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2016 Bentley Systems, Incorporated. Bentley and the B Bentley logo are registered trademarks of Bentley Systems, Incorporated or one of its direct or indirect wholly owned subsidiaries. Other brands and product names are
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Journal of Indian Water Works Association 485 Oct.-Dec. 2015
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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Journal of Indian Water Works Association 487 Oct.-Dec. 2015
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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Journal of Indian Water Works Association 491 Oct.-Dec. 2015
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
Influent conc. (ppm)
Fig. 9 Effect of influent conc. of Cr on
% at IR 0.012 m3/hr at pH 5
95
96
97
98
99
100
2 4.9 9.89 14.82 18.8
(%)Removal
Influent conc. (ppm)
Fig. 8 Effect of influent conc. of Cr on
% at IR 0.012 m3/hr at pH 4
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95
96
97
98
99
100
2.03 4.89 9.88 14.59 18.5
(%)Removal
Influent conc. (ppm)
Fig. 10 Effect of influent conc. of Cr on the
% at IR 0.012 m3/hr at pH 6
95
96
97
98
99
100
1.97 4.9 9.88 14 18.4
(%)Removal
Initial conc. (ppm)
Fig. 11 Effect of influent conc. of Cr on
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
Influent conc. (ppm)
Fig. 12 Effect of influent conc. of Cr on
% at IR 0.012 m3/hr at pH 8
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)
Fig.14Effectofinfluentconc.ofCuon
%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)
Fig.18Effectofinfluentconc.ofCuon
%
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)
Fig.24Effectofinfluent
conc.ofZnon
%
atIR0.012m
3/hrat
pH8
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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
Fig.27Effectofinfluent
conc.ofMn
on%
atIR0.012m3/h
ratpH5
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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
Influent conc. (ppm)
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
Influent conc. (ppm)
I R= 0.012 m3/hrI R= 0.024 m3/hrI R= 0.036 m3/hr
Fig. 32 Effect of influent IR on% of
different influent conc. of Cu
7075
80
85
90
95
100
16.55 34.56 55.60 71.36 85.40
(%)Removal
Influent conc. (ppm)
I R= 0.012 m3/hrI R= 0.024 m3/hrI R= 0.036 m3/hr
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
Influent conc. (ppm)
I R= 0.012 m3/hrI R= 0.024 m3/hrI R= 0.036 m3/hr
Fig. 34 Effect of influent IR on% of
different influent conc. of Mn
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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
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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
Conventional RSF Capped RSF Remark
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
Conventional RSF Capped RSF Remark
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
Conventional RSF Capped RSF Remark
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
WORLDENVIRO
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Email: [email protected],
Website: www.worldenviro.com.
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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
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Journal of Indian Water Works Association 511 Oct.-Dec. 2015
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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Journal of Indian Water Works Association 513 Oct.-Dec. 2015
(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
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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.
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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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Oct.-Dec. 2015 524 Journal of Indian Water Works Association
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,
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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
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K Patil, Madhavi Vayuvegula, Tejas S Agrawal,Jewel AG Gomes, Mehmet Kesmez, David LCocke,( 2004), Treatment of orange II azo-dye by
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17. Prakash Kariyajjanavara, Narayana Jogttappaa, b, (2011), Studieson degradation of reactive textile dyes solution
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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.
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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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Oct.-Dec. 2015 526 Journal of Indian Water Works Association
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