water Article Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks Helena M. Ramos 1,* , Alban Kuriqi 1,2,* , Mohsen Besharat 3 , Enrico Creaco 4 , Elias Tasca 5 , Oscar E. Coronado-Hernández 6 , Rodolfo Pienika 7 and Pedro Iglesias-Rey 8 1 CERIS, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal 2 Civil Engineering Department, University for Business and Technology, 10000 Pristina, Kosovo 3 School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK 4 Department of Civil Engineering and Architecture, University of Pavia, 27100 Pavia, Italy 5 School of Civil Engineering, Architecture and Urban Design, State University of Campinas, Campinas 13083-889, SP, Brazil 6 Facultad de Ingeniería, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia 7 Institute of Fluid Mechanics and Environmental Engineering, School of Engineering, Universidad de la República, Montevideo 11200, Uruguay 8 Department of Hydraulic and Environmental Engineering, Universitat Politècnica de València, 46022 Valencia, Spain * Correspondence: helena.ramos@tecnico.ulisboa.pt (H.M.R.); alban.kuriqi@tecnico.ulisboa.pt (A.K.) Abstract: One of the main factors contributing to water scarcity is water loss in water distribution systems, which mainly arises from a lack of adequate knowledge in the design process, optimization of water availability, and poor maintenance/management of the system. Thus, from the perspective of sustainable and integrated management of water resources, it is essential to enhance system efficiency by monitoring existing system elements and enhancing network maintenance/management practices. The current study establishes a smart water grid (SWG) with a digital twin (DT) for a water infrastructure to improve monitoring, management, and system efficiency. Such a tool allows live monitoring of system components, which can analyze different scenarios and variables, such as Citation: Ramos, H.M.; Kuriqi, A.; pressures, operating devices, regulation of different valves, and head-loss factors. The current study Besharat, M.; Creaco, E.; Tasca, E.; Coronado-Hernández, O.E.; Pienika, explores a case study in which local constraints amplify significant water losses. It develops and R.; Iglesias-Rey, P. Smart Water Grids examines the DT model’s application in the Gaula water distribution network (WDN) in Madeira and Digital Twin for the Management Island, Portugal. The developed methodology resulted in a significant potential reduction in real of System Efficiency in Water water losses, which presented a huge value of 434,273 m3 (~80%) and significantly improved system Distribution Networks. Water 2023, efficiency. The result shows a meaningful economic benefit, with savings of about EUR 165k in water 15, 1129. https://doi.org/10.3390/ loss volume with limiting pressures above the regulatory maximum of 60 m w.c. after the district w15061129 metered area (DMA) sectorization and the requalification of the network. Hence, only 40% of the Academic Editors: Andreas total annual volume, concerning the status quo situation, is necessary to supply the demand. The Angelakis and Jianjun Ni infrastructure leakage index measures the existing real losses and the reduction potential, reaching a value of 21.15, much higher than the recommended value of 4, revealing the great potential for Received: 9 January 2023 improving the system efficiency using the proposed methodology. Revised: 28 February 2023 Accepted: 13 March 2023 Keywords: digital twin; smart water grids; water losses; digital water; water-energy nexus Published: 15 March 2023 Copyright: © 2023 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. Water losses in water supply systems are a common and worrying worldwide issue This article is an open access article since they involve complex and intensive water–energy processes. Water losses represent distributed under the terms and a waste of treated fresh water and energy resources [1,2]. The problem of water losses in conditions of the Creative Commons municipal water networks can reach up to 80% of the conveyed water or even more in Attribution (CC BY) license (https:// some urban networks [3]. Machine learning (ML) and internet of things (IoT) platforms creativecommons.org/licenses/by/ based on digital twin (DT) solutions can help reduce water losses and energy consumption 4.0/). Water 2023, 15, 1129. https://doi.org/10.3390/w15061129 https://www.mdpi.com/journal/water Water 2021, 13, x FOR PEER REVIEW 2 of 23 Water 2023, 15, 1129 2 of 22 based on digital twin (DT) solutions can help reduce water losses and energy consumption by supplying advanced telemetry to control water-energy management toward a smart wbaytesru pnpetlwyionrgk.a Adv parnocbeldemte-lseomlveitnrgy htoolciostnictr aopl pwraotaecrh-e cnaenrg bye mpraensaegnetemde anst ato mwaanrdagaemsmeanrtt twecahtneor lnoegtyw toor sku. pApporrto ba lseemt -osfo clovnindgithioonliisntgic faacptporros,a icnhclcuadninbeg pthree sfeonlltoewd iansga: management technology to support a set of conditioning factors, including the following: • Flow management: reduction in water losses through a detection and communication • Flow management: reduction in water losses through a detection and communication system that intelligently supervises sensors, telemetry, and actuators to regulate wa- system that intelligently supervises sensors, telemetry, and actuators to regulate water ter pressure and flow in critical network points. pressure and flow in critical network points. •• Water and energy monitoring: a monitoring system transmits the pertinent data to a Water and energy monitoring: a monitoring system transmits the pertinent data to a data acquisition system, control, and management hub. data acquisition system, control, and management hub. •• Water grid control: a remote-control platform, which uses big data analytics, empow-Water grid control: a remote-control platform, which uses big data analytics, empowers ertsh ethwe awteart–eern–eenrgeyrgnye tnwetowrkormk amnaagneargteor mtoa mkeakthee thsyes steymstepmro pgrroegssrievsesliyvemlyo mreoerfefi ceiffien-t ciwenitth wreitahl -rteimale-ticmonet croolnatrnodl adnadta d-dartiav-ednridveenci sdieocniss.ions. DDuuee ttoo tthhee ggeenneerraalllyly rreedduucceedd iinnvveesstmtmeennt tmmaaddee oovveerr ththee lalasst tttwwoo ddeeccaaddeess bbyy wwaateterr mmaannaaggeemmeenntt eennttititieiess inin ssyysstteemm rreennoovvaattioionn eeffffoorrttss, ,mmoosstt wwaateterr ssyysstetemmss aarree nnooww aaggeedd, ,aanndd ththeeirir ininffrraasstrtruucctuturree isis oofftteenn oovveerrllooaaddeedd [[44]]. .WWaateter rnneetwtwoorrkkss ooftfetenn ooppeerraatete bbeeyyoonndd tthheeirir inintetennddeedd oorr pprreeddicictetedd lilfifeessppaann, ,oofftetenn wwitithh mmaainintteennaannccee nneegglelecct,t ,nnoot tkkeeeeppiningg ppaaccee wwitithh ppooppuullaattiioonn ggrroowwtthh,, aanndd ccoonnsseeqquueennttllyy ininccrreeaasseeddc ocnonsusummpptitoino,nc, acuasuinsigngsu spupplpylfya iflauirleusr,eesx, - ecxecsessivsievew wataetrelro lsossesseisn insy ssytestmems ssu sbujebcjetecdtetdo trou rputputruesreasn adnlde alekas,kasn, adnrde sreersveorvirooirv oervfleorflwosw[5s] . [S5]y. sSteymsteemffi ecffiienciceynhcya shbaes cboemcoemaen aimn pimorptoarnttacnot nccoenrcneronf mofa mnaagneamgeemntenent teintiteitsi.eTs.h Tishcisa ncabne bvei evwieewdedas aasn ano poppoprotrutnuintiytyt otoi mimpprorovveet htheem maannaaggeemmeenntt ooff wwaatteerr ssyysstteemmss bbyy iinntteeggrraattiningg nneeww ccoonncceepptst sanandd tetcehcnhonloolgoigeise [s6[]6. ]T.oT boeb aebaleb lteo taonaanlyazley zweawteart leorslsoesss, eits ,isi timispimorptaonrtt aton tbteo abweaarwe oarf ethoef tvhaerivoaursi ocuosmcpoomnpenotnse onft swoaftewra btearlabnaclea n(WceB()W aBs )aa ms aetmhoedthoolodgoylo dgeyvedleovpeeldo pbeyd thbye ItnhteerInntaetrionnaatilo WnaalteWr aAtessroAcisastoiocina t(iIoWnA()I,W wAh)i,cwh hsuicbhdsivuibddeisv aid wesataerw saytsetremsy’sst einmp’usti nvpolu-t uvmoleu imnteoi nsetovesreavl ecroaml cpoomnpenotnse (nFtisg(uFrieg u1)r e[71]). [7]. Figure 1. Typical water balance components. Figure 1. Typical water balance components. Authorized consumption refers to water consumers utilize, including domestic, com- mercAiualt,haonridzeindd cuonstsruiaml.pIttiocna nrebfersb tilol ewdaoterru cnobnislulemdebrsy uatimlizaen, aingcinlugdeingti tdyo[m8]e.stUicn, bcoilmle-d mauerthcioarli,z aenddc oinndsusmtrpiatilo. nIts cianncl ubed ebiwllaetde rour suendbfiollredfi rbeyfi gah mtinagn,asgtirneget ecnletiatnyi n[8g],. aUnndbiirlrleigda atiuo-n thofomrizuendi cciopnaslugmarpdteinosn.sW inactleurdloes wseastear eurseflde fcoter dfiirnefithgehtvinolgu, mstereoeft wclaetaenritnhga,t aenndd siruripgabteioinng olfo mstuwnitchipoault gbaerdinegnsa.s Wsoactieart elodsswesit ahrea urethfloecritzeedd inc othnes uvomlupmtioen osf awfatetrere tnhtaetr iengdsa unpe btweionrgk . lWosat tweritlhoosuset sbecianng baesscolcaisastiefide dwiatsh apupthaorerinztedlo csosenssu(AmLp)tiaonds areftaelr leonstseersin(gR La )n. eAtwLoinrkc.l uWdae-s twera ltoesrsveso lcuamn ebse ocflausnsaifiuetdh oarsi zaepdpcaorennsut lmospsteios n(AaLn)d aflnodw remale ltoesrsdees v(RicLe)m. AeLas iunrcelmudeenst werartoerrs , vwolhuimchese nodf uunpaubtehionrgizuendb ciollnesdumbyptthioenm anadn aflgoewm menetteern dtietvy,icien mclueadsiunrgemilleicnitt ecrornosrus,m wphtiicohn . eCnodn uvper sbeelyin, gR Luinnbcilluledde sbtyh ethveo lmumaneasgoefmweantet rernetsiutylt,i ningcflruodminsgt oirlaligceit taconnksouvmerpfltoiowns. ,Cleoank-s, voerrsreulpyt, uRrLes inaclolundgetsh tehnee vtwolourmke[s9 –o1f 8w].ater resulting from storage tank overflows, leaks, or ruptuDreast aalroenlagt ethdet oneatwsyosrtke m[9’–s1d8i]f.f erent components of water balance are often estimated or even unknown. Consumption and volume of water extracted from water sources have increased by 1% per year since the 1980s on a global scale, driven by a combination of pop- ulation growth, socio-economic development, and changing consumption patterns [14–18]. Water 2023, 15, 1129 3 of 22 Global water demand is expected to continue increasing at a similar rate until 2050, account- ing for 20 to 30% above the current level, mainly due to rising demand in the industrial and domestic sectors [18]. Over two billion people live in countries experiencing high water stress. About four billion people experience severe water scarcity for at least one month a year. Stress levels will continue to increase as water demand grows and the effects of climate change intensify [19]. However, the volume of water withdrawal appears to have stabilized, showing an improvement in water resource management efficiency [20–23]. Considering the abovementioned challenges, smart water grids (SWG), as an inte- grated element of smart cities, can be deployed as a new generation of water management that considers the integration of information and communications technologies (ICT), such as sensors, meters, digital controls, and analytic tools to automate the monitoring and control of water networks. When applied to the water industry, ICT can also provide an automatic remote collection of data in situ, make wireless transmission easier to analyze and improve system efficiency, quality, and reliability [6,8]. An SWG requires a more efficient and complex implementation process and manage- ment strategy than conventional grids. The combination of sensors and communication networks allows real-time monitoring, resource quality control, and optimization of distri- bution and operations. It ensures reliability and customer safety [11]. The benefits of SWGs can be stated as follows [1–23]: – Real-time monitoring of asset conditions: The data collected from advanced sensing technologies supports scheduling, planning, and maintenance. – Real-time monitoring of flow parameters: data from sensors and flow meters help control hydraulic performances. Leaks can be located, thereby minimizing water losses and mitigating the risk of pipe bursts. Actuators, as automated valves, respond to affected areas by preventing major damages, water contamination, or water losses. – Real-time water consumption information: smart water-efficient gadgets provide real-time data on water demand. An SWG structure is based on two main platforms, i.e., the water grid platform and the ICT [7]. The fundamental concept of smart water management systems starts with a global balance by comparing water demand with water resource availability. Because of that, a general framework of the modeling system and a list with the necessary modules to cover it are essential [12,13]. Although simulations and digital twins both utilize digital models to replicate a system’s behavior, a digital twin is a virtual environment, and what makes it different is that while a simulation typically studies one particular process, a digital twin can run any number of useful simulations in order to study multiple processes. The differences are obvious because simulations usually do not benefit from real-time data. In contrast, digital twins are designed around a two-way flow of information, which occurs when object sensors provide relevant data to the system processor and then ensues again when insights created by the processor are shared back with actions to improve the system behavior. By having better and constantly updated data related to a wide range of components, combined with the added computing power accompanying a virtual environment, digital twins can improve more issues than the solutions obtained by standard simulations, with a greater potential to improve system performance. The digital water concept should not be viewed as an option but as an imperative management resource since it can be integrated into every key aspect of the water cycle, from the physical infrastructure to customer service. Water systems must be understood as SWGs or cyber-physical systems made of sensors, processors, and actuators constantly com- municating with each other and reporting all relevant information in a control management system [3,6]. This concept is evolving into an open engagement ecosystem with digital inputs from external stakeholders to utility facilities and other entities. Hence, digital twins are virtual replications of a physical network, integrating virtual engineering models with city-scale reality representations and data from Geographic Information Systems (GIS). Water 2023, 15, 1129 4 of 22 Knowing the real physical data, digital twins are continuously updated with operational data from sensors, meters, and other measuring devices, resulting in a model connected to digital infrastructure that supports the management processes of smart water networks (i.e., planning, design, construction, and operation). Digital twins provide accurate and reliable data that utilities can use to analyze a water system’s lifecycle, test disruption scenarios for resilience assessment purposes, and analyze asset prognosis and health status to determine proactive maintenance measures [7,18]. Henceforward, digital twins are used in water distribution networks to solve management problems, ranging from system design to supply operation. The main capabilities of digital twins (DT) in water distribution networks (WDN) are as follows [16]: – Optimal design of network elements to minimize the overall carbon footprint and to comply with quality-of-service constraints. – Asset management to determine an optimal strategy for the renewal of the physical elements of a network. – Model-based leak detection and pre-location to reduce leaks’ duration and associated operational costs. – Determination of optimal daily operation parameters, including water velocity, pres- sure, and energy efficiency. – Early warning and informed response to emergencies to make fast and effective decisions. – Simulation of network behavior during operation and maintenance procedures. – Simulation of demands and registered consumption and the real behavior of a network according to the data registered by in situ sensors regarding water levels, pressures, and flow calibration to reproduce all the control operations’ performance in the network. – The reliability of the measurements collected by sensors to make real-time decisions and for alarm detection of critical situations. Digital twin development requires continuous adjustments and learning techniques supported by a large amount of field data stored in big-data platforms [12,15]. Therefore, this study’s main objective is to propose a DT model to help water managers reduce water losses, improve system performance, and promote energy nexus efficiency. The next sections of this paper are organized as follows: Section 2 presents the methodological approach and the information related to the case study; Section 3 presents the main findings resulting from the case study; Section 4 discusses the relevance of the findings and the general applicability of the proposed methodology; and finally, the main conclusions drawn from this research work are summarized in Section 5. 2. Materials and Methods In the case of water distribution networks, waste of resources is essentially reflected in water losses due to degraded network components, poor pressure management, pipe rup- ture, illicit connections, and measurement errors in flow meters, among other uncertainties. This paper reports a study on a water distribution system in Madeira (a Portuguese island) managed by a water municipality. The selected system’s current situation, or status quo, is extremely worrisome since only about ~20% of the incoming water is effectively billed to consumers. The remaining ~80% represents water losses. The municipal entity and Redes e Sistemas de Saneamento, Lda (RSS-rss@netcabo.pt) provided all the data used in this study. Relevant lessons can be derived from these projects and associated supporting research studies on reducing water losses, where the existing water distribution network is analyzed and reformulated. 2.1. Methodology As a virtual representation of the physical network in the heart of an SWG, DTs inte- grate virtual engineering models with city-scale reality models and Geographic Information System (GIS) data. DTs demonstrate accurate and reliable data that can be used to analyze different aspects of a water system [7,18]. DTs and SWGs foster and help digitize man- agement systems and solve problems caused by bad design or inadequate operation. DT Water 2021, 13, x FOR PEER REVIEW 5 of 23 The municipal entity and Redes e Sistemas de Saneamento, Lda (RSS-rss@netcabo.pt) provided all the data used in this study. Relevant lessons can be derived from these pro- jects and associated supporting research studies on reducing water losses, where the ex- isting water distribution network is analyzed and reformulated. 2.1. Methodology As a virtual representation of the physical network in the heart of an SWG, DTs inte- grate virtual engineering models with city-scale reality models and Geographic Infor- Water 2023, 15, 1129 mation System (GIS) data. DTs demonstrate accurate and reliable data that can be us5eodf 2t2o analyze different aspects of a water system [7,18]. DTs and SWGs foster and help digitize management systems and solve problems caused by bad design or inadequate operation. dDeTv edloevpemloepnmt reenqtu rireeqsuciorenst icnounotuinsuaodujus satdmjuensttms aenndtsl eaanrdn ilneagrtneicnhgn tiqecuhensisquupepso srutepdpobryteladr gbey filaerlgded fiaetalds tdoartead sitnorbeidg -idna btaigp-dlaattfao rpmlastf(oFrimgusr e(F2ig).ure 2). Fiigurree 22.. Sttrrucctturree off tthee prrooppoosseedd ddiiggiittaall ttwiinn.. The main components off tthhee bbigig ddaatata pplaltaftoformrm thtahta stuspupoprotsr tas Da TD aTrea raes afoslflowllosw: (si): (Gi)ISG, IwS,hiwchh ipcrhovpidroevs idinefsorimnfaotriomna rtieognardreignagr dthien glocthateiolno coaft isoynsteomf scyosmtepmonceonmts;p (oiin)e snetns-; (siio)rse, nwsohrisc,hw mhiecahsmureea shuyrderhayudlirca uplaicrapmareatmerest eorfs tohfet hweawteart enr entewtworokr;k ;(i(iiii)i )daatta acquiisiittiion ((SSCADA)),, whiich superrviises,, moniittorrs,, and conttrrolls tthe collllectted datta;; ((iiv)) smarrtt metterr-- iing,, whiicch cconttrrollss tthee neettworrk oopeerraattiion aand ccussttomeerr sseerrviiccee iin iindeepeendeentt meetteerreed aarreeaass;; aannd ((vv)) ccoomputteerriizzeed maaiinntteennaannccee maannaaggeemeenntt ssyysstteem ((CMMSS)),, whhiicchh ttrraacckkss aannd maaiinnttaaiinnss ssttaattiioonnaarryy aasssseettss.. AAs sa arerseuslut lot fo tfhteh ientiengteragtriaotnio onf othfet hfoerfmoremr seorusrocuesr ciens thine thhye- Water 2021, 13, x FOR PEER REVIEWhd yrdauraliuc lmicomdoeld, ewl,itwhi tthhet hueseu osef aorftiafirctiiafilc iinalteilnlitgeellnigceen (cAeI)( AalIg)oarligthomritsh amnsda inndfoirnmfoartimo6na o taif on2nd3 acnodmcmomunmicuantiiocnatsi otencshtneochlongoielosg (iIeCsT(sI)C, Tas D),Ta mDTodmelo idse dl eisvdeleovpeeldop (eFdig(uFrieg u3r) ew3i)thw ait hhuagheu pgoe- pteontetinatli atol tboeb eexepxlopiltoeidte [d12[1,123,1].3 ]. The methodology developed herein includes different stages, as displayed in Figure 3. The first development is a hydraulic model of the existing network and the big data collection and management provided by the municipal entities to represent the water net- work’s operation. Figure 3. Methodology of the digital twin (DT) model. TGhISe mtoeptohgordaoplhoyg yddateav ealnodp endethweroerikn einlecmluednetss'd difaftear emntusstta aglesso, base dpirsopvlaidyeed (ini.eF.,i gpuiprees3,. Tjuhnectfiiornsts,d veavlevleosp, more notthisera ehxyisdtrinagu lihcymdrod-meleochf atnhiecaelx idsetivnigcens,e trwesoerrkvoainrsd, athned beixgtedrantaal ccoolnlencetcitoinonasn).d Amddaintiaogneamlleyn, tthper ocavritdoegdrabpyhyth aenmd ufenaitcuipreasl oefn btiutiields itnogrse (pi.ree.,s etonpt tehleevwataiotenr, nneutmwboerrk ’osfo flpoeorarsti,o ann.d the existence of pumps) are required for understanding pressure distriGbIuStitoonp aongdra wphaytedr asutapapnlyd mneatnwagoerkmeelnetm. Beenstisd’ edsa ptahmysuicsatl adlsaotab, ewparteorv vidoeludm(ie.es. ,apnidp aelsl, jmuneactsiuornesd, vinaflovrems,aotirono tahreer reexqiustiirnegd htoy didroen-mtifeyc hflaonwic apladtteervnicse, sw, aretesre ruvsoaigrse, oafn cdusetxotmerenrasl, caonndn tehcet ioovnesr)a. lAl vdodluitmione ablalyla, nthcee ocaf rtthoeg sryasptheyma ancdcofredaitnugre tso othf eb udieldfininitgison(i .oef. ,wtoapteer lbeavlaatniocne., nFiunmalblye,r fioefldfl oporress,saunred mtheeaseuxrisetmenecnetso afrpeu kmeyp sto) acraelibrerqatuinirge danfodr vuanliddeartsintagn ad iDnTg mproedsseul rtoe ddiesstcrribibuet itohne arneadl wsyastteermsu upnpdlyerm ananalaygseims aecnctu. rBaetseildye. s physical data, water volumes and all 2.2. Gaula System Characteristics 2.2.1. Gaula WDS Configuration The developed DT is applied to all subsystems using the EPANET hydraulic simula- tor. It is calibrated, compiled in a single model, and then applied to a water distribution network supplied by the Gaula’s tank. All the location information and characteristics of physical elements used in elaborating the hydraulic model, such as the pipes’ site and geometry, valves, and tanks, as well as the information related to existing altimetry and cartography, were used in GIS format. The Gaula WDN, located in the southeast area of the Madeira municipality, contains 19 km of pipes. The Gaula tank is gravitationally fed by the “Funchal–Machico” pipeline. Figure 4 shows the morphology of the region where the highest elevation values in the northwest region can be observed. Water 2023, 15, 1129 6 of 22 measured information are required to identify flow patterns, water usage of customers, and the overall volume balance of the system according to the definition of water balance. Finally, field pressure measurements are key to calibrating and validating a DT model to describe the real system under analysis accurately. 2.2. Gaula System Characteristics 2.2.1. Gaula WDS Configuration The developed DT is applied to all subsystems using the EPANET hydraulic simulator. It is calibrated, compiled in a single model, and then applied to a water distribution network supplied by the Gaula’s tank. All the location information and characteristics of physical elements used in elaborating the hydraulic model, such as the pipes’ site and geometry, valves, and tanks, as well as the information related to existing altimetry and cartography, were used in GIS format. The Gaula WDN, located in the southeast area of Water 2021, 13, x FOR PEER REVIEWth e Madeira municipality, contains 19 km of pipes. The Gaula tank is gravitationally f7e dofb y23 the “Funchal–Machico” pipeline. Figure 4 shows the morphology of the region where the highest elevation values in the northwest region can be observed. FFigiguurree4 4. .L Looccaatitoionna annddt otoppooggrarapphhyyo of ft htheeG Gaauulalan netewtworokrk( l(elfetf)t)a nadndm moroprhpohloolgoygyo foMf MadaedierairaIs Ilas-nd’s Slantda’sC Sruanztma Cunruiczi pmaluitnyic(irpigahlity). (right). TThheeG Gaauulala WDDNN,,a accccoorrddininggt too muunniicciippaall waatteerr ddaattaa,, iiss aann aaggeedd ssyysstteem moossttlly ccom-- ppoosseeddo offH HDDPPEE( 5(533%%))a annddg gaalvlvaannizizeeddi riroonn( 3(355%%))p pipipeem maateterriaialsls. .F Foorrh hyyddrraauulilcics simimuulalattioionn ppuurrppooseses,s,t hteheH aHzaezne–nW–iWlliiallmiasmfos rmfourmlauwlaa swaadso patdeodpttoedca ltcou lcaatlecuhleaatde lhoessaeds ,lwosisthesa, rwouitghh a- nreosusgchoneeffissc iceonetffiofci1e4n0t foofr 1n4e0w fopri pneeswin pHipDesP Eino Hr DFFP,E1 3o0r fFoFr ,e 1x3is0t ifnogr HexDisPtiEngo rHFDF,PaEn dor1 F00F, faonrdfi b1e0r0 cfeomr fienbet ra ncedmgeanlvt aannidz egdalivraonnizdeudc tisr.onF idguucrtess. 5Fiagnudre6s p5 raensedn 6t tphreespeinpte thdeia pmipeete drsi- inamtheitserWs iDn Nthaisn Wd DthNe dainadm tehtee rddiaimstreitbeur tdioisntrtihbruotuiognh tthhreoungehtw thoerk n.eOtwftoernk,. tOhfetedni,a tmhee tderi- vaamlueetsera rvealsumeas lalerer tshmaanlltehre thmainn itmheu mminreimguulamte rdegvuallauteesd( vi.ael.,uDesN (i6.0e.,f oDrNp6o0p ufolar tpioonpsublaetlioowns 2b0e,0lo0w0 i n20h,a0b0i0t ainnhtsa)b. itants). Figures 5 and 6 show that the connections often have diameter values smaller than the minimum regulated values (DN 20), with 96% having a diameter below DN40. For 100 modeling purposes, the internal diam1e0te0rs were used according to the material of each pipe. 80 80 60 60 40 40 20 20 0 0 0 25 50 75 100 125 150 175 200 0 25 50 75 100 125 Pipe Diameter (mm) Connections Diameter (mm) Figure 5. Pipelines by diameter: along the WDN extension (left) and connections by cumulated length (right). Figures 5 and 6 show that the connections often have diameter values smaller than the minimum regulated values (DN 20), with 96% having a diameter below DN40. For modeling purposes, the internal diameters were used according to the material of each pipe. Network Extension (%) Cumulated Length (%) Water 2021, 13, x FOR PEER REVIEW 7 of 23 Figure 4. Location and topography of the Gaula network (left) and morphology of Madeira Is- land’s Santa Cruz municipality (right). The Gaula WDN, according to municipal water data, is an aged system mostly com- posed of HDPE (53%) and galvanized iron (35%) pipe materials. For hydraulic simulation purposes, the Hazen–Williams formula was adopted to calculate head losses, with a roughness coefficient of 140 for new pipes in HDPE or FF, 130 for existing HDPE or FF, and 100 for fiber cement and galvanized iron ducts. Figures 5 and 6 present the pipe di- ameters in this WDN and the diameter distribution through the network. Often, the di- Water 2023, 15, 1129 ameter values are smaller than the minimum regulated values (i.e., DN60 for populat7ioofn2s2 below 20,000 inhabitants). 100 100 80 80 60 60 40 40 20 20 0 0 0 25 50 75 100 125 150 175 200 0 25 50 75 100 125 Pipe Diameter (mm) Connections Diameter (mm) Water 2021, 13, x FOR PEER REVIEW FFiigguurree 5.. PPiippeelilnineess bbyy ddiiaameetteerr: :aalolonngg tthhee WDDNN eexxtteennssioionn ((lleefftt)) aanndd cconnneeccttiioonnss bbyy cumu 8ll aaottefe dd2 3 lleennggtthh ((rriigghhtt)).. Figures 5 and 6 show that the connections often have diameter values smaller than the minimum regulated values (DN 20), with 96% having a diameter below DN40. For modeling purposes, the internal diameters were used according to the material of each pipe. FFiigguurree6 6. .D Diiaameetteerrd disisttrribibuuttioionnt thhrroouugghhoouuttt htheeG GaauulalaW WDDSS. . 22.2.2.2.2.. GGaauullaa DDeemaanndda annddL Loosssseess OOvveerrt thheel laassttf feewwy yeeaarrss,,t thheec coonnssuummppttioionne evvooluluttiioonnh haaddn noos sigignnifiificcaannttv vaarriaiattiioonna anndd rreemmaainineedda lamlmosotscto cnosntasntat.nTt.h Terheefroerfeo,raev, aailvaabilleabvlael uveaslufreosm fr2o0m19 2w0e1r9e uwseerde inustehde DinT tmheo dDelT. Amltohdoeul.g Ah latphopuargehn taplopsasreesntty ploiscsaellsy taycpciocaulnlyt faocrcoabuonut tfo5rt oab1o0u%t o5 fttoh e10b%il loedf tchoen bsuilmledp tcioonn,- tshuemy aprteiosnu,p tphoeyse adreto sureppproesseendt ttow riecperaessemnut tcwh iacse raesa ml louscshe sasa nredasl tliollssheasv aenadv setrilyl hhaigvhe vaa vleurey. Thhiguhs, vthaelubea. sTehsucse,n tahreio b(aasles oscceanllaerdiot h(aelssota ctuaslleqdu othoer setxaitsutsin qgusoi toura teixoins)ticnogn ssiitduearteiodni)n ctohne- asnidaleyrseids iisn pthrees aennatelydsiins iTsa pbrlees1e.nted in Table 1. According to the National Statistics Institute (INE) data, the studied population corre- sTpaobnled 1s. tRoe1p3o9rt6edin rheafebrietnacnet fso.rT Gharuelea swcaetnear rvioolsuwmeesr e(inco 2n0s1i9d).e red in this study regarding the type of consumption: (i) an average situation (i.e., the occurrence of average consumption); (ii) a peak situation (i.e., the occurrence oVf ocolunmsuem ption in a high-demand situation, cal- culated throuTgyhpteh e application of the regu(lmat3o) ry instantaneous multiply(i%ng) factor); and (iii) a staItnicpsuit uVaotiluonmteh at considers a hyp5o4t8h,e2t8i6c limit scenario in which 1th0e0r e are no do- Billed 95,011 17.3 Total losses—Unbilled 453,275 82.7 Apparent Losses 19,002 4.2 Real Losses 434,273 79.2 According to the National Statistics Institute (INE) data, the studied population cor- responds to 1396 inhabitants. Three scenarios were considered in this study regarding the type of consumption: (i) an average situation (i.e., the occurrence of average consump- tion); (ii) a peak situation (i.e., the occurrence of consumption in a high-demand situation, calculated through the application of the regulatory instantaneous multiplying factor); and (iii) a static situation that considers a hypothetic limit scenario in which there are no domestic flow rates or water losses, meaning that the system is under the highest possible pressure values. The consumption flow rates and apparent losses were distributed over the nodes of the hydraulic model. These values were used while considering the model’s population Network Extension (%) Cumulated Length (%) Water 2023, 15, 1129 8 of 22 mestic flow rates or water losses, meaning that the system is under the highest possible pressure values. Table 1. Reported reference for Gaula water volumes (in 2019). Volume Type (m3) (%) Input Volume 548,286 100 Billed 95,011 17.3 Total losses—Unbilled 453,275 82.7 Apparent Losses 19,002 4.2 Real Losses 434,273 79.2 The consumption flow rates and apparent losses were distributed over the nodes of the hydraulic model. These values were used while considering the model’s population distribution per node, which were later converted to demands by applying a multiplying load factor. To calculate the population of each node, Thiessen polygons were created based on the nodes of the model, which were overlapped with BGRI Polygons (i.e., national geographic base geo-referencing) to reach a population value to be linked to each node. The multiplier to be applied to all junctions was then determined to convert inhabitants into consumption, both for the medium and peak scenarios. This multiplier was calculated by dividing the average consumption flow of each scenario by the population to be supplied, as indicated in Table 2. Table 2. Multipliers of the hydraulic model. Item Value Billed Volume (m3) 95,011 Apparent Losses in Volume (m3) 19,002 Inhabitants 1396 Regulatory Instantaneous Multiplying Load Factor (f) 3.874 Average Flow (excluding Real Losses) (L/s) 3.6 Peak Flow (excluding Real Losses) (L/s) 14.0 Multiplier—Average Consumption Scenario 0.00259 Multiplier—Peak Consumption Scenario 0.01003 The Portuguese regulatory [23] instantaneous multiplying load factor (f ) is given by √70f = 2 + (1) P where P represents the number of inhabitants in the system, when applied to the present case study, the f value corresponds to 3.874 for “domestic” consumption. 2.2.3. Real Losses The DT-based model considers the volume of real losses separately from apparent losses and demands through emitter coefficients. With this approach, the DT has different variables associated with each node. The real losses are simulated with a flow rate indepen- dent of consumption. The flow associated with each node is calculated according to the following expressions: q = K pγj f j (2) K Mf = c×∑J 1 0.5× Lij (3)= where q represents the flow rate; K f is the discharge coefficient; p is the pressure; γ is the pressure exponent that depends on the type of material of the network; c is the discharge Water 2023, 15, 1129 9 of 22 coefficient; Lij is the length of the pipe between junctions i and j; and M is the number of pipes connected to node j. The first step of the process is to convert the annual consumption and loss volumes into average flow rates (L/s). Considering Equations (2) and (3), the flow rates corresponding to real losses are distributed into each node through an iterative process until the municipality- provided values have been reached (Table 3). Table 3. DT model learning for real losses. Municipal Values (m3/year) (L/s) Tank Volume 548,286 17.39 Consumption + Ap. Losses 95,011 3.01 Real Losses 434,273 13.77 Simulation Values (m3/year) (L/s) (m3/year) (L/s) (m3/year) (L/s) (m3/year) (L/s) (m3/year) (L/s) Iterations 1 2 3 4 5 Tank Volume 682,439.04 21.64 557,241.12 17.67 534,219.84 16.94 530,120.16 16.81 529,489.44 16.79 Consumption + Ap. Losses 95,011.00 3.01 95,011.00 3.01 95,011.00 3.01 95,011.00 3.01 95,011.00 3.01 Real Losses 587,428.04 18.63 462,230.12 14.66 439,208.84 13.93 435,109.16 13.80 434,478.44 13.78 Regression Factor 0.73928 0.93952 0.98876 0.99808 0.99953 The learning process starts with an initial value of 1 × 10−5 assigned to the discharge coefficient and then applied to each node with the sum of half of the length of each connected pipe. This process results in the first group of values being applied to the emitter coefficients of the hydraulic model, and those values generate an output flow from the reservoir. Since domestic consumption and apparent losses are considered constant values and have already been determined, obtaining the real loss flow is possible. The value readjusts the emitter coefficients until the results reach the municipally provided values. The learning process is completed when the difference between the municipal RLs and the DT model reaches 0.01 L/s. 2.2.4. Pressure Reducing Valves In a water network, the pressure can be reduced and controlled by installing pressure- reducing valves (PRVs) and flow control valves (FCVs) in strategic locations. Installing such devices and replacing critical pipes promote adequate behavior and management of a hydraulic system [14,15]. PRVs are usually installed in systems to control the pressure or head, causing flow energy dissipation. These devices can operate in three ways: by locking when the down- stream pressure is higher than the value configured in the valve, thereby increasing the head loss until it reaches the established value; by opening, if the downstream pressure is lower than the established value, thereby reducing the head loss; and by acting as a check valve in the case when the downstream pressure higher than the upstream pressure [1,7]. The Gaula water distribution system has ten PRVs and one pressure drop chamber. Most existing PRVs are regulated by pressure values much higher than the “ideal regulation” (it should be equal to 2 bar), as illustrated in Table 4 and Figure 7. Only one PRV (i.e., LC PRV designation) among the ten existing PRVs, has an “ideal” regulation. This is because, among other possibilities, some areas may have low pressures and a consequent deficient supply due to insufficient diameters. These are necessary to establish high-pressure regulations to solve this problem, creating an additional excess of pressure in the rest of the network. Water 2023, 15, 1129 10 of 22 Table 4. Existing PRV characteristics in the Gaula WDS. Water 2021, 13, x FOR PEER REVIEW 11 of 23 PRV Existing Regulation “Ideal” Regulation Difference Existing/”Ideal” Designation (Bar) (Bar) (Bar) (%) LLCIII 16.9.6 2.02 .0 4.−6 0.1 33950 SP 2.8 2.0 0.8 140 R + FI 3.0 2.0 1.0 150 FII 3.5 2.0 1.5 175 GA 3.7 2.0 1.7 185 GB 3.7 2.0 1.7 185 Water 2021, 13, x FOR PEER REVIEW 11 of 23 LB 3.9 2.0 1.9 195 LI 4.9 2.0 2.9 245 LII 5.6 2.0 3.6 280 LILIIIII 6.66.6 2.20. 0 4.46. 6 333300 Figure 7. Ideal PRV and the difference between existing and ideal pressure regulation. Only one PRV (i.e., LC PRV designation) among the ten existing PRVs, has an “ideal” regulation. This is because, among other possibilities, some areas may have low pressures and a consequent deficient supply due to insufficient diameters. These are necessary to establish high-pressure regulations to solve this problem, creating an additional excess of pressure in the rest of the network. 3F.i iRguersreeu 77l.t. sII deall PRV aand tthee diiffffeerreennccee bbeettweeeenn eexxiissttiinngg aanndd iiddeeaallp prreesssuurreer reegguulalatitoionn. . 33..1.R DesTu MOnlltsodel Calibration y one PRV (i.e., LC PRV designation) among the ten existing PRVs, has an “ideal” 3r.e1g.uDTlaoTt ivMoanoli.d TealhtCiesa tilhisb ebr aeDtciaTonu mseo, damel,o tnhge ostihmeur lpaotessdi bpilrietsiessu,r seo rmeseu alrtes aws emrea yc ohmavpea lroewd pwrietshs uthrees raeanld m Taoe cavosanulsriedeqmauteeennthtts e dpDeefiTrfcmoieronmdt eesdlu, pathtp el5ys2 i dmpuoueiln atttose dionfps rutheffises cuWireeDnrtN eds.u iaFlmtigseuwtreeerr se8. cTsohhmoewspeas r aeardne wenxeictcheelstlsheanertry efi attol bmeestetwasbeuelirnseh mt hheein gmthse-paersreufsroserudmr ea ndredag ttuh5le2a tcpiaolnicnsut ltsaoot esfodtlh vveea WltuheDissN ,p w.rFoitibhgl ueamrne ,a 8cvrseehraoatwginesg oa afn n5e %axdc eedrlirlteoinort ninfial tt ehbxeect pweseesae kon f htphoreuerms.s uearesu inre tdhae nrdestth oef ctahlec unleattwedorvka.l ues, with an average of 5% error in the peak hour. 3. Results 3.1. DT Model Calibration To validate the DT model, the simulated pressure results were compared with the real measurements performed at 52 points of the WDN. Figure 8 shows an excellent fit between the measured and the calculated values, with an average of 5% error in the peak hour. FFiigguurree 88.. Meeaassuurreedd aanndd ssiimuullaatteedd pprreessssurree vaallueess.. SSiinnccee mmoosts tvavlauleuse ws ewreer me emaseuarseudre adroaurnodu n9d a.9ma.,. mth.e,steh reesseulrtess cuolrtsrecsoprornedsp toon tdhet poetahke spiteuaaktisoitnu. ation. 3.2. DT Model Response and Improvements Specific changes are necessary to improve the system under analysis. In the Gaula WDN, the low diameter values, high flow velocity, and high unit head-loss values are Figure 8. Measured and simulated pressure values. analyzed for the medium and peak situations. They are the most relevant basis for replac- ing pipes to improve overall hydraulic system efficiency. Figure 9 presents the unit head Since most values were measured around 9 a.m., these results correspond to the peak situation. 3.2. DT Model Response and Improvements Specific changes are necessary to improve the system under analysis. In the Gaula WDN, the low diameter values, high flow velocity, and high unit head-loss values are analyzed for the medium and peak situations. They are the most relevant basis for replac- ing pipes to improve overall hydraulic system efficiency. Figure 9 presents the unit head Water 2023, 15, 1129 11 of 22 3.2. DT Model Response and Improvements Water 2021, 13, x FOR PESEpRe RcEiVfiIcEWch anges are necessary to improve the system under analysis. In the Gaula 12 of 23 WDN, the low diameter values, high flow velocity, and high unit head-loss values are analyzed for the medium and peak situations. They are the most relevant basis for replac- ing pipes to improve overall hydraulic system efficiency. Figure 9 presents the unit head losses in the pelaoksssetsa tinu sthqeu poeoarke sxtiasttuins gqusiotu oart ieoxnisatinndg tshiteupatrioopno asnedd tihmep prroovpeodsesodl uimtiponro.vIted solution. It also presents thaelsroe pprlaecseedntps itphees r,esphloawceidn gpitpheesd, sirheocwt dinegle ttheeri doiursecint dfleuleentecreiooufsl oinwflduieanmcee toefr low diameter values on this WvaDluNes. oInn mthoiss tWcaDsNes., Ihni gmhoustn citasheesa, dh-ilgohs suvnaitl uheesadw-leorsesr veaplluaecse dwoerrec roemplpalcee-d or comple- mented with amnoetnhteerdp waritahll ealnpotipheer, tphaerraelbleyl cpoipnetr, othlleinregbtyh ecoonrtirgoilnlianlgfl tohwe sorreigsipnoanl sfliobwlesf orersponsible for the significant thheea dsiglonsisfiecsa.nt head losses. Figure 9. Unit hFeiagdurloe s9s.e sUinnitt heapde alokssecse nina rtihoei npetahke sctaentuasriqou ion sthiteu astaiotuns( lqeufot) ;siidtueanttioifinc a(lteioftn); oidf entification of critical pipes forcrinittiecarvl epniptieosn fo(cre inntteerr)v;eanntdionu n(citenhteeard); laonsdse usninit thheeadp elaoksssecse inna trhioe ipneathke spcernoaproiose idn the proposed solution (right). solution (right). ComplementinCgotmheppleimpeenretpinlgac tehme epnipt,et rheepnlaectewmoerkntw, tahse dnievtiwdoedrki nwtaoss dmivaildleerda irnetaos stmo aller areas to easily control aenaosmilya lcoounstrcool nasnuompatlioouns pcoanttseurnmspatniodn wpaattteerrnilsli caintdc ownanteecr tiilolincsito crobnrneeacktiaognes. or breakage. Therefore, six dTihsterriecfto-mree, tseixre ddisatrreicats-m(DeMterAed) wareeraesc (rDeaMteAd) awcecorer dcirneagtetod tahcecochrdairnagc tteor itshteic csharacteristics presented in Tapbrleese5natnedd iFni gTuabrele1 50 .aTnhde Ffligouwrem 1e0t. eTrhsea rfleoiwnd miceatteerds aarseC inodricVatiefda sasso Cci aotre Vd if associated with pressure rwedituhc ptiroenssvuarlev eresd. uction valves. Table 5. CharactTeraibzlaet i5o.n Cohfatrhaectdeirsiztraitcito-mn oeft etrheed dairsetraisc.t-metered areas. DMA DMA Flow ConFtrloolwle rCs ontrollers Extension (kEmx)tension (km) 1 C1-V21-V20-C2-V18 4.2 1 C1-V21-V20-C2-V18 4.2 2 2 C2-V12 C2-V12 4.6 4.6 3 3 V20+V21-VV2220+V21-V22 5.8 5.8 4 4 V12 V12 3.6 3.6 5 5 C3+V18 C3+V18 3.5 3.5 6 6 V22-C3 V22-C3 5.3 5.3 Total Total 27.0 27.0 WaWteart e2r02012, 31,31,5 x, 1F1O29R PEER REVIEW 12 of 22 13 of 23 FFiigguurere1 01.0D. DMMAAse csteocrtiozraitzioantiofnt hoef Gthaeu GlaaWulDaN W. DN. A huge decrease in flows can be noticed (measurements each hour) throughout 2021, with the project being implemented from March to November (Figure 11a). A real-time interaction reduced the average demand from 65 m3 when the DT was implemented in March to 27 m3 in November. The variation in the consumption volume by the hour for November, with the representation of the demand pattern, after the implementation of the digital twin model is shown in Figure 11b. A comparison of the maximum pressures before and after the DT implementation is presented in Figure 11c. Water 2023, 15, 1129 13 of 22 A huge decrease in flows can be noticed (measurements each hour) throughout 2021, with the project being implemented from March to November (Figure 11a). A real-time interaction reduced the average demand from 65 m3 when the DT was implemented in March to 27 m3 in November. The variation in the consumption volume by the hour for November, with the representation of the demand pattern, after the implementation of the Water 2021, 13, x FOR PEER REVIEW digital twin model is shown in Figure 11b. A comparison of the maximum pressures 1b4e ofof r2e3 and after the DT implementation is presented in Figure 11c. (a) (b) Figure 11. Cont. WaWterat2e0r 2230,2115, ,1131, x2 9FOR PEER REVIEW 15 of 1243o f 22 (c) FFiigguurree 1111.. CoCnosnesqeuqeunecensc eosf tohfe DthTe mDoTdeml iomdpellemimenptlaetmioenn (tfarotimon M(afrrochm toM NaorvcehmtboerN 2o0v21e)m: (bae) rim2-021): proving real-time demand (in m3) with D3T; (b) demand pattern after implementing the DT; (c) com-(a) improving real-time demand (in m ) with DT; (b) demand pattern after implementing the DT; parison of maximum pressures in the Gaula system before (top) and after implementing the DT (c(b) ocottmomp)a.r ison of maximum pressures in the Gaula system before (top) and after implementing the DT (bottom). WWaatteerr 22002211,, 1133,, xx FFOORR PPEEEERR RREEVVIIEEWW 1166 ooff 2233 W ater 2023, 15, 1129 15 of 22 IItt iiss woorrtthh meennttiioonniinngg that It is worth mentioning thatthbaetf bbeeffoorree tthhee DDTT iimpplleemeennttaattiioonn,, tthhee ssyysstteem pprreesseenntteed ore the DT implementation, the system presented 53.1%d 5533..11% ooff nnoonn--ppeerrmissible pressure (>60 m w.c.), incof non-permissible ipsrseibssleu rpere(s>s6u0rem (>w6.0c .), in.ccl.u),d iinnc lluuddiinngg 66..11% aabboovvee 1100, that is, beyond g 6.1% above 100, th0a0t, itsh, abte iyso, bnedytohned tthhee ““ppoossssiibbllee”” ffoorr tthheessee ppiippeess iinn PPN1100.. AAfftteerr tthhee DDTT iimpplleemeennttaattiioonn,, tthhee nnoonn--ppeerrmiissssiible “possible” for these pipes in PN10. After the DT implementation, the non-permissibblele pprreessssuurree cchhaannggeedd ffrroom 5533..11% ttoo 44..77%,, nnoo lloonnggeerr hhaavviinngg pprreessssuurreess aabbopressure changed from 53.1% to 4.7%, no longer having pressures abovo vvee e 1 10 0100 00 m w.c. m w .c..c. 444... D Diiissscccuuusssssiiiooonnn SSSeeevvveeerrraaalll a aannnaaallylyyssseeesss w weeerrreee c ccooonnnddduuuccctteteeddd u uusssiininnggg t ththheee d ddeeevvveeelloloopppeeeddd D DDTTT m mooodddeeelll t ttooo i ididdeeennntttiififfyyy p pprrreeesssssuuurrreee lleleevvveeelll i imimppprrrooovvveeemmeeennnttstss... F FFiigigguuurrreeesss 1 11222 a aannnddd 1 11333 p pprrreeessseeennnttt t ththheee p pprrreeesssssuuurrreee v vvaaalluluueeesss o oocccccuuurrrrriininnggg i ininn t ththheee G GGaaauuullalaa WWDDDNNb bbeeeffofoorrereea aannnddda aafftfteteerrri miimppplelleemmeeennnttitniinngggt htthheeep pprroroopppooossesededds sosoolulluutittoiioonnn. .. FFFiigigguuurreree 1 11222... P PPrrreeesssssuuurrreee v vvaaarrriiaiaattitioioonnn i ininn t ththheee GGaaauuullalaa WDDNN—aaavvveeerrraaagggeee,,, p ppeeeaaakkk,,, a aannnddd mmaaaxxxiiimmuuumm p pprrreeesssssuuurrreee s ssccceeennnaaarririoioosss ((s(ssttataattutuusssq qquuuooo)).).. FFFiigigguuurreree 1 11333... P PPrrreeessssssuuurrreee v vvaaarrriiaiaattitioioonnn i ininn t ththheee G GGaaauuullalaa W WDDDNNN——aaavvveeerrraaagggeee,,, p ppeeeaaakkk,,, a aannnddd m mmaaaxxxiimimmuuummm p pprrreeessssssuuurrreee s ssccceeennnaaarririoioosss ((p(pprroroopppooosseseeddds sosoolluluuttitioioonnn)).).. TTTaaabbbllelee6 66a naandnddF iFgFiuiggruuerr1ee4 11p44r eppsrreeenssteepnnrtt e ppsrsreuesrssesuuvrraeel u vveaaslluuineests h iienn n tthehteew nnoeertktwjuoonrrkckt ijjuounnscct(tiniooonndsse (s(nn)oodbdeteassi))n ooebdb-- ftrtaaoiimnneeddth fefrrooDmT ttmhheeo dDDeTTl, malolooddweelil,n, agallllsooowmiinenggc ossonomcluee s cicoonncscllutuossiiobonenssd trtooa wbbene ddrerragawarnnd irnreegggaatrhrddeiinnimgg ptthrhoee v iiemd-- ppprreroosvsvueedrde pplrerevessesslusu.rree lleevveellss.. Waatteerr 22002231,, 1153,, 1x1 F2O9 R PEER REVIEW 1176 ooff 2223 Table 6.. Pressure iin tthe node jjuncttiions ffrrom tthee DT moodeell rreessuullttss.. NodNeso dweistwh iPthrePsrseussrue rVe aVlauleuse sBBeellooww:: 10% 10%20%2 0% 30%3 0% 40%4 0% 50%5 0% 60%60 % 70%70 % 80%80 % 909%0% 110000%% ExistEinxgis-tAinvge-Aravgeera ge 23.002 3.0025.722 5.7231.8371 .8738.7388 .78 50.7570 .77 56.2576. 27 63.643 .4 68.6787.7 7 757.658.6 8 111144.2.200 ExistEinxgis-tPinega-kP eak 19.691 9.6935.183 5.1842.4432 .4349.549 .54 45.4435 .43 51.4521. 42 56.5361. 31 63.6037.0 7 717.215.2 5 11122.4.477 ExistEinxgis-tSintagt-Sict atic 23.742 3.7420.082 0.0830.0360 .0640.0430 .03 56.1576 .17 62.4662. 46 69.6993. 93 79.719 .1 868.261.2 1 11222.2.266 FuturFeu-tAurvee-rAavgeera ge 20.392 0.3923.692 3.6927.2207 .2030.5300 .50 33.5383 .58 37.937 .9 41.4710. 70 45.4658.6 8 545.047.0 7 6699.2.244 Future-Peak 19.71 22.80 26.26 30.00 32.72 37.04 41.08 44.60 52.90 67.56 FutuFruet-uPreea-Skt atic 19.712 0.7722.802 4.5026.2267 .8930.0300 .74 32.7324 .19 37.0349. 10 41.4028. 07 44.4670.0 0 525.950.3 5 6770.5.063 Future-Static 20.77 24.50 27.89 30.74 34.19 39.10 42.07 47.00 55.35 70.03 FFiigguurree 1144.. DTT ssiimuullaattiioonnss pprreesseenntt tthhee ppeerrcceennttaaggee ooff nnooddeess wiitthh tthhee pprreessssuurree bbeelloow.. CCoommppaarriinngg tthhee pprreessssuurree oobbsseerrvveedd iinn tthhee ssttaattuuss qquuoo sscceennaarriioo aanndd tthhee sscceennaarriioo wwiitthh iimmpprroovveemmeennttss,, tthheerreei sisa ar reedduucctitoionno of f1 .17.7b abrarc ocmompapraerdedto toth teh5e. 15.b1a bravre vriefireifideidn itnh etheex iesxtiinstg- sinitgu astiitounatfioorn5 f0o%r 5o0f%th oef pthrees spureresssubreelso wbe, lwowhe, rwe haberoeu at b2o5%ut o2f5t%h eonf ethtwe onrektw’s ojurkn’cst ijounnscthiaovnes phraevses uprreesvsualruee vsaalubeosv aeb6o0vem 60w m.c .w(.ic..e (.i,.em., amxiamxiummumre greugluatloatroyryv avlauleu)e)in ina allllt thhee sscceennaarriiooss analyzed (i.e., average, peak, and static). analyzed (i.e., average, peak, and static). By simulating the extreme pressure situation (static), where consumption and head By simulating the extreme pressure situation (static), where consumption and head losses are considered non-existent, the reduction in the mean pressure value obtained is losses are considered non-existent, the reduction in the mean pressure value obtained is from 56 m w. c. to 34 m w.c., a reduction of around 2 bar for 50% of the pressures below. from 56 m w. c. to 34 m w.c., a reduction of around 2 bar for 50% of the pressures below. Regarding the node junctions of the proposed network with pressures above the Regarding the node junctions of the proposed network with pressures above the maximum regulatory of 60 m w.c., less than 4% are in the average scenario. In the maximum maximum regulatory of 60 m w.c., less than 4% are in the average scenario. In the maxi- limit scenario of static pressure, only 6% reach those pressure values, meaning that with the DmMumA ’lsimseictt socreiznaatriioon oafn sdtatthice pnreetswsuorrke, poinpleys ’6r%eq rueaaclihfi cthatoisoen ,ptrheesseuxrcee svsalpureess,s umreeapnrionbgl ethmast awriethsu tbhset aDnMtiaAll’ys smecittiograizteadti,oans asnhdo wthne inneFtwigourrke sp9ipaensd’ r1e1q.uIat liisfiwcaotirothn,m theen teixocneisnsg ptrheastsuthree pprreosbsluermesv aarleu esus bhsatvaentniaelglyli gmibitliegvaaterdia, taios nshwoiwthnt iinm Feig(Fuirgeusr 9e a1n4d), 1a1s. iIdt eisn wtifioerdthi nmtehnetisotnaitnugs qthuaot stihtue aptiroenss.ure values have negligible variation with time (Figure 14), as identified in the stTahtuiss sqtuudo ysiatulsaotiaolnlo. ws us to analyze the relationship between ruptures in the Gaula WDNThainsd sttuhdeyo baslseor vaelldowprse sussu troe san(Failgyuzree th15e) r,ealcactoiorndsinhgipt obetthweeiennfo rrumpatutiroens pinr othveid Gedaublya tWheDmNu annicdi ptahlew oabtseerrevnedtit py,rweshsuicrhess h(Fowigsuraes 1tr5o)n, gacdceoprdenindge ntoce thbee tiwnfeoernmthaetisoent wproovvaidrieadb lbeys. Ethxec emssuivneicpipreasl swuraetsera reenrtietlya,t ewdhtiochp rsohbolwems saw stirthonhgig dhevpoelnudmeensceo fbwetawteerenlo sthseess,ew twhioch viasriaan- ibslseuse. Etoxcceosnsisvide eprrweshsuenrerse ahraeb rieliltaatteindg tot hperoWblDemNsu wsiinthg hDiTghs uvpopluomrte. s of water losses, which is an Aismsuoen tgo tchoendsiifdfeerre wnthceonm rephoanbeinlittsatoifnwg athteer WloDssNes ,urseianlgl oDsTse ssuhpapvoertth. e greatest weight, which, in the current case, is about 95% of the total volume of losses. Thus, in the analysis, it was considered that the consumption and apparent losses remained constant. Table 7 compares the volumes verified in the status quo, the hypothetical target to be achieved ac- cording to the Portuguese Supply and Residual Water Strategic Plan (PENSAAR 2020 [23]), and the suggestions of the proposed methodology. Pressure (m w.c.) Pressure (m w.c.) WWaatteerr 22002213, ,1135, ,x1 F12O9R PEER REVIEW 181 7ofo f2232 FFigiguurree 1155. .PPeercrceenntataggee oof frurupptuturreess (f(oforr ththee sstatatitcic sscceennaarrioio)). . TableA7m. Aonngnu thalev doilffumereesnitn ctohme GpaounleanWtsD oNf .water losses, real losses have the greatest weight, Situation which, in the cu3rrent case, is about 935% of the total volume of losses. Thus, iTotal Volume (m ) Billed Volume (m ) Unbilled Volume (UV) (m3) UV/Ex nis tthineg aTnValy(%si)s, it was considered that the consumption and apparent losses remained constant. Table 7 Existing situation 548,286 114,013 434,273 79% Target (PENSAAR 2020) comp1a96re,2s5 5the volumes ve1r1i4fi,0e1d3 in the status quo, 8th2,e2 4h2ypothetical target to 1b5e% achieved Difference acco−rd35in2,g0 3t1o the Portuguese0 Supply and Residua−l 3W52a,t0e3r1 Strategic Plan (PENS-AAR 2020 Proposed Scenario [23]),3 4a4n,d37 t3he suggestions1 1o4f ,0th13e proposed method2o3l0o,g36y0. 42% Difference −203,913 0 −203,913 - Table 7. Annual volumes in the Gaula WDN. By implementing theTportoapl oVsoedlumeet hoBdilolleodg yV,olnulmy 4e0 %Unobf tihlledsy Vstoelmu’ms ean UnVu/aelxviostluinmge would Sbeitunaetcieosns ary to supply(mth3e) Gaula WDN(m. 3I)n a scenario(UwVh)e (rme 3t)h e propToVse d(%P)E N- SAEAxRis2ti0n2g0 sgiotualaitsiotno be achi5e4v8e,d28(6~ 15% of w1a1t4e,r01lo3s ses), acqui4r3in4g,27o3n ly 1/3 of t7h9e%w ater Tvaorlguemt e(PeEnNteSriAngAtRh e20e2x0is)t ing 1si9t6u,a2t5i5o n would 1b1e4n,0e1c3e ssary. The p8r2o,p2o42se d scenario1a5l%lo ws a reduction of 42% of unbilled volume compared to the total volume of the existing situation. Difference −352,031 0 −352,031 - An indicator that compares real losses and reduction potential is the infrastructure Proposed Scenario 344,373 114,013 230,360 42% leakage index, which consists of the ratio between actual real losses and the “minimum” value oDf riffeaelrleonscsees (UARL). I−n2t0h3e,9e1x3i sting scenar0io , the average−p2r0e3s,s9u1r3e of 5.1 bar w- as ver- ified. The results for the unavoidable annual real losses (UARL) are illustrated in Figure 16, Water 2021, 13, x FOR PEER REVIEWw heBreyt ihmeptoletaml eUnAtiRngL tphoet epnrtoiaplorseeddu mctieotnhoisdofrloomgy2, 0o,n53ly0 4m03%/ yoefa trhteo s1y3s,t5e7m9 ’ms a3/nyneua1a9rl, ovwfo i2lt-h3 utmheem waoinulcdo mbep onneecenstsraersyp oton ssiubplepfloyr tthheis Greaduulac tWionDiNn.l oInss aes sbceeinnagritoh ewchoenrnee tchtieo npsroapnodsnedo t PiEnNthSeApAipRe 2n0e2t0w goorkali tisse tlfo. be achieved (~15% of water losses), acquiring only 1/3 of the water volume entering the existing situation would be necessary. The proposed scenario allows a reduction of 42% of unbilled volume compared to the t2o0t,a 5l3 v0olume of the existing situatio2n5,.0 00 An indicator that compares real losses and reduction potential is the inDfifrearsetnrucecture leakage2 0i,n0d00ex, which consists of the 1r0a,t i9o4 0between actual real losses and the “minimum” 6,951 value of real losses (UARL). In the existing scenario, the average pressure oFf u5t.u1r ebar was verified15. ,T0h00e results for6 t, h1e7 0unavoidable annual real losses (UARL) are illustErxaistteidng in Fig- ure 16, where the total UARL potential reduction is from 20,530 m3/year to 13,579 m3/year, 10,000 3,704 3, 419 with the main component responsible for this reduction in losses being the connections 13,579 and not in the pipe n2e,t0w89ork itself. 5,000 7,236 4,081 1,158 2,261 0 NetRweodrek ConRnaemctaioisns R Caomnaniesc (tiionnt)s ToTtoatlal (e(xetx)t) (int) UARL Components FFiiggurree 1166.. UARRLL iinn tthhee ccoompoonneennttss ooff tthee Gaaullaa WDN.. As verified in Table 1, the real losses in the Gaula WDN in the existing situation rep- resent 79% of the total water entering the system, corresponding to 434,273 m3. Consider- ing the value of unavoidable annual real losses (UARL) obtained in the existing situation (Figure 16), which is 20,530 m3, the infrastructure leakage index is 21.15 (434,273/20,530), which is much higher than the recommended value of 4, revealing that the network has a huge potential for real loss reduction. The apparent loss volume is significantly lower than the real one. Nonetheless, this volume must not be ignored. Most apparent loss values result from measuring device errors, suggesting that, to control such losses better, unreli- able measuring devices should be substituted or renewed to improve the measuring qual- ity [13]. The current WDN study identified an apparent loss of 20% of the consumption. This value is higher than the commonly verified value, but, in this case, it has no relevance in the overall volume since real losses present a significant value [11]. In conclusion, old measurement devices and/or those controlling significant consumption must be analyzed and replaced when necessary. The results of a brief analysis of municipal water flow meters are presented in Table 8 and Figure 17a, where it can be concluded that the devices’ average age is 11 years, and 25% of the devices are older than 15 years. In addition, according to the municipal water data, most of the significant consumers have old measuring devices, resulting in measured values that are different from the real ones, as presented in Table 8. According to the provided data, these significant consumers correspond to 10% of the municipality’s total water consumption, representing twice the billed consumption. Table 8 also shows that the average age of the indicated measuring devices is 17 years. Considering a 20% error in their measurements, 60,000 m3 of apparent annual losses might be associated with these consumers. The percentage relationship between the municipal- ity’s water consumers and consumption is again reinforced—a few consumers are respon- sible for higher consumption values, and the errors related to their measuring devices can be the source of considerable apparent losses. Figure 17b shows that only 1% of the mu- nicipality’s consumers are responsible for 20% of the consumed water volume. Table 8. The age of flow meters with the most significant consumption in the municipality. Consumer (No.) Consumption (m3) Age (Years) 1 51.662 23.6 2 47.810 3.3 3 31.351 27.8 4 29.998 20.4 Annual Volume (m3) Water 2023, 15, 1129 18 of 22 As verified in Table 1, the real losses in the Gaula WDN in the existing situation repre- sent 79% of the total water entering the system, corresponding to 434,273 m3. Considering the value of unavoidable annual real losses (UARL) obtained in the existing situation (Figure 16), which is 20,530 m3, the infrastructure leakage index is 21.15 (434,273/20,530), which is much higher than the recommended value of 4, revealing that the network has a huge potential for real loss reduction. The apparent loss volume is significantly lower than the real one. Nonetheless, this volume must not be ignored. Most apparent loss values result from measuring device errors, suggesting that, to control such losses better, unreliable measuring devices should be substituted or renewed to improve the measuring quality [13]. The current WDN study identified an apparent loss of 20% of the consumption. This value is higher than the commonly verified value, but, in this case, it has no relevance in the overall volume since real losses present a significant value [11]. In conclusion, old measurement devices and/or those controlling significant consumption must be analyzed and replaced when necessary. The results of a brief analysis of municipal water flow meters are presented in Table 8 and Figure 17a, where it can be concluded that the devices’ average age is 11 years, and 25% of the devices are older than 15 years. In addition, according to the municipal water data, most of the significant consumers have old measuring devices, resulting in measured values that are different from the real ones, as presented in Table 8. Table 8. The age of flow meters with the most significant consumption in the municipality. Consumer (No.) Consumption (m3) Age (Years) 1 51.662 23.6 2 47.810 3.3 3 31.351 27.8 4 29.998 20.4 5 20.686 11.8 6 19.790 12.4 7 13.153 14.8 8 12.543 20.4 9 7.519 16.8 10 7.253 13.8 According to the provided data, these significant consumers correspond to 10% of the municipality’s total water consumption, representing twice the billed consumption. Table 8 also shows that the average age of the indicated measuring devices is 17 years. Considering a 20% error in their measurements, 60,000 m3 of apparent annual losses might be associated with these consumers. The percentage relationship between the municipality’s water consumers and consumption is again reinforced—a few consumers are responsible for higher consumption values, and the errors related to their measuring devices can be the source of considerable apparent losses. Figure 17b shows that only 1% of the municipality’s consumers are responsible for 20% of the consumed water volume. Unbilled authorized consumption, corresponding to the volumes used by the munici- pality itself, could not be analyzed since no data were provided. However, it was included in the total demand in this study. This study contributes to a topic of recognized scientific relevance, given the com- plexity of water networks and their close relationship with future climate change, digital transition, and water scarcity challenges [7,8,22]. Water network losses are a common and severe worldwide difficult management problem since they involve intensive water– energy processes. Water 2021, 13, x FOR PEER REVIEW 20 of 23 5 20.686 11.8 6 19.790 12.4 7 13.153 14.8 8 12.543 20.4 9 7.519 16.8 10 7.253 13.8 Water 2023, 15, 1129 19 of 22 35 30 25 20 15 10 5 0 0-5 5-10 10-15 15-20 >20 Flow Measurement Devices - Age (years) (a) (b) FiguFrieg u17re. I1d7e.nIdtiefinctaitfiiocant ioofn corfitcirciatlic flaloflwo wmemteertse rasnadn dcocnosnusummeresr:s :(a(a) )aaggee diissttrriibbuuttiioonn ooff muunniicciippaallfl ow flowm meetetersrsa anndd( b(b) )c coonnsusummpptitoionn//ccoonnssuummeerrss ppeerrcceennttaaggee rreellaattiioonn.. UnbLilolessde asuitdheonrtiizfieedd coi nstuhme pcatisoens, tcuodrryesrpeponredsienngt taon thuen vsoaltuismfaecst oursyedf rbeysh thwea mteur nsiucp- ply ipalaitnyd itwsealfs,t ceoeunlder ngoyt [b2e0 a].nSaWlyzGesd, saisnacne ninot degatra twederel pemroevnidt eodf .s Hmoawrtecvietire, sit, wisahsi ignhclliugdhetedd as in thaen teowtalg deenmeraantdio inn othf ids isgtiutadlyw. ater management through the integration of information aTndhicso smtumduy nciocantiroibnus ttesc hton oal otgoipeisc (oICf Trse)cotognaiuzteodm sactieenthtiefimc orenlietvoarinncge,a ngidveconn tthroe lcoofmw-ater plexniteytw ofo rwkast.eTr hneetbwenoerkfist saonfdS tWheGirs calossae mrealantaiognemsheipnt wstirthat feugtyurthe actlirmeqautei rcehsaanngef, fidciigeintatla nd trancsoitmiopnl,e axnidm wplaetmere snctaartcioitny pchroaclleesnsgaerse [h7i,g8,h2l2ig].h Wteadtehre nreitnw[o1r2k] .losses are a common and severe wDorTlsdpwriodvei deiffiusceuflut lmdantagoenmaeWnt DpNro’bslleimfec syicnlcee, rtehperyo idnuvcoinlvge dinistreunpsitvioen wsacetenra–reino-s for ergyr epsrioliceenscseesa.s sessment purposes and analyzing asset prognosis and system efficiency to dLeotesrsmesi nideepnrtiofiaecdti vine tmhea cinasten satundcey/ rmeparneasgenemt aenn ut nmsaotdiseflasc.toTroy ofrfefesrhwanatefrf esuctpivpelys aonludt ion wasrteg eanredrignyg [2w0a].t eSrWloGsss,e ass, athne inptreogproatsed eDleTmmenotd oefl srmeqaurti rceitsiecso, nist ihniugohulisghatdejdu satsm ae netswa nd geneleraartinoin gofp driogcietasls wteacthern miqauneasgseumpepnotr tthedroubygha thlaer igneteagmraotuionnt ooff infifeolrdmdaatitoans atonrde dcoimn-big- mundiactaatipolnast ftoercmhns.ologies (ICTs) to automate the monitoring and control of water net- works. The benefits of SWGs as a management strategy that requires an efficient and com- 5. Conclusions plex implementation process are highlighted herein [12]. An integrated and complex methodology for evaluating WDN efficiency is presented. A real case study (Gaula WDN) is discussed and analyzed, identifying weak points in the system and potential improvements through a proposed DT model optimization. The steps of the proposed methodology are described and justified to create a benchmark for future sustainable developments related to water scarcity, climate change, water system performance efficiency, water–energy nexus control and improvements, and digital water transition. Pipe ruptures and high pressures are usually associated with high volumes of water losses, which is an important issue to consider when retrofitting water networks using DT Devices (%) Water 2023, 15, 1129 20 of 22 support. The Gaula WDN, according to municipal data, is an aged system with a large value of real losses, at around 79% of the system’s total water volume. For this investigation, the system’s characterization was identified in terms of configuration; a number of pipe branches, reservoirs, and PRVs; location and regulation of pressure reduction valves; inner diameters of the pipes; age of the system components; and the morphology and topography of the system. The types of losses were identified. Due to the large values of the unit head losses, replacing some pipes or complementing them with parallel pipes is suggested, showing the deleterious influence of low-diameter pipes. In addition, the network was divided into DMAs to control for anomalous consumption. Based on the developed DT model, several analyses were performed to identify opportunities for improving the pressure values of the WDN. Some relevant conclusions that can be drawn from the analyses undertaken in the current study are as follows: • Comparing the mean pressure observed in the average scenario of the status quo to the scenario with improvements (for the 50% of pressures below case), there is a reduction of 1.7 bar, where about 25% of the network’s junctions have pressure values above 60 m w.c. (i.e., the maximum regulatory value) in all analyzed scenarios (i.e., average, peak, and static). • By simulating the extreme pressure situation (static), when consumption and head- losses are considered non-existent, the obtained reduction in the mean pressure value is around 2 bar (for the 50% of pressures below case). • As for the junctions of the proposed network with pressures above the regulatory maximum of 60 m w.c., less than 4% are in this situation, and with the DMA sec- torization and the requalification of the network, the excess pressure problems are substantially mitigated. • With the implementation of the proposed methodology, only 40% of the total annual volume, concerning the status quo situation, would be necessary to supply the demand. • An indicator associated with real losses and potential reduction is the infrastructure leakage index (ILI), i.e., the ratio between the value of actual real losses and the “minimum” value of real losses (UARL). The infrastructure leakage index reaches a value higher than four (434,273/20,530 = 21.15), confirming that the network has great potential for real loss reduction. • Old measuring devices and/or those controlling significant consumption must be the first ones analyzed or replaced when necessary. • The relation between customers and consumption reinforces that only a few customers are responsible for large consumption. Only 1% of the municipality’s consumers are responsible for 20% of the consumed water volume. The methodology developed and applied in this study leads to a significant potential reduction in water leakage in the Gaula WDN, improving overall system efficiency. This can result in a meaningful economic benefit by saving about EUR 165k regarding water loss volume. The limitations for the practical implementation of this methodology are identified as follows: (i) reliability on data related to the system configuration, including the number of pipe branches, existent reservoirs/tanks, pump stations, existent pressure reduction valves (PRVs), location of them, type of regulation and sizes, inner diameters of the pipes, age of the system components, and morphology and topography of the system, which requires a close interaction between researchers, designers, and municipal management. On the other hand, all DT models and machine learning processes are essential for model calibration and application. However, a monetary figure does not quantify the important social and environmental benefits of a more efficient system, including avoiding supply interruption through the loss of valuable potable water. Water 2023, 15, 1129 21 of 22 Author Contributions: Conceptualization, H.M.R., P.I.-R. and A.K.; methodology, H.M.R. and P.I.-R.; validation, H.M.R., A.K. and P.I.-R.; formal analysis, H.M.R., M.B., E.C. and P.I.-R.; investigation, H.M.R. and E.C.; resources, H.M.R.; data curation, R.P. and A.K.; writing—original draft preparation, H.M.R., E.C. and P.I.-R.; writing—review and editing, H.M.R., E.C., E.T., M.B., O.E.C.-H., R.P. and A.K.; supervision, H.M.R. and P.I.-R. All authors have read and agreed to the published version of the manuscript. Funding: This research received a grant to the 2nd author through the funding UIDB/04625/2020 from the research unit CERIS. Data Availability Statement: The required data is presented in this research and no more data is available due to privacy and ethical restrictions from RSS (rss@netcabo.pt) company and Munici- pal Entity. Acknowledgments: The authors acknowledge the RSS (Redes e Sistemas de Saneamento, rss@netcabo.pt) for the data availability, support in DT analyses, graphs, and interpretation of the results. The authors are grateful for the Foundation for Science and Technology’s support to one author through the funding UIDB/04625/2020 from the research unit CERIS. Conflicts of Interest: The authors declare no conflict of interest. References 1. Giustolisi, O.; Savic, D.; Kapelan, Z. Pressure-driven demand and leakage simulation for water distribution networks. J. Hydraul. Eng. 2008, 134, 626–635. [CrossRef] 2. Al-Washali, T.; Sharma, S.; Kennedy, M. Methods of assessment of water losses in water supply systems: A review. Water Resour. Manag. 2016, 30, 4985–5001. [CrossRef] 3. Hommes, L.; Boelens, R. Urbanizing rural waters: Rural-urban water transfers and the reconfiguration of hydrosocial territories in Lima. Political Geogr. 2017, 57, 71–80. [CrossRef] 4. Ishiwatari, Y.; Mishima, I.; Utsuno, N.; Fujita, M. Diagnosis of the ageing of water pipe systems by water quality and structure of iron corrosion in supplied water. Water Sci. Technol. Water Supply 2013, 13, 178–183. [CrossRef] 5. Rokstad, M.M.; Ugarelli, R.M. Minimising the total cost of renewal and risk of water infrastructure assets by grouping renewal interventions. Reliab. Eng. Syst. Saf. 2015, 142, 148–160. [CrossRef] 6. Ociepa, E.; Mrowiec, M.; Deska, I. Analysis of water losses and assessment of initiatives aimed at their reduction in selected water supply systems. Water 2019, 11, 1037. [CrossRef] 7. Lambert, A.O.; Brown, T.G.; Takizawa, M.; Weimer, D. A review of performance indicators for real losses from water supply systems. J. Water Supply Res. Technol.—AQUA 1999, 48, 227–237. [CrossRef] 8. Abansi, C.L.; Hall, R.A.; Siason, I.M.L. Water demand management and improving access to water. In Water Policy in the Philippines; Springer: Berlin/Heidelberg, Germany, 2018; pp. 233–259. 9. Ramos, H.M.; Morani, M.C.; Carravetta, A.; Fecarrotta, O.; Adeyeye, K.; López-Jiménez, P.A.; Pérez-Sánchez, M. New challenges towards smart systems’ efficiency by digital twin in water distribution networks. Water 2022, 14, 1304. [CrossRef] 10. Arregui, F.J.; Cobacho, R.; Soriano, J.; Jimenez-Redal, R. Calculation proposal for the economic level of apparent losses (ELAL) in a water supply system. Water 2018, 10, 1809. [CrossRef] 11. Fabbiano, L.; Vacca, G.; Dinardo, G. Smart water grid: A smart methodology to detect leaks in water distribution networks. Measurement 2020, 151, 107260. [CrossRef] 12. Ramos, H.M.; McNabola, A.; López-Jiménez, P.A.; Pérez-Sánchez, M. Smart water management towards future water sustainable networks. Water 2020, 12, 58. [CrossRef] 13. Alzamora, F.M.; Carot, M.H.; Carles, J.; Campos, A. Development and Use of a Digital Twin for the Water Supply and Distribution Network of Valencia (Spain). In Proceedings of the 17th International Computing & Control for the Water Industry Conference, Exeter, UK, 1–4 September 2019. 14. Germanopoulos, G.; Jowitt, P.W. Leakage reduction by excess pressure minimization in a water supply network. Proc. Inst. Civ. Eng. 1989, 87, 195–214. [CrossRef] 15. Galdiero, E.; De Paola, F.; Fontana, N.; Giugni, M.; Savic, D. Decision support system for the optimal design of district metered areas. J. Hydroinform. 2015, 18, 49–61. [CrossRef] 16. Curl, J.M.; Nading, T.; Hegger, K.; Barhoumi, A.; Smoczynski, M. Digital twins: The next generation of water treatment technology. J. Am. Water Work. Assoc. 2019, 111, 44–50. [CrossRef] 17. Conejos Fuertes, P.; Martínez Alzamora, F.; Hervás Carot, M.; Alonso Campos, J.C. Building and exploiting a Digital Twin for the management of drinking water distribution networks. Urban Water J. 2020, 17, 704–713. [CrossRef] 18. Eggimann, S.; Mutzner, L.; Wani, O.; Schneider, M.Y.; Spuhler, D.; De Vitry, M.M.; Beutler, P.; Maurer, M. The potential of knowing more: A review of data-driven urban water management. Environ. Sci. Technol. 2017, 51, 2538–2553. [CrossRef] [PubMed] 19. Mekonnen, M.M.; Hoekstra, A.Y. Four billion people facing severe water scarcity. Sci. Adv. 2016, 2, e1500323. [CrossRef] [PubMed] 20. Bauer, P.; Stevens, B.; Hazeleger, W. A digital twin of Earth for the green transition. Nat. Clim. Chang. 2021, 11, 80–83. [CrossRef] Water 2023, 15, 1129 22 of 22 21. Xiang, X.; Li, Q.; Khan, S.; Khalaf, O.I. Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environ. Impact Assess. Rev. 2021, 86, 106515. [CrossRef] 22. Manny, L. Socio-technical challenges towards data-driven and integrated urban water management: A socio-technical network approach. Sustain. Cities Soc. 2022, 90, 104360. [CrossRef] 23. Plano Estratégico de Abastecimento de Água e Saneamento de Águas Residuais 2020 (PENSAAR 2020). Available online: https://apambiente.pt/agua/plano-estrategico-de-abastecimento-de-agua-e-saneamento-de-aguas-residuais-2020 (accessed on 19 October 2020). Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.