BetterNet: Decision Support Software for Chlorination of

Transkript

BetterNet
Decision Support Software for Chlorination of Water
Distribution Systems
Mustafa Kemal Pektürk
Hürevren Kılıç
Selçuk Soyupak
Content
Problem Definition and Research Needs
Standards for FRC Levels
Literature
Genetic Algoritms
Objective Functions
Antalya Konyaaltı WDS
Applications
Results
References
3/12/2014
Pektürk, Kılıç and Soyupak- Water Losses
Management in Water Supply SystemsSeptember 2012 ANTALYA
2
• Problem definition
Chlorine is added either at the source or at the exit of WTPs
– Clorine decays during transport of water ; at some far away
points from source, the FRC levels may be lower than the
desired minimum values; it may increase the risk of water
born diseases.
– There may be excessive FRC levels at consumers’ tap near
point of chlorine application: this may increase cancer risk.
• Aim: Keeping FRC levels within allowable ranges to protect
public health.
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Chlorine decay
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Chlorine isoconcentration areas
of a network
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A software to improve chlorine distribution in networks
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Three practical questions to be answered by the planners
and the operators of water supply systems :
1) What should be the dosage of chlorine at the source or
at the exit of WTP?
2) What should be the FRC level at any point within
WDS?
3) Does WDS need any booster station ?
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What does standards or regulations say about the FRC levels
in WDSs?
Reference
Minimum FRC
(mg/L)
Maximum FRC
(mg/L)
Ministry of Health Turkey
-
0,5
World Health
Organization(WHO)
0,2
5,0
Munavalli ve Kumar
0,2
0,4
Kooij
0,3(optimum)
-
Constans
0,1
0,5
Rouhiainan
0,1
0,6
Issam ve Lebdi
0,1
0,5
Propato
0,2
0,4
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Literature survey
• Some authors have employed optimization techniques that
utilized single objective function (Levi and Mallevialle [9];
Uber et all., [10] ; Boccelli et. all. [11]; Nace et all [12] )
• Rouhiainen et. all [13] have employed multiple objective
functions that depends on pareto.
• Rouhiainen et all employed [14] ANNtechniqe utilizing GA
with single objective function.
• Issam and Lebdi [3]; Uçaner and Özdemir [15] utilizes GA
for optimum location and numbers of booster stations.
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The unique property of BetterNet software
The unique property of this specific software :
– “To
minimize the risk of
consumption of water outside the
defined desirable range”, ( Pektürk
[16]).
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Solution Methodology: GA
• GA Methodology has been adopted for BetterNet
software.
• It is an optimization methodology.
• The methodology tries to find best solution in a
systematical way.
• The acceptable solutions can be reached very fast
even for large networks with high speed computers.
• It even find solutions that could not be solved with
other techniques
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GA-When to use?
• For complex problems with large search space.
• When it is difficult to find a solution within limited
search space where the information is limited.
• For complex problems for which classical
optimization approaches are insufficient.
• For problems for which the mathematical models that
define the problem is complex or for which
mathematical modelling is not possible.
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Logical Design of BetterNet Software
Deterministic
Modeling Tool
(EPANET)
EPS
Module
EPS
Results
Network
Description
Water
Distribution
Network
Under Study
Dosing Strategy
(Location/Amount)
Genetic Algorithm
Implementation
Dosing
Strategy
Dosing
Strategies
Descriptions
(Location
/ Amount)
(Location/Amount)
Fitness Function
Selection
Crossover
Mutation
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Pektürk,
Kılıçarchitecture
and SoyupakLosses
Figure
1. The
of Water
the solution.
13
Objective functions
The general rule of the optimization was to
keep FRC levels for any time and for any
node to be in between the desired cmax
and cmin levels.
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Centralization of FRC levels
m
T
Min ( SSD   c(i, j )  cmedian )
2
i 1 j 1
SSD(m, T)
: The sum of square of differences between FRC
concentration levels and desirable average FRC concentration value (median
of maximum and minimum allowable concentrations) throughout the EPS
process,
m : Total number of nodes,
T : EPS duration excluding the starting transition period,
i, j : Indices for the nodes and time steps,
c(i,j)
: Calculated FRC concentration level at ith node for the jth time step,
cmedian= Desirable median level,
cmin
: Minimum allowable FRC concentration level within WDS,
cmax
: Maximum allowable FRC concentration level within WDS,
Allowable range: (cmin ≤ ≤ cmax)
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Minimization of variance of FRC levels within
WDS
T
Min ( 2 
K
 (c
j 1 i 1
i, j
 c) 2
N 1
)
ci,j
: FRC concentration level of node i in time j ,
σ2
: Variance of concentrations ,concentrations within
acceptable range
i
: The index of a node ,
K
: Total number of nodes ,
T
: EPS duration excluding the starting transition
period,
: Average of the calculated concentrations ,
N
: T*K
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Minimization of risk of occurrence probability of
FRC concentration values outside of the allowable
range
T
Min ( R  (1 
KP
 Q
j 1 i 1
T
c
i, j i, j
))
NN
 Q
j 1 i 1
c
i, j i, j
Where
R : Risk of consumption of water with FRC values outside of the the allowable range
,
ci,j: Chlorine concentration level of node i in time j,
Qi,j : The amount of flow demand at node i in time j,
KP : Total number of nodes having concentration levels withinin allowable range
(where cmin ≤ ≤ cmax)
NN
: Total number of nodes,
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Minimization of trihalomethane formation risk
index
NN T
Min ( SSD  TORI   c(i, j )  cmin )
2
i 1 j 1
SSD
: The sum of square of differences between FRC concentration levels
and permitted lowest concentration values throughout the EPS process,
NN
c(i, j) : FRC concentration level at node i in time step j,
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Minimization of total FRC that reaches to consumers:
NN
T
Min (Total FRC that reaches to consumers    Qi , j Ci , j )
i 1 j 1
NN
c(i,j) : Measured chlorine concentration level at node i in time
step j,
Qi,j
: The amount of water demand at node i in time j.
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How to use BetterNet software?
We have to enter two categories of parameters:
1. GA Application Parameters
2. WDS Parameters
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GA Application
Parameters
GA parameters/switches that
can be set by the user
1)Population size
2)Cross over probability
3)Mutation probability
4)Elitisim percentage
5)Termination mode
6)Generation size
7)Ε psilon Value
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WDS Parameters
GA parameters/switches that can be set
by the user
1)Objective function
2)Solution method
3)Number of booster stations
4)Dosing regime
5)Existence of source dosing
6)Chlorination range of boosters
7)Boosting step size
8)Desirable range of chlorine
9)Transition period control
10)Transition period
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Results and discussions
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A hypothetical network
Demand pattern
1.8
1
2
Demand Factor (Average=1)
1.4
3
32
33 30 34
28
4 4
44
14 16
1.0
40
31
25
2
43
1.2
30 27
29 26
1.6
17 16
5
5
6
6
18 41
8 19
15
1
29
3 7
20
42
7
9
8 39
18 21
10
35
40
22
19
20
0.8
0.6
0.4
11 32
23 9
45
36
31
24
21
10 12
22
25
33 37
23
11 13
26
34
41
0.2
38
0.0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
36
37
Time(hours)
Number of
Nodes
Number of
Source
Reservoirs
Number
of Tanks
Number of
Pumps
Number of
Pipes
Total of Pipe
Lengths
(km)
39
2
1
1
45
43. 2
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24 27
17
28
Total Base
Nodal
Demand
(cmh)
754
12
15
14
38
13
35
39
(mm)
200400
24
One application example :
Rules of optimization:
Chlorine boosting range: 0.2-0.5.0 ppm; Number of boosting station: 4; Step size = 0.05 ppm ;
Cmin=0.2 ppm ; Cmax=0.5 ppm ;Transition duration= 24 hours;ε=0.001
The Objective Function
for which optimization
was performed →
OF 1
“Centralization of
FRC levels”
OF 2
“Minimization of
variance of FRC
levels within WDS”
OF3
“Minimization of
risk of occurrence
probability of FRC
concentration values
outside of the
allowable range”
42.20801
0.007041431
0.04558521
85.36046
42.20801
0.007041431
0.04558521
85.36046
46.57488
0.007240229
0.0463766
93.76543
58.73776
0.009021726
0.07322767
66.70158
5.053405Kg/day
5.053405Kg/day
5.157116Kg/day
4.644409Kg/day
The calculated values for
different objectives↓
SSD
σ2
R
THM Formation Risk
Index
Total chlorine
consumption
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OF4
“Minimization of
trihalomethane
formation risk
index”
25
The Objective
Function for which
optimization was
performed →
Nodes ->Dosages
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OF 1
“Centralization
of FRC levels”
7
38
28
19
→
→
→
→
0.4
0.45
0.4
0.4
OF 2
“Minimization
of variance of
FRC levels
within WDS”
7
28
19
38
→
→
→
→
0.4
0.4
0.4
0.45
OF3
“Minimization of risk
of occurrence
probability of FRC
concentration values
outside of the
allowable range”
7
33
28
19
→ 0.45
→ 0.45
→ 0.45
→ 0.45
OF4
“Minimization
of
trihalomethane
formation risk
index”
34
32
28
19
→
→
→
→
0.2
0.2
0.2
0.2
26
Effects of Number of Boosters and Optimation Rules on
Objective Function Values
“The calculated variance values under
different optimization rules and booster
numbers.”
“The calculated SSD under different
optimization rules and booster numbers”
The calculated variance values under different
optimization rules and booster numbers
0.010
The calculated SSD under different optimization rules and booster numbers
70
0.009
60
0.007
40
0.006
Variance
Sum of square of deviations
0.008
50
30
0.005
0.004
20
0.003
10
0
0
1
2
3
4
5
The SSD for
The SSD for
The SSD for
The SSD for
OF1
OF2
OF3
OF4
0.002
0.001
Number of boosters
0.000
0
1
2
3
4
5
The calculated variance for
OF1
OF2
OF3
OF4
optimization
optimization
optimization
optimization
Number of bosters
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Effects of Number of Boosters and Optimation Rules on
Objective Function Values
“The calculated exposure risk under
different optimization rules and
booster numbers.”
“The calculated TFRI under different
optimization rules and booster
numbers.”
The calculated exposure risk under different
optimization rules and booster numbers
The calculated TFRI under different optimization rules and booster
numbers
0.08
100
0.06
80
0.04
60
TFRI
Exposure risk
0.10
0.02
40
0.00
0
1
2
3
4
5
Calculated
Calculated
Calculated
Calculated
exposure risk for
exposure risk for
exposure risk for
exposure risk for
OF1 optimization
OF2 optimization
OF3 optimization
OF4 optimization
20
Number of boosters
0
0
1
2
3
4
5
Calculated
Calculated
Calculated
Calculated
TFRI for
TFRI for
TFRI for
TFRI for
OF1
OF2
OF3
OF3
Number of boosters
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ANTALYA Konyaaltı
WDS
The WDS has detailed and realistic
information.
The network can be operated
independently from other parts of
Antalya –Network.
There are enough SCADA
monitoring stations within network.
The area is important from
national and international tourism
point of view.
BOGACAY POMPA ISTASYONU
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Application properties- The following GA Parameters have been adopted
for an efficient search in solution space
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GA Application Parameter
Low
Middle
High
Cross over probability
0,85
0,90
0,95
Mutation probability
0,01
0,03
0,05
Elitisim percentage
0,1
0,2
0,4
30
Application properties- (Cont.)
•
12 Different scenarios have been established for Konyaaltı WDS.
•
7 of these 12 Scenarios represent operational conditions under low decay kinetics; 5
of these 12 Scenarios represent operational conditions under high decay kinetics.
•
The desirable FRC level at any point and at any time in WDS is above 0.2 ppm
under any operational condition while water chlorine level is 0.45 ppm after
chlorination applicaton at Bogacay Pumping Station ( TÜBİTAK 1007 G 088
project report [25] ).
•
Objective function 3 has been generally adopted to minimize the consumption of
chlorine outside the desirable range. In parallel to the satisfaction of objective
function 3 ; the magnitudes of the other objective functions have been calculated
and used as decision support arguments.
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First interface-WDS parameters
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Second Interface-GA Application Parameters
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Table 1. The properties of Application (chlorine decay rate in water kb=0.11305 day-1 v and pipe wall chlorine decay rate kw= -0.01 )
[TÜBİTAK 107G088 NO PROJECT REPORT]. Minimization of risk of occurrence probability of
FRC concentration values outside of the allowable range”
Objective
function
Algorithm
Dosing regime
Source
dosing
Number of
boosting
station
Boosting
chlorination
range
OF3
Improved GA
Continuous
Present
2
0,3-0,4
Desirable
chlorine levels
(mg/l)
Population size
Generations
Cross over
probability
Mutation
probability
Elitism
percentage
0,3-0,4
100
50
0,85
0,01
0,4
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Results related to attainable improvement
• Two booster stations should be established at
– Station No 70301 with constant feeding level of 0.4ppm, &
– Station No 70024 with constant feeding level of 0.4ppm.
• The study results have shown that the risk of consumption of
water with chlorine levels outside allowable range could be
reduced from 0/00 0.957 to 0/00 0.316 operating 2 booster
stations with above characteristics.
• The risk levels could be improved further only by improving
dead ends.
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The reduction in the of consumption of water with chlorine
levels outside allowable range by using 2 stations
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The nodes with water age of more than 30 days
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General Results
• Decision support software (for water distribution system
chlorination) that can determine the number of boosters, their
locations and operating regimes is developed [24].
• The reached solutions keeps chlorine utilization at minimum
levels in order to minimize system specific trihalomethane
formation risk index and reducing the chlorine levels to lower
values at the exit of main sources.
• Using BetterNet requires a domain expertise in water
distribution network design and analysis.
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Acknowledegements
• TUBITAK-Türkiye Bilimsel ve Teknolojik Araştırma Kurumu- (Turkish
scientific and technological instituton) for support through project 1007 G
088.
• ASAT (Antalya Water and Sewage Administration )
• Akdeniz University Administration,
• Akdeniz University Department of Environmental Engineering
• Atilim University
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Special Thanks to
• Prof.Dr.Habib Muhammetoglu for wonderful management of
the project.
•
Prof.Dr. Ayse Muhammetoglu for deterministic modelling
and kinetic studies.
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40
Thanks to
• Eng. Ethem Karadirek (Akdeniz University)
• Engineers Ismail Demirel, Tugba Ozden and Ibrahim Palanci
of ASAT
3/12/2014
41
REFERENCES
1) “Sağlık Bakanlığı, İnsani Tüketim Amaçlı Sular Hakkında Yönetmelik”, Resmi Gazete: 17 Şubat
2005 Perşembe- Sayı: 25730 ; Değişiklik: Resmi Gazete: 24.07.2005 Tarih ve 25885 Sayı.
2) Dünya Sağlık Teşkilatı (World Health Organization), 2008, “Guidelines for Drinking Water
Quality, Third Edition Incorporating The first and second addenda, Recommendations”,
Geneva.
3) N. Issam, F. Lebdi, Genetic algorithm for optimal choice of chlorine booster stations in drinking
water networks - Algorithme Genetique (AG) Pour Le Choix Optimal Des Stations D’appoint
De Chlorine Sur Les Reseaux D`eau Potable, Revue des Sciences de l`eau, no:1 (2006) 47-55.
4) G.R.Munavalli ve M.S.M.Kumar, 2003, J. Water Resour. Plan. Manage.-ASCE , 129, 493
5) V.D.Kooij , 2003. Heterotrophic Plate Counts and Drinking-water Safety. Dünya Sağlık Teşkilatı
(WHO), Editörler: J. Bartram, J. Cotrivo, M. Exner, C. Fricker, A. Glasmacher , IWA Publishing,
London, UK. ISBN : 1 84339 025 6.
6) S.Constans,1999, “Concentration en Chlore en Réseaux d’eau Potable. Simulation et
Optimisation”, Doktora Çalışması, Univ. Bordeaux I, France.
7) C.Rouhiainen ve M.O.Tade , 2003, 20th Convention of the Australian Water Association, 6-10
avril, Perth, Australia.
8) M.Propato , 2003. “Operation of Booster Disinfection Systems: From Offline Design to Online
Control”, Doktora Çalışması, Univ. Cincinnati, ABD.
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REFERENCES (Cont.)
•
•
•
•
•
•
•
•
9) Y.Levi ve J.Mallevialle , 1995, Proc. of the China Water Supply Association, Safety of Water
Supply in Transmission and Distribution Systems, Shanghai, China.
10) J.G.Uber , R.S.Summers, D.C.Boccelli, M.T.Koechling, M.E.Tryby ve L.A. Rossman, 1996,
Proc. of the AWWA Annual Conference, June 23-27, Toronto, Ontario, Canada. , Cilt: D: 197.
11 )D.L.Boccelli, M.E. Tryby, J.G. Uber, L.A. Rossman, M.L. Zierolf, ve M.M. Polycarpou, 1998,
J. of Wat. Res. Plan. and Mana., 124(2), 99
12) A.Nace, P.Harmant ve P.Villon, 2001, Proc of the World Water & Environmental Resource
Congress, May 20-24, Orlando, Florida, (Editörler Phelps ve G.Sehlke).
13) C.J.Rouhiainen, M.O.Tade, ve G.West, 2003.a. Multi Objective Algorithm for Optimal
Scheduling of Chlorine Dosing in Water Distribution Systems”, Department of Chemical
Engineering,
Curtin
University
of
Technology,
http://www.lania.mx/~ccoello/EMOO/rouhiainen03a.pdf.gz Perth, Australia.
14) C.J.Rouhiainen, M.O.Tade ve G.West, 2003b, Proc.of the 20th Convention of the
Australian Water Association, 6-10 April, 2003, Perth, Australia, 1.
15) O.N.Özdemir, ve M.E.Uçaner, 2005. Success of Booster Chlorination for Water Supply
Networks with Genetic Algorithms, Journal of Hydraulic Research, 43:3, 267.
16)M.K.Pektürk, 2010, “A Domain Aware Genetic Algorithm for Optimum Booster
Chlorination In Water Distribution Systems”, Yüksek Lisans Tezi, Bilgisayar Mühendisliği
Bölümü, Atılım Üniversitesi, Ankara, Haziran.
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REFERENCES (Cont.)
17) S.Soyupak, H.Kılıç ve M.K. Pektürk, 2011, “Daha iyi şebeke kullanıcı klavuzu Genetik algoritma
tabanlı şebeke dezenfeksiyon yazılımı sürüm (2.2)”, TÜBİTAK 1007 G 088 Nolu Proje final
rapor eki (EK-6), “İçme Suyu Dağıtım Şebekelerinde Optimum Klorlama Uygulamalarının
Matematiksel Modeller Kullanılarak Gerçekleştirilmesi ve Dezenfeksiyon Sistemlerinin
Yönetimi”.
18) D.Beasley, D.R.Bull ve R.R.Martin, 1993, University Computing, 15(2), 58
19) M.Dorigo ve T.Stützle, 2004, “Ant Colony Optimization”, MIT Press, Cambridge,MA.
20) D.Karaboğa, ve B.Akay, 2009, App. Math. and Comp., 214, 108
21) L.N. De Castro ve J.I.Timmins, 2002, “Arificial Immune Systems: A New Computational
Intelligence Approach”, Springer-Verlag, London
22) J.Hertz, A.Krogh, R.G.Palmer, 1993, “Introduction to the Theory of Neural Computation,
Lecture Notes Volume I”., Santa Fe Institute, Studies in the Sciences of Complexity, AddisonWesley Publishing Company,CA.
23) L.A.Rosmann, 2000, “EPANET-2” , National Risk Management Research Laboratory, Office of
Research and Development , USA EPA, Cincinatti , OH 45268.
24) Pektürk, M.K., Kılıç H. ve Soyupak, S., (2010). “DahaİyiŞebeke 2.2 Yazılımı Kullanıcı Kılavuzu”,
Atılım Üniversitesi, Ankara.
25) ASAT (Antalya Su ve Atıksu İdaresi Genel Müdürliğü - Akdeniz Üniversitesi Çevre Mühendisliği
Bölümü, 2011, “TÜBİTAK 1007 G 088 Nolu Proje final rapor , İçme Suyu Dağıtım
Şebekelerinde Optimum Klorlama Uygulamalarının Matematiksel Modeller Kullanılarak
Gerçekleştirilmesi ve Dezenfeksiyon Sistemlerinin Yönetimi Projesi”, Proje Yöneticisi: Prof.Dr.
Habib Muhammetoğlu.
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BetterNet: Decision Support Software for Chlorination of

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