[American Society of Civil Engineers World Environmental and Water Resources Congress 2008 -...

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Development of an Algorithm for Evaluation of a Water TreatmentPlant Performance

Mohammad Karamouz F. ASCE1, Hamed Tavakoli far2, Sara Nazif3, Kabir Rasouli4

1Professor, School of Civil Engineering, University of Tehran, e-mail: [email protected]. student, School of Civil Engineering, University of Tehran, e-mail:[email protected]. candidate, School of Civil Engineering, University of Tehran, e-mail: [email protected]., Engineering Department, Islamic Azad University- Science & Research Branch ofTehran, e-mail: [email protected]

Abstract:Water Treatment Plants (WTP) have a key role in water supply with adequatequantity and quality in desired pressure condition. Sustainable performance of thesesystems is very important and quantifies the readiness of them especially in criticalconditions. System readiness is one of the main issues that water users, stakeholdersand decision makers of water resources consider in order to decrease the probabledamages. In this paper, a methodology for assessment of a WTP performance hasbeen proposed. This methodology quantifies the readiness of the system in differentstages of plant's operation. For this purpose, three system operation indices includingreliability, resiliency and vulnerability are considered. Due to the differencesbetween the indices and especial characteristics of them, a hybrid index of them canbe a good indicator of the system performance. In this study, the mentioned indiceshave been hybridized considering the importance of water shortages in systemreadiness evaluation utilizing Artificial Neural Networks (ANNs). In order toevaluate a WTP performance, the proposed hybrid index is developed. It iscategorized into different levels for assessing the system readiness in criticalconditions. The proposed methodology has been applied for a WTP in Tehranmetropolitan area in Iran. The Results show that this algorithm can be effectivelyused for evaluation of the WTP performance.

Keywords: System Readiness, WTP, Reliability, Hybrid Performance Index,Resiliency, Vulnerability

IntroductionManagement of domestic water demand is of paramount importance for waterdistribution systems in urban areas, especially in the metropolitan areas with largepopulation. The most important issue in planning and management of operation of awater distribution system is satisfying consumer's demands. For a reliable water

World Environmental and Water Resources Congress 2008 Ahupua'a © 2008 ASCE

Copyright ASCE 2008 World Environmental and Water Resources Congress 2008: Ahupua'a World Environmental and Water Resources Congress 2008

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distribution system it is imperative to provide all users requesting water withappropriate quality at reasonable pressure in all times.It is important to provide secure water in regions of high water dependencesespecially in the cases of crises. Developing an effective approach which integratesthe different aspects of performance of a system in the form of a hybrid index can bevery useful for comprehensive evaluation of a water supply system for integrating aswell as water treatment plant (WTP). In the recent decades, due to the growth ofpopulation and technology water dependencies of societies have been increasedTherefore, water shortages in modern life can result in main crises. Consequently theperformance evaluation of water distribution and treatment system is very important.In the literature, the system performance is studied from different aspects such asreliability, resiliency and vulnerability separately, where reliability is more commonamong these indices.Mays (1989) summarized the background on performance evaluation of waterdistribution system including their concepts and assessment procedure.Goulter andBouchart (1990) classified the studies on reliability evaluation of water distributionsystem into two categories: the first studies are focused on evaluation of reliability ofwhole of the urban water system including supply, treatment, storage and distributionsystems [see Shamir and Howard (1985, 1981), Hobbs (1985)]. In the second groupof studies, water distribution system as a subsystem of the urban water supply systemhas received more attention.Germanopoulos et al. (1986) assessed the performance of water supply system withstochastic analysis using head driven hydraulic simulation method (HDSM). In thatstudy, reliability was defined as the level of system service to consumer. Penaltycurves were considered for determination of the level of service on the basis ofdesirability of head level and flow in demand nodes and velocity in pipes. Goulter(1987) defined the resiliency of water supply system as the system ability to supportdemand in failure situations and vulnerability as the maximum shortage of water infailure situations. Mays (1993) computed the reliability of a water distributionsystem by treating the demand, pressure head, and pipe roughness as randomvariables.In addition to the performance indices discussed above alternative indices have beendefined due to some computational difficulties in evaluation of system performanceindices. For example Redundancy ( see Bhave (1978) ،Ormsbee and Kessler (1990)),Graph theory (Wagner et al. (1988)، Kessler et al. (1990) ،Quimpo and Wu (1997))or Entropy (Awumah et al.(1990) ،Tanyimboh and Tempelman (1993)) are some ofthe alternative indices that are used for system performance evaluation.The quality aspects of performance of a water distribution system are also consideredin studies such as Coelho (1996). All of the studies on water distribution systemshave been focused only on one aspect of the system performance. There is not aunique definition for how well the entire system performance. In this paper, we havetried to develop a hybrid index for evaluation of WTP performance called hybridperformance index (HPI). The HPI is evaluated based on three system performanceindices including reliability, resiliency and vulnerability. The proposed indexconsiders both quality and quantity aspects of WTP performance which can be easilyadopted for evaluation of the total water distribution system performance.



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In the remainder of the paper, description on the methodology used for performanceindices evaluation and developing HPI of WTP is presented. Then the case study isintroduced and followed by discussion on the results. The paper ends with asummery and conclusion.

MethodologyWTPs have an important role in urban water supply system for supplying water withdesired quality and quantity. In the current study, an index for assessment of WTPperformance has been developed considering both quality and quantity aspects. Theproposed index can be predicted based on three indices of system performancenamely reliability, resiliency and vulnerability. These indices are defined for a WTPin specific time intervals. In the proposed index, at first water quality and quantitybased performance indices of WTP have been defined and then combined togetherby weighting. A PNN model has been developed to predict the HPI index based onsystem performance indices. In the following section the proposed index, HPI andthe procedure for its quantification and prediction are described. The result of thestudy is described in the next section.

Hybrid Performance Index, HPI, for Water Treatment PlantsBecause of the impacts of WTPs on both quality and quantity of supplied water, acombined index of system performance is developed by considering two quality andquantity performance indices.

• Quality Performance Index ( lQPI )

In this paper turbidity is used as the indicator for evaluation of water quality in WTPperformance. This selection is based on the impacts of turbidity on efficiency ofvarious steps of water treatment process such as filtration, disinfection and aestheticsof the delivered water to the users. Furthermore, turbidity indirectly represents otherquality variables such as color and total dissolved solids (TDS).Turbidity of surface water resources used for urban water supply varies from zero toten thousands nephelometry turbidity unit (NTU). Turbidity of supplied waterdepends on geomorphology of the water basin and the water supply process. Inaddition, seasonal variations of a specific surface water resource quality are so highand can highly affect the WTP performance. In quantifying performance of a WTP inelimination of water turbidity, two standard and critical thresholds are considered asfollows (Figure 1):

−−

−=

0

).(1

1

StandardAllowable

StandardOWTPl TT

TTQPI α

AllowableOWTP

StandardOWTPAllowable

StandardOWTP

TT

TTT

TT

>>≥

≤(1)

where lQPI is quality performance index of WTP.OWTP

T ,Standard

T andAllowable

T areoutflow, standard and the maximum allowed turbidity in domestic supply water



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respectively. α is Minimum performance coefficient which varies between zero andone.

Figure 1. The Schematic of QPIl variation in relation to turbidity amount.

• Quantity Performance Index ( nQPI )

In quantifying the quantity aspect of WTP performance two issues are considered.The first issue is the efficiency of water treatment process and the second is thesufficiency of system input for supplying the requested water. The quantityperformance index is then formulated as follows:

ββ ...

2

Demandin

out

Demand

Out

in

outn VV

V

V

V

V

VQPI =×= (2)

where nQPI is quantity performance index. outV and inV are outflow and inflow of the

WTP respectively ( 13. −daym ), DemandV is water demand ( 13. −daym ) and β iscorrection coefficient.

The termin

out

V

Vstands for the efficiency of a WTP which is always less than 1 due to

losses during treatment process. It depends on inflow turbidity, architecture of the

WTP and operation policies. The second term,β.Demand

out

V

V, denotes the sufficiency of

input water into WTP for supplying water demand. β is a correction factor which is

greater than one and is equal to the maximum value ofDemand

out

V

Vin WTP operation

period.

• Hybrid Performance Index for WTP ( WTPHPI )

Hybrid performance index is developed by weighted combination of two quality andquantity performance indices of WTP, lQPI and nQPI , as follows:

)).(1().( nlWTP QPIQPIHPI γγ −+= (3)

where γ is a weight that varies between 0 and 1 and shows the relative importanceof quantity and quality performance of WTP. The optional value of γ is obtained bytrail and error. HPI is used as an index of system readiness for crises and iscategorized into four main groups which are acceptable, warning, alarm, and criticalconditions of WTP performance, respectively.



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System performance indicesThree popular system performance indices namely reliability, resiliency andvulnerability are considered in this study for prediction of HPI. It should be notedthat the system performance is evaluated in weekly time steps. These indices arecalculated for the current week and are used to predict the HPI for the next week.The descriptions of these indices in this study are given in the following section.

• System reliabilityThe reliability is the probability that the system succeeds in achieving its determinedgoals in a special period. Reliability is opposite of risk in definition. Considering thisdescription, the reliability of WTP operation has been determined using thefollowing formulation.

demand

out

V

VyReliabilit = (4)

where outV and demandV are the supplied and demand water, respectively during thestudy period. For considering the effects of water quality on the performance of asystem it is assumed that the supplied water should have the standard turbidity andthe poor quality of water supply could be a system failure.

• System resiliencyThe system resiliency is quantified as follows:

F*

FS

n

nResiliency = (5)

where F*n and FSn are the total number of system failures during evaluation periodand the number of system failures which are followed by success full operation,respectively in the evaluation period. A failure happens when a WTP can not supplywater with desired quality and quantity.

• VulnerabilityThe intensity of system failures is considered as system vulnerability which isformulated as follows:

F-Demand

F-Shortage

V

VityVulnerabil = (6)

where F-ShortageV is the volume of water deficit during the evaluation period and

F-DemandV is the water demand during the same time.

Probabilistic Neural Networks (PNN) modelsRadial basis networks can require more neurons than standard feedforwardbackpropagation networks, but often they can be designed in a fraction of the timethat it takes to train standard feedforward networks. One of the main types of thesemodels are PNNs. Probabilistic neural networks can be used for classificationproblems. When an input is presented, the first layer computes distances from theinput vector to the training input vectors and produces a vector whose elements



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indicate how close the input is to a training input. The second layer sums thesecontributions for each class of inputs to produce its net output as a vector ofprobabilities. Finally, a compete transfer function on the output of the second layerpicks the maximum of these probabilities, and produces a 1 for that class and a 0 forthe other classes. The architecture for this system is shown in Figure 2. In this figure,it is assumed that there are Q input vector/target vector pairs. Each target vector hasK elements. One of these elements is 1 and the rest are 0. Thus, each input vector isassociated with one of K classes

Figure 2. The architecture for a PNN model

Case studyThe proposed methodology has been applied to Tehran metropolitan area. Tehran isthe largest city in Iran and has a population of more than 8 million people. Tehran'swater supply system consists of three surface reservoirs namely, Karaj, Lar andLatyan dams, and deep wells located around Jajrood and Tehran. The proportion ofdeep wells in Tehran water supply has increased from 5.13% in 1963 to 41.21% in1994 which affects the quality of supplied water, dramatically (Figure 3).

Figure 3. Tehran's Water Supply Resources (1955-2000)

At present, Tehran water supply system has 5 WTPs with total capacity of around19m3/s. In Table 1 specifications of these WTPs are listed. 59 storage tanks, with atotal capacity of 1.66 Mm3 have been located in different parts of Tehran area for



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drinking water storage and balancing the pressure in water distribution network. Theoldest WTP in Tehran is WTP No.1 (Jalaliyeh) which is considered in this study.This WTP has been established in 1955 with 2.7 m3/s water treatment capacity. ThisWTP supplies water for the central part of Tehran which has condensed populationand combined with governmental and business districts of the city. So the highperformance of WTP N0.1 is a matter of high priority.

Table 1. Specifications of Tehran's WTPs

WTP NameDate of Operation

Start-upClarifier

TypeNominal Capacity

(m3/s)

WTP No.1 (Jalaliyeh) 1955 Accelator 2.7WTP No.2 (Kan) Phase 1; 1963

Phase 2; 1970 Pulsator 8WTP No.3 (Tehranpars) 1968 Pulsator 4WTP No.4 (Tehranpars) 1984 Pulsator 4WTP No.5 (Lashkarak) 2004 Pulsator 15*Total Nominal Capacity of

Operating WTPs Around 29

* 2 Phases; 7.5 m3/s is supplied in each phase.

Treatment process in WTP No.1 as shown in Figure 4, consists of:- Screening, primary settlement and primary chlorination that occur in

Bilaghan intake;- Clarification process involving addition of flocculation agent (flocculation

and coagulation);- Secondary settlement;- Filtration (using open quick sand filters);- Final chlorination.

Figure 4. Schematic Plan of Treatment Process in WTP No.1 (Jalaliyeh)

5 storage tanks are directly fed as subsystem of WTP No.1. The subsystem of WTPNo.1 is connected to WTP No.2, in two points and storage tanks of WTP No.1 can becharged by WTP No.2 (Figure 5). Some deep wells are used as secondary source forwater supply in this subsystem during shortage periods and when the turbidity of



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supplied water is high. Because of some environmental problems, authorities tend toreduce the use of these deep wells. In this paper the performance of WTP No.1 isassessed from March 2004 to September 2007. During the study period arehabilitation program was implemented on filters.

Figure 5. Schematic Plan of WTP No.1 Subsystem

ResultsHPI is calculated in daily steps, while system performance indices are determined ona weekly basis. Therefore, in order to compare the results the weekly average of HPIis calculated. The values of α , β and γ are tabulated in Table 2.

Table 2. Values of HPIWTP Parametersα β γ

0.75 1.375 0.5

Figure 6 shows the variations of HPIWTP during the study period. Results show highfluctuations especially at the beginning of the spring and in the middle of the autumn.This is because of an increase in turbidity of row water due to seasonal rainfall andthe change in the weather. An improvement at the end of the study period is becauseof a rehabilitation program that has improved the performance of filters.

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 365 730 1095

Day

HPI

Figure 6. HPI of WTP No.1

Results show a consistent correlation between system performance indices and HPIvariation. For example, during the weeks of 40 to 53 which the HPI has its lowest



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values, the amount of system reliability and resiliency decrease considerably. Alsothe system vulnerability during these weeks reaches its maximum values in the studyperiod. It can be concluded that the weakness of each of the system performanceindices affects directly the amount of HPI index. The system readiness based on HPIindex in the considered case study is often normal or in warning (more than 0.6)situation.The HPI in each time step is predicted based on system performance indices ofpervious time step using the PNN model. For this purpose, the estimated values ofHPI which are between 0.7 and 1 are classified into 15 subcategories. Differentlength of calibration data sets are randomly selected for mitigation of the probableanomalies in the field data such as measurement errors. The best PNN model istrained by 50 data series. Using this model, HPI is predicted correctly in 50% ofcases and in 90% of cases the HPI is predicted with 2 subcategories relaxation. Thisshows the acceptable performance of PNN in HPI prediction (Figure 7) and alsocompatibility of HPI with system performance indices. In some cases HPI isoverestimated; this shows that PNN must be used with care and some corrections areneeded for the verification of PNN model results in a real application. Theseinvestigations are beyond the scope of this paper and are considered in the futurestudies.

0

2

4

6

8

10

12

14

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146 151 156 161 166 171 176

TheCateg

ory

ofHPI

Abserved PNN

Figure 7. The Results of using PNN for HPI prediction in comparison with ObservedData

Summary and conclusionIn this paper an integrated index called HPI is proposed for evaluation the systemreadiness. HPI is determined based on three system performance indices namely,reliability, resiliency and vulnerability. These indices are evaluated based on qualityand quantity aspects of the system performance. The proposed algorithm is appliedfor one of the WTPs in Tehran water distribution system. A PNN model is developedto predict HPI index based on system performance indices. The developed modelpredicts HPI with acceptable accuracy. The results show that there are highdependencies between system readiness and system performance indices.Appropriate rehabilitation programs can improve the readiness and performance ofthe system.



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References:Awumah, K., Goulter, I., Bhatt, S.K., (1990). "Assessment of reliability in waterdistribution networks using entropy based measures" Stochastic Hydrology andHydraulics, 4 (4), 309-320Bhave, P.R., (1978). "Optimization of single-source networks" J. ENVIRON. ENG.DIV. AM. SOC. CIV. ENG. 104 (EE4), 799-814Coelho, S.T., 1996, Performance Assessment in Water Supply and Distribution.Ph.D. Thesis, Civil & Offshore Engineering Department, Heriot-Watt University,Edinburg. UK.Germanopoulos, G., Jowitt, P.W., and Lumbers, J.P., (1986). "Assessing thereliability of supply and level of services for water distribution system." ProceedingICE, Part 1, No. 80, p.p 413-428Goulter, I.C., (1987). "Current and future use of system analysis in water distributionnetwork design." Civil Engineering Systems, 4 (4), 175-184Goulter, I.C., Bouchart, F., (1990). "Reliability-constrained pipe network model"Journal of Hydraulic Engineering, v 116, n 2, Feb, p 211-229Hobbs, B.F., (1985). "Reliability Analysis of Urban Water Supply" ASCE, 1229-1238Hobbs, B.F., (1985). "Reliability Analysis of Water System Capacity." ASCE, 341-346Kessler A., Ormsbee L., Shamir U., (1990). "Methodology for least-cost design ofinvulnerable water distribution networks" Civil Engineering Systems, 7 (1), 20-28Mays, L.W., (1989) "Reliability Analysis of Water Distribution Systems. Part 1.State-of-The-Art" Published by ASCE, 532pMays, L.W., (1993). "Methodologies for reliability analysis of water distributionsystems" Reliability and Uncertainty Analyses in Hydraulic Design, 233-268Ormsbee .L, Kessler A., (1990). "Optimal upgrading of hydraulic-networkreliability" Journal of Water Resources Planning and Management, 116 (6), 784-802Quimpo, R.G., Wu, S.J., (1997). "Condition assessment of water supplyinfrastructure" Journal of Infrastructure Systems, 3 (1), 15-21Shamir, U., Howard, C. D. D., (1981). "Water Supply Reliability Theory" Journal ofthe American Water Works Association, v 73, n 7, Jul, p 379-384Shamir, U., Howard, C. D. D., (1985). "Reliability and Risk Assessment for WaterSupply System." ASCE, p 1218-1228Tanyimboh, T.T., Templeman, A.B., (1993). "Calculating maximum entropy flowsin networks" Journal of the Operational Research Society, 44 (4), 383-396Wagner, J.M., Shamir, U., Marks, D.H., (1988). "Water Distribution reliability:Analytical methods." Journal of Water Resources Planning and Management, 114(3), 253-275Wagner, J.M., Shamir, U., Marks, D.H., (1988)." Water Distribution reliability:simulation methods." Journal of Water Resources Planning and Management, 114(3), 276-294



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