Water treatment

in which ∆C is the concentration change in mass (or number of particles) per unit volume during the time ∆t; Q, constant filtration rate; ∆σ a , absolute specific deposit, volume of particles deposited per unit volume of the bed; ∆LA, volume of filtration element. ∆σ a = β∆σ, in which β is the bulk factor, and can be interpreted as a conversion factor to obtain the specific deposit σ (mass or number of particles per unit volume). Irrespective of the units of C, the bulk factor helps in obtaining estimates of σ (Sembi and Ives, 1982, quoted by Ohja and Graham, 1994). Rearranging equation 2.1 and using the differential form, ∂C A ∂σ ∂C β ∂σ ∂C ∂σ + β = 0 ⇒ + = 0 ⇒ V + β = 0 (2.2) ∂L Q ∂t ∂L V ∂t ∂L ∂t in which V is the approach velocity (LT -1 ). In eq. 2.2, the change of σ with time can be estimated if the change of C with L is known. On the basis of experimental data on SSF units, Iwasaki (1937) presented the eq 2.3, in which λ (L -1 ) is the impediment modulus also called filter coefficient. ∂ C = −λC (2.3) ∂L In clean filter conditions λ becomes λ 0 , and in the integrated form of the eq. 2.3 C 0 is the initial concentration of particles. Then, the profile of C in the liquid phase throughout the filter depth is logarithmic for a bed of uniform grain size, as shown by eq. 2.4 −λ L 0 0 C = C e (2.4) The parameter λ is important in filtration studies and occurs in all of the theories of filtration (Amirtharajah, 1988). Since σ, λ, and C are functions of time in the differential equations 2.2 and 2.3, a third equation is necessary to determine concentration as a function of time. Since theoretical considerations are of little help, λ has to be determined from filtration experiments, with which ∂c/∂L at various combinations of L and t can be estimated and plotted against σ, to obtain graphs as those shown in figure 2.5. With this approach the entire filtration cycle can be modelled but needs extensive experimentation to determine many of the parameters included in the models. Research papers present λ = f (λ 0 , σ), with empirical coefficients and because of differences in theoretical considerations or in experimental conditions, many have found different results. Some examples follow (based on Huisman, 1986), λ 1 = λo + k σ λ = λ + k σ − k o 1 2 σ p − σ 0 2 (Stein, 1940) (2.5) (Ives, 1960) (2.6) σ λ = λ0(1 + ) 1 − p 0 a σ b σ c (1 − ) (1 − ) p σ 0 u (Ives, 1969) (2.7) 24

in which p 0 is the clean media porosity; σ u is the ultimate or saturation value of the specific deposit ratio; and k 1 , k 2 , a, b, and c are coefficients or exponents to be determined experimentally. λ= λ + κ σ λ λ= λ + κ σ − κ Figure 2.5. A. Variation of filter coefficient λ with specific deposit ratio, σ (Fox and Cleasby, 1966). B. Variation of concentration C with filter length L and time t (Ives, 1982) The eq. 2.6 includes a second term to account for the increase in removal efficiency during the initial filter ripening period as a result of increased surface area caused by deposits collecting on the filter grains. The third term accounts for the subsequent decrease in removal efficiency in RF units with constant-filtration rate due to the increasing pore velocities caused by deposits. Fox and Cleasby (1966, quoted by Ginn et al, 1992) found that in conventional water treatment the alum and ferric flocs produced were not adequately modelled by eq. 2.6, mainly due to the third term accounting for the clogging process. Many of the equations proposed for λ can be derived from the general eq. 2.7, obtained after assuming that the changes in filter efficiency were due to changes in pore geometry, and the increase in interstitial velocity after the narrowing of the pore flow paths (Lebcir, 1992) Adin and Rebhun (1982), quoted by Amirtharajah (1988) presented a different approach incorporating attachment and detachment terms explicitly in the model equation. Besides eq. 2.2, they used eq. 2.8 for the removal efficiencies, ∂σ = k3VC( F −σ ) − k4σI ∂t (2.8) in which k 3 is the accumulation coefficient; F is the theoretical filter capacity or the mass retained per unit bed volume that could clog the pores completely; k 4 the detachment coefficient; and I the hydraulic gradient. The F and I values are obtained from the porosity of the filter and Darcy´s Law. The accumulation term, k 3 VC(F-σ) is similar to eq. 2.3. The third equation used by Adin and Rebhun to relate σ to F, was eq. 2.9, k ko 3 ⎡ 0.5 ⎢ 1 σ ⎤ − ( ) ⎥ ⎦ = ⎣ F (2.9) in which k and k 0 are hydraulic conductivity of the filtering bed with and without (clean bed) deposits. The parameters have to be determined experimentally for specific conditions. 25

- Page 1 and 2: Development and Evaluation of Multi
- Page 3 and 4: ACKNOWLEDGEMENTS To my supervisor,
- Page 5 and 6: ABBREVIATIONS ABNT: Acuavalle: ACV:
- Page 7 and 8: SOCs: Synthetic Organic Chemicals S
- Page 9 and 10: u c V V f Vs uniformity coefficient
- Page 11 and 12: TABLE OF CONTENTS 1. INTRODUCTION 1
- Page 13 and 14: 4 MULTISTAGE FILTRATION EXPERIENCIE
- Page 15 and 16: 1 INTRODUCTION Water is essential f
- Page 17 and 18: Table 1.2 Access to WS&S in Colombi
- Page 19 and 20: Table 1.5 Safe drinking water cover
- Page 21 and 22: 1.2 Multiple Barriers Strategy and
- Page 23 and 24: 2 OVERCOMING THE LIMITATIONS OF SLO
- Page 25 and 26: adjustment, are among the technolog
- Page 27 and 28: On January 14, 1829, Simpson’s on
- Page 29 and 30: With increasing life expectancy, en
- Page 31 and 32: Table 2.2 Treatments steps recommen
- Page 33 and 34: In table 2.3, WHO guideline values
- Page 35 and 36: 2.5 The Slow Sand Filtration Proces
- Page 37: When the particles are very close t
- Page 41 and 42: Yao et al (1971) related the remova
- Page 43 and 44: compensate for the increase in the
- Page 45 and 46: can be applied, but intermittent op
- Page 47 and 48: Table 2.4 Comparison of design crit
- Page 49 and 50: Although accepted as indirect indic
- Page 51 and 52: 50% when the temperature falls from
- Page 53 and 54: Figure 2.9 Flow diagram of the wate
- Page 55 and 56: ut higher running costs, since more
- Page 57 and 58: Headloss and flow control. Final he
- Page 59 and 60: Figure 2.13 Influence of flow condi
- Page 61 and 62: Operation and maintenance (O & M).
- Page 63 and 64: in parallel (Galvis, 1983; Smet et
- Page 65 and 66: cleaning simple, DyGF should behave
- Page 67 and 68: case of Dortmund (Germany), the HGF
- Page 69 and 70: Table 2.9 Data about three experien
- Page 71 and 72: Some points of discussion about HGF
- Page 73 and 74: and 600-800 NTU) and different filt
- Page 75 and 76: the HGF units of Aesch (see table 2
- Page 77 and 78: in spite of the low removal efficie
- Page 79 and 80: order to overcome the water quality
- Page 81 and 82: full-scale units. In this research,
- Page 83 and 84: 3 MULTISTAGE FILTRATION STUDIES WIT
- Page 85 and 86: in the case of UGFL. Initially, it
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l Figure 3.7 Plan view of Cinara's

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The present research work was divid

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Table 3.1. Design parameters, grave

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Figure 3.9. Piezometer distribution

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were used to collect samples for DO

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were poured into a funnel using fil

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H 0 : H a : Treatment levels workin

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3.2 Results and Specific Discussion

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3.2.2 Dynamic gravel filtration (Dy

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Mean faecal coliform removal effici

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Table 3.10 Comparative analysis of

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DyGF-A had flow reductions in the r

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The experimental data used to produ

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Previous observations were further

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ates (figure 3.17 B). However, at t

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Longer “initial-ripening” perio

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Table 3.17. Descriptive statistics

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100 Filtration rate = 0.3 mh -1 100

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After the present experience, faeca

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nature of the organic matter and th

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Table 3.24 Comparative analyses of

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3.2.4.3. Filtration run lengths and

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deep bed filter (data not included

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and operational considerations Pard

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than in sand samples from other SSF

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Step dose tracer tests were made at

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for HGFS and from 3 to 5 for HGF. T

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Constant and declining filtration r

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The efficiency levels summarised be

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Surface area of CGF and SSF units.

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community based organisations and l

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systems. All these systems were fed

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Parts of the suburban settlements o

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Figure 4.2. Layout of Retiro MSF pl

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Traditionally, in the WS&S of Colom

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Photo 4.10. Partial cleaning activi

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Figure 4.3 Location of full-scale M

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4.4.1.3 Main characteristics of mul

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Figure 4.4 Layout of Restrepo MSF p

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Figure 4.6 Layout of Javeriana MSF

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Figure 4.9 Layout of Cañasgordas M

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Figure 4.11. Layout of Ceylan MSF p

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Table 4.4 Descriptive statistics fo

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Water sources in the coffee region

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Filterability results seem to under

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Table 4.8 Mean removal efficiencies

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The length of this ripening period

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in Peru (Pardon, 1989) and Colombia

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Photo 4.24 Drainage facilities in u

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the Cauca Valley. This is not the c

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Pardon (1989) reports similar evide

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5. COST OF MULTI-STAGE FILTRATION P

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ecame formally established as WS en

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Models for assessing construction q

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MSF system can then be calculated o

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5.7 Cost Model for the Cali Area an

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Table 5.8. Annual labour costs due

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5.8 General Discussion The followin

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systems. The differences between MS

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guideline for colour is < 15 PCU (W

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Table 6.1. Individual (at each trea

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Table 6.3. Individual (at each trea

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As shown in tables 6.1 and 6.3, col

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UGFL 0.45 UGFS 0.45 (32;51;85) (44;

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Table 6.4. An example of identifica

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MSF technology showed great flexibi

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In harmony with the new development

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epresents the risk the community ha

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The selection of MSF alternatives i

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scouring and transporting away prev

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REFERENCES ABNT, (1989) NB-592 Proj

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Craun, G.F., Bull, R.J., Clark, R.M

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Drinking Water Disinfection, ed. by

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Huisman, L. (1989) Plain Sedimentat

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Mendenhall, W. and Sincich, T. (199

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Ridley, J.E. (1967) Experience in t

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Visscher, J.T. and Galvis, G. (1992

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ANNEXES Annex 1: Accessories for Mu

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aw water. The red colour is used fo

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Annex 2: Design of Manifolds Manifo

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+ q 2 Q1 (1.2 qn + qn) (2.2 qn) = =

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R 1 = (total orifice area / lateral

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0.30 0.25 0.20 0.15 0.10 0.05 0.00

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Table A.4-2 General notation for th

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Box A4-3. Sum of Square Error (SSE)

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Annex 5: Residence times in coarse

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Table A5-1 Percentage of incoming w

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Annex 6 Number and Type of Valves N

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Table A7-1. Descriptive statistics

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Tables A7-3 Removal efficiencies of

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Tables A7-5 Removal efficiencies of

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Construction quantities of DyGF com

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Net present value (US$) of MSF and