Journal of Indian Water Works Association (JIWWA, Vol-4, Issue-47)_Oct-Dec-2015

7/26/2019 Journal of Indian Water Works Association (JIWWA, Vol-4, Issue-47)_Oct-Dec-2015

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Journal of Indian Water Works Association 483 Oct.-Dec. 2015


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Oct.-Dec. 2015 484 Journal of Indian Water Works Association


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JOURNALOF INDIAN WATER WORKS ASSOCIATION

Published Quarterly in Jan-Mar, April-June,

July-Sept & Oct-Dec

ISSN 0970-275X

Founder President : Late D.R. Bhise

PresidentEr. Bappa Sarkar

Hon. Secretary General

Er. Parmod Nirbhavane

Hon. Editor (Journal)

Dr. Ulhas Naik

Hon. Editor (Midstream)

Er. G.V. Patade

Members of Review Board

Er. Ulhas DivekarDr. Abhaykumar WayalDr. Syeda UnnissaDr. R.K. ShrivastavaDr. D.D. OzhaDr. H.K.Rama RajuProf. Dr. Parag SadgirProf. Dr. Upendra KulkarniEr. Ulhas ParanjapeEr. Ghanshyam PatadeDr. Prashanth Reddy Hanmaiahgari

Prof. R.V. Saraf

Price: Rs. 18/-For Member Only.

Indian Water Works AssociationMCGM Compound, Pipeline Road,Vakola, Santacruz (East),Mumbai - 400 055.Tel: 91-22-26672665,26672666Fax: 97-22-26686113Email: [email protected] [email protected]

Website: www.iwwa.info

Cover Design :

"Catch Them Young, Make Them

Aware on Water Conservation"

Universal High School Malad

students visit on 5th Nov. 2015

to IWWA HQ for Rain Water

Harvesting System Training.

Vol. XXXXVII No. 4 October-December 2015

INDEX

From the Editors Desk ..................................................................................487

Removal of Select Heavy Metals from Polluted Water

Gajanan Khadse, Awadhesh Kumar, Pawan Labhasetwar.................................. 491

Comparison of the ability of Crushed Coconut shell and

Anthracite Coal as Capping Media

Manoj H. Mota, Shashiraj S. Chougule, SachinPatil .......................................... 503

Surface Water Quality Changes for EC in

Jayakwadi Reservoir, India

Purushottam Sarda, P. A. Sadgir ........................................................................ 510

Decolorization of Reactive Dye by Electrochemical

Oxidation Using Graphite Electrode

Rekha H. B., Usha N. Murthy ............................................................................ 517

AMRUT Mission Guidelines : Review and Recommendations

for Development of Resilient Water Infrastructure

Suneet Manjavkar.............................................................................................. 525

Midstream........................................................................................................535

A Comparative Study on Treatment of Simulated and

Actual Dye Wastewater by Coagulation Process

Aakanksha Soni, Priya Mundada, Dr. Urmila Brighu......................................... 543

Up gradation and Modernization of Water Treatment

Plants (WTPs) at Bhopal City, Madhya Pradesh, India

Santosh Kumar Kharole, Dr. Suresh Singh Kushwah,

Dr. Sudhir Singh Bhadauria............................................................................... 550

Discussion On Article .......................................................................................557


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Dear Members and Readers of Journal,

On behalf of the Editorial Board, it is a great pleasure to present your issueof Journal for Oct-December 2015.

We have already launched the online facility for submission of papers andit has been received well. We are pleased to share with you that this IWWAJournal issue contains most articles received through the online facility.

As promised, we shall have scheduled to launch the IWWA JournalArchives facility in the forthcoming IWWA annual convention, 2016Mumbai in the inauguration function.

This issue WISE WORDS are from another laurate and renownedpersonality, emirate Professor Soli Arceivala.

The articles in this issue primarily focuses more on treatment technologiesand advances. It also covers review and recommendation on AMRUTMission Guidelines. AMRUT mission is central government ambitiousmission for urban infra structure developments.

Another important buzz in urban infra structure development sector inIndia is Smart Cities. The Mission will cover 100 cities and its duration Three tier area-based Smart City development have been envisaged. achieve Smart City, Redevelopment shall effect a replacement of theexisting built-up environment and enable co-creation of a new layout withenhanced infrastructure using mixed land use and increased density, while previously vacant area (more than 250 acres) using innovative planning, reconstitution) with provision for affordable housing, especially for thepoor. We trust this central government mission shall certainly bring a lotimprovements to the water and sanitation sectors in all aspects.

On the outset of severe changes in climate pattern in India, the criticalissue in the center stage is Global warming. This topic is being churned atvarious national and international forum over last decade and now posinga serious threat leading to disrupt in common mans life; environmental,economic and social. Indian media can contribute to increased awarenessof climate change and related issues.

We appeal our readers to contribute articles on these above topics to makethe awareness propagate from IWWA platform.

Wish you all a Happy and prosperous new year 2016 !!!

(Ulhas S. Naik)

Hon. Editor Journal


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Removal of Select Heavy Metals from Polluted Water

Abstract

Removal of select heavy metals viz. chromium, copper, manganese and zinc from synthetic wastewater

with economically feasible materials with adsorption was investigated. Adsorption isotherms are

beds using sand as adsorbent for the different solutes. Solutions of varying concentrations of selected

heavy metals of chromium and copper (2-20 ppm), manganese (2-10 ppm) and zinc 15-85 ppm were

increased with increasing pH while it decreased with increasing metals concentration and injection

can successfully be used for heavy metal removal from water and wastewater.

Keywords

1. Introduction

Rapid industrialization and urbanization

have been contaminating the existing water

resources by discharging wastewater containingorganics, colour and heavy metals. Heavy

metals contamination exist in aqueous waste

streams of many industries, such as metal

mining, chemical manufacturing, pesticides,

fertilizers, dyes, pigments, tanning, and battery

manufacturing (Rao et al. 2001; Kang et al. 2007;

Lesmana et al. 2009). Heavy metals are reported

as priority pollutants, due to their mobility in

natural water ecosystems. Water pollution with

heavy metals is a source of danger to the health

of people living in developing countries. Some of

these metals are potentially toxic or carcinogenic

human health hazards if they enter the food chain.

Investigations have been made of the extent

of the heavy metal pollution of surface water,

groundwater, soils, air, and vegetation by mining

and associated industrial activities, thermal power

plants and open-cast coal mines (Khan et al. 2005).

Conventional methods for removing dissolved

heavy metal ions include chemical precipitation,

exchange, electrochemical treatment, application

of membrane technology and evaporation

recovery. However, these technology processes

have considerable disadvantages including

incomplete metal removal, requirements for

expensive equipment and monitoring system,

high reagent or energy requirements or generation

of toxic sludge or other waste products that

require disposal (Rorrer, 1998,Aksu et al. 2002;

Benguella and Benaissa, 2002; Bai and Abraham,

2003). In advanced countries, removal of heavy

metals in water and wastewater is normally

achieved by advance technologies such as ion


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or electrochemical deposition do not seem tobe economically feasible for such industriesbecause of their relatively high costs. This needsto investigate an alternative low-cost method,which is effective and economic. The study

method for removal of selected heavy metals viz.

1.1 Advantages of Sand Filtration Technique

and biological process and work on straining,sedimentation and adsorption phenomena. Designand operation simplicity as well as minimalpower and chemical requirements make the sand

suspended organic and inorganic matter. These

cloudiness, and organic levels - thus reducingthe need for disinfection and, consequently, the

water. Other advantages include: Minimal sludgehandling problems, Close operator supervisionis not necessary, No power requirement, Use oflocally available materials and labour.

1.2. Chemical and Biological Activities in

Sand Filtration

treated is essential. Biological oxidation of organicmatter in an aerobic environment contributes to

role. In the presence of sunlight they are able tobuild up cell materials from simple minerals such

as water, CO2, nitrates and phosphate and in the

process produce oxygen which in turn facilitatesbio-degradation of organic matter. Although most

called Schmutzdecke (the top 10-20 mm of

2. Guidelines for Drinking Water Quality

Water quality standards are the foundation of thewater quality-based pollution control programmandated by the Clean Water Act. Water quality

designating its uses, setting criteria to protectthose uses, and establishing provisions to protectwater bodies from pollutants. Various guidelinesvalues of selected heavy metals for drinking

water according to IS 10500:1991, WHO (2006),CPHEEO, EPA are given inTable 1.

3. Materials and Methods

3.1 Sand Filter Unit

To carry out the experimental investigation a

Fig. 1) comprised of an overhead tank

tank. Locally available sand and gravels of 9.5

average diameter of 41.5 cm, total height of 45 2

with gravels up to 5 cm at bottom followed byordinary sand up to 40 cm height, after washingwith substantial amount of water and followed by1% acid water and again with water properly toremove all impurities. Thereafter, it was dried indirect sun light.

Table 1 Guidelines for drinking water quality

Elements/

Symbol

IS 10500:1991 WHO

(2006)

(mg/L)

CPHEEO

EPA (mg/L)Desirable

limit (mg/L)

Permissible

limit (mg/L)

Acceptable

(mg/L)

Cause for

rejection (mg/L)

Chromium 0.05 No relaxation 0.05 0.05 0.05 0.1

Copper 0.05 1.5 2 0.05 1.5 0.05Manganese 0.1 0.3 0.4 0.05 0.5 0.3

Zinc 5 15 no 5 15 5


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size (D10

(U=D60

/D10

) less than 3. In stock sand that does

10

and D60

sizes, there is usable portion (Puse

), a portion that

f), and a portion that is too coarse (Pc).Therefore,

Puse

+ Pf+P

c= 100

3.2 Sieve Analysis

The sieve analysis of stock sand is done as shown

in Table 2.

Table 2 Sieve analysis of stock sand

SieveNo.

SieveSize

(mm)

Mass retainedon each sieve,

Wn (g)

% of massretained on

each sieve, Rn

Cumulative (%)

Cumulative (%)

8 2.35 65.21 13.81 13.81 86.1812 1.68 26.23 5.55 19.36 81.054

16 1.18 25.23 5.34 24.71 75.2820 0.85 72.02 15.25 39.96 60.0330 0.60 54.05 11.44 51.41 48.5840 0.425 69.54 14.72 66.14 33.8550 0.3 92.16 19.52 85.67 14.32100 0.15 46.50 9.84 95.52 4.47pan 21.15 4.47 99.99 0

472.10 100

Engineering properties of sand used for the presentinvestigation is given in Table 3.

Table 3 Engineering Properties of Sand

S.N. Properties Values (%)

1. Liquid limit NP2. Plastic limit NP3. Plasticity Index NP4. 2.65

5. Permeability 7.7 X 10-3cm/sec7. OMC 6.08. MDD 1.5069. C 010. 320

3.3 Elemental Composition Analysis of Sand

The distribution of elemental composition of the sand wasanalyzed using X-ray diffraction (XRD). XRD spectrum ofsand showed the presence of silicon, oxygen and a smallpercentage of aluminum and other metals as shown inFig. 2.

Fig.2XRD spectrum of sand


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3.4 Heavy Metals Selected and Chemicals

Used for Making Standards

Four heavy metals viz. Chromium (Cr), Copper

(Cu), Manganese (Mn), and Zinc (Zn) were

selected to study their removal through sand

[CrCl

3.6H

2O] with a purity of 96% was selected

as a source of Cr ions, Cupric nitrate [Cu

(NO3)

2.3 H

2O] with a purity of 95% was selected

as source Cu ions, Zinc nitrate hexahydrate

[Zn (NO3)

2.6 H

2O] with a purity of 96% was a

selected as a source of Zn ions and Manganese

sulphate [MnSO4.H

2O] was selected as a source

of Mn ions because of good solubility in water.

All the chemicals manufactured by Qualigens

ACROS. All solutions were prepared in distilledwater.

3.5 Application Procedure and Estimation

sand and fed from over head tank (reactor) with

synthetic solution of different concentration of all

selected heavy metals prepared in the laboratory.

A tap was attached to overhead tank controlled

were collected in bottles of polyethylene from

all the sampling ports at regular time intervals.

after adsorption) using Flame Atomic Absorption

Spectrometer (AAS) (Perkin Elmer, USA,

Model-A analyst 800).

3.6 Operation of Filter

To perform the experiment, 60 L working solution

of different concentrations of Cu, Cr, Zn, Mn were

prepared by dissolving the metals compound asmentioned above. The synthetic water samples

different injection rates 0.012, 0.024, and 0.036

m3/hr adjusted by tap were considered to study

the effects of injection rates on metal removal

8 adjusted by 40% conc. HCl and NaOH solution

rate 0.012 m3/hr. Treated samples were collected

at different time intervals.

) of heavy metals

%) = [(Co - Ct) / Co]*100

where, Co and Ct are the metal concentrations inthe sample before and after treatment respectively.

3.7 Cleaning of Sand Filter Unit

will need to be cleaned or backwashed. It was

after two or three week, however, it can be used

after 2ndweek by scrapping upper 2 to 5 cm sand

layer daily.

3.8 Adsorption Experiments of Selected

Heavy Metals

Batch experiments for selected heavy metals

(Cr, Cu, Mn and Zn) were carried out in 600 mLbeaker at room temperature (27 2). Heavy metals

adsorption as a function of equilibrium time, pH,

amount of adsorbent and initial concentration was

studied. In order to optimize contact time, 30 g

of the adsorbent was stirred with 100 mL of 20

ppm Cr solution at different time intervals (2, 5,10, 20, 40, 60, 80, 100 and 120 min). At the end

of the stirring period the samples were centrifuged

To study the effect of pH on Cr adsorption, 100

mL of 20 ppm Cr solutions were adjusted to

different pH values (3, 4, 5, 6, 7, and 8) using 40%

conc. NaOH and HCl. Then, 30 g adsorbent was

equilibrated with these solutions for 60 min. and

Cr adsorption. The effect of adsorbent dosage was

also studied by varying the amount of adsorbent

(15, 30, 45 and 60 gm) on an initial concentration

of 20 ppm at pH 7 for a contact time of 60 min. In

another set each 100 mL of Cr solutions at varying

concentrations (20, 40, 60, 80, 100 ppm) were

introduced into the beaker containing 30 g of the


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were analysed for the effect of Cr concentration.

The same experiment was also carried out for Cu,

Mn, and Zn.

4. Results and Discussion

and

injection rate (IR).

4.1 Effect of pH on Heavy Metal Removal

Solutions of various concentrations of Cr, Cu,

Mn and Zn were prepared at pH values of 3, 4,

5, 6, 7 and 8. For IR of 0.012 m 3/hr, the effect

of pH was compared for each concentration. It

was found that the removal of heavy metals isslightly higher at pH 8 as compared at pH 3. This

difference increase with increasing concentration

m3/hr, initial concentration of Cr 2 0.3 ppm,

the removal is 99.38 99.71% at all pH values;

whereas at a concentration of 20 2 ppm, 95.91

% of Cr is removed at pH 3 and 98.10 % at pH 8

(Fig. 3). The effect of pH 4-7 on removal of Cr is

in between.

Similarly, in case of Cu, initial concentration ofCu 2 0.3 ppm, the removal is 98.5% at all pH

values; whereas at a concentration of 20 2 ppm,

93% of Cu is removed at pH 3 and 94.86% at a

solution pH 8 (Fig. 4). The effect of pH 4-7 on

removal of Cu is in between. In case of Zn, initial

concentration of Zn 20 5 ppm, the removal is

93.92% at pH 3 and 97.21% at pH 8; whereas at

a concentration of 85 4 ppm, 78.05% of Zn is

removed at pH 3 and 84 % at pH 8 (Fig. 5).The

effect of pH 4-7 on removal of Zn is in between.In case of Mn, initial concentration of Mn 2 0.2

ppm, the removal is 67.27% at pH 3 and74.3% at

pH 8; whereas at a concentration of 10 2 ppm,

64.58 % of Mn is removed at pH 3 and 70.05%

at solution pH 8 (Fig. 6). The effect of pH 4-7 on

removal of Mn is in between.

94

95

96

9798

99

100

3 4 5 6 7 8

(%)Removal

pH

Co= 1.97 (ppm)Co= 4.86 (ppm)Co= 9.89 (ppm)Co= 14.47 (ppm)Co= 18.55 (ppm)

Fig. 3: Effect of pH on removal of influent Cr

conc.

9293949596979899

100

3 4 5 6 7 8

(%)Removal

pH

Co= 1.81 (ppm)Co= 4.84 (ppm)Co= 9.80 (ppm)Co= 14.39 (ppm)Co= 18.60 (ppm)

Fig. 4: Effect of pH on removal of influent Cu

conc.

75

80

85

90

95

100

3 4 5 6 7 8

(%)Removal

pH

Co= 16.64 (ppm)

Co= 34.66 (ppm)

Co= 52.68 (ppm)

Co= 72.48 (ppm)

Co= 86.40 (ppm)

Fig. 5: Effect of pH on removal of influent Zn conc.


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Fig. 6: Effect of pH on removal of influent Mn

conc.

The results agree with those of Zeng (2002)

which show that the pH does not have a majoreffect on the removal of metals from solution.

pH of solution is attributed to the precipitationof Metal (M) hydroxide [M(OH)3] at higher

pH. Increasing the pH implies a proportionalincrease in the concentration of hydroxide ions

in solution and hence disturbs the equilibrium.Therefore, the system adjusts to cancel this effect

(Le Chateliers principle) by precipitating moreand more hydroxide out of the solution. This

precipitate, although not permanently adsorbed bythe sand particles, is nevertheless retained/trapped

the metals into direct contact with the externalenvironment.

Metal Removal

each metal at an injection rate (IR) of 0.012 m 3/hrat different pH values 3-8. Five different concentrations 2, 5, 10, 15 and 20 2 ppm of

Cr was considered to have comparison of sandadsorption behaviour at different pH. Maximum

removal (99.38 - 99.71%) was observed for an

concentration of Cr. Even at concentration of 20

at pH 3 and 98% at pH 8. As usual, the effect of

somewhere in between (Fig. 7-12).

94

95

96

97

98

99

100

1.9 4.8 9.9 14.86 18.82

(%)Removal

Influent conc. (ppm)

Fig. 7 Effect of influent conc. of Cr on

% at IR 0.012 m3/hr at pH 3

95

96

97

98

99

100

2. 03 4.89 9. 88 14. 59 18. 5

(%

)Removal




95

96

97

98

99

100

2 4.9 9.89 14.82 18.8

(%)Removal





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95

96

97

98

99

100

2.03 4.89 9.88 14.59 18.5

(%)Removal


Fig. 10 Effect of influent conc. of Cr on the


95

96

97

98

99

100

1.97 4.9 9.88 14 18.4

(%)Removal

Initial conc. (ppm)


the % at IR 0.012 m3/hr at pH 7

95

96

97

98

99

100

1.97 4.89 9.87 14.21 18.3

(%)Removal




concentrations 2,5, 10, 15 and 20 2 ppm of Cu were considered,maximum removal (98.58 - 94.8%) was observedfor 2 ppmat all pH. Even at concentration of 20

at pH 3 and 94.86% at pH 8 (Fig. 13 -18). In caseof Zn, concentrations 15, 35,55, 75, and 85 6 ppm of Zn were considered;maximum removal (97.12 %) was observed atpH 7. Even at concentration of 85 ppm removal

and 84% at pH of 8(Fig. 19 -24). In case of Mn, concentrations 2, 4, 6, 8 and10 2 ppm of Mn, were considered, maximumremoval 74.31% of 2 ppm at pH 8. Even at

falls up to 64.58% at pH 3. As usual, the effect of

somewhere in between (Fig. 25 -30).

This can be explained by the fact that as theconcentration of metal ions increases so does themetal loading on the adsorbent. For example, aconcentration of 85 ppm will have higher surfaceloading as compared to concentration of 15 ppm.Because it causes an equal increase in number ofmetal ions coming in contact with sand increases

during same interval of time while on the otherhand the no of adsorbing sites available foradsorption are constant for all concentrations.

more number of ions will be competing for sameadsorption sites and will go through without beingadsorbed.

4.3 Effect of Injection Rateon Heavy Metal

Removal

3/hr, 0.024 m3/hr

and 0.036 m3/hr) were studied at constant sand bed

solution. It was found that maximum removal wasobserved at IR 0.012 m3/hr, as compare to theother two IRs. e.g. 98% removal of Cr of 20 2

m3/hr, and 95% at 0.036 m3/hr and at 0.024 m3/


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90

92

94

96

98

100

1.

9

4.

85

9.

86

14.

55

18.

75

(%)Removal

Influentconc.(ppm)

Fig.13Effectofinfluentconc.ofCuon

%

atIR0.012m3/hra

tpH3

9293949596979899

1.

8

9.

85

14.

39

18.

7

(%)Removal

Influentconc.(ppm)


%a

tIR0.012m3/hratpH4

91

92

93

94

95

96

97

98

99

100

1.

8

4.

71

9.

92

14.

4

18.

6

(%)Removal

Influentconc.(pp

m)

Fig.15Effectofinfluentco

nc.ofCuon

%

atIR0.012m3/hratpH5

92

93

94

95

96

97

98

99

1.

82

4.

76

9.

87

14.

53

18

(%)Removal

Influentconc.(ppm)

Fig.16Effectofinfluen

tconc.ofCuon

%

atIR0.012m3/hr

atpH6

92

93

94

95

96

97

98

99

1.

79

4.

9

9.

44

14.

21

18.

5

(%)Removal

Influentconc.(ppm)

Fig.17E

ffectofinfluentconc.ofCuon

%

atIR0.012m3/hratpH7

93

94

95

96

97

98

99

1.

75

4.

92

9.

88

14.

28

18.

4

(%)Removal

Influentconc.(ppm)


%

atIR0.012m3/hratp

H8


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70

75

80

85

90

95

100

16.

73

34.

66

55.

26

72.

48

86.

4

(%)Removal

Influent

conc.(ppm)

Fig.19Effectofinfluentconc.ofZnon

%

atIR0.012m

3/hr

atpH3

70

75

80

85

90

95

100

16.

73

34.

5

55.

68

72.

36

86.

1

(%)Removal

Fig.20

Effectofinfluentconc.ofZnon

%

at

IR0.012m

3/hratH4

70

75

80

85

90

95

100

16.

49

34.

12

55.

94

71.

8

86

(%)Removal

Influentconc.

(ppm)

Fig.21Effectofinfluent

conc.ofZnon

%

atIR0.012m

3/hrat

H5

70

75

80

85

90

95

100

16.

88

34.3

56.

5

71.

68

85.

8

(%)Removal

Influentconcentration(ppm

Fig.22Effectofinflu

entconc.ofZnon

%

atIR0.012m

3/h

ratH6

75

80

85

90

95

100

16.

65

34.

56

55.

6

71.

36

85.

4

(%)Removal

Influentconc.(ppm)

Fig.23Effectofinfluentconc.ofZnon

%

atIR0.012m

3/hratH7

75

80

85

90

95

100

16.

37

34.

08

55.

1

71.

92

(%)Removal

Influen

tconc.(ppm)


conc.ofZnon

%

atIR0.012m

3/hrat

pH8


20/80


62

64

66

68

70

72

74

2.

1

3.

89

5.

92

6.

88

9.

67

(%)Removal

Influentconc.(ppm)

Fig.28Effectofinfluen

tconc.ofMnon

%

atIR0.012m3/hratpH6

63

64

64

65

65

66

66

67

67

68

2.

08

3.

94

5.

89

6.

91

9.

84

(%)Removal

Influentconc.(ppm)

Fig.25Effectofinfluentconc.ofMn

on

%

atIR0.012m

3/hrat

H3

6

4

6

5

6

5

6

6

6

6

6

7

6

7

6

8

1.

99

3.

9

5.

92

6.

89

9.

55

(%)Removal

influentconc.(ppm)

Fig.

26Effectofinfluentconc.ofMnon

%

atIR0.012m3/hratH4

64666870727476

2.

04

3.

94

5.

9

6.

85

(%)Removal

Influentconc.(ppm)

Fig.29

Effectofinfluentconc.ofMnon

%

atIR0.012m3/hratH7

67

68

69

70

71

72

73

74

75

1.

94

3.

9

5.

87

6.

82

9.

72

(%)Removal

Influentconc

.(ppm)

Fig.30Effectofinfluentconc.ofMnon

%

atIR0.012m3/hratpH

8


conc.ofMn

on%

atIR0.012m3/h

ratpH5


21/80


hr the removal was in between (Fig.31). SimilarlyCu removal was 94% and 90% at 0.012 m3/hr

and 0.036 m3/hr respectively (Fig. 32). In case

of Zn removal of 83.25 % and 78.26 %at 0.012

m3/hr and 0.036 m3/hr respectively (Fig. 33) was

observed. In case of Mn 67.84% and 59.67%

removal was observed at IR of 0.012 m3/hr and

0.036 m3/hr respectively (Fig. 34).

5. Summary and Conclusion

Heavy metals viz. Cr, Cu Mn, and Zn in aquaticenvironment are a major concern because of

their toxicity and threat to plant and animal life

disturbing the natural ecological balance. Sand

removal of heavy metals from the water. Therefore

the present investigation was undertaken to study

the removal of selected heavy metals with sand

and

injection rate. It was found that the removal of

heavy metals is slightly greater at a pH of 8 as

compare to a pH of 3. This difference increase with

solution e.g. at a injection rate 0.012 m3/hr. The

removal of Cr is 99.38% and 98.10%, Cu is 93%

94

95

96

97

98

99

100

1.97 4.90 9.88 14.00 18.40

(%)Removal


I R= 0.012 m3/hrI R= 0.024 m3/hrI R=0.036 m3/hr

Fig. 31 Effect of influent IR on% of

different influent conc. of Cr

86889092949698

100

1.89 4.90 9.43 14.21 18.50

(%)Removal


I R= 0.012 m3/hrI R= 0.024 m3/hrI R= 0.036 m3/hr


different influent conc. of Cu

7075

80

85

90

95

100

16.55 34.56 55.60 71.36 85.40

(%)Removal



Fig. 33 Effect of influent IR on % of different

influent conc. of Zn

50

55

60

65

70

75

80

2.04 3.94 5.90 6.85 9.67

(%)Removal




different influent conc. of Mn


22/80


and 98%, Zn is 93.92% and 97.21% and Mn is67.27% and 74.3% at pH of 3 and 8 respectively, 0.3

ppm, and Zn 203ppm. While removal of Crwas 99.71%, Cu 93%, Zn 78.05% and Mn 64% at

854 ppm and Mn 102 ppm. When the injectionrate increased, the hydraulic loading rate was also

observed in Cr 99.54% to 95%, Cu 98.5% to 90%,Zn 96.69% to 92% and Mn 67.63% to 59.46%, at

the hydraulic loading rate of 0.08 m/hr to 0.169 m/hr respectively.

Based on this study, the following conclusions

were drawn:

Mn and Zn. Since a higher pH results inprecipitation of Cr rather than permanent

adsorption, it is recommended to acidify the

decreased as the injection rate increased.Although sand is quite effective even at arate of 0.036 m3/hr, yet the conventional rate

of 0.012 m3/hr is strongly recommended.

Sand has showed very high adsorptioncapacities of metals. It was observed that theadsorption of the selected heavy metals isin the order of Cr > Cu > Zn > Mn, and canbe successfully used for treatment of waterand wastewater. Since this method involves

it is practicably feasible for developingcountries. The results of investigation will

be useful for the removal of metals from

References:

1. Aksu, Z., F. Gnen and Z. Demircan (2002).Biosorption of chromium (VI) ions by MowitalB3OH resin immobilized activated sludge in a

packed bed: comparison with granular activatedcarbon,Process. Biochem.38 (2002), pp. 175186.

2. APHA, AWWA & WPCF (2005). Standard Method

for the Examination of water and waste water,

21st edition, American Public Health Association,

3. Bai, R.S., E. Abraham (2003). Studies on chromium

(VI) adsorptiondesorption using immobilized

fungal biomoss, 87 (2003), pp.

1726.

4. Benguella, B., H. Benaissa (2002). Effects of

competing cations on cadmium biosorption by

chitin, Colloid Surf. A:Physicochem. Eng. Aspects

201, pp. 143150.

5. BIS:10500 (1991). Bureau of Indian Standards

(BIS), Drinking Water Quality Standards.

6. Kang, S., J. Lee, and K. Kima. 2007. Biosorption

of Cr(III) and Cr(VI) onto the cell surface of

pseudomonas aeruginosa.Biochemical Engineering

Journal, 36: 5458.

7. Khan, R., Israili, SH., Ahmad, H. and Mohan, A.

(2005), Heavy metal pollution assessment in

surface water bodies and its suitability for irrigation

around the Nayevli lignite mines and associated

industrial complex, Tamil Nadu, India, Mine Water

and the Environment, Vol. 24, pp. 155-161.

8. Lesmana, Sisca O., Novie Febriana, Felycia E.

Soetaredgo, Jaka Sunarso, and Suryadi Ismadji.

2009. Studies on potential applications of biomass

for the separation of heavy metals from water and

wastewater. Biochemical Engineering Journal,44:19-41.

9. Rao, M., A.V. Parwate, A.G. Bhole. 2001. Removal

of Cr6+ and Ni2+ from aqueous solution using

WasteManagement, 22: 821

830.

10. Rorrer, G.L. 1998. Heavy metal ions removal

from wastewater. Encyclopaedia of Environmental

Analysis and Remediation,4: 21042128.

11. World Health Organization (WHO), Geneva,

Guidelines for drinking Water Quality, (1984).

12. World Health Organization (WHO) (2004).Guidelines for Drinking-Water Quality: Vol.1

Recommendations, 3rd edition. World Health

Organization, Geneva.

13. Zeng, L. (2002). Preliminary Study of Multiple

Heavy Metal Removal Using Waste Iron Oxide

Tailings. Proceedings of the Remediation

Technologies Symposium, October 16-18, Banff,

Alberta.


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Comparison of the ability of Crushed Coconut shell and

Anthracite Coal as Capping Media

Manoj H. Mota* ** ***

Email: [email protected], Mob: 9272195932

*** Associate professor, Civil Engg. Dept., Ashokrao Mane Group of Institute, Vatharturf, Vadgaon, KolhapurEmail: [email protected], Mob: 9767503463

Abstract

by a media of comparatively coarser in nature but less in density as compared to conventional sand

used as monomedia. It is easier method to improve the performance of conventional rapid sand

treatment plant.Keywords-

Introduction

Different unit processes and unit operations

utilized in most of the conventional water

treatment plant (WTP) in India includes aeration,

process. Most of the turbidity though removed

particles able to pass through that are removed by

water produced by any WTP is the function of the

Most of the conventional water treatment plants

are overloaded due to increased demand. They

are facing the problems like substandard overall

performance and unsatisfactory water supply

besides unsatisfactory operation and maintenance.

suffering by the problems like mud ball formation,

can overcome these limitations of rapid sand

materials apart from sand.

promising method of improving the performance

caps such as Anthracite coal, Bituminous coal,

Crushed coconut shells, etc. Capping involves

the replacement of a top portion of the sand with

inferior to the originally designed dual media


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[1]

The study has made by installing a pilot plant at

Ichalkaranji municipal WTP. The coconut shell aswell as anthracite coal were used as the capping

media. The results obtained are very encouraging.The comparison of two materials on the basis of

its performance as capping media has been done.

is possible along with smaller ripening period,

Materials and methods

two glass columns, each of an inside area of 0.15m

X 0.15m (as Side of column/effective size of sand>50) [5] along with associated piping and valves

0.5 HP was used for proper back washing of sand

less than 5m/hr. The backwashing rate was keptas 0.7 m/min [2, 7] .The pilot model was installedat Ichalkaranji water treatment plant, where the

evaluation of capped RSF.

Fig 1. Photograph of installed pilot plant

from the stock sand available at Ichalkaranji WTP.The required sand was initially washed and sun

prepared by sieving and mixing in appropriate

used was 1.5 and the effective size was 0.6mm. [2]

The effective size of capping media was

determined by considering the fact that the settlingvelocity of the sand particle of effective size tobe more than that of capping media particles. The

about 1.0 (i.e. particles of more or less uniformin size) and effective size was 1.91 mm. while in

of capping media used was again kept around1 and effective size was 1.51 mm. The depth ofcapping was kept as 10cm. in both cases.

Coconut shells of required size and uniformitywas obtained by crushing and sieving it. Thecrushed coconut shell was charged by heatingbefore use. [3]

Capped sand media with coconut shell as

capping media

Capped sand media with anthracite coal as

capping media

Fig 2. Photograph of capped sand media


25/80


were collected from for the conventional pilot

and the turbidity of these were checked usingNephelometer. Along with this comparison was

backwash and ripening period.

Results and discussion

During the study following results were obtained

Coconut shell as capping media

to be slightly lesser than the conventional rapid

by the coarser media. But the clear advantage was

run. The conventional RSF was clogged within 14hrs while the capped RSF was able to run for more

than 22 hrs which is evident in graph no1.

rate for conventional RSF was kept 5m/hr. In that

case even the performance of capped RSF was

Graph no1. Comparison of performance of Conventional R.S.F. and Coconut shell capped R.S.F with

Graph no 2. Comparison of performance of Conventional R.S.F. and Coconut shell capped R.S.F. with

Note:


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remained almost same as that was observed in case

(though acceptable) as compared to lesser

was observed in which this trial which was quite evident in graph no 2. No escaping of media oflesser density was observed with normal rate ofbackwashing i.e. 600-700mm/min.

Anthracite coal as capping media

In case of anthracite coal used as capping media,

Table No.1. Ripening period** for coconut shell capped RSF

Time in

minutes

Conventional RSF Capped RSF Remark

Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

0 6.8 7.2 6.8 7.9 ---

5 6.8 7.5 6.8 7.0

10 6.8 6.4 6.8 5.0 Ripening period forcapped RSF-10 minute

15 6.8 4.9 -- -- Ripening period forconventionalRSF-15minute

Table No.2. Backwash periods for coconut shell capped RSF

Time in

minutes


Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

0 2.9 63 2.9 68 --

5 2.9 39 2.9 20

10 2.9 21 2.9 3.1 Backwash time forcapped RSF-10 minute

15 2.9 3.0 -- -- Backwash time forconventionalRSF-15minute

case of coconut shell used as in capping media.The reason for the observed behavior is same.

the breakthrough and not because of the high head

rate of 5m/hr as well as 7m/hr. These are evidentin graph no.3 and graph no 4.


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Table No.3 Ripening periods for RSF using anthracite coal as capping media

Time in

minutes


Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

0 6.9 7.4 6.9 7.9 ---5 6.9 7.9 6.9 7.0

10 6.9 6.3 6.9 6.1

15 6.9 5.1 6.9 4.9 Ripening period for bothconventional and cappedRSF-15minute

anthracite coal


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Summary of the results obtained:

Sr.no.

Particularfor

comparison

ConventionalRSF

RSFwithcoconut

shellcapping

RSFwith

anthracitecappin

g

1. Filter run (Hrs) 13.5 (max) 22.5 19.5

2. Backwash time(min)

15 10 13

3. Ripening period 15 10 13

Conclusions:

From the study made to evaluate the effect of

capping of RSF following conclusions were

made..

1. For Crushed coconut shell used as capping

media..

a) The capping of RSF using the crushed

coconut shell as capping media can

b) Backwash requirement for coconut

shell capped RSF is less as compared

to conventional RSF by 33%.

c) Ripening period for capped RSF is

less as compared to conventional RSF

by 33%.

2. For Anthracite coal used as capping

media..

a) The capping of RSF using theanthracite coal as capping media can

%.

b) Backwash requirement for anthracite

coal capped RSF is less as compared

to conventional RSF by 15%.

c) Ripening period for capped RSF was

almost same.

3. Capping of RSF using crushed coconut shell

coal as capping media.

quality. Thus the capping of conventional

RSF can be very effective tool in case of

overloaded conventional plants where

Future scope

The coconut shell used as a capping media was

media to extend its life by reducing the decaying

effect. The charged media is also capable to offer

more resistance to the bacterial action. The long

term study about the life of such media is essential.

Table No.4 Backwash period for anthracite coal as capping media

Time in

minutes


Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

Turbidity of

( NTU)

0 2.7 59 2.7 61 --

5 2.7 35 2.7 29

10 2.7 23 2.7 19

13 2.7 12 2.7 2.7 Backwash time forcapped RSF-13 minute

15 2.7 2.8 -- -- Backwash time forconventional RSF-15minute


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The bacterial effect can also taken care by using

chlorinated water for backwashing once in a week

or so. Again this is a subject which will need a

comprehensive long term study.

AcknowledgementThe authors are very much thankful to the

Mr.Babasaheb Choudhari, Hydraulic Engineer,

Ichalkaranji Municipal Council and Mr. Bajirao

Kamble for allowing their team to work at

Ichalkaranji Municipal Water Treatment Plant and

providing all possible help during study period.

References

1. Al-Rawi S.M.

turbidity removal for potable water treatment

plants. Environment Research Center (ERC),Mosul University, Mosul, Iraq, 2009.

2. Dr. B.C. Punmia et al., Water supply engineeringLaxmi Publications (P) Ltd, 311-360, 1995.

3. Dr. J.N. Kardile, Simple methods in water, Filters.1987.

4. Larson J.H. capping.Clean Water Enterprises, Inc. Syracuse

5. Lang, John S.; Giron, Jonathan J.; Hansen, AmyT.; Trussell, R. Rhodes; Hodges, Willie E. Jr.Investigating, Filter Performance as a Functionof the Ratio of Filter Size to Media Size, Journalof American water works assocoation,Vol.85(10) pg122-130,1993.

6. O. Fred Nelson, Capping Sand Filters, Journal ofAmerican Water Works Association Vol.61(10), ,

pp. 539- 540.1969

7. Qasim, S.R., Motley E.M., and G.Zhu, Water WorksEngineering, PHI private ltd,867-949, 2002

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Website: www.worldenviro.com.


30/80


Surface Water Quality Changes for EC in

Jayakwadi Reservoir, India

Purushottam Sarda1 2

Abstract

of Jayakwadi reservoir. Jayakwadi reservoir serves multiple purposes such as water for drinking,

fast and continuous measured parameters from 2001-2012 at Pategaon observation station is

and two different ANN strategies, Feedforward Neural Network (FFNN) and Cascade Correlation

Error (RMSE), Mean Absolute Error (MAE), Percent of Prediction within a Factor of 1.1(FA1.1),

criteria. Comparison of the results indicate that the FFNN performed slightly well than the CCFF for

Abstract: Cascade Correlation Feedforward; Electrical Conductivity; Feedforward Neural Network;

Statistical Analysis; Water Quality.

1.0 Introduction

The water is an important natural resource for

different purposes such as drinking, irrigation,

therefore, it requires at least an acceptable

level of water quality [Alam et al. (2007);Emamgholizadeh et al. (2014)]. The need of study

of surface water quality is one of the major issues

today due to increase in the load of pollution from

industrial, commercial and residential sectors with

its effects on human health and aquatic ecosystems

[Diamantopoulou et al. (2005); Choudhary et al.

(2011)]. Rankovic et al. (2010) stated that basic

problem in the case of water quality monitoring isthe complexity associated with analyzing the large

number of variables. Palani et al.(2008) predicted

the water quality key factor in the water quality

management of stream and it enables a manager

Electrical conductivity (EC) is considered to be arapid and good measure of dissolved solids which

of the aquatic body [Gupta et al.(2007); Heydari et

al.(2013)]. Najah et al.

changes in EC parameters and concluded that EC

is an indicator of too much salt in the polluted

stream of water.

In this paper, the objective is to check the surface

water quality changes in EC using various

combinations of input parameters; Temperature,pH, TDS, DO and BOD. Another objective is

to determine the best input parameter among all

for predicting EC. Performances of strategies

are compared by statistical criteria Root Mean

1 Research Scholar, Government College of Engineering, Aurangabad, India


31/80


Square Error (RMSE), Mean Absolute Error

(MAE), Percent of Prediction within a Factor

of 1.1(FA1.1), Index of Agreement (IA) 2) for each

combination of parameters.

2.0 Study Area

Jayakwadi reservoir is located on Godavari River

a multipurpose project, and mainly used to irrigate

agricultural land in the drought-prone region of

the state. It also provides water for drinking, hydro

and industrial usage. The surrounding area ofthe dam has a garden and a bird sanctuary. It isbounded by latitude 192755N and longitude

752427E with catchment area of 21,750 sq. km,

length of 10.20 Km and gross storage capacity of2909 Million cubic meters. Reservoir receives

water from Godavari River and its tributaries in

the upstream catchment.

3.0 Methodology

The monthly water quality data collected from

2001-12 at Pategaon observation station andstatistical variation of dataset has been calculatedby statistical analysis i.e. mean, mode, median andstandard deviation. After knowing the variation ofdataset, the value of dataset has been comparedwith soft-tools such as ANN. The ANN is a dataprocessing system, based on an idea similar to theprocessing of the human brain that treats data asa steady network parallel to each other in order tosolve a problem. With the networks, the structure ofdata is designed to help programming knowledge

in which the behavior is as same as natural neural

consists of three components, including weighting(w), bias (b) and transfer function (f). These threecomponents are unique for each neural system.The network topography consists of a set of nodes(neurons) connected by links and are usuallyorganized in number of layers. The basic structure

Fig. 1 Location plan of Jayakwadi Reservoir


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of an ANN usually consists of three layers viz.,

an input layer, output layer, and hidden layer(s)

between the input and output layers as shown in

Fig. 2 Basic Structure of ANN Model

The transfer function can transform the nodes net

input in a linear or non-linear manner. Commonly

used transfer functions in hidden layer are

sigmoid transfer function and hyperbolic tangent

transfer function, these were tansig (Hyperbolic

tangent sigmoid transfer function) and purlin

( Following parameters for the modeling of waterquality by ANN has been workout for variouscombinations of models. The combination ofmodels for predicting monthly EC is as shown in

table 1.For processing the dataset, MATLAB 2012soft tool has been used with following differentarchitectures. For models construction, twodifferent kinds of networks such as FeedforwardBackpropogation Neural Network (FFNN) andCascade Correlation Neural network (CCFF) areproposed for developing all models. The numberof iterations represents the time needed fornetwork training. If the training time is shorter, the

only a small number of iterations were representedas 1000 epochs in this study as shown in table 2.

Table 2 Initial parameter setting for

implementing the ANNs models

General Setting

Network FFNN, CFNN

Max. Epoch 1000

Training Algorithm Levenberg-Marquardt(trainlm)

Transfer Function

PerformanceFunction

R2,RMSE, MAE,IA,FA1.1

Adaption LearningFunction

LEARNGDM

No. of Neurons 2 to 20

No. of Hidden Layers 2

Table 1 Combinations investigated for predicting monthly EC

Combination of Model Input Abbreviation output

1 (TDS)t T (EC)t

2 (TDS)t, (Temp.)t TT (EC)t

3 (TDS)t, (Temp.)t, (DO)t TTD (EC)t

4(TDS)t, (Temp.)t, (DO)t,(BOD)t

TTDB (EC)t

5(TDS)t, (Temp.)t, (DO)t,(BOD)t, (pH)t

TTDBP (EC)t


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(Where X = Normalized data value, x = Datavalue, X

min= Minimum data value in available

dataset, Xmax

= Maximum data value in available

dataset, n = No. of Datasets, Cp and Co are thePredicted and Observed dataset respectively)

The data has been initially normalized andperformance of model is observed with statistics

indicators with formulae as shown in Table.3.

The RMSE can provide a balanced evaluation of

sensitive to larger relative errors, the best valueof which is zero (RMSE=0). The MAE has range

Table 3 Statistical metrics used in model performance evaluation

Measures Mathematical Expression

Normalized Data

R2

RSME Root Mean Squared Error

MAE Mean Absolute Error

IA Index of Agreement

FA1.1 Percent of Prediction within a Factor of 1.1

2, which ranges from 0 to 1.0, is a

statistical measure of how well the regression line

2=1)

observed data. FA should lie in domain 0.9 to 1.1,

if is more or less than the above limits it is not

be equivalent to R2values.

3.0 Results and Discussion:

3.1 Statistical Analysis

Statistical analysis gives an idea about water

quality and its tendency. So analysis has done

Table 4 Statistical analysis of water quality parameter

Statistical Parameters TDS Temp DO BOD pH EC

Mean 258.73 26.86 6.21 5.44 8.07 365.41

Median 242.00 27.00 6.20 2.33 8.10 344.00

Mode 240.00 27.00 6.40 1.40 8.20 510.00

SD 93.24 2.48 0.98 6.87 0.42 126.80

Kurtosis 0.24 0.70 1.60 3.87 -0.42 0.22

Skewness 0.83 0.04 -0.02 2.09 -0.33 0.66

Minimum 110.00 20.00 2.90 0.50 7.00 163.00

Maximum 562.00 35.30 9.30 32.00 8.90 815.00

As per BIS/ ICMR/WHO 500 15-35 5 5 6.5-8.5 300

BIS ICMR/ WHO ICMR/ WHO ICMR/ WHO BIS/ ICMR ICMR/ WHO


34/80


for study area and results are shown in table 4.

The values of water quality parameter are also

compared with the standards limits given by

various agencies.

Fig.5 Mean, Median and Mode of Model

Parameters

It is observed that the TDS, BOD and DO haveexceed the limits by standards and standarddeviation 126.80 for EC; it means observationseries have less homogeneous and inconsistentwhile curve is platykurtic and positive distribution.

The radar curve represents the status of monthlyEC concentration have been greater range than

comparison of Mean, Mode and Median is shown

3.2 Neural Network

In order to model EC concentration, availablemeasured dataset were divided into two partitionsas training and testing for each model. Forvalidation of partitions various partitions has

been made and results are as shown in table 5 and C

pand C

oare the predicted and observed

concentrations, respectively.

Table 5 Summary of Different Percentage

Ratio of Training and Testing

Partitions

TrainingR2

TestingR2

Equation

70-30% 0.850 0.835 Cp= 0.866 C

o+ 49.40

80-20% 0.841 0.779 Cp= 0.845 C

o+ 56.69

50-50% 0.879 0.585 Cp= 0.880 C

o+ 53.26


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The dataset after normalized and with selectedarchitecture results has been calculated andthe amount of error for predicting EC has beenworkout. Results of performance indicators ofR2, RMSE, MAE, FA1.1 and IA of Architecture and (M-

are shown in

Comparison of predicted value with observed

been seen that EC with one input i.e. TDS givesbetter prediction results using FFNN with as performance indictor are less as compared toother models. Moreover, keeping in mind thatANNs require less prior knowledge of the systemunder study, it is expected that it will be a more

powerful tool in capturing interrelations betweenwater quality variables.

Table 6 Result summary of FFNN and CCFF model for the training and testing dataset of EC with

different input Combinations

Model ArchitectTraining Testing

RSME MAE R 2 IA FA1.1 RSME MAE R 2 IA FA1.1

M1 FF_Tan_10

46.768 38.300 0.867 0.782 1.048 36.889 27.057 0.917 0.811 0.983

44.007 35.272 0.903 0.809 1.037 42.364 31.526 0.626 0.770 0.958

49.325 39.083 0.774 0.817 0.990 89.691 67.204 0.782 0.696 0.929

48.091 30.765 0.957 0.854 1.031 58.534 29.103 0.590 0.882 1.022

44.348 35.615 0.895 0.829 1.018 43.080 36.784 0.824 0.753 0.987

M2 CF_Pur_6

48.084 40.774 0.853 0.788 1.056 50.156 43.737 0.838 0.703 0.976

48.667 41.299 0.861 0.782 1.071 49.063 42.568 0.838 0.719 0.981

48.225 40.602 0.849 0.788 1.070 46.149 40.458 0.861 0.706 0.976

56.129 46.468 0.851 0.752 1.113 50.701 40.745 0.854 0.690 0.968

50.973 41.456 0.837 0.779 1.097 58.479 50.123 0.714 0.779 1.061

Fig.7 Observed and Predicted dataset of EC from FFNN and CCFF


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4.0 Conclusions

In this study, the dependency of water qualityparameters on each other has been calculatedusing the statistical analysis and ANN. It wasobserved that the TDS, BOD and DO have

exceed the limits by BIS standards and StandardDeviation is 126.80 for EC; means observationseries have less homogeneous and inconsistentwhile curve is platykurtic and positive distributed.The performance of various combinations forFFNN and CCFF have been studied and comparedon the basis of performance indicators. Result ofFFNN shows lesser amount of errors than CCFF.Assessments of RMSE, MAE, IA and FA1.1 havebeen found to be 36.889, 27.057, 0.811 and 0.983respectively for FFNN. TDS parameter givesbetter prediction of surface water quality changesin EC with lesser amount of error in this study.

5.0 Acknowledgement

This material is based upon work supported by

Engineer, Data Analysis Circle, Water ResourcesDepartment Nasik.

6.0 References

1. Alam M.J.B., Islam M.R., Muyen Z., Mamun M.and Islam S., Water quality parameters alongrivers, International Journal Environmental , Vol. 4(1), 2007, pp.159-167

2. Bureau of Indian Standards (BIS), IS: 10500:2012, nd Revision),Drinking Water Sectional Committee, FAD25, May2012, India, pp.1-11.

3. Diamantopoulou M.J., Papamichai D.M. andAntonopoulos V.Z., The Use of a Neural

Network Technique for the Prediction of WaterQuality Parameters, Operational Research, An

International Journal, ASCE, Vol. 5 (1), 2005, pp.115-125

4. Emamgholizadeh S., Kashi H., Marofpoor I.and Zalaghi E., Prediction of water quality

intelligence-based models, Springer, International , Vol.11,2014, pp.645656 DOI 10.1007/s13762-013-

0378-x.5. Gupta P., Vishwakarma M. and RawtaniP. M.,

Assessment of water quality parameters of KerwaDam for drinking Suitability, International , Vol. 1(2), 2007, pp. 53-55.

6. Heydari M., Olyaie E., Mohebzadeh H. and KisiQ., Development of a Neural Network Techniquefor Prediction of Water Quality Parameters in theDelaware River, Pennsylvania, Middle-East Vol. 13 (10), 2013,

pp. 1367-1376.7. ICMR, Manual of standards of quality for drinking

water suppliesIndian Council of Medical Research,Spec. Rep. No. 44, 1975, New Delhi.

8.

Prediction, Neural Computational & AppliedScience Springer, Vol. 22 (1), 2013, pp. 180-201

9. An ANNapplication for water quality forecasting, Elsevier,Marine Pollution Bulletin, Vol.56 (15), 2008, pp.861597 DOI: 10.1016/j.marpolbul.2008.05.021.

10. Choudhary R., Ratwani P. and Vishwakarma M.,Comparative study of Drinking Water QualityParameters of three Manmade Reservoirs i.e.

Kolar, Kaliasote and Kerwa DamCurrent WorldEnvironment, Vol. 6(1), 2011, pp.145-149.

11. WHO, International Standards for DrinkingWater, th Edition World Health Organization,Geneva, Switzerland, 2004.

12. Rankovic V., Radulovic J., Radojevic J., Ostoji A.

dissolved oxygen in the Gruza reservoir, Serbia,

Ecological Modeling ELSEVIER Vol. 221, 2010,pp. 1239-1244.


37/80


Decolorization of Reactive Dye by Electrochemical

Oxidation Using Graphite Electrode

**

Abstract

graphite anode can be used for the removal of color in textile wastewater treatments.

Keywords: Color; COD; pH; Reactive dye.

1. Introduction

Dyes constitute a small portion of the totalvolume of waste discharged in textile processing,

for textile industry because of several reasons,the presence of even a small fraction of dyes inwater is highly visible due to high tinctorial valueof dyes and affects the aesthetic merit of streamsand other water resources (Joshi et.al, 2003).

Most of the dyes used in ancient times werediscovered by accident, they often consist ofnatural plants that were common in society. Asdyes were developed and experimented with,people became more adventurous and wouldattempt different mediums as dyes. Hence, the

dyeing industry developed. Some well-knownancient natural dyes include indigo, madder,and cochineal. Today, with the invention ofsynthetic materials used in textiles, many new

* Asst. Professor, Department of Civil Engineering, University Visvesvaraya College of Engineering, Bangalore University,

** Professor, Department of Civil Engineering, University Visvesvaraya College of Engineering, Bangalore University,Bangalore-560056, Karnataka, India.

types of dyes have been developed and put intoregular use. There are two basic ways to colortextiles: dyes and pigments. Pigments are not a

The majority of natural dyes are from plantsources roots, berries, bark, leaves, and wood,

dye, mauveine, was discovered serendipitouslyby William Henry Perkin in 1856, the result ofa failed attempt at the total synthesis of quinine(Charity Goetz, 2008).

and chemical structure. They are composed of agroup of atoms responsible for the dye color,called chromophores, as well as an electron

withdrawing or donating substituents that causeor intensify the color of the chromophores calledauxochromes (Christie, 2001). The most importantchromophores are azo (-N=N-), carbonyl (- C=O),


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methane (-CH=), nitro (-NO2) and quinoid groups.

The most important auxochromes are amine

(-NH3), carboxyl (-COOH), sulfonate (-SO3H)

and hydroxyl (-OH). It is worth to mention that

the sulfonate groups confer very high aqueous

solubility to the dyes. The auxochromes canbelong to the classes of reactive, acid, direct,

basic mordant, disperse, pigment, vat, anionic and

ingrain, sulphur, solvent and dispers dye (Andre

et al. 2007). The biggest problem relates to the

dyeing of cotton with reactive and sulphur dyes

as shown in table1.

1.1 Impacts of reactive dyes

Reactive dyes have been found to be problematic

characterized by their readily water solubility

as well as their high stability and persistence,

essentially due to their complex structure and

synthetic origin. Since they are intentionally

designed to resist degradation, they consequently

offer a large.

Table 1: Exhaustion Range of Various Dye Classes

Dye class Fibre Degree

of

%

Loss to

%

Acid Polyamide 80-95 5-20

Basic Acrylic 95-100 0.5

Direct Cellulose 70-95 5-30

Disperse Polyester 90-100 0-10

Metal-complex Wool 90-98 2-10

Reactive Cellulose 50-90 10-50Sulphur Cellulose 60-90 10-40

Vat Cellulose 80-95 5-20

Resistence against chemical and photolytic

degradation. Moreover, as many of textile

dyes, reactive dyes are usually non biodegradable

under typical aerobic conditions found in

conventional biologic treatment systems. Among

them the reactive azo dyes family is of special

interest. Although they are usually of non toxic

nature, they may generate under anaerobic

condition breakdown products as aromaticamines considered to be potentially carcinogenic,

mutagenic and toxic (Julia, 2007).

dyes causes serious environment pollution

because, the presence of dyes in water is

highly visible and affects their transparency

and aesthetic even if the concentration of the dyes

is low. Reactive dyes cause respiratory and nasal

symptoms, asthma rhinitis and dermatitis, allergic

contact dermatitis. Adverse effects have also beendetected from aquatic environment. Dyes have a

very low rate of removal ratio for BOD to COD

(less than 0.1) (Shyamala et.al, 2014). Most dyes

have complex aromatic structure resistant to light,

biological activity, ozone and not readily removed

by typical waste treatment processes (Joshi

et.al, 2003). The removal of dyes is therefore a

challenge to both the textile industry and the

wastewater treatment facilities that must treat it

before its disposal into water bodies.In recent years, the electrochemical techniques

have received greater attention, because all types

of pollutants could be removed effectively. In

electro oxidation, the main reagent is the electron

without generating any secondary pollutants

(Bhaskararaju et.al, 2008)

Generally, oxidation of organic matter by

direct oxidation at surface of anode and indirectoxidation distant from the anode surface; processes

Recently, oxides anode have been of interest

because of higher conductivity and oxidizability

(Chuanping Feng et.al., 2003). The energy

supplied to an electrochemical reactor plays an

important role in any electrochemical process.


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The energy supplied to an electrode undergoes the

following steps during the process:

1. The electro active particle is transferred to

the electrode surface from the bulk solution.

2. The electro active particle is adsorbed on tothe surface of the electrode.

3. Electron transfer occurs between the bulk

and the electrode.

4. The reacted particle is either transported to

the bulk solution (desorption) or deposited

at the electrode surface.

From the above, the transfer of electrons between

the solution and electrode surface plays an

important role in the electrochemical process as

the electrical energy is converted into chemical

energy at the interface of the electrode. A

generalized scheme for direct and indirect electro-

et.al., 2001).

Fig1. Schematic Representation of Direct and Indirect

Electro-Oxidation Process (Mohan et.al, 2001)

From an electrochemical point of view the choice

of electrode material is of fundamental importance.

Graphite electrodes were used as anode and

cathode by many researchers for the application

in organic oxidation (Prakash et.al., 2011).Hence, there is an interest in electrochemical

and eco-friendly alternative for the degradation

of dyestuffs (Martinez et.al., 2009). In the past,

graphite was frequently used as an anode for the

electrochemical degradation of textile dye as it is

relatively cheaper and gives satisfactory results

(Szpyrkowicz et.al., 1995). Also graphite anode

is used because when carbon react with oxygen

liberated at anode it forms CO2 gas which is a

exothermic reaction and maintain the temperature

of the process.

Synthetic dye solutions had been used by mostresearchers in their investigation of treatment

technologies since synthetic solutions was useful

in obtaining information on how individual dyes

react to different types of treatment. Apart from

this, constant composition of a synthetic solution

on a particular treatment technology. Hence the

of electrochemical oxidation using graphite anode

for the removal of reactive azo dye.

2. Materials and Methodology

The commercially available reactive dye Remazol

Red RB 133 ( RR RB 133) was obtained from

a textile industry, Bangalore, Karnataka, India

the characteristics of of remazol red rb 133 are

summarized in table 2. Distilled water was used to

prepare the desired concentration of dye solutions

and all the reagents. Graphite was purchased from

SLV industries, Bangalore, Karnataka, India.

Standard solution of simulated dye wastewater

containing reactive red was prepared by

dissolving 1g of dye in one lit of distilled water.NaCl was used as an internal electrolyte. The

conductivity and pH of the solution were

measured before and after each experiment. The

pH was adjusted using either 0.1 N NaOH or0.1 N H2SO4. The experimental set-up (Fig.3)

consisted of a glass beaker of 500 ml capacity, in

which two electrodes having an inter-electrode


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gap of 2 cm were placed vertical and parallel toeach other. Commercially available graphite ofdimension 5 cm x 5 cm was used as anode and

cathode. The effective area of electrode was 25cm2 (0.0025 m2).

Table 2:

Characterization of the Remzaol Red RB 133

Sl no Parameter Value

1 Colour index REACTIVE REDRB 133

2 Chromophore Azo

3 Molecular formula C27H18ClN7Na4O16S5

3 Reactive anchorsystems *MCT and VSa

4 Molar mass(nonhydrolyzeddye)

984.21

Water solubility at293 K(g/L)

70

5 Percentage of puredye

63%

9 pH value (at 10g/L water)

7

10 COD value (mg/g) 540

11 BOD value (mg/g)


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3. Results and Discussions

Fig 4 shows the spectrum graph of absorbancevalues at different wavelength. At 510 nm apeak absorbance of 1.082 was observed. For

absorbance was measured at that particularwavelength.

3.1 The effects of electrolyte concentration

The addition of NaCl would lead to the decreasein power consumption because of the increase

in conductivity. Therefore effect of electrolyteconcentration on electrochemical oxidation ofreactive dye were investigated. Fig 5 showsthe results of variation of NaCl with respect toremoval of color.

Fig 5: Percent Color Removal at Different Moles of

Sodium Chloride.

Fig 5 indicates that the percent color removal

when the electrolyte concentration was increasedfrom 0.02 M to 0.1 M. Similar effects were

reported by Lin and peng, 1996, Kobya et al,

2003, Mollah et.al., 2004. Conductivty of the

solution was also increased linearly from 2.40 to

10.57 mS/cm with electrolyte concetration.

The effect of increase in conductivity of the

exhibited similar behaviour as in the case of

increasing electrolyte concentration. Subsequent

experiments were carried out with 0.02 M NaCl

solution in order to minimize the addition of

excess Cl ions in solution as well as to lower thecurrent density.

3.2 Determination of Optimum Electrolysis

Duration

electrolysis duration at which maximum colour

removal takes place. During the experiment, the

were carried till the decolorization of the dye. The

samples were collected at regular time intervals

increased between 50 min and 70 min, and

observed. With 70 min of electrolysis duration,

color removal of 96.37% was achieved which is

considered as optimum electrolysis duration. The

decrease in color removal at later stage might be


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due to the exhaustion of hypochlorite and freechlorine generation in situ in the reactor.

3.3 Effect of applied current.

To study the effect of varying current on colorand COD, experiments were carried out at 0.14,0.24, 0.34, 0.44 and 0.54 A. Based on previousexperiments 70 min of electrolysis duration wasmaintained. Fig 7 and 8 shows the variation of

absorbance decreases with increasing electrolysistime. As current intensity increases, the pollutantdegradation rate increases initially. However,once the current intensity reaches a certainvalue, referred as limiting current intensity,the degradation rate does not increase anymore

and is determined by the mass transfer rate

increases gradually at varying applied current. Ata current of 0.44A, 89.94 % color removal and61.12 % of COD removal was achieved. Fig8 shows that at different applied current thereis a decrease in COD also. Table 3 shows energy

applied current of 0.44 A was selected as optimum

Electrolysis Duration

Fig 7: Percent variation of color at different current,

graphite anode.

Fig 8: Percent COD variation at different current,

graphite anode.

Table 3: Energy Consumption and Anodic

Applied

current

voltage

Anode

consump

tion

grams

Energy

consump

tion

kWh/kg

ofCOD

Kgof

dyeremovedper

kgofanode

0.14 5.20 0.0310 4.85 0.857 2.30

0.24 7.10 0.0339 9.94 0.571 2.286

0.34 8.40 0.0415 14.17 0.474 1.879

0.44 10.00 0.0485 18.66 0.428 1.855

0.54 12.30 0.0533 25.83 0.381 1.707

2) at different

applied currents.

increases energy consumption increases and

increases. Fig 10 and 11 shows the SEM

images of surface of graphite anode before and

after treatment by electrochemical oxidation.


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3.4 Effect of pH

Study was also carried out to know the effect

pH of the solution was adjusted using 0.1 NH2SO4 and 0.1 N NaOH. The effect of pH was

investigated between 3 and 9 under optimizedconditions at 0.44 A. The electro-oxidationshowed a considerable degradation of the dyestructure which is in accordance with the CODremoval percentages observed for this process. Rateof color removal was higher than COD removaldue to the faster azo bond degradation. Thefact that decolorization occurs at substantiallygreater rates than COD conversion implies thatelectrochemical degradation by- products aremore resistant to electrooxidation than the originaldyes (Milica, 2013). Similar results regarding therelative rates of electrochemical decolorizationand mineralization have also been reported by

several other investigators (Awad et.al, 2005,

Shen et.al, 2001). At pH 5 the degradation rate

was higher compared to other pH ranges. The

removal rate of color was 95.47% with duration

of 30 min.

4. Conclusions

Electrochemical oxidation is an effective

treatment process for color removal from reactive

dyes. Graphite as an anode can be used forthe removal of color and COD. The optimized

conditions for the reactive dye were 0.44 A at pH

5 with reaction time of 30 min as it gives a color

References

1. Andre B dos santos, Francisco J Cervantes,

Jules B vam ;oer, (2007), Review paper on

current technologies for decolourisation of

textile wastewaters: perspectives for anaerobic

biotechnology, Bioresource technology, vol 98, pp

2369-2385.

2. Awad H. S.and Abo Galwa N, 2005,

Electrochemical degradation of Acid Blue andBasic Brown dyes on Pb/PbO2 electrode in the

presence of different conductive electrolyte and

effect of various operating factors, Chemosphere,

61, pp 1327-1335.

Fig 10: SEM image of graphite anode before treatment Fig 11: SEM image of graphite anode after treatment.

Fig 12: Variation of color removal at different pH


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3. Bhaskar Raju G , M. Thalamadai Karuppiah,S.S. Latha, S. Parvathy, S. Prabhakar, (2008),

Treatment of wastewater from synthetic

textile industry by electrocoagulation

electrooxidation, Chemical Engineering Journal,

vol 144, pp 5158.

4. 4. Can O.T, Bayramoglu M, and Kobya

M, (2003), Decolorization of Reactive Dye

Solutions by Electrocoagulation Using Aluminum

Electrodes, Ind. Eng. Chem. Res. Vol 42, pp 3391-

3396.

5. Charity Goetz, (2008), Textile Dyes: Techniques

and their Effects on the Environment with a

Recommendation for Dyers Concerning the Green

Effect, Liberty University.

6. Christies R, 2001, Colour chemistry, the

Royal Society of Chemistry, Cambridge, United

Kingdom.7. Chuanping Feng, Norio Sugiura, Satoru Shimada,

Takaaki Maekawa, (2003), Development of a high

performance electrochemical wastewater treatment

system,J Haz Materials, B103, pp. 65-78.

8. Joshi M, Bansal R and Purwar R, (2004), color

Journal of

9. Julia Garcia Montano, 2007, Combination of AOP

and biological treatments of commercial reactive

azo dyes removal, Barcelona University.

10. Kobya M, O T Can, M bayramoglu,(2003),Treatment of textile wastewaters by

electrocoagulation using iron andaluminum

electrodes, J haz mat, B (100), pp 163-178.

11. Lin S H, Peng CF, (1996), Continuous treatment

of textile wastewater by combined coagulation,

electrooxidation and activated sludge, Water

research, 30, pp 587- 592.

12. Martinez-Huitle C A, Brillas E, 2009,

Decontamination of wastewaters containing

synthetic organic dyes by electrochemical methods:

a general review, Appl. Catal. B Environ. 87,pp105145.

13. Miled W, Haj and Roudesli S, (2010),Decolorization of high polluted textilewastewater by indirect electrochemical oxidationProcess, J of textile and apparel technology and

management, vol 6, issue 3, pp 1-6.

14. Milica Jovic, Dalibor Stankovic, Dragan

Biljana Dojcinovic, Goran Roglic, (2013), Studyof the Electrochemical Oxidation of ReactiveTextile Dyes Using Platinum Electrode, Int. J.Electrochem. Sci., vol 8, pp 168 183.

15. Mohan N, Balasubramanian N, and Subramanian V,(2001), Electrochemical Treatment of Simulated

16.

K Patil, Madhavi Vayuvegula, Tejas S Agrawal,Jewel AG Gomes, Mehmet Kesmez, David LCocke,( 2004), Treatment of orange II azo-dye by

B109, pp 165-171.

17. Prakash Kariyajjanavara, Narayana Jogttappaa, b, (2011), Studieson degradation of reactive textile dyes solution

by electrochemical method Journal of HazardousMaterials 190, pp 952961.

18.

A, (2001), Degradation of dye solution by an

J. Hazard. Mater. B, 84, pp107-16.

19. Shyamala Gowri R, Vijayaraghavan R andMeenambigai1 P, (2014), Microbial degradationof reactive dyes- A Review, Int.J.Curr.Microbiol.App.Sci, Volume 3, Number 3 , pp. 421-436.

20. Szpyrkowicz L, Naumczyk J, Zilio-Grndi F, (1995),Electrochemical treatment of tannery wastewaterusing Ti/Pt and Ti/Pt/Ir electrodes, Water Res. 29,

pp 517524.


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AMRUT Mission Guidelines : Review and Recommendations

for Development of Resilient Water Infrastructure

Suneet Manjavkar

Abstract

mission cities. Document provide insight of issues, challenges, and opportunities to make mission

successful. It recognizes water projects development withholistic ecosystem. It has put forth the

possible prioritization and resourcing with mix of technologies needed for cities transformation.

Article proposes indispensable elements to upkeep project transitions with recent learnings from

project progressfor building spirited basic services with provision of water services for all and water

for people.

Introduction

In India, pace of urbanization is much higher than

development of basic obligatory infrastructure

needed to support civic centers. Demand for

public services are growing across all the sections

of societies. It imposes great stress on existing

water infrastructure, surrounding environment

and meet expectations of political masters forservice delivery. Ministry of Urban Development

(MoUD) endorses learning from earlier mission in

its spirit for Infrastructure creation and further laid

down the operational guidelines under the three

landmark missions in June 2015 -

1. Smart Cities Mission (SCM): Area based

development for urban transformation

2. Atal Mission for Rejuvenation and Urban

Transformation (AMRUT): Project basedendeavour to build and strengthen basic

infrastructure services to cities

3. Housing for All Mission (HAM): Shelter

for every citizen

Urban water professional (MSc-Urban Water Engg & Mgmt,UNESCO-IHE, The Netherlands),

AMRUT guidelines proposes infrastructuredevelopment for needs of people from small tolarge sized towns and cities with sets of reforms.Reforms address improvement in service delivery,mobilization of resources and making municipalfunctioning more transparent. Guidelines laiddown directives for provision of public servicesand intends to map gradual progress by service

level benchmarking. AMRUT empowers Urbanlocal bodies (ULB) to operate at independent levelunder recommendations of central governmentsand allows integration with other central and stateschemes to channelize the development of urbancenters in country.

Objective of Article :Article reviews AMRUTguidelines to build Water infrastructure ofproposed 500 cities as a holistic and closedloop ecological system for human necessities.

The aim of this document is to provide insightof issues, challenges, and opportunities to makemission successful. It attempts to put forwardpossible prioritization and resourcing with mixof technologies needed for cities transformation.Article proposes indispensable elements to


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upkeep project transitions with recent learnings

from Indian water sector and allied project

execution practices. Consideration of these

recommendations will certainly bridge gap in

pursuit of better outcomes.

About AMRUT Mission :

mission milestones on foundation of

Journal of Indian Water Works Association (JIWWA, Vol-4, Issue-47)_Oct-Dec-2015

Documents

Transcript of Journal of Indian Water Works Association (JIWWA, Vol-4, Issue-47)_Oct-Dec-2015