As industrial water demand continues to grow, effective monitoring and management become critical for sustainable resource use. This paper explores how Digital Twins can enhance industrial water management by integrating insights from existing research with findings from five real-world case studies. We derive common functional and non-functional requirements, as well as relevant Key Performance Indicators, based on industry needs. Guided by the ISO 23247 standard, we propose a Digital Twin architecture and a supporting data model designed for water-related applications. The resulting framework helps bridge theoretical approaches with practical challenges, offering a foundation for implementing “Digital Water” solutions. Industry stakeholders and analysts can also use the identified use cases and Key Performance Indicators to align Digital Twin implementations with strategic objectives.
van VlietMTJonesERFlörkeM, et al.Global water scarcity including surface water quality and expansions of clean water technologies. Environ Res Lett2021; 16: 024020.
2.
SpiraDRaoK. Water tight 3.0 - the top issues in the global water sector, 2021.
3.
LevinsonMLeeEChungJ, et al.Global equity research: Watching water a guide to evaluating corporate risks in a thirsty world, 2008.
4.
FigueiredoIEstevesPCabritaP. Water wise–a digital water solution for smart cities and water management entities. Procedia Comput Sci2021; 181: 897–904.
5.
MounceSPedrazaCJacksonT, et al.Cloud based machine learning approaches for leakage assessment and management in smart water networks. Procedia Eng2015; 119: 43–52.
6.
KrauseAPerciavallePJohnsonK. Intelligent water platforms for water abundance. GE Retrie November2017; 14: 2019.
7.
OwenDAL. Smart water technologies and techniques: data capture and analysis for sustainable water management. Hoboken, New Jersey, US: John Wiley & Sons, 2018.
8.
JhaMKSahRKRashmithaM, et al.Smart water monitoring system for real-time water quality and usage monitoring. In: 2018 International conference on inventive research in computing applications (ICIRCA), 2018, pp.617–621. IEEE.
9.
SenguptaPK. Industrial Water Resource Management: Challenges and Opportunities for Corporate Water Stewardship. Hoboken, New Jersey, US: John Wiley & Sons, 2017.
10.
WangZSongHWatkinsDW, et al.Cyber-physical systems for water sustainability: challenges and opportunities. IEEE Commun Maga2015; 53: 216–222.
11.
Moreno SantamariaBdel Ama GonzaloFPinetteD, et al.Application and validation of a dynamic energy simulation tool: A case study with water flow glazing envelope. Energies2020; 13: 3203.
12.
GongLYanJChenY, et al.An iot-based intelligent irrigation system with data fusion and a self-powered wide-area network. J Indust Inform Integr2022; 29: 100367.
13.
JiHLiJZhangS, et al.Research on water resources intelligent management of thermal power plant based on digital twins. In: 2021 IEEE 6th International conference on cloud computing and big data analytics (ICCCBDA), 2021, pp.557–562. IEEE.
14.
AtouiMACohenA. Coupling data-driven and model-based methods to improve fault diagnosis. Comput Indust2021; 128: 103401.
15.
GuptaADPandeyPFeijóoA, et al.Smart water technology for efficient water resource management: A review. Energies2020; 13: 6268.
16.
GrievesM. Digital twin: manufacturing excellence through virtual factory replication. White paper2014; 1: 1–7.
17.
Al FaruqueMAMuthirayanDYuSY, et al.Cognitive digital twin for manufacturing systems. In: 2021 Design, automation & test in Europe conference & exhibition (DATE), 2021, pp.440–445. IEEE.
18.
BécueAMaiaEFeekenL, et al.A new concept of digital twin supporting optimization and resilience of factories of the future. Appl Sci2020; 10: 4482.
19.
SharmaAKosasihEZhangJ, et al.Digital twins: State of the art theory and practice, challenges, and open research questions. J Indust Inform Integ2022; 30: 100383.
20.
BanerjeeSJesubalanNGKulkarniA, et al.Developing cyber-physical system and digital twin for smart manufacturing: Methodology and case study of continuous clarification. J Indust Inform Integr2024; 38: 100577.
21.
Conejos FuertesPMartínez AlzamoraFHervás CarotM, et al.Building and exploiting a digital twin for the management of drinking water distribution networks. Urban Water J2020; 17: 704–713.
22.
PedersenANBorupMBrink-KjærA, et al.Living and prototyping digital twins for urban water systems: towards multi-purpose value creation using models and sensors. Water2021; 13: 592.
23.
WangJLiSXiongG, et al.Application of digital technologies about water network in steel industry. Resou Conserv Recy2011; 55: 755–759.
24.
LeeSWSarpSJeonDJ, et al.Smart water grid: the future water management platform. Desal Water Treat2015; 55: 339–346.
25.
RadhakrishnanVWuW. Iot technology for smart water system. In: 2018 IEEE 20th International conference on high performance computing and communications; IEEE 16th International conference on smart city; IEEE 4th International conference on data science and systems (HPCC/SmartCity/DSS), 2018, pp.1491–1496. IEEE.
26.
BoulosPFWalkerAT. Fixing the future of wastewater systems with smart water network modeling. J-Am Water Works Assoc2015; 107: 72–80.
27.
RaoPMcKaneAde FontaineA. Energy savings from industrial water reductions, 2015.
28.
WalshBPCusackDOO’SullivanD. An industrial water management value system framework development. Sustain Product Consum2016; 5: 82–93.
29.
VatikiotisSAvgerinosIPlitsosS, et al.A decision support system for optimised industrial water management. Expert Syst Appl2025; 271: 126673.
30.
RamosHMMoraniMCCarravettaA, et al.New challenges towards smart systems’ efficiency by digital twin in water distribution networks. Water2022; 14: 1304.
31.
ChacónECruz SalazarLACardilloJ, et al.A control architecture for continuous production processes based on industry 4.0: water supply systems application. J Intell Manuf2021; 32: 2061–2081.
32.
PhamTTMaiTDPhamTD, et al.Industrial water mass balance as a tool for water management in industrial parks. Water Resou Indust2016; 13: 14–21.
33.
MartinsMAAmaroCASouzaLS, et al.New objective function for data reconciliation in water balance from industrial processes. J Clean Prod2010; 18: 1184–1189.
34.
KlemešJJ. Industrial water recycle/reuse. Curr Opin Chem Eng2012; 1: 238–245.
35.
Mohammad RozaliNEWan AlwiSRMananZA, et al.Process integration for hybrid power system supply planning and demand management – a review. Renew Sustain Energy Rev2016; 66: 834–842.
36.
CreswellJWPothCN. Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, California, US: Sage publications, 2016.
37.
ZampouEPlitsosSKaragiannakiA, et al.Towards a framework for energy-aware information systems in manufacturing. Comput Indust2014; 65: 419–433.
38.
SeigerRMalburgLWeberB, et al.Integrating process management and event processing in smart factories: A systems architecture and use cases. J Manuf Syst2022; 63: 575–592.
39.
KarkouETeoCJSavvakisN, et al.Industrial circular water use practices through the application of a conceptual water efficiency framework in the process industry. J Environ Manage2024; 370: 122596.
40.
BonillaCAZanfeiABrentanB, et al.A digital twin of a water distribution system by using graph convolutional networks for pump speed-based state estimation. Water2022; 14: 514.
41.
YuWPatrosPYoungB, et al.Energy digital twin technology for industrial energy management: Classification, challenges and future. Renew Sustain Energy Rev2022; 161: 112407.
42.
NiggemannODiedrichAKühnertC, et al.A generic digitaltwin model for artificial intelligence applications. In: 2021 4th IEEE International conference on industrial cyber-physical systems (ICPS), 2021, pp.55–62. IEEE.
43.
KartakisSAbrahamEMcCannJA. Waterbox: A testbed for monitoring and controlling smart water networks. In: Proceedings of the 1st ACM International workshop on cyber-physical systems for smart water networks, 2015, pp.1–6.
