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Conventional Filtration

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1 Conventional Filtration
CE 547 Conventional Filtration

2 Filtration is a unit operation of separating solids from liquids.
Types of Filters (to create pressure differential to force the water through the filter): Gravity filters Pressure filters Vacuum filters In terms of media Perforated plates Septum of woven materials Granular materials (such as sand) Sand Filters Slow sand filters (1.0 – 10 m3/m2.d) Rapid sand filter (100 – 200 m3/m2.d)

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5 Medium Specification for Granular Filters
1. Medium is the most important component of granular filters Small grain sizes tend to have higher head losses Large grain sizes may not be effective in filtering The most effective grain sizes are found from previous experience 2. Effective size of medium is specified in terms of : Effective size Uniformity coefficient

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7 What is Uniformity Coefficient?
What is Effective Size? It is the size of sieve opening that passes the 10% finer of the medium sample (the 10th percentile size, P10) What is Uniformity Coefficient? It is the ratio of the size of the sieve opening that passes the 60% finer of the medium sample (P60) to the size of the sieve opening that passes the 10% finer of the medium sample (P10). It is P60 to P10. For Slow Sand Filters P10 = 0.25 – 0.35 mm P60/P10 = 2 – 3 For Rapid Sand Filters P10 = 0.45 mm and higher P60/P10 = 1.5 and higher

8 Example 7.1

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10 Linear Momentum Equation Applied to Filters (Fig 7.10)
Movement of water through a filter bed is similar to the moment of water in parallel pipes, except that the motion is not straight but tortuous. If momentum equation applied on water flow in the downward direction of the element, then:

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12 F2 = unbalanced force in downward (z) direction (inertia force)
p = hydrostatic pressure A = cross-sectional area of the cylindrical element of the fluid Fg = weight of water in the element Fsh = shear force acting on the fluid along the surface areas of the grains dV = volume of element dl = differential length of the element As = surface area of the grains k = factor which converts As into an area such that ( kAsdl = dV )  = porosity of the bed a2 = acceleration of fluid element in downward (z) direction  = fluid mass density  = fluid element velocity in the (z) direction t = time

13 Since Ki = proportionality constant V = average water velocity

14 Since Ps = drop in pressure due to shear force  = liquid viscosity
l = length of pipe D = diameter of pipe

15 Ks = proportionality constant
rH = hydraulic radius = (area of flow / wetted perimeter) then Since Fg is constant, it can be included in Ki and Ks and removed from the equation. Then: This is a good linear momentum equation which can be applied to any filter

16 If particles are spherical
d = diameter of particle

17 Thus rH = (area of flow / wetted perimeter) = (volume of flow / wetted area) if N = number of grains vp = volume of each grain Since V can be expressed as = (vs / ) vs = superficial velocity

18 Head Loss in Grain Filters
There are two categories head loss in clean filters head loss due to the deposited materials A. Clean-filter Head Loss Sp = surface area of a particle N = number of grains in the bed Volume of bed grains = S0l(1-) S0 = empty bed or surficial area of the bed  = porosity of bed l = length of bed

19 if vp is volume of a grain, then:

20 Now we have

21 Substitute (2), (3), and (4) in (1)

22 fp is a form of friction factor. Since:

23 Since particles are not spherical and
 = shape factor dp = sieve diameter After backwash, the grain particles are allowed to settle. That means, the particles will deposit layer by layer, and hence particles will be of different sizes.

24 The bed is said to be stratified, and the head loss will be the sum of head losses of each layer. Then: If xi is the fraction of the di particles in the ith layer, then Assume  is the same throughout the bed, then

25 Example 7.2

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29 Head Losses Due to Deposited Materials
If q = deposited materials per unit volume of the bed, then: If hL0 = clean-bed head loss, then C = concentration of solids introduced into bed l = length of the filter bed

30 How to determine (a) and (b) in:
It is clear that the equation represents a straight line if ln (hd) was plotted against ln (q).

31 This means that only two data points are needed in order to determine (a) and (b). If we have:

32 Examples

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42 Backwashing Head Loss in Granular Filters
During backwashing, the filter bed expands. For this to happen, a force must be applied: le = expanded depth of the bed l’ = the difference between the level at the trough and the limit of bed expansion hLb = backwashing head loss w = specific weight of water

43 weight of suspended solids is:
e = expanded porosity of bed p = specific weight of particles weight of column water

44 Weights of suspended grains and column water are acting downward against the backwashing force. Thus: Solving for backwashing head loss:

45 Then, and according to the following empirical equation:
If vb = backwashing velocity v = settling velocity of grains Then, and according to the following empirical equation:

46 for stratified bed if xi = fraction of particles in layer I, then: So,

47 Assuming expanded mass = unexpanded mass; then:
Then, the fraction bed expression is:

48 Study Example 7.6

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52 Cake Filtration This is happening when solids precipitate on the surface of the filter medium. In cake filtration, the general momentum equation can be applied:

53 Because solids are tightly packed in cake filtration, the first term is equal to zero due to the fact that the inertia force must disappear upon entrance of flow into the cake and the flow is not accelerated. So:

54 because A = S0; and 36Ks = 150; then:

55 if p is the density of solids, the mass (dm) in the differential thickness of cake (dl) is:
 = specific cake resistance

56 integrating from P2 to P1 mc = total mass of solids collected on the filter bed If the resistance of the filter medium to be included, then: Rm = resistance of medium

57 Determination of  V = volume of filtrate collected at any time, t
if mc = cV c = mass of cake collected per unit volume of the filtrate, then  = average specific cake resistance (since  changes with time)

58 the equation represents a straight line with slope m of :
by determining the slope of the line from the experimental data,  can be determined as:

59 Where n1 and n2 are the number of elements in the respective group

60 Example 7.7

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63 Design of Cake Filtration Equation
Practically, the resistance of the filter medium is negligible. Then: Introducing a new parameter called the filter yield, Lf, defined as: Lf = the amount of cake formed per unit area of the filter per unit time

64 If t was called the formation time (tf) that is when cake starts to form. Also some filters operate on a cycle. Call cycling time as tc. In such cases tf may be expressed as a fraction of tc. So: Then In vacuum filtration, f is equal to the fraction of submergence of the drum.

65 For compressible cakes,  is not constant and the above equation must be modified. In such cases:
s = measure of cake compressibility If s = zero, then the cake is incompressible and  = 0. For compressible cakes:

66 Determination of Cake Filtration Parameters
What design parameters? Lf -P f tc c  , s

67 This is a straight line equation with slope = s

68 Examples 7.8 and 7.9

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