Getting digital twins right in wastewater operations

Water and wastewater utilities across the United States are operating in an environment of accelerating complexity. Advances in sensor technology, cell-enabled data transmission, and computational modeling have created the conditions for a new class of operational tools. Sensors are smaller and less expensive than they once were, and the range of parameters that utilities can continuously monitor has expanded considerably.

At the same time, operational demands are increasing. Aging infrastructure, workforce turnover, and growing expectations from the communities we serve are compressing the margin for error in day-to-day system management. For utilities, these pressures manifest as a need for better situational awareness, faster response times to system events, and more defensible operational decision-making.

Without tools that reflect how systems are performing in real time, utilities risk making decisions based on outdated information, responding to failures rather than anticipating them, and losing operational knowledge as experienced staff leave the workforce.

The digital twin value proposition

The term "digital twin" has entered the vocabulary of utility management with growing frequency, and the interest is well-founded. As sensor networks expand, data transmission costs fall, and computational modeling becomes more accessible, utilities are right to ask how these capabilities can be put to work in day-to-day operations.

A clear, working definition is the starting point: A digital twin is a model of a system that is continuously connected to live data from that system. It ingests operational information, including pressures, flows, tank levels, pump status, and biological indicators, and uses those data to maintain a running representation of system conditions.

The model interpolates between measurement points, applying the governing hydraulics of the system to characterize conditions that are not directly measured. By connecting operational-grade models to live data, utilities gain a continuously updated picture of how the system is performing and where it is trending, a capability that static, planning-level tools cannot provide.

Where utilities are finding value today

Extracting operational value from technology requires a clear understanding of what it enables and how to integrate it into existing workflows and decision-making processes. Without that integration, even a well-calibrated digital twin risks becoming a tool that engineering staff consult periodically rather than a resource that informs day-to-day operations.

The most immediate benefit for most utilities is direct: knowing what is happening in the system before a customer picks up the phone to complain. When a main breaks and pressure drops, most utilities learn of the failure through calls and social media posts from the public; a digital twin changes that dynamic by flagging anomalous behavior across a pressure zone before it surfaces externally. This allows staff to respond to the public with confidence that the condition has been identified and is being addressed.

The continuous nature of the model also enables capabilities that static tools cannot support, including the ability to simulate operational changes, predict near-term demands, and test mitigation scenarios against a live system state rather than a theoretical baseline. By integrating these capabilities into routine operations, utilities can shift from reactive management to a posture of proactive situational awareness, with material benefits for service reliability, energy consumption, and public trust.

The institutional knowledge problem

The challenge of workforce continuity adds another dimension to the value proposition for digital twins. Utilities across the country are working through the same demographic reality: long-tenured operators who have spent decades learning the particular behaviors of specific systems are retiring, and the workforce entering the industry is smaller, less experienced, and less likely to remain at a single utility for the length of a career. Much of what keeps complex systems running reliably is not documented in any formal record. It resides in the accumulated experience of individuals who know how a specific pump behaves under load, or where a particular weather pattern might cause system surcharges.

Without structured mechanisms to capture and transfer that knowledge, utilities face growing operational exposure as experienced staff leave. To address this, a well-built, well-calibrated digital twin can encode some of that operational logic within the model itself. When the behavior of a lift station under peak flow, or an aeration basin following a chemical feed adjustment, is observable and documented in the twin, a newer operator can develop a working understanding of system dynamics without depending solely on years of hands-on experience to acquire it. By embedding institutional knowledge in a living, continuously updated model, utilities can protect operational continuity in ways that written documentation alone cannot achieve.

The path to implementation

The challenges of implementing a digital twin should not be understated. Many utilities are still working to assess the reliability of their sensor networks, validate the consistency of their data pipelines, and confirm that the right parameters are being measured at the right locations. Selecting and configuring appropriate modeling platforms requires technical expertise and ongoing investment. Staffing constraints often limit the internal capacity available to design, build, and maintain a working system.

Moreover, utilities must align the tool to the needs of multiple user groups, since an engineering team using a digital twin to prioritize capital investments requires different outputs and interfaces than an operations team managing a chemical feed system in real time. An application that does not match how its users work will not get used.

These realities demand a disciplined and sequential approach: establishing a reliable data foundation before building the model, beginning with a discrete and well-bounded system such as a single lift station or aeration basin, building that first implementation to a working standard before expanding scope, and defining clear success criteria at each stage so that lessons learned carry forward. There is a meaningful economy of scale once the data infrastructure and organizational familiarity are in place; the marginal cost of adding a second process, then a third, is substantially lower than the investment required to build the first.

The case that matters most

For utilities evaluating where digital twin investment delivers the greatest return, emergency management is the most compelling answer. Complex infrastructure systems will experience failure; that is not a risk to be eliminated but a certainty to be managed. When a significant system upset occurs, the ability to run mitigation scenarios against a live system state changes the character of the response entirely. Operators can determine what happens to an upset treatment train when an adjustment is made, what flow looks like at downstream stations under current conditions, and where the system is most vulnerable before any field decision is made. In that moment, a calibrated digital twin provides a window into system behavior that would otherwise be opaque under stress, reducing the need to rely on trial and error under an emergency response scenario.

Prevention remains the primary operational objective, but utilities increasingly recognize that prevention alone is not a complete strategy. Infrastructure fails, contamination events occur, and weather imposes conditions that no maintenance program fully anticipates. The ability to respond quickly and intelligently to a system upset, with scenario-testing capability grounded in a live system state rather than a static baseline, is a core operational requirement today.

The technology to support that capability exists. For utilities facing the compounding pressures of today's operating environment, the message is clear: a digital twin is not a technology experiment but a core operational capability, and it is one now within reach of utilities ready to pursue it with planning, transparency, and discipline.