanofluids. It is very important to note that the temperatures of the base fluid and the nanofluid should be the same while comparing the thermal conductivities between them. With an increase in the thermal conductivity of the base fluid, the thermal conductivity enhancement ratio decreases. Considering the effect of the size of the inclusion, there exists an optimal value for alumina nanofluids, while for the CNT nanofluid, the thermal conductivity increases with a decrease of the average diameter of the included CNTs. The thermal characteristics of nanofluids might be manipulated by means of controlling the morphology of the inclusions, which also provide a promising way to conduct investigation on the mechanism of heat transfer in nanofluids. The additives like acid, base, or surfactant play considerable roles on the thermal conductivity enhancement of nanofluids.
CNTs: carbon nanotubes; DNPs: diamond NPs; DW: deionized water; DWNTs: double-walled CNTs; EG: ethylene glycol; KOH: potassium hydroxide; LP: liquid paraffin; MWNTs: multi-walled CNTs; NPs: nanoparticles; PVP: poly(vinylpyrrolidone); SDS: sodium dodecyl sulfate; SHW: short hot wire; SSA: specific surface area; SWNTs: single-walled CNTs; THW: transient hot wire; TCNTs: treated CNTs.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HQ supervised and participated all the studies. He wrote this paper. WY carried out the studies on the nanofluids containing copper nanoparticles, graphene, diamond nanoparticles, and several kinds of oxide nanoparticles. YL carried out the studies on the nanofluids containing other oxide nanoparticles. LF carried out the studies on the nanofluids containing carbon nanotubes.
This study was supported by the National Science Foundation of China (50876058), Program for New Century Excellent Talents in University (NCET-10-883), and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.
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Huaqing Xie*, Wei Yu, Yang Li and Lifei Chen
- *Corresponding author: Huaqing Xie firstname.lastname@example.com
School of Urban Development and Environmental Engineering, Shanghai Second Polytechnic University, Shanghai 201209, China
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Nanoscale Research Letters 2011, 6:124 doi:10.1186/1556-276X-6-124
The electronic version of this article is the complete one and can be found online at:http://www.nanoscalereslett.com/content/6/1/124
|3 September 2010
|9 February 2011
|9 February 2011
© 2011 Xie et al; licensee Springer.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Testing Hydroponics System's Nutrient Solution – MyronLMeters.com
TweetTDS meter Whether or not you're a newcomer to hydroponic growing, keeping your hydroponic system's nutrient solution properly balanced with a satisfactory nutrient concentration can be tough. Regular testing of one's t solution is required if you want to keep the hydroponic system balanced and your plants healthy and growing. The simplest way to keep […]
TDS meter
Whether or not you're a newcomer to hydroponic growing, keeping your hydroponic system's nutrient solution properly balanced with a satisfactory nutrient concentration can be tough. Regular testing of one's t solution is required if you want to keep the hydroponic system balanced and your plants healthy and growing. The simplest way to keep your nutrient solution balanced is via testing. You must check your solution's pH level and nutrient concentration no less than every couple of days. To be able to try out your solution you need a few basic devices. You need to get a trusted pH tester and either an overall total Dissolved Solids (TDS) meter or perhaps a Conductivity (EC) meter.
pH tester
It is generally agreed that the pH of one's nutrient solution should be kept slightly acidic using a pH range of 5.5-6.0. You will find exceptions for this generalization. If you are unsure what are the best pH range is for the plants you might be growing, there are many resources open to guide you. You can find three basic means of testing pH. The least expensive technique is paper testing strips. They're simple to use but could be difficult to learn. Typically the most popular testing way is liquid test kits. This method is extremely accurate and easier to see than paper testing strips but it is also more expensive. An electronic digital pH meter may be the last available option. Digital pH meters are available in various shapes, sizes, and price ranges. The benefit of an electronic pH meter is that it can be really user friendly, fast, and accurate. However, they are the most costly of the testing options, they can break easily, plus they has to be calibrated frequently if you'd like them to remain accurate.
TDS tester
Both conductivity meters and TDS meters are used to look at the strength, or concentration, of your hydroponic nutrient solution. Even though it is crucial that you know the concentration of your solution, this is because measurements ought to be used being a guideline only. EC meters will almost always be measured much the same way. Two sensors they fit within the solution being tested along with a little bit of electricity is emitted by one sensor and received by the other sensor. How well the electricity travels is then based on the EC meter. The harder electricity conducted, the greater the power of solids in the solution. A TDS meter uses the EC after which calculates the amount of solids inside the solution according to among three conversion factors. Considering that the TDS is dependant on a calculation, it really is only a quote of solids in the nutrient solution.
With this particular basic comprehension of the main difference between TDS and conductivity meters you can determine which measurement process is best for you. When you use a packaged nutrient solution, browse the product label to learn which kind of meter the maker recommends. In the event the manufacturer recommends a TDS, they'll also inform you which conversion step to use as well as the recommended concentration range for his or her product. If you use a homemade nutrient solution plus a TDS meter, a great general guideline is to keep your TDS between 800 and 1200 ppm (ppm). If you work with an EC meter to test your homemade nutrient solution, a good range is 1.0 to 3.0 mS/cm (milisiemens per centimeter).
This information will help keep your hydroponics nutrient solution balanced and your plants healthy.
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Microporous Silica Based Membranes for Desalination – MyronLMeters.com
Tweet Microporous Silica Based Membranes for Desalination Abstract: This review provides a global overview of microporous silica based membranes for desalination via pervaporation with a focus on membrane synthesis and processing, transport mechanisms and current state of the art membrane performance. Most importantly, the recent development and novel concepts for improving the hydro-stability and separating […]
Microporous Silica Based Membranes for Desalination
Abstract: This review provides a global overview of microporous silica based membranes for desalination via pervaporation with a focus on membrane synthesis and processing, transport mechanisms and current state of the art membrane performance. Most importantly, the recent development and novel concepts for improving the hydro-stability and separating performance of silica membranes for desalination are critically examined. Research into silica based membranes for desalination has focussed on three primary methods for improving the hydro-stability. These include incorporating carbon templates into the microporous silica both as surfactants and hybrid organic-inorganic structures and incorporation of metal oxide nanoparticles into the silica matrix. The literature examined identified that only metal oxide silica membranes have demonstrated high salt rejections under a variety of feed concentrations, reasonable fluxes and unaltered performance over long-term operation. As this is an embryonic field of research several target areas for researchers were discussed including further improvement of the membrane materials, but also regarding the necessity of integrating waste or solar heat sources into the final process design to ensure cost competitiveness with conventional reverse osmosis processes.
Keywords: desalination; pervaporation; microporous silica; metal oxide silica; hybrid silica; carbon template silica
- 1. Introduction
Water is essential for life and the rapid increase in the global population, and corresponding urbanization has seen the demand for both the quantity and quality of fresh water increase dramatically. One of the major challenges of the 21st century, if not the most important of all, is water scarcity, with the security of social and economic development of a country closed linked to its water resources. Nearly every industrial sector is dependent upon the availability of water, and water shortages have a resounding impact on all levels of society from the general public to health and politics. Indeed, the major problems encountered by water shortages include drought and famine, loss of production in primary industries, loss of job opportunities, poor health and hygiene as well as an increase in the cost of fresh water. This situation is made more complex by the fact that, according to the World Health Organization, more than 15% of the world's population have no access to potable water and more than 37% have no access to sanitation [1]. Against this backdrop, desalination is becoming an increasingly important tool in the fight to the global demand for clean water.
Membrane technologies have long been an attractive approach to separation in industry, because they are fast and relatively energy efficient processes. In addition, they frequently offer high operational stability, low operating costs and are simple to integrate and control within larger industrial process trains. Indeed, they have been successfully applied to the desalination industry with such vigor that they have long overtaken traditional thermal processes to become the gold standard [2]. In general, there are three main types of membrane processes that are currently applied including reverse osmosis (RO), membrane distillation (MD) and pervaporation (PV) [3]. RO depends on the ability of the 'dense' membrane to repel salt ions whilst allowing the passage of water molecules. The transport is governed by a solution-diffusion mechanism with the driving force being an external pressure difference large enough to overcome the osmotic pressure of the salt water. On the other hand, MD is a thermal process that requires a porous, hydrophobic membrane wherein the passage of water vapour only is permissible. PV, by contrast, uses molecular sieve type of membranes that allows only passage to water molecules but relies on a water vapour pressure difference. Both of these desalination processes require very different types of membranes with vastly different properties and configurations. Currently, there are two main types of membranes for water desalination, namely polymeric (e.g., polyamide-, polysulfone-, polyfurane- and cellulose-based for RO and polytetrafluoroethylene for MD) and inorganic composite or ceramic membranes (alumina-, zirconia-, titania-, zeolite-, silica- and carbon-based). Between these two classes of membranes, polymeric membranes are the most mature and well-established in the desalination industry due to their low cost, manufacturability, simple module design and improved permeability and selectivity [4,5]. However, these membranes suffer from swelling phenomenon, a short life-span due to biofouling as well as poor thermal and chemical resistance [2].
Inorganic membranes, on the other hand, are more resistant to process conditions. In addition, they are by their very nature, porous and hence desalinate via different transport mechanisms to polymeric membranes, based primarily on their pore size. In particular, zeolites and amorphous silica based membranes are attractive candidates for water desalination due to the advantages of their tunable pore sizes and morphology thereby offering higher selectivity. Furthermore, interest in amorphous silica based membranes is gaining momentum because of their simple fabrication techniques, relatively low cost and excellent molecular sieving properties as demonstrated in studies where they are utilized to separate gas molecules [6–9]. In these cases, microporous silica membranes have molecular-sieving structures with pore sizes on the order of the kinetic diameter of the species to be separated (dp = 3–5 Å) and therefore the membrane acts via PV as selective barrier between the water molecule (dk = 2.6 Å) and the hydrated salt ions (e.g., Na+: dk = 7.2 Å and Cl−: dk = 6.6 Å) [10,11], thus allowing the separation of water and salt. However, due to the amorphous nature of the silica material, when exposed to water the silica matrix may undergo dissolution and/or densification [12]. This is a major problem for using silica based membranes in desalination as the effect decreases the overall separation performance and ultimately the quality of the desalinated water. Therefore, a concerted effort has been devoted to improving the hydro-stability of these membranes for various industrial applications.
Many recent reviews have been published for membrane desalination and desalination technologies which are both exhaustive and comprehensive [2,4,5,13–16]. Amongst them, polymeric membranes and zeolites have played a major role. Thus, the contribution of this review is to cover recent studies of non-crystalline microporous silica based membranes for desalination and the new strategies focusing on improving hydro-stability and membrane properties for potential water desalination applications.
2. Membrane Processing and Transport Mechanisms for Water Desalination
Water desalination is a process in which fresh water is extracted from aqueous solutions such as seawater, brackish water and brine, which contain dissolved salts and other minerals. For water molecules to diffuse through a membrane, a driving force must be established, otherwise water molecules will remain mixed in the aqueous salt solution. The driving force is associated with concentration, pressure and temperature difference between the feed and permeate sides of the membrane. In the case of RO processes, the water molecules must overcome the osmotic pressure to diffuse through dense polymeric membranes. As the osmotic pressure of typical saline solutions ranges from 0.2 MPa to 3 MPa for brackish water to seawater respectively, RO desalination processes are generally pressure intensive with pressures of between 6 MPa and 8 MPa commonly used for seawater applications [4]. In contrast, MD does not attempt to overcome the osmotic pressure and so does not require a pressurised feed, although being a thermal process the water flux is proportional to the vapour pressure difference across the membrane. MD generally uses porous hydrophobic membranes, where pore size ranges between 1 µm and 100 Å, and the water vapour permeating via the pores is subsequently condensed downstream to produce fresh water [17]. MD operates at lower temperatures (up to 70 °C) when compared to conventional thermal process such as multi-stage flash or multi-effect distillation. Finally, the PV process, when applied to desalination, employs molecular-sieving (dp = 3–5 Å) ceramic membranes with a narrow pore distribution smaller than the diameter of the hydrated salt ions (>6 Å). Therefore they have the potential to completely reject salt ions while permitting water molecules to permeate. MD and PV are similar processes that can be chiefly identified by the way in which the membrane functions. If the membrane is simply a support structure that allows a meniscus to form on the feed side and plays no role in separation then the process is MD. If however, the membrane actively participates in the separation process then the process is PV. To further provide clarity between these three membrane processes, Figure 1 shows a diagram as comparison of RO, MD and PV in desalination processes.
Figure 1. Schematic representation of transport mechanism through a membrane via
(A) reverse osmosis, (B) membrane distillation and (C) pervaporation for seawater desalination [3].
PV is a well-established water separation technique particularly in alcohol dehydration, although under those circumstances dense polymeric membranes are typically employed [18]. In PV separation processes, the transport resistance is governed by the sorption equilibrium and mobility of water molecules in the silica membrane based on a molecular sieving mechanism [3,15,16,19,20]. Therefore, the transport of the larger hydrated salt ions is excluded through the membrane [21]. In a typical PV process, the membrane acts as a molecular scale selective barrier between the two phases which consist of the liquid phase in the feed and the vapour phase in the permeate side. In order to create a driving force, vacuum is applied on the permeate side of the membrane while the feed side is kept at atmospheric pressure and temperature. The water molecules permeate through the membrane to the exclusion of the salt ions, evaporate on the permeate side and are then convectively transported to the condenser. Fundamentally, the condenser functions to reduce the water vapour pressure on the permeate side by changing the water phase from vapour to liquid. This function allows for a steady state driving force to be maintained throughout the PV operation.
Similar to MD, PV can operate in several different arrangements. The most common MD operational arrangements have been well reviewed elsewhere [17]. The most common PV arrangements are shown in Figure 2 to provide context and include: (i) vacuum; (ii) air gap and (iii) sweep flow. PV can operate using any setup that allows a vapour pressure gradient to form but does not allow the permeate to flow back into the feed.
Figure 2. Pervaporation (PV) processes in various operational arrangements.
The PV process variables that are commonly investigated include temperature, pressure, total dissolved solids concentration and the ionic strength of the feed solution. The effect of these variables on water transport through the membrane is measured by two important factors which determine the overall membrane performance: (1) flux of the water and (2) selectivity or rejection of the salt ions. The permeate water is captured in a condenser and the flux (kg m−2 h−1), F, of water during a given period of time is calculated using Equation (1):
F= M/S.t
where M is the permeate mass (kg), S is the membrane surface area (m2) and t is the testing time (h). The salt rejection (%), R, of the membrane is determined by using Equation (2):
R = (Cf – Cp/ Cf) ×100%
where Cf and Cp are the salt concentrations in the feed and permeate solutions, respectively, measured from solution conductivity. Both of the equations are used prolifically in the literature to provide comparison measure for the overall membrane performance in both MD and PV experiments for water desalination. Based on the theory of MD and PV, the salt rejection should equate to 100% since the salt ions will not vapourise under the typical testing conditions. Instead they will crystallize on the inner surface of the membrane on the permeate side if they also find passage across the membrane. There are several reasons that this could occur, but for silica-based membranes this is primarily the result of imperfections in the top layer as a result of poor membrane preparation or silica disintegration in the aqueous environment. Therefore, several research groups have taken this into account by flushing the permeate salt when determining the overall salt rejection [19,22].
As previously alluded to, amorphous silica membranes present an interesting classification problem for membrane desalination technologies, because despite being porous, the water transport through the membrane cannot be described as a conventional MD. One of the major reasons is that in PV using silica based membranes, the pore sizes are too small to effectively form a meniscus associated with a liquid surface tension as it is the case in MD processes. In this case, the Kelvin equation for the liquid-vapour equilibrium is not applicable, as the pure liquid saturation pressure above a convex liquid surface is essentially the same as the pressure above a flat surface. In other words, the pressure of the water molecules at the pore entrance is possibly the same as in the feed bulk liquid phase (i.e., hydrostatic pressure). Having said that, silica based membranes for PV desalination cannot truly be described as activated transport either, as is the case for these membranes in gas separation [23]. Increasing feed bulk liquid pressure results in almost no water flux changes [19] as expected because changing the bulk feed pressure has a negligible effect on the vapour pressure of the feed; yet changing the vapour pressure of the feed, by increasing its temperature, delivers water flux improvements. Hence, in this case PV closely complies with Darcy's law (N = K ΔP°) where the water flux (N) is proportional to the water vapour pressure (ΔP°) and coefficient K, which are in turn temperature dependent. Silica derived membranes are hydrophilic materials and the water transport can be described by a sorption-diffusion mechanism. In the case of silica-based membranes for PV, water molecules must preferentially access the pore entrances of the silica matrix to permeate through the membrane, a surface adsorption process. Hence, the water transport can be summarised in four successive steps, namely, (1) selective surface adsorption from the bulk liquid mixture, (2) selective access of water to the pore entrance at the membrane interface on the feed side, (3) diffusion of water from the feed side to the permeate side, (4) desorption of water into vapour phase at the membrane interface of the permeate side. Therefore, the physico-chemical properties of the silica membranes as well as their interaction with the water molecules are equally influential.
3. Features of Silica Based Membranes for Desalination
3.1. Features of Silica Based Membranes for Desalination
Amorphous silica materials that can be tailored to pore sizes in the range of 3–5 Å are highly suitable for selective membranes in water desalination applications. Several techniques have been widely developed to effectively control the pore size of silica derived membranes, including sol-gel methods [24–31] and chemical vapour deposition (CVD) [32–35]. Although remarkable progress in gas separation applications have been reported using both methods, to date only silica membranes derived via sol-gel processes have been investigated for desalination applications. One of the major reasons is that the sol-gel method is one of the most simple and cost effective routes, which still offers the flexibility to tailor the required porosity. Traditionally, the sol-gel method is a wet chemical process to fabricate metal oxide powders starting from a chemical solution which acts as a precursor for an integrated network (gel). This method is frequently adopted in membrane synthesis or membrane pore modification due to its controllability and homogeneity [24,30,36–38], and it includes various steps such as sol preparation, gel formation, drying and thermal treatment. Many types of silicon alkoxide precursors have been utilized, but the clear majority of research describes work using tetraethoxysilane (TEOS) [39–41]. The sol gel synthesis has been well described in a variety of reference materials [42], and so briefly it involves the hydrolysis (Equation (3)) and condensation reactions (Equations (4) and (5)) of a metal alkoxides to form a network. In the hydrolysis reaction, the alkoxide groups (OR, where R is an alkyl group, CxH2x+1) are replaced with hydroxyl groups (OH). The silanol groups (Si-OH) are subsequently involved in the condensation reaction producing siloxane bonds (Si-O-Si), alcohols (R-OH) and water. The desired microporous structure of the silica layer is thus partially determined by both the reactivity and the size of the precursors, but also by the appropriate selection of the precursor, water, alcohol and catalyst concentrations.
|≡Si-OR + H2O ↔ ≡Si-OH + ROH
|≡Si-OR + HO-Si≡ ↔ ≡Si-O-Si≡ + ROH
|(Alcohol condensation)
|≡Si-OH + HO-Si≡ ↔ ≡Si-O-Si≡ + H2O
|(Water condensation)
Hydrolysis and condensation reactions are commonly catalysed by the use of a mineral base or acid. In the case of a silicon alkoxide, acidic conditions usually produce sols with fractal-like structures which have been shown to be more favorable for the formation of microporous silica with smaller pore sizes [38]. Indeed, when the fractal dimension of those species is low enough, their interpenetration is not restricted during the gelation stage, which gives rise to the formation of weakly-branched structures with small pores [42]. By contrast, basic conditions will otherwise favour the production of highly branched fractal structures and/or colloidal particles. This leads to the production of networks with larger pore sizes and is generally not used to prepare molecular sieveing silica membranes.
3.2. Membrane Preparation
Silica membranes are ultra-thin films (~250 nm) that are traditionally prepared on top of a support for mechanical strength to form an asymmetric structure (as depicted in Figure 3). The support quality plays a major role in the final morphology of the silica derived films as its homogeneity is fundamental in preparing thin films without defects. To achieve this aim, the substrate must have (i) small pore sizes, (ii) low surface roughness and (iii) low defect or void concentration [43]. Substrates with large pores, voids and rough surfaces tend to induce mechanical stress in the films resulting in micro-cracks or pin-hole defects. In order to overcome support roughness, interlayers with smaller pores sizes are typically employed. According to the literature, only a few combinations of support and interlayers have been explored for silica-based membranes for PV desalination. Indeed, supports prepared from α-Al2O3 powders are currently the substrates of choice due to their high porosity and relatively low cost and high mechanical stability. Mesoporous γ-Al2O3, consisting of much smaller pore sizes of ~4 nm are as used in 2 μm thick intermediate layers, and are able to minimize the defect rate observed [44]. However, γ-Al2O3 exhibits low hydrothermal stability [45], which is of concern if these materials are to be used in applications containing water vapour. Alternate intermediate layers include silica-zirconia composites developed by Tsuru and co-workers [46,47], which are typically more hydro-stable than γ-Al2O3 layers.
The coating of the substrate (or support) using the sol-gel process can be carried out by dip coating, spin coating and the pendulum method. Due to its flexibility to coat both flat and tubular geometries, in addition to small or large substrates, dip coating has been the preferred process to prepare silica based membranes. Scriven [48] extensively reviewed the dip coating process and proposed five stages: immersion, start-up, deposition, drainage and evaporation. Upon immersion of a substrate to a silica sol, the sol starts adhering to the surface of the substrate. During the withdrawal step, the sol deposits on the surface of the substrate leading to drainage of excess liquid and evaporation of the sol to forming a gel on the support surface. Brinker et al. [49] proposed that there is a sequential order of structural development that results from drainage accompanied by solvent evaporation, continued condensation reactions and capillary collapse. According to Brinker et al. [50] the concentration of the deposited film increases 18–36 fold due to evaporation. This causes the formed film to undergo very fast gelation and drying, thus suggesting structural reorganization of the film matrix.
Figure 3. SEM micrograph of the cross-section of a high quality asymmetric membrane structure—Reproduced by permission of The Royal Society of Chemistry (http://dx.doi.org/10.1039/B924327E) [51].
The withdrawal speed of the substrate from a sol, in addition to the viscosity of the sol, plays an important role in determining the silica thin film formation. Generally, withdrawal speeds reported by several research groups vary between 1 and 20 cm min−1, whilst prepared sols are diluted with ethanol up to 20 times the original sol volume. In this case (low withdrawal speed and low viscosity), the thickness of a film (h) is proportional to U2/3 (where U is the product of the viscosity and withdrawal speed), in accordance to the Landau and Levich equation [52]. Hence, increasing the speed of withdrawal in the dip coating process will yield thicker films and vice versa. As the production of thicker films tends to lead to cracking upon evaporation and gelation, thinner sols of low viscosity with low withdrawal speeds are preferred.
Upon film coating, the membranes are calcined at high temperatures, generally up to 600 °C, in order to fix the silica structure. Higher temperatures tend to densify the silica film, resulting in extremely low fluxes. The calcination process can lead to thermal stresses between the substrate and the thin silica film, possibly causing film cracking and defects. Hence the heating ramp rate is of considerable importance and is typically low at around 1 °C min−1, although recent developments in rapid thermal processing for silica membranes in other applications are challenging this long held view [53,54]. As the thickness of the silica films are generally in the region of 30–50 nm, and possibly a single film may contain defects caused by either inhomogeneity in the support or interlayers, or calcination stresses, or environmental dust; the dip coating and calcination process is generally repeated at least 2–3 times to produce high quality membranes. As environmental dust affects thin film formation, de Vos and Verweij [55] demonstrated that the quality of silica membranes was greatly improved by simply coating in a clean room environment.
4. Novel Silica Based Membranes in Desalination
4.1. Hydro-stability and Current Strategies
Owing to the affinity of amorphous silica for water adsorption, silica derived membranes undergo structural degradation when exposed to water, leading to a loss of selectivity [56]. Briefly, silica surface materials are prone to rehydration via a mechanism of physisorption of H2O molecules on silanol groups (Si-OH), followed by reaction with a nearby siloxane (chemisorption) [57,58]. As a result, H2O assists the breakage of siloxane groups, allowing for dissociative chemisorption via the hydrolysis reaction (the reverse of Equation (5)) [59]. Therefore, hydrolysed surface siloxanes may become strained, which act as strong acid–base sites, having a rapid uptake of water and becoming mobile [60]. As the silica seeks to reduce its surface energy under hydrothermal conditions [61], Duke and co-workers [60] proposed that the mobile and strained hydrolysed siloxane groups migrate to smaller pores where they undergo re-condensation to block the pore, whilst the larger pores become even larger. Hydro-stability is therefore a serious problem for the deployment of silica based membranes for water desalination. To address this problem, researchers have attempted to modify the surface properties of the silica, to minimize the interaction of water molecules with the membrane structure. A summary of the main strategies employed is displayed in Figure 4.
One strategy to solve this challenging problem is introducing non-covalently bonded, organic templates into the pure silica matrix [62–64]. Indeed, the presence of carbon moieties embedded into the silica framework can prevent the mobility of soluble silica groups under hydrolytic attack and consequently inhibits micropore collapse. This was demonstrated by Duke et al. [60] who successfully prepared carbonized-template molecular sieve silica membranes (CTMSS) by introducing the ionic surfactant (C6 hexyltriethyl ammonium bromide) during the silica sol synthesis. The carbon moieties trapped in the CTMSS matrix were formed by carbonization of the surfactant under vacuum or an inert atmosphere, leading to a hybrid silica/carbon membrane. Although CTMSS membranes still retained their hydrophilic properties, the resultant membranes showed great potential for attaining hydro-stability without compromising the selectivity for wet gas separation [65]. Based on this approach, CTMSS membranes were subsequently tested for desalination performance, demonstrating high salt rejection from seawater [19].
In a similar study, Wijaya et al. [66] investigated the effect of the carbon chain length of ionic surfactants in CTMSS membranes for desalination by preparing sol-gels with hexyltriethyl ammonium bromide (C6), dodecyltrimethyl ammonium bromide (C12) and hexadecyltrimethyl ammonium bromide (C16). It was found that the CTMSS membrane prepared with the surfactant with the longest carbon chain (C16) delivered the highest salt rejection, whilst also given the largest pore volume and surface area, although interestingly, the average pore sizes were similar for the three surfactants used. These results suggest that the embedded carbon has a beneficial role in silica matrices and the amount embedded has a direct impact in terms of desalination performance, since the carbon content of the added surfactant is directly related to the amount of carbon remaining following carbonization. However, if the concentration of ionic surfactants is too high they form micelles [67] which drastically limits the possibility of using the sol-gel to dip coat substrates. In order to increase the carbon content in the silica framework, Ladewig et al. [68] proposed the use of a non-ionic surfactant such as a tri-block copolymer like polyethylene glycol–polypropylene glycol–polyethylene glycol (PEG-PPG-PEG), a high molecular weight polymer. Silica samples were mixed with 1–20 wt % PEG-PPG-PEG, and increasing the loading of the tri-block copolymer to 10 wt % effectively doubled the pore volume and surface area compared to pure silica, whilst still maintaining microporosity. Further increases in tri-block copolymer loading to 20 wt % altered the structure of the CTMSS materials to produce mesopores. Of greatest relevance to both the preceding studies and future research directions, the CTMSS membranes prepared with 10 wt % PEG-PPG-PEG (i.e., the highest carbon content sample, whilst still remaining microporous) also delivered high salt rejections and water fluxes.
Figure 4. Schematic representation of various strategies for silica modification.
Another approach to increase the hydrothermal stability of the pure silica membrane is by incorporating terminal methyl groups (≡Si-CH3) via various precursors used during the sol-gel synthesis (Figure 5). This was firstly reported by de Vos et al. [69] who synthesized methylated silica membranes derived by the copolymerization of TEOS and methyltriethoxysilane (MTES) in the presence of ethanol and water, with an acid catalysis. Again, the membranes were calcined under a non-oxidising environment to retain the carbon moieties in the silica matrix. These membranes showed remarkable stability for alcohol dehydration for 18 months, though severe degradation occurred at testing temperatures of ≥95 °C thereafter [70]. Although the addition of methyl ligand groups to silica rendered hydrophobicity, the counter effect was the formation of larger micropores. Duke et al. [19] investigated the effect of both methyl ligand and non-ligand C6 surfactant as templates in silica membranes for desalination. They found that the CTMSS membrane outperformed the methylated-silica membrane, suggesting that carbonizing the C6 surfactants led to the formation of smaller pores than the covalently attached methyl groups.
Figure 5. Precursors used for the preparation of pure (TEOS), methylated (MTES) and hybrids (BTESE) silica membranes.
Following on from the methyl ligand work, significant hydrothermal improvement can be achieved when the siloxane bridges (Si-O-Si) are partially replaced by organic bridges (Si-CH2-CH2-Si) such as BTESE in Figure 5. In this method, alkyl groups (ethylene groups in Figure 5) between Si atoms, which cannot be hydrolyzed, can be used as a "spacer" to control the silica network size while minimizing the hydrophilicity of the silica pore surface. The sol synthesis for such membrane layers was first developed by Castricum et al. [71] and consisted of a two-step acid hydrolysis of BTESE/MTES mixtures. In this work they showed that the durability of the membrane network for the dehydration of n-butanol by PV was greatly improved by incorporating hydrolytically stable organic groups as integral bridging components into the nanoporous silica. These hybrid organosilica membranes were able to withstand long-term PV operation of up to 2 years at 150 °C. Recently, Tsuru et al. reported the potential of such BTESE membranes in RO and PV desalination processes [72].
Alternate efforts have focused on modifying the silica structure through the addition of metal oxides [73–77]. Recently Lin et al. [21] reported for the first time the potential of cobalt oxide silica (CoOxSi) membranes for desalination of waters from brackish to brine concentrations. CoOxSi xerogels were synthesised via the sol-gel method using TEOS, cobalt nitrate hexahydrate and hydrogen peroxide, at a range of pH from 3 to 6. The pH was altered by addition of ammonia during the sol-gel process. Initial hydrothermal exposure (<2 days) at 75 °C of xerogels resulted in the reduction of pore volume and surface area, although subsequent exposure proved that the pore structure of the xerogels was no longer significantly altered. The CoOxSi synthesized at pH 5 was the most resistant to the hydrothermal degradation, remaining stable and delivering high salt rejections for 570 hours of testing at temperatures up to 75 °C and NaCl salt concentrations up to 15 wt %.
4.2. Membrane Performance: Effect of Testing Conditions
A summary of the reported membranes performance in term of water flux and salt rejection is listed in Table 1. It must be stressed that comparing these results gives an indication of the general performance only. One should be aware of that these results are dependent upon several parameters related to testing condition including feed concentration, salt used, feed temperature, feed flow rate, cross-flow velocity, permeate vapour pressure and fouling/scaling tendencies. In addition, these listed membranes may have different geometries (flat or tubular and sizes) and architecture (thickness of top film, number interlayers number, porosity and substrate). As such, all these factors play a role in the final performance of the tested membranes.
a Feed pressurizing up to 7 bar and permeate vacuum pumping; b Permeate vacuum pumping, resulting in a pressure difference ΔP across the membrane less than 1bar; * Sea water.
The majority of membranes listed in Table 1 were tested for feed synthetic solutions containing NaCl dissolved in deionised water with concentrations ranging from 0.3 to 3.5 wt % in order to simulate the typical salt concentration of brackish water (0.3–1 wt %) and sea water (3.5 wt %). CTMSS membranes gave similar water fluxes varying from 1.4 to 6.3 kg m−2 h−1 with high salt rejections greater than 84%, depending on the operating conditions. Hybrid membranes (i.e., those prepared with terminal methyl groups or covalently bound carbon bridges) also gave similar water fluxes and salt rejections. The hybrid membranes prepared with BTESE delivered considerable high water fluxes at 34 kg m−2 h−1 at 90 °C and excellent salt rejection 99.9%. However, these membranes were tested at very low salt concentration (NaCl 0.2 wt %) and high feed temperature and when cooler feed temperatures (30 °C) were used, the water fluxes reduced considerably (one order of magnitude). In the only study of its kind so far, CoOxSi based silica membranes were also investigated for brine
processing conditions where the salt concentrations ranged from 7.5 to 15 wt %. In this case, an increase in salt concentration in the feed from 0.3 to 15 wt % resulted in a decline of the permeate flux from 1.8 to 0.55 kg m−2 h−1 at 75 °C. However, despite the high salt feed concentrations, the salt rejection remained high suggesting they were stable under these harsh testing conditions.
Analyzing the results reported in Table 1, the trends are very clear with increasing temperature yielding increased water flux whilst increasing salt concentration results in decreasing water flux. For instance, at 0.3 wt % salt feed concentration, the water flux increased by 77% (from 0.4 to 1.8 kg m−2 h−1) as the feed temperature was raised from 20 °C to 75 °C. This can be explained through the thermodynamics of the system in that as the temperature increases, so does the water vapour pressure in the feed stream, leading to an increase in the driving force for water permeation across the membrane. Likewise the water vapour pressure decreases as a function of the salt concentration, partially explaining the decreased flux observed under seawater and brine feed concentrations. However, the water vapour pressure change as a function of the salt concentration at constant temperature is not large enough to justify the large reduction of flux as reported by several groups in Table 1. For instance, in the case of carbonized template CTMSS (ionic C6), experiment was conducted at a fixed temperature of 20 °C [68]. Indeed, water flux was reduced by more than half (56%) by increasing the feed concentration from 0.3 wt % to 3.5 wt %. In that case, the change in vapour pressure driving force of an ideal salt solution will change from 2.3 kPa to 2.28 kPa, representing a decrease of 0.08% [78] far smaller than the decline in flux, thus demonstrating that salt and temperature polarization are also likely occurring. In this case a boundary layer of more concentrated salt forms at the membrane surface due to the permeation of water through the membrane being faster than the diffusion for fresh water from the bulk to the membrane surface. Likewise thermal boundary layers can form through the conduction and convection of sensible heat and the transfer of latent heat through the vapourisation of water through the membrane. This phenomenon, along with temperature polarization, is commonly observed for MD processes. Interestingly, temperature polarization, whereby the heat flow across the membrane from conduction and convection is sufficient to reduce the temperature at the membrane surface in comparison to the bulk feed, is the more commonly reported problem [79]. The fact that salt concentration polarization is strongest suggest that (a) the silica-based membrane is more insulating than typical polymeric MD counterparts, and that (b) the cross flow velocities investigated were not sufficient to disturb or reduce the mass transfer boundary layer.
The purity of the water in the permeate stream is a fundamental parameter in terms of potable water. As the salt rejection is generally a ratio of salinities (Equation (2)), a high salt rejection for a high feed salt stream does not necessarily translating into potable water. According to the World Health Organization portable water should have a factor called total dissolved solids (TDS) < 600 ppm with an upper limit of TDS < 1000 ppm [1]. To assess the performance of the membranes in Table 1 in terms of water quality, the permeate water concentration was calculated as shown in Figure 6. All the membranes listed in Table 1 produce good quality drinking water (TDS < 600 ppm) for slightly saline water conditions (0.3 wt %). However, only the CoOxSi silica base membranes were able to meet the requirement of 600 ppm for seawater and brine feed conditions. For those membranes with TDS in excess of 600 or 1000, a second pass becomes necessary to achieve potable water requirements. As discussed previously the theory of PV operation necessitates that the permeate stream should be free of salt, regardless of the feed conditions.
The observation that the vast majority of silica-based membranes tested under PV desalination conditions do not give pure water in the permeate stream is strong evidence that research focusing on improving the hydro-stability of the silica as well as the integrity of the membrane layer itself should continue to receive high priority. However, only a handful of authors have reported preliminary stability measurements as listed in Table 1. In the longest performance evaluation reported so far, Lin et al. sequentially tested cobalt oxide silica (CoOxSi) membranes with solutions containing salt at 1 wt % (288 h), 3.5 wt % (144 h), 7.5wt % (72 h) and 15 wt % (72 h), leading to a total of 575 hours [21]. Despite a significant variation in water flux observed during the first 120 h, the water flux tended to stabilize after 5 days of measurement. This was attributed to initial textural and/or structural changes in the CoOxSi matrix and was also observed in nitrogen sorption and FTIR analyses. However, this long term testing successfully demonstrated the improved hydro-stability of CoOxSi membranes at several temperature points and feed concentrations. In the only other studies reported thus far, Duke et al. reported stable performance over 5 h of the CTMSS (Ionic 6) membrane [19]; and Ladewig et al. showed stable performance over 12 h, suggesting the benefit of the carbonized templating method to improve the hydro-stability of amorphous silica membranes [68].
Figure 6. Comparison of water quality in the permeate stream.
4.3. Future Challenges
Silica based membranes for desalination applications are still at the embryonic stages of research and development. Therefore, this type of membrane requires significant improvements to be able to compete against both alternate membranes and alternate technologies. Indeed, the RO process using polymeric membranes is now a mature technology, having undergone major research, development and deployment in the last 30 years. This developmental advantage implies that RO will continue to dominate the large desalination plants around the world. However, RO cannot process all feed concentrations, in particular the pressure requirements for brine processing are prohibitive and can even destroy the polymeric membranes. Thus silica based membranes (especially metal oxide silica membranes, such as CoOxSi) operating under PV conditions, could have a niche market in the processing of brines or even the processing or drying of mineral salts such as potash or lithium brines.
In order to be able to compete against polymeric RO membranes, the water fluxes of silica based membranes for processing seawater (NaCl 3.5 wt %) must be significantly increased, by an order of magnitude on average. At the moment high water fluxes in excess of 20 kg m−2 h−1 have been demonstrated for BTESE silica membranes only, although only for slightly saline feed concentrations (NaCl 0.2 wt %) and high temperatures at 90 °C. This raises the second major impediment to silica based membrane PV, the issue of temperature, and ultimately energy consumption. PV is a thermal process and raising the temperature of feed translates into higher vapour pressures which should likewise increase water flux and water production. The problem here is that heat must be generated to increase the temperature of the water (and ultimately vapourise it), which together with the energy required to condense the water vapour explains why the PV process uses more energy per liter of water produced than RO processes which use only pump energy to pressurize the saline water feed. If this heat is supplied through conventional means, the cost will be prohibitive. However, there are several options available to reduce the cost of energy by utilising waste heat from industrial sites and thermal power plants, salt gradient solar ponds or solar heat [80–84]. These options may be attractive to deploy PV using silica based membranes.
A vital aspect of any membrane technology is long term operation and stability. At the moment, CoOxSi silica membranes have demonstrated stability up to 575 hours of operation. Similar tests must also be undertaken for CTMSS and hybrid silica based membranes to show proof of concept. To some extent, the CoOxSi silica membranes showed superior performance than MFI zeolites, which may be viewed as a competing membrane technology. In a recent study, Dobrek and co-workers [22] reported the dissolution of both S-1 and ZSM-5 top layers in MFI zeolite membranes after 560 hours testing in PV desalination. This was attributed to the combined effects of ion exchange and water dissolution mechanisms. The loss of membrane performance due to the quality of the saline waters can therefore cause deterioration of the materials such as in zeolite membranes, or fouling and scaling as is the case of polymeric membranes [85]. Currently, there is no fouling work reported for silica based membranes mainly due to the embryonic nature of the testing which has occurred under laboratory conditions using synthetic salt solutions. Given the scale of the problem for RO membranes, this is a problem that will require substantial research to ensure that silica based membranes can be deployed in an industrial context to process saline waters to potable quality.
5. Conclusions
Microporous silica based membranes have been shown to provide excellent molecular sieving properties for gas separation applications but their reported use in water treatment processes, such as desalination, have been limited, primarily due to the lack of stability when exposed to water. However, innovative concepts have been developed in the last two decades to realize the potential of silica based membranes for desalination via PV. In particular, research into silica based membrane desalination has focussed on three distinct methods of stabilising the structure including carbon templated silica, hybrid organic-inorganic silica and metal oxide silica. Whilst these methods have all been successfully trialed for desalination via PV, only metal oxide silica membranes have demonstrated significant potential with high salt rejections under all feed concentrations, reasonable fluxes and unaltered performance for over 575 hours of operation. Indeed they were the only membranes capable of producing potable water from highly concentrated brine feed streams. The target areas of research for membrane scientists is therefore on the materials development to further improve water fluxes (in order to compete with RO processes), to stabilize the silica structure to ensure no reductions in long term performance and to produce defect-free membranes to ensure high salt rejections, at low cost. The final challenge for the membrane research community is to establish the conditions under which PV desalination using silica based membranes is most technically and economically viable. The energy requirements of PV systems are considerable in comparison to RO processes and analysis of the thermodynamics indicates that parity will never be reached when utilizing primary energy sources. However, if PV processes are successfully integrated with waste heat or solar heat sources then the technology may be attractive for niche applications such as brine processing or salt recovery. Regardless, the separation and purification of potable water from desalination is a paramount task which the membrane research community must endeavour to address before water supply becomes a global crisis.
Muthia Elma specially thanks for the scholarship provided by the University of Queensland. The authors acknowledge financial support from the Australian Research Council (DP110101185). Simon Smart also acknowledges funding support from the Australian Research Council (DP110103440).
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Muthia Elma, Christelle Yacou, David K. Wang, Simon Smart and João C. Diniz da Costa *
Films and Inorganic Membrane Laboratory, School of Chemical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia; E-Mails: firstname.lastname@example.com (M.E.); firstname.lastname@example.com (C.Y.); firstname.lastname@example.com (D.K.W.); email@example.com (S.S.)
* Author to whom correspondence should be addressed; E-Mail: firstname.lastname@example.com; Tel.: +62-7-33656960; Fax: +62-7-33654199.
© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Testing Fountain Solution Conductivity – MyronLMeters.com
Tweet WHY TEST FOUNTAIN SOLUTIONS? Accurate fountain (dampening) solution concentration control is essential for consistent, high-quality results in lithography. Low concentration can cause drying on the non-image area of the plate resulting in tinting, scumming, blanket piling, etc. High concentrations, on the other hand, bring about over-emulsification of the ink. This results in weakening of […]
Accurate fountain (dampening) solution concentration control is essential for consistent, high-quality results in lithography. Low concentration can cause drying on the non-image area of the plate resulting in tinting, scumming, blanket piling, etc. High concentrations, on the other hand, bring about over-emulsification of the ink. This results in weakening of color strength and changes in ink rheology (body and flow properties). Correct concentration will allow the non-image areas of the plate to be appropriately wetted.
Traditionally, pH was the test relied on to determine fountain solution concentration. Today, however, conductivity testing is recognized as a much more accurate method. Many modern dampening solutions are pH stabilized (or buffered), so only small changes in pH are seen even when dramatic changes occur in solution strength. Conductivity measurement is a fast and easy test which is more indicative of fountain solution concentration than pH. This is true for all neutral, alkaline, and many acid type solutions.
pH is still important, however, with unbuffered acid fountain solutions. Checking both conductivity and pH can provide valuable information. Acid fountain solution is a mixture of gum arabic, wetting agents, salts, acids, buffers, etc. Conductivity will tell you if the proper amount of most ingredients are present, but pH is necessary to check acid concentrations. pH will also determine how effective one ingredient, gum arabic, will be.
What is conductivity? Conductivity is the measurement of a solution's ability to conduct an electrical current. It is usually expressed in microsiemens (micromhos). Absolutely pure water is actually a poor electrical conductor. It is the substances dissolved in water which determine how conductive the solution will be. Therefore, conductivity is an excellent indicator of solution strength.
To properly measure the conductivity of fountain solutions:
1. Test and write down the conductivity of the water used to prepare the solution.
- Mix the fountain solution concentrate with the water, using the manufacturer's recommendations or as experience dictates.
- Measure the conductivity of the mixed solution.
- Subtract the water conductivity value obtained in step 1. This is necessary because tap water quality can change from day to day.
The resulting number is an accurate indicator of fountain solution strength. Caution: because alcohol will lower a solution's conductivity, always test solution conductivity before and after the addition of alcohol.
Determining the best concentration of fountain solution is mostly "trial and error." It can be very useful to make a graph, recording readings for every one-half or one ounce of concentrate added to a gallon of water. Record readings on a graph with the vertical axis representing conductivity values and the horizontal axis representing ounces/gallon. Such a graph will help "fine tune" your system during future press runs.
For "on the spot" fountain solution tests, Myron L handheld instruments are fast, accurate, and reliable. Measurements are made in seconds simply by pouring a small sample of solution into the instrument cell cup and pressing a button. Automatic temperature compensated accuracy and famous Myron L reliability have made our instruments popular in pressrooms worldwide.
pH TESTING
Even though pH usually is not the best method to check the concentration of fountain solution, it is still very important and must be checked regularly. The pH of acid dampening solution affects sensitivity, plate-life, ink-drying, etc. Also, pH can change during a run if the paper has a high acid or alkaline content. pH, therefore, must be maintained at the proper level for good printing.
A convenient and accurate way to test pH (as well as temperature) is Myron L's waterproof Ultrameter II™ Model 6PFCE or TechPro II™ TH1. The 6PFCE has a 100 reading memory and the TH1 has a 20 reading memory to store test results on site. The 6P also measures conductivity. All electrodes are contained in the cell cup for protection. Model M6/PH also measures pH and conductivity.
For continuous monitoring and/or control of fountain solution concentration, Myron L offers a complete series of in-line conductivity instruments. These economical, accurate, and reliable models use a remotely installed sensor and a panel/wall mount meter enclosure. Most contain an adjustable set point and heavy duty relay circuit which can be used to activate alarms, valves, feedpumps, etc. All models contain a 0-10 VDC output for a chart recorder or PLC (SCADA) input, if required, (4-20 mA output is also available).
The 750 Series II with dual set point option has become quite popular in pressrooms. The two set points allow a "safe zone" for controlling fountain solution concentration.
Ultrameter II 6PFCE, 512M5 and M6/PH are available with the useful LITHO-KIT™. This accessory includes a foam-lined, rugged all-plastic carry case with calibrating solutions and buffers. In addition, a syringe to simplify drawing samples and a thermometer for testing fountain solution temperature are also included.
|Improperly mixed fountain solution
|Carefully follow manufacturer's directions, checking both water and mixed solution with a conductivity instrument
|Ultrameter 4PII, 6PIIFCE and 9PTK; ULTRAPEN PT1; and TechPro II TPH1 or TP1 all test 0-9999 ppm TDS or microsiemens conductivity, and temperature. 512M5 por- table DS Meter™ with a 0-5000 microsiemens conductivity range.
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|Check pH of fountain solution to determine if it's too acidic
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|Ultrameter 4PII, 6PIIFCE and 9PTK; ULTRAPEN PT1; and TechPro II TPH1 or TP1 all test 0-9999 ppm TDS or microsiemens conductivity, and temperature.
|Scum streaks across plate after 10,000 – 20,000impressions
|Check acid/gum levels infountain solution
|Ultrameter 6PIIFCE and 9PTK; ULTRAPEN PT2; and TechPro II TH1.M6/PH portable pDS Meter. Ranges: 0-5000 microsiemens and 2-12 pH.
|Personnel unable to test fountain solution concentration
|Continuously control fountain solution with conductivity Monitor/controller
|758II-123 (0-5000 µS) in-lineMonitor/controller.
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