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.

Water scarcity and the low quality of wastewater produced in industrial applications present significant challenges, particularly in managing fresh water intake and reusing residual quantities. These issues affect various industries, compelling plant owners and managers to optimise water resources within their process networks. To address this cross-sector business requirement, we propose a Decision Support System (DSS) designed to capture key network components, such as inlet streams, processes, and outlet streams. Data provided to the DSS are exploited by an optimisation module, which supports both network design and operational decisions. This module is coupled with a generic mixed-integer nonlinear programming (MINLP) model, which is linearised into a compact mixed-integer linear programming (MILP) formulation capable of delivering fast optimal solutions across various network designs and input parameterisations. Additionally, a Constraint Programming (CP) approach is incorporated to handle nonlinear expressions through straightforward modeling. This state-of-the-art generalised framework enables broad applicability across a wide range of real-world scenarios, setting it apart from the conventional reliance on customised solutions designed for specific use cases. The proposed framework was tested on 500 synthetic data instances inspired by historical data from three case studies. The obtained results confirm the validity, computational competence and practical impact of our approach both among their operational and network design phases, demonstrating significant improvements over current practices. Notably, the proposed approach achieved a 17.6% reduction in freshwater intake in a chemical industry case and facilitated the reuse of nearly 90% of wastewater in an oil refinery case.