On the way to the customer’s tap, many water utilities are losing an estimated 20% to 35% of the fresh, treated drinking water they produce.
Source: Nickel Institute5
Can you further explore how data management and high-quality data help water utilities address system challenges such as waste?
David Rawlinson: When demand increases but supply is constrained, attention naturally turns to being more careful about waste, such as transmission losses. This means that we need to measure more carefully and pay better attention to anomalies and trends to find and prevent losses. Accurate and detailed data can reduce waste and improve efficiency.
Anna Dahlman Petri: In order to reduce waste, it is necessary to see where waste is occurring or where it is likely to happen in the system. This need points to one of the greatest challenges facing all water utility owners—almost all the water distribution system is underground, making it much more difficult to spot and fix any issues, often requiring costly digging operations and road closures.
In view of this reality, the best operational method is not to work on the physical network first but to create a model, such as a digital twin, of the water distribution network. A high-quality database vastly reduces the cost of creating a model while increasing the usefulness of the model in forecasting anything from probable weak pipes to network performance during predicted peak loads.
Once it is finished, a model is a great tool for developing reliable scenarios for decision making. These scenarios help us to understand possible impacts to the surroundings and what customers might experience in the case of a breakdown in aging infrastructure. Scenario development enables us to be prepared and take measures at the right time and place to mitigate potential damages. The consequences of a network failure might be anything from physical damages due to erosion from underground leakage, to the societal costs of leaving people without access to clean water.
Data management is therefore itself an effective tool to avoid time-consuming investigations and the need to rehabilitate damages. And no tool is better than its input data.
Matthew Gallaugher: The preventative role of data management is significant; more effective management of aging infrastructure can provide high value and reduce risks for asset owners. In fact, the data demands of aging infrastructure may be greater than those of modern infrastructure.
First, aging infrastructure suffers a comparative lack of sensing and measurement devices designed into the system—sensors have become much cheaper and more readily networked, meaning that we can automatically have a continual stream of relevant measurements. In older systems, retrofitted sensors, or ad-hoc measurement procedures, typically make data more indirectly or sparsely acquired. This situation creates a more heterogeneous and complex dataset.
Second, with infrastructure that has aged, there is greater uncertainty about the condition of the infrastructure due to material degradation and damage caused by external environmental factors over time. We generally know less about material quality and processes of older constructions, and quality control was less consistent. This means we need to generate new, additional data to measure these things in-situ or try to infer endpoints directly without having a precise model of the infrastructure.
Both these factors tend to require more complex and bespoke inference to gain a clear and accurate picture of the infrastructure. In a modern system, data streams neatly into a well- designed database with little technical debt. However, in older systems, a patchwork of different technologies provide data to various legacy systems; as discussed earlier, harmonizing data can be a significant challenge. Often, the first task is to provide digital infrastructure—such as databases—that capture all key data sources and present a coherent, unified view. We also need to keep the data up-to-date as new measurements arrive, performing curation and quality control. Frequently, the data remains fragmented, or is only periodically re-integrated with other sources. That is why digital maturity indices emphasize continual demonstration of data provenance, versioning, backup, and disaster recovery. None of these things are possible without good data management.
Morten Engedal Sørensen: Aging infrastructure certainly brings greater complexity. The expected conditions of the infrastructure must be validated from insights based on relevant data collected from different sources.
By correlating data that conveys the age of the pipes with data about soil composition, groundwater level, vegetation, and measured flow, it is possible for data scientists to provide a valid understanding of the condition of the infrastructure. In addition, retrieving data from the surrounding SCADA and IoT systems and applying the logic and rules from the data model paves the way for the application of artificial intelligence and machine learning; this process leads to valuable predictions about the current state of the assets underground and provides vital information for supporting maintenance decisions or replacement activities.
What role does data have in helping utility operators control costs and manage demand?
Matthew Gallaugher: Not all demands are equally important, and demand overall is elastic. Generally speaking, demand emerges from a population of users who have different needs and requirements. Data is critical to understanding these users, when and where their demand varies and how responsive the population is to various price levels at different times. Input costs such as electrical power also vary over time, meaning that optimum management is a juggling act of forecasting demand to adjust pricing. For many reasons, an element of uncertainty is unavoidable, which means that a variety of outcomes must be considered and “priced in” at all times.
Anna Dahlman Petri: A key challenge that water utilities face is they must be able to deliver on peak days when the demand can be twice the average. If that peak could be predicted and managed, the risk of failure would be mitigated, and the size and complexity of the water system could be optimized.
Accurate, real-time data could be used to create price models benefitting all parties—of course, with regulations in place and relevant data available; for example, a forecast can lead to a reduced price during windy or sunny days when sustainable electricity is more abundant, allowing industry to optimize their usage, and thereby helping the overall network.
A good understanding of the demand base and the actual supply can also allow for more accurate tariff rates on a more granular basis. This is especially useful when considering areas with the most-water-intensive industries, which tend to be more responsive to water price adjustments.
Morten Engedal Sørensen: Within the network, the key indicators concerning flows and pressures in all parts of the network are known to the utility operators by collecting data from the many infrastructural components in many different locations. By using the data, it is possible to make corrections to the parameters in the network and thereby take different actions to adjust the water supply to flow to particular areas or sites.
To be cost-effective, it is necessary to predict how much water will be consumed. Data can be used for that prediction; and aligned pressure potentially translates into lower electricity cost for utilities. By using data, and artificial intelligence, operators can optimize pump use and use of other components in the system to deliver water in the most efficient way.
All of the above is only possible when the water supply is sufficient. In case of breakage or leaks, recent events show how local disasters can cause huge losses of water and lead to extreme scarcity of water in regions that normally have a steady supply of water. In such cases, a digital twin can compute an area isolation plan to ensure that the part of the main with the leak or breakage could be isolated to prevent loss of water, while areas that are not affected can remain supplied.