Digital twins and AI: the future of efficiency and security in seawater desalination plants
Water scarcity remains one of the most urgent global challenges, with more than two billion people lacking access to safe drinking water. Seawater desalination, particularly reverse osmosis, has become an indispensable technology to address this crisis. However, desalination plants face two fundamental hurdles: the high energy demand, which can represent up to 70% of operating costs, and their vulnerability as critical infrastructure, making cybersecurity a pressing priority.
Tedagua’s digital twin initiative was designed to confront these challenges directly. The project integrates advanced technologies such as artificial intelligence, Internet of Things (IoT), cloud services, and edge computing to deliver smarter, more secure, and more efficient operations. At the core lies a digital twin enhanced with AI capabilities, capable of simulating, predicting, and optimising desalination processes continuously.
Tedagua’s digital twin initiative aimed to create a standardised technological ecosystem to optimise reverse osmosis desalination plants
Launched under the Red.es 2021 Call for Proposals for R&D projects in artificial intelligence and other digital technologies and their integration into value chains, and co-financed by NextGenerationEU funds, Tedagua’s Digital Twin Platform for Desalination Plants (2022–2024) has demonstrated that efficiency and security can advance hand in hand in critical water infrastructure.
Project objectives and scope
The overarching aim was to create a standardised technological ecosystem to optimise reverse osmosis desalination plants. This ecosystem integrates IoT devices, advanced data analysis tools, evaluation criteria, and operational protocols, ensuring improved efficiency while reinforcing cybersecurity. Real data from several Tedagua desalination plants was used for validation.
The general goal was supported by three interrelated specific objectives:
- Advanced artificial intelligence application: Use of machine learning, deep learning, and neural networks for predictive analysis and real-time optimisation, supported by big data technologies, high-performance computing, cloud services, and natural language processing for technical documentation.
- Identification of operational improvement points: Systematic detection of optimisation opportunities through detailed process analysis, with emphasis on environmental sustainability, particularly energy consumption, and enhancing cybersecurity.
- Development of a continuous improvement philosophy: Establishment of a framework to guide ongoing process evolution across the plant lifecycle, based on identifying complex patterns emerging from continuous data analysis.
Technological architecture of the solution
Tedagua collaborated with leading partners: Grant Thornton, Universidad Pablo de Olavide, Tecnalia, and Quantia.
Digital twin platform
At the core of the solution is a Digital Twin platform that virtually represents all components and processes of a desalination plant. Using high-fidelity 3D modelling, it provides detailed representations of facilities and equipment, drawing on existing 3D models from Tedagua’s desalination plants. Real-time inputs from IoT sensors keep the model synchronised with the actual state of the facility, while its role as a dynamic knowledge base ensures that new data, detected patterns, and lessons learned are continuously incorporated into operations.
Built on the digital twin, an artificial intelligence layer enables intelligent process analysis and predictive capabilities
Artificial intelligence layer
Built on the digital twin, this layer enables intelligent process analysis and predictive capabilities. Machine learning models detect anomalies and optimise parameters, while deep neural networks uncover complex relationships in operational data. Simulation environments further allow operators to test scenarios safely before applying changes to real operations.
Supporting Infrastructure
The platform is supported by a robust infrastructure designed for scalability and reliability. Cloud computing provides the capacity for intensive data processing and scalable storage of both historical and real-time information. Edge computing complements this by reducing latency and enabling immediate responses to critical operating conditions. Finally, a microservices architecture ensures a modular design, allowing components to evolve independently and new functionalities to be integrated without compromising system stability.
Implementation approach
Tedagua developed the platform following a structured methodology divided into five interconnected work packages. Each package tackled a different aspect of the system, ensuring both robustness and adaptability.
Automated information system
The first work package focused on reliable data acquisition. A network of IoT sensors was deployed to monitor key parameters such as pressure, flow, energy consumption, and water quality. Data transmission followed secure industrial protocols to guarantee confidentiality and integrity. Real-time analytics enabled rapid detection of anomalies, allowing operators to respond promptly.
Digital twin development
The second work package concentrated on building the digital twin itself. By integrating Building Information Modeling (BIM) practices, Tedagua ensured accurate, continuously updated digital representations. Bidirectional synchronisation mechanisms allowed real-world data to refresh the digital model automatically, while proposed operational adjustments could be simulated virtually before implementation.
Resilient data architecture
The third work package addressed data management. A decentralised architecture distributed information across multiple nodes, eliminating single points of failure. Horizontal scalability allowed capacity expansion as more data or plants were integrated. Redundancy and automatic recovery protocols were introduced to guarantee the uninterrupted availability of critical systems.
Pattern detection system
The fourth work package introduced advanced analytical tools. Time series analysis revealed trends and anomalies in operational variables. Correlation mapping uncovered relationships between variables not immediately apparent, while predictive simulations enabled foresight into potential operational issues before they occurred.
Process optimization
The final work package focused on transforming data and insights into action. Adaptive algorithms dynamically adjusted operating conditions to minimise energy use while ensuring water quality. Interactive dashboards provided operators with an intuitive view of the plant’s status. The system also generated AI-driven recommendations, offering concrete strategies for improvement based on ongoing performance monitoring.
A network of IoT sensors was deployed to monitor key parameters such as pressure, flow, energy consumption, and water quality
Results achieved
The implementation of the digital twin platform has delivered measurable benefits across energy efficiency, cybersecurity, and decision-making. These outcomes confirm the value of combining advanced analytics with real-time operational data in desalination plants.
Energy efficiency gains
The platform delivered notable reductions in energy consumption, which directly lowered operating costs. AI models optimised operating pressure, reducing energy demand without compromising output quality. Demand forecasting tools allowed scheduling that aligned with lower electricity tariffs. Predictive maintenance reduced unplanned downtime, extending membrane life and ensuring efficiency remained consistent.
Strengthened cybersecurity
The system significantly enhanced cybersecurity. Machine learning models continuously scanned for abnormal behaviour that could indicate cyber intrusions. Network segmentation isolated critical systems, limiting exposure. Automated response protocols further reduced vulnerability by neutralising threats in real time, often without requiring operator intervention.
Smarter decision making
The platform transformed decision-making into a data-driven process. Operators gained comprehensive visibility over plant performance through unified dashboards. Scenario simulation tools enabled informed “what-if” analysis, minimising risks associated with operational decisions. Moreover, the platform served as a living repository of institutional knowledge, ensuring that expertise and lessons learned remain available for future use.
Broader implications for the desalination industry
The project represents a significant shift in the management of desalination plants, moving them from reactive operations to proactive, intelligent systems. This transformation not only improves current performance but also establishes a model for how desalination facilities can evolve in the coming years, with greater capacity to anticipate challenges and adapt to changing conditions.
The implementation of the digital twin has delivered measurable benefits across energy efficiency, cybersecurity, and decision-making
Another important outcome is the solution’s scalability and replicability. Thanks to its modular architecture and the use of open standards, the platform can be adapted to desalination plants of different sizes and complexities, from small municipal systems to large industrial facilities. This flexibility positions Tedagua to extend the benefits of the project across its global portfolio, offering a solution that can be tailored to diverse operational contexts.
At the same time, several factors must be considered for successful adoption. Digital transformation requires a structured approach to change management, including training and support for plant operators. While the investment in infrastructure and development is significant at the outset, the long-term savings and improvements in resilience make the case compelling. Equally important is ensuring compatibility with existing systems, such as SCADA and CMMS, to minimise disruption during the transition to this new way of operating.
Conclusions and future directions
The Digital Twin Platform for Desalination Plants demonstrates that advanced AI and digital twin technologies can radically enhance the efficiency, resilience, and security of desalination facilities.
Looking ahead, Tedagua will expand AI capabilities to integrate external variables such as supply chains, energy markets, and waste management. The integration of renewable energy sources into plant operations is also a priority, aligning desalination processes with sustainability goals.
By combining operational efficiency with environmental responsibility and cybersecurity, Tedagua has established a new benchmark for intelligent water management. This project not only addresses current challenges but also lays the foundation for future advances in the desalination industry, positioning Tedagua at the forefront of digital innovation.
