Many utilities today are investing in advanced metering infrastructure (AMI). However, many of these same utilities often don’t realize the breadth of capabilities that a smart utility network (SUN) now offers. In the past, AMI networks were typically used for accurate metering and billing. Today, a smart utility network builds upon traditional AMI to provide multiple advantages across the full water cycle — and beyond.
Many utilities are embracing the concept of smart utility networks to make their systems more efficient and enhance customer service. Smart utility networks provide the ability to improve every part of the water cycle. However, having the right metrology for each application is critical to ensuring the reliability and accuracy of the data being collected.
If A Pipe Leaks In A Forest, Does It Make A Sound? (Part I) addressed condition assessment and leak detection from the perspective of surprises that can arise when “hidden” problems are revealed by acoustic technology. This article introduces several more eye-opening experiences with permanent and mobile acoustic leak detection equipment, plus examples of just how expensive undetected leaks can become when they turn into full-blown water main breaks.
Utility managers are facing increasing financial and sustainability pressures regarding water loss throughout their systems. An American Water Works Association (AWWA) white paper titled The State of Water Loss Control in Drinking Water Utilities notes that “all utilities incur inefficiencies, or losses, in both supply- and customer-related functions of their operations.”
As need for efficiency expands, there is greater expectation for utility network performance and data reliability. Although having access to more data more quickly can be a benefit of a smart utility network, making the most of the optimization possibilities within a water system relies on having the right data for the right applications delivered at the right time. Such data becomes vital information, giving water professionals the ability to make better decisions.
Beyond the existential philosophy implications, the consequences of a pipeline leaking in a forest when no one is around highlight the desirability of leak detection systems in water distribution utilities as a whole. As the following experiences show, leak detection can have its entertaining side. On the other side of the coin, however, the consequences of not monitoring leaks can also trigger a tsunami of costs far beyond the expense of pipeline repair alone.
Most people accept that major purchases in life — e.g., housing, automobiles, appliances — come with cyclical budgeting impacts that require amortizing costs over the long term, through savings or borrowing. Why should anyone expect the national water infrastructure to be any different?
Water treatment plant (WTP) and wastewater treatment plant (WWTP) operators are increasingly supporting better decision-making by leveraging digitalization capabilities available through instrumentation and plant management systems. Successful adoption of digitalization starts with identifying the right balance of tools (i.e., web-networked instrumentation) and systems (i.e., analytical data management software). Maximizing the value, however, is as much about exercising a mind-set of efficiency as it is about tracking raw numbers.
As industries expand, they typically need to increase the capacity of their wastewater treatment facilities. Increasingly stringent regulatory requirements, such as lower nitrogen limits, may also signal the need to boost treatment capacity. Installing additional tanks and larger equipment not only adds capital costs but increases operating costs as well.
From the largest metropolitan water treatment plant (WTP) or wastewater treatment plant (WWTP) operations to the smallest rural systems, the goals are essentially the same — achieve regulatory compliance and the most efficient results at the lowest practical cost. The most feasible (i.e., affordable) control solutions vary by process, plant size, and budgetary limitations. Here are several high-level guidelines to achieving a common strategy that works across virtually all applications: good data, properly analyzed, yields good results.
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