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EG2605 UROP Report National University of Singapore 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: 10/05/2008-03/02/2009


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: http://en.wikipedia.org/wiki/Membrane_bioreactor

2 Hitachi Plant Technologies Ltd Official Website:

http://www.hitachi-pt.com/products/ip/industrial/original/mbr.html

http://en.wikipedia.org/wiki/Membrane_bioreactor



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.

Figure 1. Typical Membrane Bioreactor

3

2. Membrane Fouling

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: http://www.hitachi-pt.com/products/ip/industrial/original/mbr.html.



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

http://en.wikipedia.org/wiki/Image

http://en.wikipedia.org/wiki/Digital_image

http://en.wikipedia.org/wiki/Digital_image_processing


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:


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: 4.0 – 4.5 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 sub-

critical 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 Na2CO3) 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 0.665 ml/(L.water) for both nutrient B and C.

Nutrient A is added with 0.24g/L NaHCO3 into the tank together.

The nutrients are prepared as the following:

Nutrient A contains 238.85g NH4Cl and 70.51g C2HPO4 per 5 liter prepared;

Nutrient B contains 8g CoCl, 2.46g Na2MoO4 and 10 MgCl2 per 1 liter prepared;

Nutrient C contains 29.2g CaCl2 and 27g FeCl3 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.


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


Data Extracted From Green Reactor Using ISA

Time (d)

TMP (kPa) AP FD

ADD

MDD HRL VRL Perimeter E TE H

0.00 0 0.6182 1.2335 470.04 2092.08 1163.25 1033.90 411247.2 0.02627 4.2543 0.8835

0.04 3 0.4315 1.3626 856.93 3026.83 1226.73 1030.74 529526.5 0.01014 5.2429 0.7344

0.25 4.5 0.7308 1.3572 57.37 322.97 108.69 114.28 159273.9 0.02677 4.3451 0.8296

1.00 4.4 0.4464 1.2800 349.97 1714.50 1309.67 1278.79 506962.8 0.03195 4.1526 0.8478

3.00 2.4 0.7130 1.5816 94.98 629.33 241.85 173.04 1427480 0.01305 5.3264 0.6257

7.00 2.8 0.8689 1.0983 162.25 643.76 391.75 341.85 359177.2 0.04024 4.1606 0.8179

9.00

14.00 3.4 0.7841 1.5459 94.96 719.67 185.36 147.22 1279800 0.01933 4.8237 0.7123

21.00 3.9 0.6004 1.5693 113.91 859.53 307.81 247.34 1466109 0.01912 4.8700 0.6963

28.00 3.4 0.8529 1.2092 149.42 698.50 286.87 229.58 551370.5 0.02757 4.4768 0.7697

35.00 4 0.4361 1.3624 341.88 1714.37 988.05 794.83 687916.7 0.01591 4.8617 0.7232 Table 1. Data Extracted from Green Reactor Images with DPI 1200

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.8278 1.2024 96.03 329.49 204.86 202.56 106886.4 0.04805 3.8246 0.8838

0.04 3 0.7362 1.4626 44.53 359.72 54.75 51.14 540231.5 0.01184 5.1060 0.6348

0.25 4.5 0.7437 1.4337 83.09 459.98 82.72 74.52 373517.6 0.01482 4.9530 0.6924

1.00 4.4 0.8569 1.1506 45.33 201.50 58.02 53.06 261196.7 0.03028 4.3489 0.7208

3.00 2.4 0.7405 1.3617 25.95 204.26 60.11 46.13 585290.1 0.00905 5.4541 0.5536

7.00 2.8 0.9995 0.7691 10.71 31.75 33.64 27.99 1460.5 0.06439 3.6146 0.8566

9.00 14.00 3.4 0.7943 1.3989 27.01 174.63 38.97 34.57 561239.5 0.01556 4.9460 0.6006

21.00 3.9 0.7870 1.4257 28.17 206.38 39.52 33.88 590576.5 0.01544 5.0301 0.6054

28.00 3.4 0.8725 1.1760 42.74 180.23 68.58 55.08 216810.2 0.03562 4.2138 0.7314


Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.8262 1.2070 23.59 79.13 51.81 49.61 27057.6 0.05114 3.7720 0.8842

0.04 3 0.7462 1.4461 11.03 83.08 13.95 13.04 126914.0 0.01194 5.0974 0.6404

0.25 4.5 0.7709 1.4300 18.71 108.29 17.93 16.11 94316.0 0.01614 4.8809 0.6926

1.00 4.4 0.8604 1.1374 11.83 50.27 15.24 14.28 60139.8 0.02935 4.3756 0.7222

3.00 2.4 0.7545 1.3239 6.15 43.33 15.17 12.21 133764.1 0.01039 5.4043 0.5690

7.00 2.8 0.9995 0.7473 2.70 8.05 8.61 7.21 366.4 0.06580 3.5989 0.8572

9.00


14.00 3.4 0.8252 1.3190 7.36 43.66 10.10 9.03 113538.0 0.01566 4.9673 0.5995

21.00 3.9 0.8166 1.3695 6.32 46.30 9.54 8.29 128463.1 0.01574 5.0187 0.6068

28.00 3.4 0.8740 1.1664 10.59 45.15 17.50 14.10 52349.1 0.03706 4.1797 0.7368


Data Extracted From Blue Reactor Using ISA

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.5783 1.1998 911.34 3220.74 1962.25 2076.36 242189.0 0.02049 4.5033 0.8742

0.04 5 0.4363 1.3864 561.48 3029.50 1005.57 880.80 655277.7 0.01505 4.9082 0.7634

0.25 8.5 0.3932 1.6120 202.25 1637.65 507.41 351.31 1601575.8 0.01133 5.1368 0.6287

1.00 24.4 0.5126 1.5992 149.85 1190.05 421.27 287.54 1539515.2 0.01564 4.8987 0.6831

3.00 30.4 0.5851 1.4113 192.97 960.93 481.48 447.21 935418.5 0.01993 4.6253 0.7563 Table 4. Data Extracted from Blue Reactor Images with DPI 1200

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.7396 1.2917

0.02086 4.5183 0.8411

0.04 5 0.8453 1.2368 51.21 275.17 64.35 57.77 268599.7 0.01173 5.1522 0.6091

0.25 8.5 0.8272 1.2302 32.71 206.38 39.72 35.52 446299.2 0.00989 5.2136 0.5090

1.00 24.4 0.8322 1.2497 31.95 232.35 45.48 38.16 419941.4 0.01362 5.0052 0.5612


Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.6890 1.3538 25.50 152.68 46.40 43.88 60166.3 0.02099 4.5110 0.8413

0.04 5 0.8638 1.2083 10.99 53.33 18.16 17.11 53727.6 0.01221 5.1419 0.6166

0.25 8.5 0.8348 1.2151 8.42 50.29 9.93 8.89 106124.4 0.00999 5.2070 0.5095

1.00 24.4 0.8275 1.2598 8.30 56.89 11.53 9.65 107144.3 0.01326 5.0282 0.5580


Data Extracted From Black Reactor Using ISA

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.6969 1.1741 394.41 1566.33 1084.18 962.86 341651.2 0.03609 3.9670 0.8950

0.04 4.3 0.6187 1.4420 246.82 1279.84 502.42 404.66 897382.0 0.02235 4.5499 0.7878

0.25 5.7 0.6138 1.4791 180.62 1274.40 405.73 292.73 1165944.7 0.01677 5.0952 0.6786


1.00 7.7 0.5216 1.5042 232.24 1397.00 564.69 444.34 1012698.0 0.01913 4.8870 0.6790

3.00 8.6 0.5865 1.5468 137.01 1100.67 370.40 308.26 1273069.2 0.01774 5.1938 0.6075

7.00 10.7 0.5051 1.5875 173.21 1442.60 363.00 362.39 1411202.8 0.01558 5.1849 0.6167

9.00 35.5 0.9943 0.7531 148.94 529.17 202.26 241.12 21844.0 0.01278 5.1396 0.5980 Table 7. Data Extracted from Black Reactor Images with DPI 1200

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.8245 1.2230 99.49 424.03 208.90 167.24 127296.3 0.04223 3.8696 0.8847

0.04 4.3 0.8362 1.1477 66.10 365.13 52.36 47.63 334745.5 0.02007 4.6176 0.6589

0.25 5.7 0.7530 1.3268 43.61 311.53 64.69 49.83 476144.2 0.00945 5.3988 0.5881

1.00 7.7 0.6917 1.5110 32.21 324.22 45.31 36.15 819266.4 0.00796 5.3445 0.5187

3.00 8.6 0.6156 1.5774 26.30 261.92 52.42 42.45 955479.2 0.00527 5.7029 0.4606

7.00 10.7 0.5822 1.4483 34.71 306.92 64.16 63.13 733610.2 0.00720 5.4042 0.5350


Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.8599 1.2160 22.39 87.84 48.00 39.63 26963.7 0.04388 3.8624 0.8820

0.04 4.3 0.8362 1.1477 16.52 91.28 13.09 11.91 83686.4 0.02007 4.6176 0.6589

0.25 5.7 0.7609 1.3179 10.63 78.15 15.96 12.31 116357.1 0.00961 5.3808 0.5898

1.00 7.7 0.7046 1.4834 8.70 79.39 11.28 9.16 194815.4 0.00802 5.3502 0.5213

3.00 8.6 0.6342 1.5670 6.05 55.83 12.55 10.22 235118.0 0.00542 5.6767 0.4638

7.00 10.7 0.6562 1.4382 6.29 54.24 12.30 12.21 191105.9 0.00816 5.3006 0.5319


Data Extracted From Yellow Reactor Using ISA

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.6992 1.2338 377.22 2001.90 1143.61 1109.98 326326.5 0.04334 3.8545 0.8900

0.04 2.7 0.8342 1.2741 162.64 816.77 282.27 240.51 597281.0 0.03131 4.2728 0.7475

0.25 2.6 0.6861 1.3563 183.07 952.50 380.71 328.61 932688.0 0.02083 4.6814 0.7695

1.00 3 0.3725 1.2908 534.76 2429.01 1622.48 1471.43 465158.7 0.02119 4.5515 0.7952

3.00 2.7 0.4361 1.4686 300.64 2256.91 732.16 558.31 1005501.3 0.01401 4.9376 0.7136

7.00 2.9 0.8522 1.2602 176.14 795.65 385.99 328.73 412517.2 0.02331 4.6653 0.7651

9.00 14.00 3.1 0.8615 1.2694 164.86 738.41 368.24 324.98 405849.7 0.02545 4.5951 0.7760

21.00 3 0.4512 1.2392 495.31 2172.03 1246.35 1270.92 479340.3 0.02634 4.4215 0.8007

28.00 3.3 0.4177 1.3332 625.89 2857.50 1067.50 1009.52 571373.0 0.02357 4.5399 0.7813


35.00 3.6 0.4022 1.2371 653.96 2034.20 1419.09 1381.49 434276.5 0.02817 4.3987 0.8088 Table 10. Data Extracted from Yellow Reactor Images with DPI 1200

Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.6512 1.2258 106.30 534.46 273.18 213.19 200866.4 0.03488 4.0361 0.8722

0.04 2.7 0.6411 1.4543 49.80 373.62 92.68 79.30 518054.2 0.02094 4.5488 0.7369

0.25 2.6 0.8606 1.1554 56.21 211.47 86.18 84.06 175445.2 0.03136 4.3833 0.7401

1.00 3 0.8406 1.2668 48.76 218.76 68.24 67.38 255688.0 0.02770 4.4299 0.7266

3.00 2.7 0.6620 1.4170 43.34 390.44 73.80 56.56 616108.8 0.01621 4.7675 0.6843

7.00 2.9 0.8573 1.2615 62.43 280.46 110.71 93.61 168724.8 0.02681 4.4858 0.7522

9.00

14.00 3.1 0.8698 1.2299 60.44 275.17 117.18 99.32 141747.9 0.03212 4.3520 0.7682

21.00 3 0.7343 1.4128 56.67 433.92 81.75 71.62 411173.1 0.03073 4.2126 0.7975

28.00 3.3 0.6827 1.4624 52.21 421.28 76.30 69.88 514741.6 0.02572 4.3428 0.7711


Time (d)

TMP (kPa) AP FD

ADD


0.00 0 0.6491 1.2297 26.67 132.09 68.52 52.98 50643.9 0.03460 4.0426 0.8713

0.04 2.7 0.8080 1.3318 9.73 60.45 15.27 12.98 94874.3 0.02731 4.3713 0.7369

0.25 2.6 0.8544 1.1615 14.02 52.92 20.51 20.42 47746.7 0.03238 4.3530 0.7394

1.00 3 0.8262 1.2818 12.50 55.56 16.61 16.21 71659.8 0.02856 4.4053 0.7286

3.00 2.7 0.8172 1.2745 8.87 50.20 12.43 10.28 110065.3 0.01865 4.6939 0.6793

7.00 2.9 0.8652 1.2584 15.83 69.02 29.13 24.92 37994.2 0.02715 4.5063 0.7489

9.00 14.00 3.1 0.8747 1.2355 15.76 54.43 31.49 27.68 31800.3 0.03291 4.3554 0.7673

21.00 3 0.8618 1.2542 11.32 55.56 17.51 15.59 57042.8 0.03943 4.0469 0.7954

28.00 3.3 0.8521 1.2543 10.39 59.53 13.58 12.16 75300.4 0.03148 4.1980 0.7657


2. 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, 19200. For example, a plot of AP


against time for Green Reactor with image dpi 1200 is showed as follow:

Figure 2. AP against Time for Green Reactor Images with DPI 19200

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 R2

Green 1200 y = 0.0005x + 0.6428 0.0015

Green 4800 y = 0.004x + 0.7923 0.2741

Green 19200 y = 0.0039x + 0.8047 0.3118

Blue 1200 y = 0.0366x + 0.4696 0.2955

Blue 4800 y = -0.0435x + 0.82 0.5221

Blue 19200 y = -0.0031x + 0.8014 0.0032

Black 1200 y = 0.0221x + 0.5842 0.2406

Black 4800 y = 0.0044x + 0.7423 0.0137

Black 19200 y = 0.0061x + 0.7582 0.0333

Yellow 1200 y = -0.007x + 0.6776 0.1955

Yellow 4800 y = 0.0014x + 0.7503 0.0347

Yellow 19200 y = 0.0031x + 0.7979 0.3233 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

y = 0.000x + 0.642R² = 0.001

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 40

Days

AP for Green 1200


correlation between AP and time which is shown by the low R2

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 sub-

critical flux condition, Blue reactor was running on critical condition, Black reactor was

running on super-critical condition.

Time (d) Green Blue Black Yellow

0.000 0 0 0 0

0.042 3 5 4.3 2.7

0.250 4.5 8.5 5.7 2.6

1.000 4.4 24.4 7.7 3

3.000 2.4 30.4 8.6 2.7

Pressure Condition

Sub-critical

Super-critical Critical

Sub-critical

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

0.000 0.6182 0.5783 0.6969 0.6992

0.042 0.4315 0.4363 0.6187 0.8342

0.250 0.7308 0.3932 0.6138 0.6861

1.000 0.4464 0.5126 0.5216 0.3725

3.000 0.7130 0.5851 0.5865 0.4361

Average 0.5880 0.5011 0.6075 0.6056 Table 15. AP against Time for Four Reactors


Figure 4. AP against Time for Four Reactors

By comparing four different reactors, we can see that the average AP for Blue (average

0.5011) is lower than the other three (average value 0.5880, 0.6075, 0.6056 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, 0.6075, 0.6056

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

0.000 1.2335 1.1998 1.1741 1.2338

0.042 1.3626 1.3864 1.4420 1.2741

0.250 1.3572 1.6120 1.4791 1.3563

1.000 1.2800 1.5992 1.5042 1.2908

3.000 1.5816 1.4113 1.5468 1.4686

Average 1.3630 1.4417 1.4293 1.3247 Table 16. FD against Time for Four Reactors

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500

AP

vlu

e

Time (d)

AP agianst time for Different Reactors

Green

Blue

Black

Yellow


Figure 5. FD against Time for Four Reactors

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 (1.3630 and 1.3247) 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:

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

1.4000

1.6000

1.8000

0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500

FD v

lue

Time (d)

FD agianst time for Different Reactors

Green

Blue

Black

Yellow


Figure 6. MDD against TMP for Blue Reactor with dpi 1200

Therefore, we listed out the Coefficient of Regression (namely R2) of those graphs:

Reactor dpi AP FD ADD MDD HRL VRL Perimeter E TE H

Green 1200 0.0355 0.0364 0.2196 0.1662 0.1506 0.1788 0.1776 0.0049 0.0265 0.2276

Green 4800 0.2384 0.0609 0.4841 0.5042 0.3609 0.3626 0.0122 0.0195 0.0142 0.1437

Green 19200 0.29400 0.09020 0.49460 0.49750 0.34540 0.35660 0.02920 0.02300 0.01720 0.15030

Blue 1200 0.12790 0.21000 0.61830 0.82160 0.56310 0.48760 0.24200 0.02780 0.00140 0.13770

Blue 4800 0.15260 0.40540 0.62920 0.39260 0.01410 0.01370 0.65590 0.08070 0.07180 0.20490

Blue 19200 0.04060 0.18660 0.53390 0.36750 0.45990 0.46990 0.78770 0.05400 0.04790 0.23140

Black 1200 0.60010 0.52560 0.31880 0.83860 0.44400 0.29980 0.26520 0.41210 0.27110 0.39850

Black 4800 0.25290 0.19100 0.31520 0.74510 0.20750 0.16820 0.11110 0.11720 0.10530 0.13660

Black 19200 0.25600 0.13090 0.31620 0.52490 0.23120 0.20980 0.09510 0.13340 0.13780 0.14630

Yellow 1200 0.08150 0.02120 0.04500 0.00005 0.00007 0.00030 0.02840 0.48060 0.44610 0.40610

Yellow 4800 0.20890 0.05380 0.76620 0.33430 0.87960 0.86050 0.03120 0.10030 0.17110 0.38180

Yellow 19200 0.91740 0.01770 0.64820 0.88030 0.74720 0.63620 0.00080 0.00008 0.05130 0.38890

Average N.A 0.26715 0.16081 0.44911 0.50607 0.36696 0.33700 0.20303 0.12113 0.11348 0.24615

Table 17. Coefficient of Regression for Graphs with Different Parameter against TMP

y = -72.85x + 3002.R² = 0.821

0

500

1000

1500

2000

2500

3000

3500

0 5 10 15 20 25 30 35

TMP (kPa)

MDD for Blue 1200


1- 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.

0.00000

0.10000

0.20000

0.30000

0.40000

0.50000

0.60000

1 2 3 4 5 6 7 8 9 10

Ave

rage

Co

eff

icie

nt

of

Re

gre

ssio

n

Average Coefficient of Regression for

Different Parameters against TMP



(The red figures highlighted are the ones with coefficient of regression larger than 0.5)

Figure 8. Average Coefficient of Regression for Different dpi

From figure 26 we can see that images with dpi 19200 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.

0

0.1

0.2

0.3

0.4

0.5

1 2 3Ave

rage

Co

effi

cien

t o

f R

egre

ssio

n

1-dpi1200 2-dpi 4800 3-dpi19200

Average Coefficient of Regression for Different Parameters against TMP

Green

Blue

Black

Yellow

Reactor dpi AP FD ADD MDD HRL VRL Perimeter E TE H Average

Green 1200 0.0355 0.0364 0.2196 0.1662 0.1506 0.1788 0.1776 0.0049 0.0265 0.2276 0.1224

Green 4800 0.2384 0.0609 0.4841 0.5042 0.3609 0.3626 0.0122 0.0195 0.0142 0.1437 0.2201

Green 19200 0.29400 0.09020 0.49460 0.49750 0.34540 0.35660 0.02920 0.02300 0.01720 0.15030 0.22980

Blue 1200 0.12790 0.21000 0.61830 0.82160 0.56310 0.48760 0.24200 0.02780 0.00140 0.13770 0.32374

Blue 4800 0.15260 0.40540 0.62920 0.39260 0.01410 0.01370 0.65590 0.08070 0.07180 0.20490 0.26209

Blue 19200 0.04060 0.18660 0.53390 0.36750 0.45990 0.46990 0.78770 0.05400 0.04790 0.23140 0.31794

Black 1200 0.60010 0.52560 0.31880 0.83860 0.44400 0.29980 0.26520 0.41210 0.27110 0.39850 0.43738

Black 4800 0.25290 0.19100 0.31520 0.74510 0.20750 0.16820 0.11110 0.11720 0.10530 0.13660 0.23501

Black 19200 0.25600 0.13090 0.31620 0.52490 0.23120 0.20980 0.09510 0.13340 0.13780 0.14630 0.21816

Yellow 1200 0.08150 0.02120 0.04500 0.00005 0.00007 0.00030 0.02840 0.48060 0.44610 0.40610 0.15093

Yellow 4800 0.20890 0.05380 0.76620 0.33430 0.87960 0.86050 0.03120 0.10030 0.17110 0.38180 0.37877

Yellow 19200 0.91740 0.01770 0.64820 0.88030 0.74720 0.63620 0.00080 0.00008 0.05130 0.38890 0.42881



(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 19200.

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 1200. 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

Reactor dpi AP FD ADD MDD HRL VRL Perimeter E TE H Average

Green 1200 0.0355 0.0364 0.2196 0.1662 0.1506 0.1788 0.1776 0.0049 0.0265 0.2276 0.1224

Green 4800 0.2384 0.0609 0.4841 0.5042 0.3609 0.3626 0.0122 0.0195 0.0142 0.1437 0.2201

Green 19200 0.29400 0.09020 0.49460 0.49750 0.34540 0.35660 0.02920 0.02300 0.01720 0.15030 0.22980

Blue 1200 0.12790 0.21000 0.61830 0.82160 0.56310 0.48760 0.24200 0.02780 0.00140 0.13770 0.32374

Blue 4800 0.15260 0.40540 0.62920 0.39260 0.01410 0.01370 0.65590 0.08070 0.07180 0.20490 0.26209

Blue 19200 0.04060 0.18660 0.53390 0.36750 0.45990 0.46990 0.78770 0.05400 0.04790 0.23140 0.31794

Black 1200 0.60010 0.52560 0.31880 0.83860 0.44400 0.29980 0.26520 0.41210 0.27110 0.39850 0.43738

Black 4800 0.25290 0.19100 0.31520 0.74510 0.20750 0.16820 0.11110 0.11720 0.10530 0.13660 0.23501

Black 19200 0.25600 0.13090 0.31620 0.52490 0.23120 0.20980 0.09510 0.13340 0.13780 0.14630 0.21816

Yellow 1200 0.08150 0.02120 0.04500 0.00005 0.00007 0.00030 0.02840 0.48060 0.44610 0.40610 0.15093

Yellow 4800 0.20890 0.05380 0.76620 0.33430 0.87960 0.86050 0.03120 0.10030 0.17110 0.38180 0.37877

Yellow 19200 0.91740 0.01770 0.64820 0.88030 0.74720 0.63620 0.00080 0.00008 0.05130 0.38890 0.42881


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: 395-413.

2. Beyenal, H., Lewandowski, Z. and Harkin, G. Quantifying biofilm structure:

Facts and fiction. 2004b. Biofouling 20: 1-23.

3. Yang, X. M., Beyenal, H., Harkin, G. and Lewandowski, Z. Quantifying biofilm

structure using image analysis. 2000. Journal of Microbiological Methods 39:

109-119.

4. Yang, X. M., Beyenal, H., Harkin, G. and Lewandowski, Z. Evaluation of biofilm

image thresholding methods. 2001. Water Research 35: 1149-1158.

5. 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 109-115.

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