Technical Performance Evaluation of Water Distribution Networks ...

Available online at www.sciencedirect.com

ScienceDirect Procedia Engineering 70 (2014) 1201 – 1210

12th International Conference on Computing and Control for the Water Industry, CCWI2013

Technical performance evaluation of water distribution networks based on EPANET J. Muranhoa,b,c*, A. Ferreirad, J. Sousac,e, A. Gomesa,b, A. Sá Marquesc,f a b

Departamento de Informática, Universidade da Beira Interior, Rua Marquês d'Ávila e Bolama, 6201-001 Covilhã, Portugal Instituto de Telecomunicações, Universidade da Beira Interior, Rua Marquês d'Ávila e Bolama, 6201-001 Covilhã, Portugal c IMAR - Instituto do Mar,Dep. Ciências da Vida, Universidade de Coimbra,3004-517 Coimbra, Portugal d U.T.C. Eng. Civil,Instituto Politécnico de Castelo Branco,Av. do Empresário,6000-767 Castelo Branco, Portugal e Dep.Engenharia Civil, Instituto Politécnico de Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal f Dep. Engenharia Civil, Universidade de Coimbra, Rua Luís Reis Santos - Pólo II, 3030-788 Coimbra, Portugal

Abstract This paper explores the use of Technical Performance Indexes (TPIs) to assess the operational performance of Water Distribution Networks (WDNs) and exemplifies its suitability to easily identify problematic zones. The paper also points out some problems associated with the existent TPIs and proposes new analysis tools to avoid these difficulties. These new analysis tools (such as state variable slack and constraint violation) are conjugated with the pressure-driven simulation model for illustration purposes, including operational performance assessment during pipe bursts or firefighting scenarios, and to identity and account for the “demand not satisfied” due to those events. © © 2013 2013 The The Authors. Authors.Published Publishedby byElsevier ElsevierLtd. Ltd. Selection Committee. Selection and and peer-review peer-reviewunder underresponsibility responsibilityofofthe theCCWI2013 CCWI2013 Committee Keywords: EPANET, Performance Assessment, Performance Indicators, Water Distribution Networks, WaterNetGen.

* Corresponding author. Tel.: +351 275 242081; fax: +351 275 319899 E-mail address: [email protected]

1877-7058 © 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the CCWI2013 Committee doi:10.1016/j.proeng.2014.02.133

1202

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

1. Introduction Water distribution networks play an important role in modern societies being its proper operation directly related to the population’s well-being. However, water supply activities tend to be natural monopolies, so to guarantee good service levels in a sustainable way the water supply systems performance must be evaluated. The incorporation of performance assessment methodologies in the management practices creates competitiveness mechanisms that lead to the culture of efficiency and the pursuit of continuous improvement. The desired sustainability involves both water services and water infrastructures. The optimal management of urban water infrastructures is an unavoidable issue that needs to be addressed given their intrinsic value (water infrastructures represent an important portion of the municipal public infrastructures) and the potential consequences of the service disruption. Water infrastructure management aggregates management, information and engineering knowledge domains and is differently viewed by the different stakeholders. For example, the maintenance manager can see the water infrastructure as a collection of assets that needs to be maintained while the operation manager sees the network as a whole that needs to be operated. Anyway, as pointed by Alegre and Coelho (2012), these partial views must be conciliated to produce an integrated infrastructure asset management, driven by the need to provide adequate levels of service in a sustainable way in the long-term. Infrastructure asset management is nowadays a real practice for many water utilities and an active research field (Alegre, 2010; Alegre and Coelho, 2012; Cardoso et al., 2012; Carriço et al., 2012). The performance assessment is the key towards sustainability, where performance assessment can be defined as “any approach that allows for the evaluation of the efficiency or the effectiveness of a process or activity through the production of performance measures” (Alegre and Coelho, 2012). Performance assessment is currently a wellestablished practice in the water sector - water supply and wastewater collection (Cardoso et al., 2004; Kanakoudis and Tsitsifli, 2010; Sadiq et al., 2010) and is also giving the first steps in solid waste services (Alegre et al., 2009). There are essentially two main types of performance evaluation tools available to water and wastewater utility managers (Cardoso et al., 2004): systems of performance indicators and technical performance assessment tools. Performance evaluation methodologies, such as the International Water Association (IWA) Performance Indicators System (PIS) (Alegre et al., 2000, 2006), are used to evaluate the water utility’s performance where a performance indicator is defined as a “quantitative measure of a particular aspect of the water undertaking’s performance or standard of service”. Performance indicators are used to assess the performance of the whole service, covering all its sectors of activity (Skarda, 1997; van der Willigen, 1997; Alegre et al., 2000, 2006; Guérin-Schneider and Brunet, 2002; Matos et al., 2003; Crotty, 2004; Lafferty and Lauer, 2005; Kun et al., 2007; Radivojevic et al. 2007; Quadros et al., 2010). Technical performance assessment tools are related to specific aspects of the system (e.g. hydraulic behaviour). Alegre and Coelho (1992, 1995) and Coelho (1997a) propose technical performance indexes (TPIs) to evaluate the hydraulic performance of WDNs. Coelho (1997b) presents a TPI to evaluate water quality performance. TPIs have been also used in other research domains: urban sewer systems (Cardoso et al., 2005), evaluation of the effect of pressure dependent analysis on quality performance assessment of WDNs (Tabesh and Dolatkhahi, 2006); evaluation of the WDNs performance for different pipe materials (Tamminen et al, 2008; Ramos et al., 2009); assessment of the operational performance of water treatment plants (Vieira et al., 2010); among others. The data required to compute TPIs can be produced by simulation tools and exported to other tools (like spreadsheets) for post-processing. However, this procedure is time burden and the data manipulation can introduce errors. To avoid these drawbacks, this paper follows a different approach: compute and represent the TPIs inside the EPANET software (Rossman, 2000) in a manner completely integrated with the EPANET user interface. This paper introduces the concepts of “slack variable”, “constraint violation” and “number of storeys” of a junction node. The “number of storeys” of a junction node expresses the number of storeys of the buildings in the surroundings of that node (an indirect way to define the building’s height). These new “evaluation tools” and the TPIs are integrated in WaterNetGen (Muranho et al., 2012) and are used here to explore the WDNs behaviour under certain critical events, like pipe bursts.

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

1203

Nomenclature PIS TPI WDN

Performance Indicators System Technical Performance Index Water Distribution Network

D Dk Lk N NN NP P(N) Pi Pimin Piref Qi TPIpress TPIvel V(D) W[·] hmax hmin pi pk qiavl qireq vmax wi wk α

Pipe diameter (in millimetres) Diameter of pipe k Length of pipe k Number of storeys above ground Number of nodes Number of pipes Pressure of a node with N storeys Current pressure of node i Pressure value below which no demand can be satisfied at node i Reference (or service) pressure necessary to fully satisfy required demand at node i Water demand of node i Technical performance index for nodal pressure state variable Technical performance index for pipe flow velocity state variable Maximum allowed water velocity for a pipe with diameter D Generalization operator Maximum allowed pressure Minimum required pressure (service pressure) Performance of node i Performance of pipe k Available (or satisfied) pressure at node i Required demand at node i Maximum allowed pipe flow velocity Performance weight of node i Performance weight of pipe k Pressure-demand relationship exponent

2. Evaluation tools The evaluation of the technical performance intends to classify the WDNs behaviour in accordance to a merit scale. The TPIs evaluate the behaviour of each model element (node, link) by comparing their current value with reference values to obtain their performance value. This performance evaluation methodology is based on three components (Coelho, 1997a): 1) state variable; 2) penalty (or performance) curve; and 3) generalization operator. The state variable is related to the specific aspect in consideration. The penalty curve states a relation between the variable values and the performance classification scale (that is, evaluates the merit of each element). The generalization operator aggregates the individual performance values to produce the global system performance index. The generalization operator can be the average (or weighted average) aggregate function, or other more suitable depending on the purpose of the analysis. Several TPIs can be combined to obtain the WDN global TPI. Fig. 1 shows two examples of penalty curves for the state variables nodal pressure and pipe flow velocity. The performance is set based on the relative position of the state variable value to some reference marks: hmin and hmax are the minimum required and maximum allowed pressures, respectively, and vmax is the maximum allowed pipe flow velocity.

1204

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

Fig. 1. Performance curves for nodal pressure and pipe flow velocity state variables. hmin and hmax represent the minimum required and maximum allowed pressures. vmax refers to the maximum allowed pipe flow velocity.

The performance curves produce performance values between 0% (no service) and 100% (optimum service). These curves try to capture the relative importance of the state variable value for the feature under analysis. These curves are drawn in an empirical basis, based on the expert experience. The performance curve for pressure tries to capture the idea that nodal pressures close to the minimum required (service pressure) are preferable as they represent lower energy states, the system elements are under less mechanical stress, and certainly, correspond to a lower level of water losses. The performance curve for the pipe flow velocity intends to state that values above some maximum recommended are undesirable (increased pipes wear ̶ abrasion, and higher head loss, noise and hydraulic transients). The global TPIs are computed by applying the generalization operator (W[·]). The TPIs for the pressure and velocity state variables can be computed using weighted averages, considering weights for the nodes proportional to their consumptions and for the pipes proportional to their water volume. That is, the global performance indexes for pressure (TPIpress) and velocity (TPIvel) can be computed by expressions (1) and (2), respectively.

TPI press = W [ pi ] =

TPI vel = W [ pk ] =

NN

∑w p i

i

∧ wi =

i =1

NP

∑wk pk ∧ wk = k =1

Qi

∑

NN j

Qj

Lk Dk2

∑

NP j

L j D 2j

(1)

(2)

where NN is the number of nodes in the network, pi is the performance value, wi is the weight, Qi is the water demand of node i, NP is the number of pipes in the network, pk is the performance value, wk is the weight, Lk and Dk are the length and diameter of pipe k. As previously mentioned, each junction node has a new property: the number of storeys. With this new field the minimum required pressure can be expressed as a function of the storeys. In this work, this relationship is expressed by the formula P(N) = 100 + 40·N, where N is the number of storeys above ground, with P in kPa. For example, for N = 1, P(1) = 140 kPa ≈ 14.29 m and for N = 5, P(5) = 300 kPa ≈ 30.61 m. To compute the pressure performance (pi) for a junction node (i) using the pressure performance curve in Fig. 1, it is necessary to compute the hmin value using the above formula and replacing N with the number of storeys of the node (i). In a similar way, it can be used a formula for the maximum allowed pressure. In this work, the maximum allowed pressure (hmax) is set to 600 kPa (≈ 61.22 m). The computation of the performance (pk) for a pipe (k) using the (flow velocity) performance curve in Fig. 1, needs to express vmax as a function of the pipe characteristics, in this case as a function of the pipe diameter. In this

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

1205

work, the maximum allowed velocity is given by the formula V(D) = 0.127·D0.4, with D in millimetres and V in m/s. The above minimum required pressure and maximum allowed velocity formulas can be used to produce limit values dependent on the element (node, pipe) under analysis. A constraint violation occurs when the state variable value overcomes the limit value (e.g., the nodal pressure falls below the minimum required pressure). The concept of slack variable is introduced to represent the unused amount of resource (e.g., the minimum pressure slack is the amount of service pressure above the minimum required pressure). 3. Performance evaluation – case study The above performance analysis tools are applied to a WDN model generated with the WaterNetGen software. The example network comprises 200 junctions, 218 pipes, and 2 reservoirs. The network supply different sectors with buildings ranging from one to five storeys – see Fig. 2.

Fig. 2. Water distribution network supplying different sectors (buildings with one to five storeys).

The network is supposed to supply 30,000 inhabitants with a daily per capita consumption of 200 litres, which corresponds to an average demand of 69.44 ls-1, with the demand pattern shown in Fig. 3.

Fig. 3. Demand pattern for the example network.

1206

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

3.1. Geographical representation of the performance In heterogeneous networks, supplying sectors with buildings of very different heights, the map of pressures isn’t enough to infer about the quality of the service provided. Considering, for example, the map of pressures at the start of the simulation period (0:00) shown in Fig. 4, it is not obvious which nodes present low pressure performance, and this occurs because the performance value depends on the node hmin value (which depends on the number of storeys of the node).

Fig. 4. Nodal pressure map – 0:00 Hrs.

So it is not obvious if a yellow node has a good or a bad performance. The geographical representation of the performance can relieve this problem. In this case the performance of each junction node (or pipe) is computed and represented with a colour code – see Fig. 5.

Fig. 5. Nodal pressure performance – 0:00 Hrs.

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

1207

In summary, the integration of the performance evaluation process in the simulation tool allows a spatial view of the network behaviour sensible to the context (for example, considering the number of storeys above the ground or the pipe diameters) and a quick identification of the elements with poor performance. It should be noted, however, that a good performance value can hide service levels problems. For example, reporting to the pressure performance curve of Fig. 1, a performance of 75% can result from an excess of energy (current pressure equal to hmax) or from a deficit of energy (current pressure below hmin). The new “evaluation tools”, proposed in this work, can be used to surpass this problem. 3.2. Slack variables and constraint violations The slack variables represent the amount of resource available, that is, indicates how far the state variable is from its limit value. The slack map shows the slack of each element. For example, Fig. 6 shows the slack map for the minimum required pressure at the hour of greatest consumption (7:00 Hrs).

Fig. 6. Network slack map for the minimum pressure – 7:00 Hrs.

The slack map can be used to choose preferable points for future network expansions, initiating the selection process by the nodes with greater slack. Another map that is very useful is the violations map, as it put in evidence the elements which violate some constraint. For example, the constraint violations map can assist in the determination of the consequences, in terms of service levels, of a pipe burst – see Fig. 7.

1208

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

Fig. 7. Minimum pressure constraint violations map – burst in pipe P50.

The constraint violations map highlights the affected zones. This information can be used in criticality studies to evaluate the impact of element outages taking into account the specific zone context (hospital, school, services, industry, military, etc.). For this kind of scenarios, the pressure-driven simulation mode of WaterNetGen is explored to compute the demand that can be satisfied under the pressure-deficient situation. The available demand (qiavl) is computed based on the following pressure-relationship (Wagner et al., 1988):

⎧ 1 ⎪ α min ⎞ avl req ⎪⎛ Pi − Pi qi ( Pi ) = qi × ⎨⎜ ref min ⎟ ⎪⎝ Pi − Pi ⎠ ⎪ 0 ⎩

Pi ≥ Pi ref Pi min < Pi < Pi ref

(3)

Pi ≤ Pi min

where Piref is the reference (or service) pressure necessary to fully satisfy the required demand qireq, Pimin is the pressure below which no water can be supplied, α (= 0.5) is the exponent of the pressure-demand relationship, Pi is the current pressure at node i, and qiavl is the available demand (satisfied). 3.3. Demand not satisfied The consequence of a pipe burst can also be analysed in terms of the difference between the demand required and the demand satisfied (qireq - qiavl). Fig. 8 illustrates the amount of demand not satisfied as a consequence of the pipe burst scenario represented in Fig. 7.

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

1209

Fig. 8. Demand required but not satisfied – burst in pipe P50 (Fig. 7).

4. Conclusion This paper presents and explores new performance evaluation tools. The traditional TPIs based on state variables have been implemented in WaterNetGen, an extension of the EPANET software. The new elements of analysis based on slack variables and constraints violation have been also incorporated in WaterNetGen. The evaluation tools have been explored through a case study to show how to easily identify model elements with poor performance, identify the preferable points for network expansion, help previewing the consequences of eventual pipe bursts, and to compute the demand required but not satisfied and identify the network nodes where this occurs. The authors believe that the inclusion of the performance evaluation elements in WaterNetGen can benefit a large user community and open new application fields. Availability WaterNetGen can be downloaded from http://www.dec.uc.pt/~WaterNetGen. Acknowledgements This work has been produced with the partial support of the Portuguese Foundation for Science and Technology, through POPH/FSE funding, under Doctoral Grant No. SFRH/BD/48252/2008. References Alegre, H., 2010. Is strategic asset management applicable to small and medium utilities?. Water Science & Technology, 62, 9, 2051-2058. Alegre, H.,Baptista, J.M., Cabrera Jr, E., Cubillo, F., Duarte, P., Hirner, W., Merkel, W., Parena, R., 2006. Performance Indicators for Water Supply Services (Manual of Best Practice Series), 2nd ed., IWA Publishing, London, UK.

1210

J. Muranho et al. / Procedia Engineering 70 (2014) 1201 – 1210

Alegre, H., Cabrera Jr., E., Merkel, W., 2009. Performance assessment of urban utilities: the case of water supply, wastewater and solid waste. Journal of Water Supply: Research and Technology – AQUA, 58, 5, 305-315. Alegre, H., Coelho, S.T., 1992. Diagnosis oh hydraulic performance of water distribution networks, in “Pipeline Systems”. In: Coulbeck, B., Evans, E.P. (Ed.), Kluwer Academic Press, London, UK. Alegre, H., Coelho, S.T., 1995. Hydraulic performance and rehabilitation strategies, in “Improving Efficiency and Reliability in Water Distribution Systems”. In: Cabrera, E., Vela, A.F. (Ed.), Kluwer Academic Press, London, UK. Alegre H., Coelho, S.T., 2012. Infrastructure Asset Management of Urban Water Systems, in “Water Supply System Analysis - Selected Topics”. In: Ostfeld, A. (Ed.), InTech, ISBN: 978-953-51-0889-4, InTech. Available from: http://www.intechopen.com/books/water-supplysystem-analysis-selected-topics/infrastructure-asset-management-of-urban-water-systems. Alegre, H., Hirner, W., Baptista, J.M., Parena, R., 2000. Performance Indicators for Water Supply Services. Manual of Best Practice Series. IWA Publishing, London, UK. Cardoso, M.A., Coelho, S.T., Matos, R., Alegre, H., 2004. Performance assessment of water supply and wastwater systems. Urban Water Journal, 1, 1, 55-67. Cardoso, M.A., Coelho, S.T., Praça, P., Brito, R.S., Matos, J., 2005. Technical performance assessment of urban sewer systems. Journal of Performance of Constructed Facilities, 19, 4, 339-346. Cardoso, M.A., Santos Silva, M., Coelho, S.T., Almeida, M.C., Covas, D., 2012. Urban water infrastructure asset management – a current structured approach in four water utilities. Water Science & Technology, 66, 12, 2702-2711. Carriço, N., Covas, D., Almeida, M.C., Leitão, J.P., Alegre, H., 2012. Prioritization of rehabilitation interventions for urban water assets using multiple criteria decision-aid methods. Water Science & Technology, 66, 5, 1007-1014. Coelho, S., 1997a. Performance indicators in water distribution through mathematical modelling, IWA Workshop on Performance Indicators for Transmission and Distribution Systems, LNEC, Lisbon, Portugal. Coelho, S.T., 1997b. Water quality performance in distribution networks, 21st International Water Supply Congress and Exhibition, IWSA. Madrid, Spain. Crotty, P., 2004. Selection and definition of performance indicators for water and wastewater utilities. American Water Works Association Research Foundation, ISBN: 1-58321-304-X, USA.. Guérin-Schneider, L., Brunet, E., 2002. Performance Indicators for the Regulation of the Water and Sewerage Services: the French Experience, Enviro 2002 IWA World Water Congress, April 7-12, Melbourne, Australia. Lafferty, A.K., Lauer, W.C., 2005. Benchmarking – Performance indicators for water and wastewater utilities: survey data and analyses report. American Water Works Association, ISBN: 1-58321-366-X, USA. Kanakoudis, V., Tsitsifli, S., 2010. Results of an urban water distribution network performance evaluation attempt in Greece. Urban Water Journal, 7, 5, 267-285. Kun, O.B., Abdul-Talib, S., Redwan, G., 2007. Establishment of Performance Indicators forWater Supply Services Industry in Malaysia. Malaysian Journal of Civil Engineering, l 19, 1, 73-83. Matos, R., Cardoso, A., Ashley, R., Duarte, P., Molinari, A., Shulz, A., 2003. Performance Indicators for Wastewaters Services, IWA Publishing, London, UK. Muranho, J., Ferreira, A., Sousa, J., Gomes, A., Sá Marques, J., 2012. WaterNetGen – An EPANET extension for automatic water distribution networks models generation and pipe sizing. Water Science and Technology: Water Supply, 12, 1, 117-123. Quadros, S., Rosa, M.J., Alegre, H., Silva, C., 2010. A performance indicators system for urban wastewater treatment plants. Water Science & Technology, 62, 10, 2398-2407. Radivojevic, D., Milecevic, D., Tetrovic, N., 2007. Technical Performance Indicators, IWA Best Practise for Water Mains and the First Steps in Serbia. Facta Universitatis Series: Architecture and Civil Engineering, 5, 2, 115-124. Ramos, H., Tamminen, S., Covas, D., 2009. Water Supply System Performance for Different Pipe Materials Part I: Water Quality Analysis. Water Resources Management, 23 , 2, 367-393. Rossman, L., 2000. EPANET2 users’ manual. Drinking Water Research Division, Risk Reduction Engineering Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, USA. Sadiq, R., Rodríguez, M.J., Tesfamariam, S., 2010. Integrating indicators for performance assessment of small water utilities using ordered weighted averaging (OWA) operators. Expert Systems and Applications, 37, 4881-4891. Skarda, B., 1997. The Swiss experience with performance indicators and special viewpoints on water networks, IWA Workshop on Performance Indicators for Transmission and Distribution Systems, LNEC, Lisbon, Portugal. Tabesh, M., Dolatkhahi, A., 2006. Effects of Pressure Dependent Analysis on Quality Performance Assessment of Water Distribution Networks. Iranian Journal of Science & Technology, Transaction B, Engineering, 30 (B1). Tamminen, S., Ramos, H., Covas, D., 2008. Water Supply System Performance for Different Pipe Materials Part II: Sensitivity Analysis to Pressure Variation. Water Resources Management, 22 , 11, 1579-1607. van der Willigen, F., 1997. Dutch experience and viewpoints on performance indicators, IWA Workshop on Performance Indicators for Transmission and Distribution Systems, May 5-7, LNEC, Lisbon, Portugal. Vieira, P., Rosa, M.J., Alegre, H., Lucas, H., 2010. Assessing the operational performance of water treatment plants – Focus on water quality treatment efficiency, 7th IWA World Water Congress, September 19-24, Montréal, Canada. Wagner, J.M., Shamir, U., Marks, D.H., 1988. Water distribution reliability - Simulation methods. Journal of Water Resources Planning and Management, ASCE, 114, 3, 276-294.