NATIONAL UNIVERSTIY OF SINGAPORE

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1 NATIONAL UNIVERSTIY OF SINGAPORE Division of Environmental Science and Engineering EG2605 UROP Lab Report Studies on membrane fouling in MBR Supervisor: Professor NG How Yong Instructor: NG Tze Chiang Albert Name: Zhang Zhaoxuan Matriculation No: U067444W Registration Date: 05/09/2008 Duration of the UROP: 10/05/ /02/2009 1

2 Introduction Membrane fouling is a common phenomenon that limits the widespread application and acceptance of membrane bioreactors (MBRs) in the industry. It remains one of the most challenging issues facing further MBR development. There is, thus, a need to understand and control the fouling process during water filtration. Understanding the mechanism at which fouling is induced and subsequently develops may help to generate ideas at which membrane fouling can be alleviated by controlling certain parameters. This study aims to identify the parameters responsible for membrane fouling on the membrane surface, and relate them to actual lab-scale operating parameters of an MBR system so that we can minimise the effects of membrane fouling in MBRs system in the industry. Objectives The objectives of this research study are listed below: 1. To develop a fast and efficient method to describe the state of membrane fouling 2. To investigate if the method is capable of reflecting the flux conditions in an MBR Literature Review 1. Membrane Bioreactor (MBR) MBR is the combination of both a process as microfiltration or ultrafiltration and a suspended growth bioreactor (with suspended activated sludge inside). The system can maintain the activated sludge concentration at a relatively high-levels in the reaction tank without use of a sedimentation tank for separation of liquids mixed with the activated sludge. After the filtration, the treated water has a very high quality. Moreover, it is space saving and reduces operation costs. Since sludge control in a sedimentation tank is eliminated, this treatment system has a feature of easier maintenance. The technology is widely used for both municipal and industrial wastewater treatment. 1 The main feature of MBR can be listed as below: 2 High-quality treated water: The MF membrane (with a pore size of 0.4 μm) is used for filtration, and thereby of coliform bacteria can be completely removed. The treated water can be reused for landscape maintenance. Space saving: As the membrane separates solids and liquids, no sedimentation tank is required. 1 Membrane Bioreactor, Wikipedia: 2 Hitachi Plant Technologies Ltd Official Website: 2

3 Due to the shorter treatment time, the system boasts a more compact design. Thickening tank is not required since excess sludge is in high concentration and can be dehydrated directly. Easy maintenance: The system has a simple, flat membrane structure whereby impurities are difficult to twine around. Easy maintenance and remote monitoring are possible, since the sludge settling control is not required. By employment of elements with a large membrane area as modules, easy installation, inspection and replacement are facilitated. High reliability: The membrane element is a sturdy, integral structure receiving resin treatment. Energy saving: Membrane modules are installed in two-step so that the air for membrane cleaning surface can be utilized more efficiently. 2. Membrane Fouling Figure 1. Typical Membrane Bioreactor 3 Membrane fouling is defined as the process where particles in water deposit onto the membrane surface or within membrane pores after which membrane performance is adversely affected. It serves as the main obstacle that holds back the use of membrane technology. 3 Graph extracted from: 3

4 Membrane fouling can cause significant flux decline and it will affect the volume of effluent produced. Severe membrane fouling requires chemical cleaning or replacement of the membrane which greatly increase the cost for the water treatment plant. Factors that may affect membrane fouling are: 1. Membrane properties such as pore size, hydrophobicity, pore size distribution and membrane material; 2. Solution properties such as concentration, nature of components and particle size distribution; 3. Operating conditions such as ph, temperature, flowrate and pressure. Measure of membrane fouling: Flux and trans-membrane pressure (TMP) are usually the main indicators of membrane fouling. Under constant flux operation, TMP increases to compensate for the fouling. On the other hand, under constant pressure operation, flux declines due to the occurrence of membrane fouling. Fouling control: Although membrane fouling is an inevitable phenomenon which affects membrane filtration, it can be controlled by different ways such as cleaning, appropriate membrane selection and control of operating conditions. Membranes can be cleaned either physically, biologically or chemically. Physical cleaning includes sponge, water jet or back-flushing. Biologically cleaning uses biocides to kill microorganisms attached, whereas chemical cleaning involves the use of acids and bases to remove foulants and impurities. Another strategy to control membrane fouling is using the appropriate membrane for specific operation. It is always necessary to understand the nature of the influent and therefore a membrane that is less prone to fouling to that solution is selected For example: during aqueous filtration, hydrophilic membrane is preferred. Moreover, operating conditions during membrane filtration are vital as they may affect fouling conditions during filtration. For instance, cross-flow filtration is always preferred rather than dead end filtration because turbulence generated during the filtration provides thinner deposit layer and therefore controls fouling. 3. Image Structure Analyzer (ISA) Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image Structure Analyzer developed by the Biofilm Structure and Function Research Group is used in this experiment to qualify the biofilm structure numerically. It calculates textural entropy, homogeneity, energy, contrast, correlation, areal porosity, run lengths, diffusion distances and fractal dimension from digital biofilm images. A detailed introduction of the parameters it extracts from the images is listed as the following. Area Parameters 4

5 1. Describe morphological characteristics of biofilms 2. Measures unique characteristics of the cell cluster or void in the biofilm 3. Concerned with the size and shape of the consequent parts Areal Porosity (AP): Areal Porosity = Number of Void Pixels / Total Number of Pixels Fractal Dimension (FD): The higher the fractal dimension value, the more irregular the perimeter of the object. The rougher the biofilm boundary, the higher the fractal dimension is. The number varies between 1 and 2. Average Diffuse Distance (ADD): Average of the minimum distance from each cluster pixel to the nearest void pixel over all cluster pixels in the image. Maximum Diffuse Distance (MDD): Maximum of the minimum distance from each cluster pixel to the nearest void pixel over all cluster pixels in the image. Textual Parameters 1. The textural parameters measure the microscale heterogeneity in the biofilm by comparing the size, position and/or orientation of the biofilm constituents. 2. Texture is broadly defined as the rate and direction of change of the chromatic properties of the image, and could be subjectively described as fine, coarse, smooth, random, rippled, irregular, etc. Horizontal Run Length (HRL) Vertical Run Length (VRL) Energy (E) Textual Entropy (TE): TE is a measure of the pure randomness in the gray scale image. The higher the textural entropy value, the more heterogeneous the biofilm is. Heterogeneity (H): It measure the how heterogeneous the biofilm is. Experiment Method and Procedure 1. MBR Maintenance (1) MBRs parameters: 5

6 MBRs require frequent maintenance and the detailed procedure of maintenance depend on the actual MBRs used. The MBRs used in this experiment is designed as the following: a. Reactor volume: 7L b. Sludge retention time: 20 days c. Desludge twice a day with 175 ml each time d. Air flow rate: 2L/min 2. DO concentration: mg/l e. HRT varies according to the operating flux (the four reactors were run under different flux: sub-critical, sub-critical, critical and super-critical respectively, HRT for subcritical is longer and for super-critical is longer) f. Membrane material: Polyolefin g. Nominal Pore size: 0.45 m (2) Maintenance Procedure a. Desludge twice a day with 175 ml each time. Make sure the pipes are disconnected and cleaned during desludging to avoid clogging. b. Level sensors are cleared everyday. Fresh water are poured inside to clean and scrub the sensors. Squeeze the end of the connected pipe to avoid clogging. c. ph control bottles need to be filled with ph buffer (0.5M Na 2 CO 3 ) to maintain the ph in MBRs. d. Scrub the wall of MBRs everyday to get rid of attached bacteria. We need floating bacteria to degrade the BOCs in the water efficiently. e. Air-flow meters are checked everyday to maintain an air-flow of 1.5L/min. f. Glucose feeding: feed the glucose tank with glucose concentration of 400g/L. MilliQ water or Distill water are used in this process since glucose are vulnerable to bacteria or any limited amount of microorganism in the tap water. g. Nutrient tanks are frequently filled with three types of nutrients. The concentration for nutrient A is 2.66 ml/(l.water) and ml/(l.water) for both nutrient B and C. Nutrient A is added with 0.24g/L NaHCO 3 into the tank together. The nutrients are prepared as the following: Nutrient A contains g NH 4 Cl and 70.51g C 2 HPO 4 per 5 liter prepared; Nutrient B contains 8g CoCl, 2.46g Na 2 MoO 4 and 10 MgCl 2 per 1 liter prepared; Nutrient C contains 29.2g CaCl 2 and 27g FeCl 3 per 1 liter prepared. 2. Image Acquisition Images were obtained periodically by removing the membrane module from the MBR and placing it in a glass tank filled with effluent. Effluent of the MBR was used to as to maintain similar physico-chemical properties as in the MBR. The images were scanned with a high resolution paper scanner (Brand: Cannon) at various resolutions. The images were saved and analysed with ISA-2. Images were obtained without knowing the flux conditions in each MBR such that the subsequent data can be analysed to determine if the method is capable of describing membrane fouling based on the type of operating flux. 6

7 3. Data Extraction After the pictures are taken, data is extracted from the pictures using ISA. After running the program, we set the parameters as following: Directory: Input the directory of the picture Image type: Transmitted Light Result File: Name the file according to the picture name Result Write Mode: Write Mode GL Filter size: 0 BW Filter size: 1 INF: N.A Special Filter: None Threshold Method: Interative Selection: Image Order: Top First Interpolation Method: Linear dxy: Length of each dot (calculated from dpi, measured by micrometer) dz: 1 pmaxt: 0.05 Distance Mapping Method: Quasi Euclidean Map Colour: Hot % Image Size: 0.7 Image Contrast: 3 After adjusting the parameters, change the Folder Options by clicking Show hidden files and folders and unselecting Hide protected operating system files (recommended). Then click ISA button. The program will automatically generate a text file containing the numerical parameters of the picture. Repeat the same operation to every picture. 4. Data Analysis 1) Cut and paste the data from the text file onto one excel file base on different operating condition. 2) Arrange the data according to different operating condition, different dpi and chronologically. 3) Plot different parameters against time and observe if there exist any correlation between membrane fouling and time. 4) Obtain the TMP (trans-membrane pressure) and plot different parameters against TMP. Then observe and do further discussion. Result and Discussion 1. Results listed 7

8 Data Extracted From Green Reactor Using ISA Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 1. Data Extracted from Green Reactor Images with DPI 1200 Time (d) TMP (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 2. Data Extracted from Green Reactor Images with DPI 4800 Time (d) TMP (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H

9 Table 3. Data Extracted from Green Reactor Images with DPI Data Extracted From Blue Reactor Using ISA Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 4. Data Extracted from Blue Reactor Images with DPI 1200 Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 5. Data Extracted from Blue Reactor Images with DPI 4800 Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 6. Data Extracted from Blue Reactor Images with DPI Time (d) Data Extracted From Black Reactor Using ISA TMP (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H

10 Table 7. Data Extracted from Black Reactor Images with DPI 1200 Time (d) TMP (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 8. Data Extracted from Black Reactor Images with DPI 4800 Time (d) TMP (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 9. Data Extracted from Black Reactor Images with DPI Data Extracted From Yellow Reactor Using ISA Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H

11 Table 10. Data Extracted from Yellow Reactor Images with DPI 1200 Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 11. Data Extracted from Yellow Reactor Images with DPI 4800 Time TMP (d) (kpa) AP FD ADD MDD HRL VRL Perimeter E TE H Table 12. Data Extracted from Yellow Reactor Images with DPI Questions and Discussion 1) In reality, Areal Porosity (AP) should decrease with time. However, why from the data extracted we observe a general trend of increase of AP with time? In reality, AP should decrease with time since as time goes on, impurities in the water treated will slowly attach to the voids which decreases AP. From the data collected, we observed that AP has been increasing with time for Green Reactor with image dpi 1200, 4800, 19200; Blue Reactor with image dpi 1200; Black Reactor with image dpi 1200, 4800, 19200; Yellow Reactor with image dpi 4800, For example, a plot of AP 11

12 against time for Green Reactor with image dpi 1200 is showed as follow: 1 AP for Green y = 0.000x R² = Days Figure 2. AP against Time for Green Reactor Images with DPI To facilitate the analysis, we plot other graph same as figure 15, and obtained the relationship between AP and time for different reactors and different dpi. A table describing the relationship between AP and time is listed as below: Reactor dpi Correlation of AP against time R 2 Green 1200 y = x Green 4800 y = 0.004x Green y = x Blue 1200 y = x Blue 4800 y = x Blue y = x Black 1200 y = x Black 4800 y = x Black y = x Yellow 1200 y = x Yellow 4800 y = x Yellow y = x Table 13. Correlation between AP and Time As showed as we observe that 9 out of 12 graphs have an increased AP against time. Firstly we doubt that it might because the ISA was set Reflected Light mode instead of Transmitted Light mode during the extraction of the data. However, after re-extraction, I obtained the same results. Thus I guess the inaccuracy may be due to the following reasons: Firstly, the most possible reason may because with dpi 1200, 4800, 19200, the images cannot reflect the fouling of the membrane very well. This can be seen from the low 12

13 correlation between AP and time which is shown by the low R 2 in figure 16. If we try other dpi or cut the image into smaller image and do further analysis under other scales, we may get more reasonable results. The second possible reason may be due to the sampling of the images. The results will only be accurate when the images are representative. If the images represent special case on the membrane, the whole results may not be correct. The third possible reason may because as time goes on, the TMP also increase to maintain certain flux. An increase of TMP may counteract the process of fouling and enlarge the voids. I guess that an increase of TMP will wash some of the impurities attached to the membrane during reversible fouling. However, this mechanism is not supported by any journals that I have found thus it remain as a disputable reason. 2) With the TMP absence, is it possible to deduct which reactor is under what kind of pressure condition (sub-critical, critical, super-critical)? With the TMP given, it obvious that Green and Yellow reactors were running on subcritical flux condition, Blue reactor was running on critical condition, Black reactor was running on super-critical condition. Time (d) Green Blue Black Yellow Pressure Condition Subcritical Supercritical Critical Subcritical Table 14. TMP against Time for Four Reactors However, without the TMP value given, we also can deduct this information by simple comparison of the AP value. With the same run time, a lower AP value should correspond to a higher TMP running condition. This is simply because running at higher pressure with higher flux will promote membrane fouling. The comparing table is listed as below: Time (d) Green Blue Black Yellow Average Table 15. AP against Time for Four Reactors 13

14 AP vlue AP agianst time for Different Reactors Time (d) Figure 4. AP against Time for Four Reactors Green Blue Black Yellow By comparing four different reactors, we can see that the average AP for Blue (average ) is lower than the other three (average value , , respectively) thus the blue reactor should have been running under super-critical condition. However, since the other three reactors are with similar average AP value (0.5880, , respectively) it is difficult for us to tell the difference. To further tell the difference between the other three reactors, we have to compare the FD values. Usually, the higher FD value, the more irregular the perimeter of the object is. Moreover, the higher TMP, the less irregular the biofilm should be due to the scouring mechanism. Therefore, if we observe a higher value of FD we can tell it have a less TMP. The comparing table is as below: Time (d) Green Blue Black Yellow Average Table 16. FD against Time for Four Reactors 14

15 FD vlue FD agianst time for Different Reactors Time (d) Figure 5. FD against Time for Four Reactors Green Blue Black Yellow From Figure 20, it can be seen that Blue Reactor has the highest average FD (1.4417) which means that it have the least irregular biofilm attached to the membrane. This confirmed it to be the reactor that ran at super-critical flux condition. Black reactor, which has the moderate average FD value (1.4293), has a moderate regularity thus should be the one that ran under critical flux condition. The Green and Yellow reactor, which have the similar values for FD ( and ) are with the most irregular biofilm thus can be confirmed as reactors that ran under sub-critical condition. Moreover, it can be easily seen from Figure 21 that the line for FD value is generally above the other three indicating a high FD value. The lines for FD for Green and Yellow reactors nearly overlap each other and lies at the bottom indicating smallest FD value. Overall, even with the TMP value absent, we still can work out the flux condition that the reactors were running at by comparing the values like AP and FD. However, owing to the uncertainties in the data collected, we sometimes have to compare several parameters to obtain the correct results. 3) Which do you think is a better descriptive parameter for membrane fouling, if any? Since TMP is a good indicator of membrane fouling. Thus we draw graphs with parameters against TMP. Then we obtain the correlations between those parameters and TMP. Those have the largest correlations with TMP will be better descriptive parameter for membrane fouling. Example of such graphs is shown as below: 15

16 MDD for Blue 1200 y = x R² = TMP (kpa) Figure 6. MDD against TMP for Blue Reactor with dpi 1200 Therefore, we listed out the Coefficient of Regression (namely R 2 ) of those graphs: Reactor dpi AP FD ADD MDD HRL VRL Perimeter E TE H Green Green Green Blue Blue Blue Black Black Black Yellow Yellow Yellow Average N.A Table 17. Coefficient of Regression for Graphs with Different Parameter against TMP 16

17 Average Coefficient of Regression Average Coefficient of Regression for Different Parameters against TMP AP, 2-FD, 3-ADD, 4-MDD, 5-HRL, 6-VRL, 7-Perimeter, 8-E, 9-TE, 10-H 2- Figure 7. Average Coefficient of Regression for Different Parameter against TMP From Figure 23 and 24 we can see that ADD, MDD (Average Diffuse Distance and Maximum Diffuse Distance) have the highest average value of coefficient of regression (both above 0.4) which means that they have the highest correlation with TMP thus they should serve as better indicative parameters for membrane fouling than other parameters. Usually they are used to describe the growing size of a biofilm, thus the larger the values are, the more serious membrane fouling is. However, the value of coefficient of regression is still below what has been expected. The reason for this has already been analyzed in Question 1. However, it seems that ADD and MDD still seem to be relative better than other parameters. 4) Of the three dpi values, which is better? Are there any differences between each parameter? Similar to Question 3, we have to summarize the coefficient of regression for parameters against TMP and compare those values to see which dpi is better for describing membrane fouling. 17

18 Average Coefficient of Regression Reactor dpi AP FD ADD MDD HRL VRL Perimeter E TE H Average Green Green Green Blue Blue Blue Black Black Black Yellow Yellow Yellow Table 18. Coefficient of Regression for Graphs with Different Parameter against TMP (The red figures highlighted are the ones with coefficient of regression larger than 0.5) Average Coefficient of Regression for Different Parameters against TMP dpi dpi dpi19200 Figure 8. Average Coefficient of Regression for Different dpi Green Blue Black Yellow From figure 26 we can see that images with dpi generally have a higher coefficient of regression for Green and Yellow reactors, while images with dpi 1200 have higher coefficient of regression for Blue and Black reactor. However, none of these average values went beyond 0.5 indicating a poor correlation between those parameters and TMP. Thus to find the true result, changing of scale of the images are required. 18

19 Reactor dpi AP FD ADD MDD HRL VRL Perimeter E TE H Average Green Green Green Blue Blue Blue Black Black Black Yellow Yellow Yellow Table 19. Coefficient of Regression for Graphs with Different Parameter against TMP (The blue figures highlighted are the ones with the highest coefficient of regression among three dpi for a certain parameter and a certain reactor.) By observing figure 25, we also can see that if we highlight the coefficient of regression with a value that is larger than 0.5, more of these values (8 out of 20) fall in the images with dpi However, it is difficult to see which parameter favor which dpi. From figure 25, if we highlight larger coefficient of regression for each parameter and each dpi, we only can observe that TE and H favors dpi 1200 more as three out of the four reactors have higher correlation between parameters and TMP with dpi For the other parameters it is very difficult to get a general conclusion. Conclusion Study of membrane fouling is of great importance in the field of water and wastewater treatment as it is the bottle neck of the membrane technology. We extracted data from different images taken from the membrane to find an indicative parameter which can reflect the process of membrane fouling. From the data, we get a general observation how different parameters vary under different flux conditions. However, with the data given, it is difficult to draw a general conclusion which parameter is better for describing membrane fouling since those parameters showed poor correlation with TMP which is a proved indicator for membrane fouling. This may due to the improper scale of the images. Thus further analysis by cutting the images into small pieces may required to obtain better results. Through this UROP, I obtained knowledge in image analysis and data analysis which will 19

20 help me in my further research. Acknowledgement I would like to thank Professor NG How Yong for providing this opportunity and thank NG Tze Chiang Albert for his patient instruction during the whole UROP period. References 1. Beyenal, H., Donovan, C., Lewandowski, Z. and Harkin, G. Three-dimensional biofilm image analysis. 2004a. Journal of Microbiological Methods 59: Beyenal, H., Lewandowski, Z. and Harkin, G. Quantifying biofilm structure: Facts and fiction. 2004b. Biofouling 20: Yang, X. M., Beyenal, H., Harkin, G. and Lewandowski, Z. Quantifying biofilm structure using image analysis Journal of Microbiological Methods 39: Yang, X. M., Beyenal, H., Harkin, G. and Lewandowski, Z. Evaluation of biofilm image thresholding methods Water Research 35: Lei Wang, Xudong Wang, Study of membrane morphology by microscopic image analysis and membrane structure parameter model, Journal of Membrane Science, Volume 283, Issues 1-2, 20 October 2006, Pages