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# galvis

Water treatment

## Box A4-3. Sum of Square

Box A4-3. Sum of Square Error (SSE) SSE = ⎡ ⎢ ⎢ ⎣ + 2 2 2 ( Y1,1 − Y1. − Y.1 + Y) + ( Y1,2 − Y1. − Y.2 + Y) + ( Y1,3 − Y1. − Y.3 + Y) + 2 ( Y1,4 − Y1. − Y.4 + Y) + .......... .......... ............ + ( Y1,33 − Y1. − Y.33 + Y) 2 2 2 ( Y2,1 − Y2. − Y.1 + Y) + ( Y2,2 − Y2. − Y.2 + Y) + ( Y2,3 − Y2. − Y.3 + Y) + ( Y Y Y Y) 2 2,4 − 2. − .4 + + .......... .......... .............. + ( Y2,33 − Y2. − Y.33 + Y) ⎡ ⎢ ⎢ ⎣ + 2 2 2 ( Y3,1 − Y3. − Y.1 + Y) + ( Y3,2 − Y3. − Y.2 + Y) + ( Y3,3 − Y3. − Y.3 + Y) + 2 ( Y3,4 − Y3. − Y.4 + Y) + .......... .......... .............. + ( Y3,33 − Y3. − Y.33 + Y) ⎡ ⎢ ⎢ ⎣ = 1190.54+ 660.40+ 1457.29 = 3308.23 2 ⎤ ⎥ ⎥ ⎦ 2 2 ⎤ ⎥ ⎥ ⎦ ⎤ ⎥ ⎥ ⎦ V 1 = 0.9 mh -1 V 2 = 1.3 mh -1 V 3 = 1.4 mh -1 Box A4-4. Sum of Square Total (SST) 3 33 SST = ∑ ∑ ( Yij − Y ) + + i = 1 j = 1 2 2 2 ( Y1,1 − Y ) + ( Y1,2 − Y ) + ........ + ( Y1,33 − Y ) 2 2 ( Y2,1 − Y ) + ( Y2,2 − Y ) + ....... + ( Y2,33 − Y ) ( Y 2 − Y ) + ( Y 2 − Y ) + ....... + ( Y − Y ) 3,1 3,2 = 3912 .55 + 4739 .62 + 9247 .22 = 17889 .40 3,33 2 2 2 V 1 = 0.9 mh -1 V 2 = 1.3 mh -1 V 3 = 1.4 mh -1 The calculations required by the ANOVA technique are summarised in A.4-4, using the result previously obtained for SSTreat, SSBlocks, SSError, and SSTotal Table A4.4 ANOVA data for the comparison of DyFG’s in suspended solids. Source of Variation Degrees of freedom Sum of squares (SS) Mean square (MS) F c Treatment (between) 2 3095.26 1547.638 29.94 Blocks (between) 32 11496.14 359.25 6.95 Error (within) 64 3308.23 51.69 Total 98 17889.40 With level of significance of α = 0.01, F (0.01,2,64) = 4.98. Therefore, considering that Fc = 29.94 > 4.98, the null hypothesis (Ho) is rejected, meaning that observed values do not support the hypothesis that different DyGF filtration rates do not have an impact on mean SS removal efficiencies. As Ho was rejected and all the treatment levels have the same number of observed values, the Tukey Test is used to do all possible comparisons of mean removal efficiencies originated from the different DyGF filtration rates. Tukey test is used when all treatment levels have the same number of observed A4-4

values (Mendenhall, 1997). Tukey test is employed to make all possible comparisons of means based on the Minimum Significance Difference (MSD) calculated as follows (Reyes, 1980) = ( α ∗ MSD q , t, n1) ( MSE b ) (A.4-2) In which q (α, t, n1) is a critical value of Studentized Range. α is the significance level; t is the number of means being compared; n 1 = 64 are the degrees of freedom of MSE (Mean Square of within); and n is the data number for each treatment level. The difference between each pair of means (D) is calculated as D = ⎺Y i – ⎺Y j for i ≠ j. The decision rules are as follows: If D > MSD, the two means are statistically different (⎺Y i ≠⎺Y j ), otherwise they are not statistically different (⎺Y i = ⎺Y j ). For the example being presented, q (0.01,3,64) = 4.28 MSD = 4.28 x 51.69 33 = 5.35 For this application Y 1 = V 1 ; Y 2 = V 2 and Y 3 = V 3 then D = 1 − Y 2 = 9 . 8 ; D = Y 2 − Y 3 = 3 . 5 ; = Y 1 − Y 3 = 1 Y 2 D 3 7 . 7 Furthermore, with significance level of 0.01: (3.4) D 1 > MSD, then the means SS removal efficiencies Y 1 and Y 2 are statistically different. D 2 < MSD, then the means SS removal efficiencies Y 2 and Y 3 are statistically similar, and D 3 > MSD, then the means SS removal efficiencies Y 1 and Y 3 are statistically different. Therefore, Based on the Tukey Test results, mean SS removal efficiencies associated with different filtration rates (V), can be presented in the following hierarchical order: V 1 Is the best filtration rate and different to V 2 and V 3 V 2 Is the second and statistically alike V 3 . This is, with significance level α = 0.01 and MSD = 5.35, Mean SS removal efficiency associated with V 1 = 79.4 (1) is the best. Mean SS removal efficiency associated with V 2 = 69.7 (2) is the second and similar to V 3 . Mean SS removal efficiency associated with V 3 = 66.2 (2) is also the second and similar to V 2 . References Mesa.E. (1999) Modelos Estadísticos: In Curso-Taller Diseño de Experimentos. Universidad del Valle: Cali, Colombia. Mendenhall, W. and Sincich, T. (1997) Probabilidad y Estadística para Ingeniería y Ciencias. México. Prentice Hall, Reyes, C.P. (1980) Diseño de Experimentos Aplicados. México. Editorial Trillas, Rowntree, D. (1981) Statistics without Tears. A Primer Non-Mathematicians. Great Britain. Penguin Books, Vargas, V. (1991) Metodología para el Manejo del la Información en el Proyecto Integrado de Investigación y Demostración de Métodos de Pretratamiento para Sistemas de Abastecimiento de Agua. Statitician Thesis. Universidad del Valle. A4-5

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Development and Evaluation of Multi

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ACKNOWLEDGEMENTS To my supervisor,

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ABBREVIATIONS ABNT: Acuavalle: ACV:

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SOCs: Synthetic Organic Chemicals S

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u c V V f Vs uniformity coefficient

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4 MULTISTAGE FILTRATION EXPERIENCIE

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1 INTRODUCTION Water is essential f

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Table 1.5 Safe drinking water cover

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1.2 Multiple Barriers Strategy and

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2 OVERCOMING THE LIMITATIONS OF SLO

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On January 14, 1829, Simpson’s on

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With increasing life expectancy, en

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Table 2.2 Treatments steps recommen

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In table 2.3, WHO guideline values

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2.5 The Slow Sand Filtration Proces

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When the particles are very close t

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in which p 0 is the clean media por

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Yao et al (1971) related the remova

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compensate for the increase in the

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can be applied, but intermittent op

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Table 2.4 Comparison of design crit

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Although accepted as indirect indic

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50% when the temperature falls from

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Figure 2.9 Flow diagram of the wate

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ut higher running costs, since more

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Headloss and flow control. Final he

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Figure 2.13 Influence of flow condi

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Operation and maintenance (O & M).

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in parallel (Galvis, 1983; Smet et

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cleaning simple, DyGF should behave

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case of Dortmund (Germany), the HGF

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Table 2.9 Data about three experien

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Some points of discussion about HGF

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and 600-800 NTU) and different filt

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the HGF units of Aesch (see table 2

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in spite of the low removal efficie

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order to overcome the water quality

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full-scale units. In this research,

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3 MULTISTAGE FILTRATION STUDIES WIT

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in the case of UGFL. Initially, it

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• Bigger and better-instrumented

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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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• Page 214 and 215: Table 6.1. Individual (at each trea
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• Page 222 and 223: Table 6.4. An example of identifica
• Page 224 and 225: MSF technology showed great flexibi
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• Page 228 and 229: epresents the risk the community ha
• Page 230 and 231: The selection of MSF alternatives i
• Page 232 and 233: scouring and transporting away prev
• Page 234 and 235: REFERENCES ABNT, (1989) NB-592 Proj
• Page 236 and 237: Craun, G.F., Bull, R.J., Clark, R.M
• Page 238 and 239: Drinking Water Disinfection, ed. by
• Page 240 and 241: Huisman, L. (1989) Plain Sedimentat
• Page 242 and 243: Mendenhall, W. and Sincich, T. (199
• Page 244 and 245: Ridley, J.E. (1967) Experience in t
• Page 246 and 247: Visscher, J.T. and Galvis, G. (1992
• Page 248 and 249: ANNEXES Annex 1: Accessories for Mu
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• Page 252 and 253: Annex 2: Design of Manifolds Manifo
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• Page 256 and 257: R 1 = (total orifice area / lateral
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• Page 260 and 261: Table A.4-2 General notation for th
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• Page 268 and 269: Annex 6 Number and Type of Valves N
• Page 270: Table A7-1. Descriptive statistics
• Page 274 and 275: Tables A7-3 Removal efficiencies of
• Page 276 and 277: Tables A7-5 Removal efficiencies of
• Page 278 and 279: Construction quantities of DyGF com
• Page 280: Net present value (US\$) of MSF and