A TEST METHOD FOR FILTERING AND BACK WASHING

EFFICIENCIES OF MICRO IRRIGATION FILTERS

UNE MÉTHODE POUR TESTER L’EFFICACITE DE LA FILTRATION

ET DU LAVAGE A CONTRE COURANT DES FILTRES DE MICRO-

IRRIGATION

A S van Niekerk Pr. Eng.1

ABSTRACT

The ARC-Institute for Agricultural Engineering has 23 years of experience on the testing of micro irrigation filters. A special instrument called the Dirtiness Index Meter was developed to quantify the clogging potential of irrigation water for micro irrigation filters. With this instrument as reference, a test method was developed and a test bench build at the Institute.

Two types of tests are normally conducted. The first test is to verify the friction loss of micro irrigation filters and the second is to verify the dirty water filtration capacity of these filters. Pine sawdust is used as clogging agent. While measuring the dirty water filtration capacity of a filter the filtration efficiency of the filter relatively to a certain particle size (in µm) is also measured. This is done by measuring the dirtiness index of the in-going water and the dirtiness index of the out-going water of the filter, relative to the same micron size as the element of the filter.

Five tests are done at different dirtiness indexes, ranging from 2% up to 50% dirtiness of the water, and a graph is drawn of volume water filtered against dirtiness index at a head loss increase of 50kPa over the filter. For these tests the filter elements are thoroughly hand-cleaned before each clogging test. After these tests, the same tests are repeated, but this time the filters are back-washed and not hand-cleaned. It was found that a reduction of the filtration capacity of the filters happens, and the % reduction is a direct indication of the back-washing efficiency of the filter.

In the paper the test bench and the test method will be presented and some test results will be shown. The filtration efficiency of filters will also be measured in the field in the same way and that will be compared with the lab results.

RESUME ET CONCLUSIONS

L'indice de saleté est une mesure du potentiel obstruant de l'eau pour les filtres utilisés en micro d'irrigation. Ce n'est pas une mesure de l'aspect visuel de l'eau, mais une mesure de l'effet de colmatage que l'eau aura sur un filtre, en tenant compte de la de la maille du filtre. Le potentiel obstruant de l'eau est l'effet combiné du type de saleté présent dans l’eau et de sa concentration. La mesure de l’indice de saleté est cependant indépendante du type de saleté et de sa concentration.

L'indice de saleté est mesuré sur une échelle de 1 à 100. La valeur 100 est attribuée arbitrairement à une eau naturelle jugée exceptionnellement sale (par exemple rencontrée dans un cours d’eau en crue). L'eau usuellement rencontrée dans la nature est, en moyenne, caractérisée par un indice de saleté variant de 2 à 3 (eau à 2-3% sale). Dans de telles conditions un filtre se colmate en une semaine

1 Head of ILI Hydrolabs, ARC-Institute for Agricultural Engineering, Private Bag X519, Silverton, 0127, PRETORIA, SOUTH AFRICA, asvn@ing1.agric.za

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environ. Pour une saleté de 15%, il s’obstruera en un jour. Pour 30% de saleté, en une heure. Et en quelques minutes pour un taux de 60%.

La mesure d'indice de saleté se fait généralement grâce à un essai obstruant accéléré dans des conditions normalisées et contrôlées. Un instrument spécial, appelé le compteur d'indice de saleté, a été développé par l’Institut du génie rural (ILI) de l’ARC afin de mesurer la saleté de l'eau d'irrigation et de lui attribuer une valeur numérique. C'est un instrument très simple et bon marché qui a été développé pour faire des mesures sur le terrain. Au Laboratoire Hydraulique d’ILI (Hydrolabs), une version automatisée de l’‘appareil a été développée, et constitue un élément essentiel du banc d’essai. On utilise deux de ces appareils afin de contrôler la saleté de l'eau à l’entrée et à la sortie du filtre. L'efficacité de filtration est ainsi mesurée. En utilisant un facteur d’écran spécifique dans la formule de calcul de l’indice de Saleté, la valeur numérique pour la saleté de la même eau est calculée, indépendamment de la maille de l’écran utilisée dans l’appareil. Lorsque l’on fait un test sur un filtre, la maille de l’écran dans l’appareil de mesure de l’Indice de saleté doit être la même que celle de l’élément du filtre lui-même.

Pour mesurer l'efficacité du lavage à contre courant d'un filtre, un graphique de capacité de filtration pour le filtre doit d'abord être préparé. Pour ceci, cinq essais obstruants sont réalisés sur le filtre, aux indices de saleté de 2%, 7%, 15%, 30% et 50%. Avant chaque essai, l'élément filtrant est complètement nettoyé à la main. Puis le s mêmes essais sont reproduits, mais cette fois le filtre subi un lavage à contre courant avant chaque nouvel essai et la capacité de filtration est tracée sur le même graphique que pour les essais précédents. En dehors de ces conditions de test en banc d'essai, il est impossible de faire deux essais l’un après l’autre exactement au même indice moyen de saleté. Si ça se produisait, ce serait en grande partie du au hasard. Par conséquent des ajustements de courbe sont faits sur les cas de nettoyage manuel et de lavage à contre courant. Ces points sont employés pour calculer la capacité de filtration du filtre dans les deux conditions au même indice de saleté. Ces valeurs sont employées pour calculer l'efficacité de lavage à contre courant du filtre.

Conclusions

Pendant les essais d’obstruction du filtre effectués à l’Hydrolabs d’ILI, l'indice de saleté de l'eau est mesuré en permanence en amont et en aval du filtre. Si un filtre s’obstrue, on constatera que la différence de pression avant le filtre augmente graduellement et sans interruption. A l’Hydrolabs, un essai de filtre commence avec une différence de pression de 10kPa (en raison de la perte de charge dans le filtre) et il est observé jusqu'à ce que la différence de pression avant-filtre atteigne 60kPa.

Quelques observations très intéressantes ont été faites pendant les essais d'efficacité de filtration qui ont été réalisés sur des filtres à l'institut:

• On a constaté que l'efficacité de filtration varie en fonction de la différence de pression à laquelle est soumit le filtre. Pour un élément propre soumit à une faible différence de pression, l'efficacité de filtration est généralement grande. À mesure que la différence de pression augmente, il y a une réduction de l'efficacité de filtration. Pour une différence de pression variant entre 30kPa et 40kPa, il y a un changement de tendance et l'efficacité de filtration peut soit augmenter légèrement soit rester constante et, au-delà de 50kPa, il y a de nouveau une baisse dans l'efficacité de filtration.

• Quand le débit dans le filtre est réduit, l'efficacité de filtration augmente.

• Avec un indice de saleté faible, l'efficacité de filtration est plus faible qu'avec un indice de saleté élevé.

• A des indices de saleté élevés, certains filtres ont affiché, lorsqu’ ils subissent un lavage à contre courant, des efficacités de filtration plus élevées que lors du nettoyage manuel.

Le lavage à contre courant des filtres d'irrigation est une pratique courante, mais dont l’efficacité est encore méconnue. De ces essais d'efficacité de lavage à contre courant une information intéressante a été collectée:

• Un fait alarmant qui est ressorti de ces essais de filtres est que le lavage à contre courant des filtres

3

annulaires est seulement efficace à environ 30%. Ceci signifie qu'un filtre s’obstruera trois fois plus rapidement après un lavage à contre courant qu’après un lavage manuel.

• L'efficacité du lavage à contre courant est plus ou moins la même pour les différents filtres qui ont été examinés et relativement constante quand l'indice de saleté de l'eau est supérieur à 10%. En dessous de ce niveau de saleté de 10%, l’efficacité diminue progressivement, jusqu’à environ 15% pour un indice de saleté de moins de 2%.

INTRODUCTION

The invention of the Dirtiness Index Meter made it possible that a numeric value can be attached to the dirtiness of irrigation water. This value gives a very good indication of how a filter will react to the water. The fact that it can be measured makes it possible that it can be controlled in a test bench set up. Once this is possible, it becomes possible to conduct very accurate filter clogging tests, but before that is possible it is necessary that the factors that influence these tests be discussed and explained.

THE DIRTINESS INDEX CONCEPT

The Dirtiness Index is a measure of the clogging potential of irrigation water to micro irrigation filters. It is not a measure of the visual appearance of the water, but a measure of the effect that the water will have on a filter, taking into account the fineness of the element of the filter. The clogging potential of water is the combined effect of the kind of dirt in the water and it’s concentration. The dirtiness index measure is thus independent of the kind of dirt in the water or it’s concentration. It is measured on a scale of 1 to 100 and can thus be regarded as a percentage of dirtiness of the water compared to the dirtiest condition in nature which will measure 100 and which will be 100% dirty, like a river in heavy flood.

The Dirtiness Index measurement is in principle an accelerated clogging test under standardized, controlled conditions. A special instrument, called The Dirtiness Index Meter, was developed by the ARC-Institute for Agricultural Engineering (ILI) to measure the dirtiness of irrigation water so that a numerical value can be linked to it. It is a very simple and cheap instrument that was developed to do measurements in the field. In the Hydrolabs of ILI an automated version of the meter was developed which forms the heart of the filter test bench.

The Dirtiness Index Meter

Figure 1 is a diagram for the construction of the Dirtiness Index Meter. Any 20mm household water meter can be used in the meter. When a dirtiness index measurement is to be made, valve A is closed at first and valve B fully opened. The inlet side of the meter is then connected to the irrigation pipeline. Valve B is now adjusted so that the reading on the pressure gauge stands between 110kPa and 120kPa.

A screen with the correct micron size is now placed between the two brass washers and this “sandwich” secured to the water meter inside the water meter’s connecting nut. Accredited nylon screens from Switzerland with different micron sizes are used and pieces are cut from them to match the outer dimension of the brass washers. The inner diameter of the washers is 12mm and the outer diameter should fit neatly inside the connecting nut of the water meter.

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Figure 1: The Dirtiness Index Meter (compteur d'Indice de Saleté)

The Dirtiness Index Formula

After the Dirtiness Index Meter is set up as described in the previous section, the reading on the dial of the water meter is noted. Valve A is opened fully which will cause the pressure to drop to between 40kPa and 50kPa. As the screen clogs up, the reading on the pressure gauge will start to rise. When it reaches 100kPa, valve A is closed again and the new reading on the water meter is noted. The difference of these two readings is calculated in liters and that result used in the dirtiness index formula to calculate the dirtiness index of the water. The formula reads as follows:

)(log

1FfactorScreen

litersinvolumegingcTheIndexDirtiness ×= 1

A B

5

When the dirtiness index of water is measured for a specific filter, the fineness of the screen that is used in the Dirtiness Index Meter should be the same as that of the filter element. Figure 2 shows the values for factor F in the formula, for a number of micron sizes of screens that are commonly used in the Meter:

Screen Size (µm) Screen Factor (F)

50 23

75 55

80 63

100 100

130 174

200 429

250 686

300 1000

0100200300400500600700800900

10001100

0 50 100 150 200 250 300 350

Fineness of the Screen (µm)S

cree

n F

acto

r (F

)

F = 6,32 x 10-3 . µm 2,1

Figure 2: Relation between the value of “screen factor” F in the dirtiness index formula and the micron size of the screen (Relation entre la valeur du facteur d’écran F dans la formule d'indice de saleté et la maille de l'écran)

From the discussion so far it is clear that the micron size of a filter’s element must be known before a dirtiness index measurement can be made for that filter. The fineness of many filter’s elements are specified in mesh. With sand filters it is also a problem to know what the fineness of filtration is for such filters. To be able to do dirtiness index measurements for these filters figures 3 and 4 shows what the relation is between mesh and microns and also what the theoretical fineness is for a sand filter with a certain size of graded sand as filter medium:

Microns Mesh

22 450

80 200

100 155

130 120

200 75

300 50

500 30

800 20

1200 12

0

50

100

150

200

250

300

350

400

450

500

0 200 400 600 800 1000 1200 1400

Microns

Mes

h

Mesh = 10187 x µm –0,9327

Figure 3: Relation between the mesh - and micron sizes of screens (Relation entre la maille et la taille en micron des écrans)

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Sand Particle (mm)

Filtration Fineness (µm)

0,2 39

0,4 68

0,6 97

0,8 126

1,0 155

1,2 184

1.4 213

1,6 242

1,8 271

2,0 300

0

50

100

150

200

250

300

350

0.0 0.5 1.0 1.5 2.0 2.5

Sand Particle Size (mm)F

iltra

tio

n F

inen

ess

(µm

)

µm = 10 + 145 . mm

Figure 4: Theoretical relation between the sand particle size and the filtration fineness of sand filters (Relation théorique entre la taille des particules de sable et la finesse de filtration des filtres à sable)

Most sand filters have 0,6mm sand as medium. From figure 4 it can thus be seen that the filtration fineness of irrigation sand filters is about 100µm.

The Dirtiness Index of irrigation water in practice

The discussion in this section will be to give the reader a better feeling and understanding of the dirtiness index value of irrigation water. From an irrigation filter’s perspective, table 1 will give an indication of how the dirtiness index values effect the performance of filters in general. Figure 5 illustrates the relation between the dirtiness index of water and the better-known concentration of dirt in parts per million (PPM) on a volumetric basis. These PPM values are not the same as total suspended solids (TSS), but are particle size related. Figure 6 will show how the dirtiness index of irrigation water typically looks on a day to day and a yearly basis.

Table 1: The effect of the dirtiness index of irrigation water on filter performance (L'effet de l'index de saleté de l'eau d'irrigation sur l'exécution de filtre)

Dirtiness Index of the water (%) Time it will take for a filter to clog

2 More or less one week.

15 More or less one day.

30 More or less one hour.

60 Only a few minutes.

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PPM (Volume basis)

Dirtiness Index (%)

0,001 0,5

0,01 2

0,1 9

1 35

3 70 0

1020

3040

5060

7080

0 0.5 1 1.5 2 2.5 3 3.5

PPM (Volumetric)

Dir

tines

s In

dex

(%)

DI = 35,612 x PPM 0,6183

Figure 5: Relation between the dirtiness index measurement and the concentration of size related particles in the water in PPM on a volumetric basis (Relation entre la mesure d'indice de saleté et la concentration en particules (d’une certaine taille) en PPM, sur une base volumétrique)

0

5

10

15

20

25

30

35

1 31 61 91 121

151

181

211

241

271

301

331

Number of days

Dir

tines

s In

dex

(%)

Figure 6: Daily variation of the dirtiness index of an irrigation reservoir’s water over time (Variation journalière de l'indice de saleté de l’eau d’un reservoir d' irrigation)

In figure 6 it shows that the dirtiness index can vary between 3% and 23% even in one day’s time (see day 331). This can be explained by the fact that dirt is not uniform in irrigation water, but it moves around in lumps of algae, water grass, fish eggs etc. When such a lump passes the point of extraction, there is a sudden increase in the dirtiness index that lasts for a short time after which the dirtiness index falls back to normal again. Most filters can “survive” such spikes on condition that they do not last for too long.

8

FILTER TESTS AT THE ILI HYDROLABS

The Filter Test Bench

Figure 7 is a simplified diagram of the filter test bench at the ARC-Institute for Agricultural Engineering (ILI) in Pretoria, South Africa. It is a re-circulating system, consisting mainly of two reservoirs, a pump, pipes, valves, two Dirtiness Index Meters, electronic pressure - and flow sensors and instrumentation that display all the signals and that have two-way communication with the controlling computer. Water circulates from the pump to the other side of the test bench. Part of the water circulates back into the pump reservoir through a center pipe, blows onto the floor and keeps the dirt in suspension. On the other side water is distributed over a large area of the sedimentation reservoir. It moves slowly through the reservoir and the dirt settles out into this reservoir so that clean water returns to the pump reservoir. When the dirtiness of the water in the pump reservoir needs to be increased, the center pipe in the sedimentation reservoir is activated for a few seconds, blows some dirt into suspension which flows over to the pump reservoir and tops-up the dirt in it. In such a way good control over the dirtiness index of the water in the pump reservoir is achieved. When the desired dirtiness index in the pump reservoir is reached, the water flowing into the sedimentation reservoir is shut off, the valve leading to the filter under test is opened and the filter clogging test starts.

Figure 7: The irrigation filter test bench of the ILI Hydrolabs (Le banc d'essai du filtre d'irrigation de l'ILI Hydrolabs)

The Filter Test

At first the dirtiness index of the water in the test bench is lowered to 1% and a friction loss test is done on the filter. Out of this test the flow rate that causes a friction loss of 10kPa is determined. The clogging tests on the filter are going to be done at this flow rate.

Once the clogging test on the filter is initiated, the flow rate is checked and the pressure difference over the filter is measured and noted. An automatic valve keeps the flow rate constant for the whole duration of the test. For the rest of the test the computer monitors the Dirtiness Index Meters, takes the readings from them and backwashes them after which new readings immediately start again. The dirtiness index of the in-going and out-going water of the filter is measured continuously in this way.

The computer also monitors the pressure difference over the filter and at every 5kPa increase of the

9

pressure difference, the following measurements are noted:

• The total volume of water that was filtered so far since the beginning of the test.

• The flow rate through the filter at that point in time.

• The pressure difference over the filter.

• The latest dirtiness index measurements before and after the filter. From these measurements the filtration efficiency of the filter at this point of clogging of the filter is calculated.

This will continue till the total pressure difference over the filter reaches 60kPa and then the test is terminated and the data stored on the hard disk of the computer. The valve leading to the filter is closed again and the water is circulated again through the sedimentation reservoir. Control of the dirtiness index of the water also stops. It is though important that the pump should keep running to maintain the balance of the test bench till the next test is done. Only after a test series is finished and no tests will be done for two or more months, then the pump is stopped. If a new series of tests is to be done, the test bench needs a running-in period of about a week to get it in ba lance again. Only then are successful tests possible again. Usually, immediately after a test has finished, the filter is cleaned and the next clogging test at the next dirtiness index level is done.

The test is controlled from a LOTUS 123 spreadsheet. The test program in the computer consists of a number of running macros in the spreadsheet. Figure 8 is a copy of the spreadsheet of one of the tests that were done for this paper. On the spreadsheet all the readings from the instruments are shown. In the top left column the target pressures at which measurements must be recorded, are put in before the test as well as the flow rate at which the test must be run. The micron size of the screens in the Dirtiness Index Meters as well as the target dirtiness index for the test to follow is also put in and the test is started from the spreadsheet. The program will then, with the aid of the Dirtiness Index Meter before the filter, adjust the dirtiness index of the water in the test bench, make sure it is stable, start the test and take all the readings during the test. While the test is running the dirtiness index is monitored and the necessary actions taken when necessary to keep it at the given level.

Figure 8: An example of a filter clogging test spreadsheet (Un exemple de résultat d’un test obstruant de filtre)

On the spreadsheet in figure 8 a very interesting observation is made: In cell D23 a negative filtration efficiency is shown. This means there is more dirt after the filter than before the filter. The only explanation for that is that dirt that had accumulated on the element has gone through the element,

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causing a huge rise in the dirtiness index of the water after the filter. This happened between the 50kPa and 55kPa levels of clogging of the filter, leaving a “clean patch” on the filter element. A close look at the graph of pressure difference against filtered volume shows a deviation of the gradient of the graph between these pressures. This means that relatively more water had to be filtered to cause the pressure difference increase from 50kPa to 55kPa than was necessary between 45kPa and 50kPa and also between 55kPa and 60kPa. This proves that part of the element was cleaner and had to be clogged first before the pressure difference could rise further and that dirt was indeed forced through the element at that high pressure difference across the element.

The Clogging Agent

At the beginning of the paper it was explained that the dirtiness index of the water is independent of the kind of dirt in the water. This leaves the possibility open that any clogging agent can be used in the test bench to do the clogging of the filters, as long as it is measured by the Dirtiness Index Meter and that one is free to choose the most suitable agent for this purpose.

Experimentation and experience proved that pine sawdust produced by the circular saw in a carpenter shop is the best to use. It was found that most successful tests could be done with it, but the real composition of such sawdust was uncertain. The ideal situation is that there must be an even volumetric percentage in the water of all the particle sizes from 50µm up to 1000µm. Visual analyses that varied from 5x up to 100x magnification of five samples of sawdust were done. A thin layer of sawdust was spread evenly onto an A4 overhead projector flimsy and the particles covered with clear sticky tape to keep them in place. Five samples of 1,5cm x 1,5cm were chosen randomly on this area and examined. Table 2 shows the results:

Table 2: Particle size analyses of five samples of pine sawdust (Granulométrie de cinq échantillons de sciure de pin)

Particle sizes

(µm)

Sample 1 (Number of particles)

Sample 2 (Number of particles)

Sample 3 (Number of particles)

Sample 4 (Number of particles)

Sample 5 (Number of particles)

Up to 50 101 121 193 331 215

50–100 86 154 242 354 238

100–150 89 120 238 311 158

150–200 232 137 279 256 212

200–300 133 94 182 186 136

300–400 88 59 84 106 93

400–600 53 29 43 56 42

600–800 19 14 12 16 19

800–1000 11 5 5 4 7

1000–1200 7 2 3 3 5

1200–1500 4 1 2 1 1

The five samples were averaged per particle size and number-percentages were calculated. Arbitrary volumes based on the particle sizes were assigned to them and volume-percentages were calculated. Figure 9 shows the results graphically. A scaled photograph of the particles of sample 4 is also shown:

11

0

5

10

15

20

25

0 200 400 600 800 1000 1200 1400 1600

Particle Size (µm)

Per

cen

tag

e o

f S

amp

le (

%)

Number-of-particles-Percentage Bulk-volume-Percentage

Figure 9: Particle size analysis of pine sawdust (Granulométrie de la sciure de pin)

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TWO FILTERS COMPARED

For the purpose of this paper two disc filters of different construction and having two different kinds of discs were tested and compared. Filtration efficiency tests were done on them when they were hand-cleaned and back-washed. These two tests were also used to test the back-wash efficiency on both of them. Figure 10 shows the filters with the discs used in their elements:

Filter B

Details

Filter A

Details

Figure 10: Filter A and Filter B with the discs used in their elements (Filtre A et filtre B avec les disques utilisés dans leurs éléments)

The Filtration Efficiency Tests

In figure 8 the data that is generated by a clogging test is shown. This is a test at a dirtiness index of 30%. Filter A and Filter B were tested at this dirtiness index, but also at the following dirtiness indexes: 2%, 7%, 15% and 50%. For theses tests they were hand-cleaned before every test. After that the same tests were repeated, but before every test they were only back-washed. The data that was generated by all these tests can be displayed in various ways:

• Filtration efficiency against pressure difference buildup across the filter.

• Average filtration efficiency per test against the dirtiness index of the water.

• Back-wash efficiency against the dirtiness index of the water.

Figure 11 shows the results of the filtration efficiency tests on both filters against the pressure difference across the filters. It must be kept in mind that these efficiencies are always related to one

13

specific particle size which corresponds with the fineness of the filter’s element. As was mentioned earlier in the paper do the Dirtiness Index Meters have the same micron size screens in them as that of the filter element. The filtration efficiency is calculated as follows:

1001 ×

−=

FilterbeforeIndexDirtiness

FilterafterIndexDirtinessEfficiencyFiltration

01020304050607080

15 20 25 30 35 40 45 50 55 60

Pressure Differential (kPa)

Filtr

atio

n E

ffic

ienc

y (%

)

DI = 2% DI = 7% DI = 15%

DI = 30% DI = 50%

0

10

20

30

40

50

15 20 25 30 35 40 45 50 55 60

Pressure Differential (kPa)

Filtr

atio

n E

ffic

ienc

y (%

)

DI = 2% DI = 7% DI = 15%

DI = 30% DI = 50%

Figure 11: Filtration efficiencies of Filter A (left) and Filter B (right) against pressure difference when they were hand-cleaned (Efficacités de filtration du filtre A (gauche) et du filtre B (droit) vs différences de pression après lavage manuel)

0

10

20

30

40

50

60

70

15 20 25 30 35 40 45 50 55 60

Pressure Differential (kPa)

Filt

rati

on

Eff

icie

ncy

(%

)

DI = 2% DI = 7% DI = 15%

DI = 30% DI = 50%

0

10

20

30

40

50

15 20 25 30 35 40 45 50 55 60

Pressure Differential (kPa)

Filt

rati

on

Eff

icie

ncy

(%

)

DI = 2% DI = 7% DI = 15%

DI = 30% DI = 50%

Figure 12: Filtration efficiencies of Filter A (left) and Filter B (right) against pressure difference when they were back-washed (Efficacités de filtration du filtre A (gauche) et du filtre B (droite) vs différences de pression après lavage à contre-courant)

From the graphs it can be seen that the filtration efficiency of a filter is effected by the pressure difference over the filter and the dirtiness index of the water. Although there seems to be no constancy, certain trends can be observed:

• The higher the dirtiness index of the water, the better the filtration efficiency.

• There is a general decline in the filtration efficiency of a filter when the pressure difference across it increases.

When the data points in the previous graphs are averaged vertically, the average filtration efficiencies of the two filters over the whole dirtiness index spectrum against pressure difference can be seen and other, less visible trends can be observed. In figure 13 the average filtration efficiencies of the filters, when they are hand-cleaned and back-washed, are shown on the same graphs. Comparison of the filters themselves and amongst each other is then possible:

14

0

10

20

30

40

50

60

15 20 25 30 35 40 45 50 55 60

Pressure Differential (kPa)

Filtr

atio

n E

ffic

ienc

y (%

)

Filter A Hand- cleaned Filter A Back- washed

0

5

10

15

20

25

30

35

15 20 25 30 35 40 45 50 55 60

Pressure Differential (kPa)

Filtr

atio

n E

ffic

ienc

y (%

)

Filter B Hand-cleaned Filter B Back- washed

Figure 13: Average filtration efficiencies of Filter A (left) and Filter B (right) against pressure difference when they were hand-cleaned and back-washed (Efficacités moyennes de filtration du filtre A (gauche) et du filtre B (droite) vs différence de pression après lavage manuel et lavage à contre-courant)

From the graphs it can be seen that the filtration efficiency of a filter does not stay the same when the pressure difference over the filter increases. With a clean element and a low pressure difference, the filtration efficiency is usually high. As the pressure difference increases, there is a reduction in the filtration efficiency. At a pressure difference of between 30kPa and 40kPa there is a turning point and the filtration efficiency can either increase a bit or stay constant and after 50kPa there is again a drop in the filtration efficiency. This tendency can be theoretically explained as follows: When the element is clean with a low pressure difference across it, it can keep the dirt back. When the pressure difference increases, dirt is forced through the

0

10

20

30

40

50

60

0 10 20 30 40 50

Dirtiness Index (%)

Filtr

atio

n E

ffic

ienc

y (%

)

Filter A Hand- cleaned Filter A Back- washed

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50

Dirtiness Index (%)

Filtr

atio

n E

ffic

ienc

y (%

)

Filter B Hand- cleaned Filter B Back- washed

Figure 14: Average filtration efficiencies of Filter A (left) and Filter B (right) against dirtiness index when they were hand-cleaned and back -washed (Efficacités moyennes de filtration du filtre A (gauche) et du filtre B (droite) vs indice de saleté après lavage manuel et lavage à contre-courant)

element. When the element is thoroughly dirty (resulting in a high pressure difference) the dirt on the element itself acts as a “filter” that is finer than the filter’s element, and the size of particles, for which the Dirtiness Index Meter after the filter is on the outlook, will not pass through, and a high filtration efficiency is measured. If the pressure difference gets too high though, the “dirt filter” breaks up and a lot of dirt is pushed through the element.

The filtration efficiencies can further be compared against the dirtiness index of the irrigation water. If the average of all the dirtiness index measurements before the filter are taken and also that of the measurements after the filter for a specific clogging test, an average filtration efficiency for the whole test at the average dirtiness index for that test, can be calculated. The graphs in figure 14 show that for Filter A and Filter B when they are hand-cleaned and back-washed.

15

The Backwash Efficiency Tests

To measure the back washing efficiency of a filter, a filtration capacity graph for the filter must first be compiled. For this, five clogging tests are done on the filter namely at dirtiness indexes of 2%, 7%, 15%, 30% and 50%. Before each test the filter element is thoroughly hand-cleaned. After this the same tests are done on the filter, but this time the filter is back-washed before each test and the filtration capacity is plotted on the same graph as for the tests preceded by hand-cleaning. Figure 15 shows the filtration capacities for the two filters when they are hand-cleaned and back-washed. The volume of water that each filter can clean, with a 50kPa pressure difference buildup over each filter, are shown on the graphs against the different dirtiness indexes:

10

100

1000

10000

1 10 100

Dirtiness Index (%)

Vol

ume

(m3 )

Hand-cleaned Back-washed

1

10

100

1000

1 10 100

Dirtiness Index (%)V

olum

e (m

3)

Hand-cleaned Back-washed

Figure 15: Filtration capacities for Filter A (left) and Filter B (right) against dirtiness index when they were hand-cleaned and back-washed (Capacités de filtration pour le filtre A (gauche) et le filtre B (droite) vs l'indice de saleté après lavage manuel et lavage à contre-courant)

Out of the nature of the tests and the test bench it is impossible to do two tests after one another at exactly the same average dirtiness index. If it does happen, it will largely be by accident. Therefore curve fittings are done on the hand-cleaned and the back-washed data points and these are used to calculate the filtration capacities for each of the two filters, at the same dirtiness index, for each of the two treatments. In figure 16 the lines and the power equations are shown for all the curves in figure 15 for Filter A and Filter B when they are hand-cleaned and back-washed:

y = 4910.9x-1.5497

R2 = 0.9881

y = 28063x-1.7237

R2 = 0.9998

1

10

100

1000

10000

1 10 100

Dirtiness Index (%)

Vol

ume

(m3 )

Hand-cleaned Back-washed

Power (Back-washed) Power (Hand-cleaned)

y = 448.74x-1.1328

R2 = 0.9964

y = 3042.1x-1.3449

R2 = 0.9976

1

10

100

1000

10000

1 10 100

Dirtiness Index (%)

Vol

ume

(m3 )

Hand-cleaned Back-washed

Power (Back-washed) Power (Hand-cleaned)

Figure 16: Curve fittings for the filtration capacity curves of Filter A (left) and Filter B (right) against dirtiness index when they are hand-cleaned and back-washed (Ajustements de courbe pour les courbes de capacité de filtration du filtre A (gauche) et du filtre B (droite) vs l'indice de saleté après lavage manuel et lavage à contre-courant)

Using the curve fittings to calculate the volumes, the backwash efficiency of Filter A and Filter B can be calculated at the intended dirtiness indexes at which the filter was tested, using equation 3:

100×−−

=cleaningHandafterFilteredVolume

washingBackafterFilteredVolumeEfficiencyBackwash 3.

16

Table 3: Calculating the backwash efficiencies of Filter A at different dirtiness indexes (Calcul des efficacités de lavage à contre-courant du filtre A pour différents indice de saleté)

Dirtiness Index

(%)

Volume after hand cleaning (m3)

Volume after back washing (m3)

Backwash efficiency (%)

2 8496,7 1677,5 19,7

7 980,5 240,7 24,6

15 263,6 73,9 28,0

30 79,8 25,2 31,6

50 33,1 11,4 34,6

Table 4: Calculating the backwash efficiencies of Filter B at different dirtiness indexes (Calcul des efficacités de lavage à contre-courant du filtre B pour différents indice de saleté)

Dirtiness Index

(%)

Volume after hand cleaning (m3)

Volume after back washing (m3)

Backwash efficiency (%)

2 1197,6 204,6 17,1

7 222,1 49,5 22,3

15 79,7 20,9 26,2

30 31,4 9,5 30,4

50 15,8 5,3 33,8

Using the data in table 3 and 4, the backwash efficiencies of Filter A and Filter B can be compared in figure 17:

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50

Dirtiness Index (%)

Bac

k W

ash

Eff

icie

ncy

(%

)

Filter A Filter B

Figure 17: Comparison between the back -washing efficiency of Filter A and Filter B against the dirtiness index of the water (Comparaison entre l'efficacité de lavage à contre-courant du filtre A et du filtre B vs indice de saleté de l'eau)

17

“The proof of the pudding lies in the eating”

Coincidentally two tests on Filter A happened at the same average dirtiness indexes as two similar tests on Filter B. This presented an ideal opportunity to compare the two filters and to check on the accuracy of the tests with real measured values.

Table 5: Assessment of the accuracy of the test program, test method, test bench and the instrumentation used by the ILI Hydrolabs for the testing of irrigation filters (L'évaluation de la précision du programme d'essai, de la méthode d'essai, du banc d'essai et de l'instrumentation employée par l'ILI Hydrolabs pour les essais de filtres l'irrigation)

Test 1:

(Dirtiness Index:

50%)

Filter element surface area: (m2)

Element area ratio: Filter A Filter B

Volume filtered till clogged: (m3 )

Average filtration efficiency for test:

(%)

Filtered volume ratio: Filter A Filter B

Volume ratio “times” efficiency ratio-1 :

Filter A 0,19 35,14 50

Filter B 0,15 1,26

18,33 33 1,92 1,27

Test 2:

(DI: 15%)

Filter A 0,19 244,00 34

Filter B 0,15 1,26

86,04 15 2,84

1,25

In table 5 a cross-check is made on the accuracy of the filter tests as being done by the Hydrolabs of ILI. At first sight, one would assume that the volume of water that two filters can clean under identical circumstances, should be in the same ratio as the surface areas of their elements. This was proved wrong!, as can be seen in table 5 which is quite self-explanatory. All the values differ! The reason: The filtration efficiencies of the two filters are not the same, and that changes the whole picture! Only if that is also taken into account one can see in the table that the numeric average of the two numbers in the last column is 1,26 and that is the same as the element surface area ratio of the two filters. This proves that the filtration capacity of a filter is a function of the surface area of the element and the filtration efficiency of the element and that the tests, as being done by ILI, are accurate.

Some Test Results from the Field

Although a fair number of measurements were made in the field on the two filters, not many field conditions favored comparison with similar conditions in the labs under which the filters were tested. Also could be seen in the paper so far that the filtration efficiency of filters could be very variable. In spite of all this, it was indeed possible to make a few comparisons where there were agreements between measurements, but also differences as can be seen in table 6:

18

Table 6: Comparison between field measurements and laboratory measurements of the filtration efficiencies of Filter A and Filter B (Comparaison entre les mesures sur le terrain et les mesures en laboratoire, des efficacités de filtration du filtre A et du filtre B)

Filter A Filter B

Situation:

Dirtiness Index

(%)

Pressure drop (∆∆P) (kPa)

Filtration efficiency (%)

Dirtiness Index

(%)

Pressure drop (∆∆P) (kPa)

Filtration efficiency (%)

Lab: 10 60 68 16 20 47

1 30 22 14 45 28

Field: 10 60 91 16 - 66

1 30 24 14 45 59

From the efficiency tests on Filter A and Filter B it was seen that the filtration efficiency of filters can vary even while the filter is blocking and it just depends on the point in time when the measurements were taken, what the result would be. Certain trends though do show up in the tests: When the dirtiness index of the water increases, there’s an increase in efficiency and visa versa. When the pressure drop over the filter increases then the following pattern emerges: At first the filtration efficiency is high, then there is a slight drop between 25 kPa and 40 kPa after which an increase happens up to 45 kPa from where it varies between increases and decreases and from 55 kPa it drops from slightly to severe. These same trends show up in the field tests. That is why the field measurements are sometimes close to the lab measurements, but they can also vary markedly from the lab measurements in other cases. Although the exact numerical values may show differences, the same trends are though maintained.

AUTOMATIC FILTERS

With automatic filters it is also possible to measure the filtration efficiencies in the same way as for the hand-cleaned filters that were discussed in the paper. It is though not possible to evaluate the back-wash efficiencies in the same way, because most of their elements can not easily be removed for hand-cleaning. In their case it is more practical to measure the volume of their back-wash water as a percentage of total volume filtered, against the dirtiness index of the water and to compare them on that basis.