Digital Twins Reshape the Future of Water Infrastructure Efficiency
Water utilities sit at the intersection of two growing pressures: the need to secure reliable drinking water supplies and the rising cost of energy required to treat and deliver it. Against this backdrop, researchers at Oak Ridge National Laboratory are advancing a new approach that blends digital intelligence with physical infrastructure to improve efficiency without compromising safety.
Working with partners including University of California Irvine and Orange County Water District, the team has developed a paired system combining a real-world treatment facility with a continuously updated digital counterpart. This arrangement, often referred to as a digital twin, allows water treatment processes to respond dynamically to changing conditions such as electricity pricing and operational demands. Rather than relying on static operating schedules, the system introduces a level of adaptability that could redefine how utilities manage both cost and performance.
Briefing
- Digital twin technology is being applied to water treatment to reduce energy consumption and operational costs
- Real-time feedback between physical and virtual systems enables continuous optimisation
- The pilot project operates within a live municipal water reuse environment
- Data-driven models replace traditional computationally intensive simulations
- The approach could scale across desalination, reuse, and conventional water treatment systems
A Shift Towards Intelligent Water Operations
Water treatment has traditionally been a rigid, energy-intensive process. Facilities often operate at fixed flow rates, with manual intervention required to respond to fluctuations in demand or cost. This structure has long limited efficiency gains, particularly as electricity prices become more volatile in increasingly complex energy markets.
The introduction of digital twin systems signals a notable shift. By linking a physical treatment plant with a virtual model that continuously monitors performance, operators gain the ability to fine-tune processes in near real time. The virtual model analyses incoming data, including energy pricing and system performance, and adjusts operational parameters accordingly. This feedback loop enables facilities to operate more intelligently, aligning production with cost conditions without disrupting output quality.
At a broader level, this reflects a growing trend across infrastructure sectors. Digital twins are already being deployed in transport networks, manufacturing, and urban planning. Their application in water infrastructure, however, carries particular significance given the sectorβs heavy energy footprint. According to the International Energy Agency, water and wastewater systems account for a substantial share of municipal energy use worldwide, making efficiency gains both economically and environmentally valuable.
From Simulation to Real-Time Decision Making
Conventional digital twin models often rely on complex physics-based simulations. While effective in controlled environments, these systems can be computationally demanding and slow to deploy. They typically require extensive datasets and long calibration periods, limiting their practicality for many utilities.
The ORNL-led approach departs from this model. Instead of building exhaustive simulations, the researchers developed streamlined, data-driven algorithms capable of predicting system behaviour using a smaller set of operational inputs. This allows the digital twin to respond more quickly and operate with reduced computational overhead.
βDigital twins are increasingly used as platforms for safely testing how new approaches affect complex systems,β said Subrata Mukherjee, who leads the project for ORNL. βThis project pairs a digital twin with a physical system, so they provide constant feedback to each other while operating. This unique approach supports data-driven decision making for water utility owners and operators.β
This distinction is more than technical nuance. Faster deployment and lower computational requirements make the technology accessible to a wider range of utilities, including those without advanced modelling capabilities. It also enhances responsiveness, allowing systems to adapt to hourly or even more frequent changes in operating conditions.
Real-World Validation in Orange County
The projectβs credibility rests on its application within an operational environment. Hosted by the Orange County Water District, the pilot system serves as a scaled-down representation of a full drinking water reuse facility. This controlled yet realistic setting allows researchers to test the digital twin under conditions that closely mirror real-world operations.
The pilot plant, developed and managed by University of California Irvine, demonstrates how dynamic control can influence system performance. By adjusting flow rates in response to changing electricity prices, the digital twin effectively shifts energy-intensive processes to more cost-efficient periods. Over time, this can translate into meaningful savings for utilities, particularly in regions with time-of-use energy pricing.
Importantly, the system operates without compromising water quality or reliability. Maintaining regulatory compliance remains a non-negotiable requirement for utilities, and the digital twin must operate within these constraints. Early indications suggest that intelligent optimisation can coexist with strict safety standards, opening the door to wider adoption.
Implications for Infrastructure and Investment
The significance of this development extends beyond water utilities. Infrastructure investors and policymakers are increasingly focused on systems that deliver both resilience and efficiency. Digital twins align with this objective by providing a platform for continuous improvement without the need for major physical upgrades.
In financial terms, the ability to reduce operating costs while extending asset life is particularly attractive. Energy represents a major share of operational expenditure for water utilities. Even modest reductions can have a substantial impact over time, especially when scaled across large networks.
From a policy perspective, the technology supports broader sustainability goals. Governments worldwide are seeking to reduce emissions and improve resource efficiency. Water systems, often overlooked in energy discussions, represent a significant opportunity. By integrating digital intelligence, utilities can contribute to climate targets while enhancing service delivery.
The involvement of organisations such as the National Alliance for Water Innovation underscores the strategic importance of the project. Backed by the US Department of Energy and regional agencies including the California Department of Water Resources, the initiative reflects a coordinated effort to modernise water infrastructure through innovation.
Scaling Beyond a Single Application
One of the most compelling aspects of the approach lies in its adaptability. While the current pilot focuses on water reuse, the underlying methodology can be applied to a wide range of treatment systems. Desalination plants, wastewater facilities, and conventional drinking water systems all share similar operational challenges, particularly in balancing energy use with performance requirements.
The flexibility of data-driven models makes them suitable for diverse environments. Unlike highly specialised simulations, which may need to be rebuilt for each application, the ORNL approach can be tailored with relatively modest adjustments. This opens the possibility of widespread deployment across different geographies and infrastructure types.
As water scarcity intensifies in many regions, the need for efficient treatment solutions will only grow. Technologies that enable smarter operation without extensive capital investment are likely to gain traction. Digital twins, in this context, offer a pathway to incremental yet impactful improvements.
A Data-Led Future for Water Utilities
The evolution of water infrastructure is increasingly tied to digital transformation. Sensors, data analytics, and automation are becoming integral components of modern systems. Digital twins represent a convergence of these technologies, providing a unified platform for monitoring, analysis, and control.
For operators, this translates into greater visibility and control over complex processes. For investors, it offers a means of enhancing asset performance and reducing risk. And for policymakers, it provides a tool for achieving sustainability objectives without compromising service quality.
As the ORNL project demonstrates, the transition to intelligent infrastructure does not require a complete overhaul of existing systems. Instead, it builds on current assets, layering digital capabilities to unlock new efficiencies. In doing so, it points towards a future where infrastructure is not only built to last but designed to learn and adapt over time.
