How Miami-Dade’s water utility is using cloud ad IoT analytics to manage the wastewater management for a community of millions.
Name of Organization: Miami-Dade County Water and Sewer Department
Industry: Utilities
Location: Miami, Florida
Opportunity or Challenge Encountered: Along with the pumping and supplying water to almost three million residents over 6,000 miles of pipes via 1,000 pumping stations, the Miami-Dade County Water and Sewer Department (WASD) has a challenge few other water authorities face – much of its service area sits roughly one meter above sea level.
As related in a recent case study, that means relying on more than 1,000 separate sewer pump stations — many with more than two pumps — to push residents’ wastewater through more than 4,000 miles of infrastructure, some sections of which are over 90 years old.
See also: Case study — Barrick Mining takes WiFi deep underground
There is a lot of data to work with. For the past 22 years, WASD has relied on a supervisory control and data acquisition (SCADA) system to operate and collect data from their water pipes, sewer lines, meters, and pumps. This has resulted in millions of rows of data every day—flow rates, pressure, usage, providing data for engineers, operations staff, maintenance staff, and regulatory reporting. With these metrics, WASD administrators are able to “measure usage, identify trends and defects, predict problems, and record work orders and maintenance,” the case study reports.
WASD sought to find a way to increase its awareness and fix leaks and spills as they happen – or preferably, before they even happen. However, no one was capable of reacting to data in real time, instead, relying on analysis through spreadsheets, SQL tools, and basic reports. It often took a team of WASD engineers upwards of days, weeks, or months to process data from a single pump station.
How This Opportunity or Challenge Was Met: Technology has come to the rescue, and with the Internet of Things (IoT), a cloud database, and thousands of sensors gathering data such as water pressure, flow rates, and rainfall. Propelling this effort was a mandate from the US Environmental Protection Agency to manage and operate its sewer system more effectively.
The EPA expects reports demonstrating WASD’s infrastructure investments, along with maintenance, operator, and management dashboards that demonstrated their improvement. The WASD’s Information Technology team proposed a technology solution to solve the agency’s data problem, meet the EPA requirements, and rebuild their infrastructure in the cloud, with an IoT solution built on Microsoft Azure SQL Data Warehouse and Power BI.
Since adopting their cloud solution, WASD has loaded over 15 billion rows of SCADA historical data into their Azure SQL Data Warehouse and are adding over four million more every day. “This not only equips WASD to better analyze and act on its existing infrastructure but will allow for better insight which can help the planners, operations and maintenance staff identify and respond,” according to the case study. “If the pumps are overworked, this can be quickly identified so a maintenance crew to respond.”
Benefits From This Initiative: The new platform equips the WASD’s information technology team with the scalability, insight, and power to process data in seconds rather than days, the case study relates. Formerly, with spreadsheets only capable of handling an extract of three years’ worth of data of one metric of one pump station. With the cloud-based BI, engineers can look at data from more than 1,000 pump stations from the past 22 years in a matter of seconds.
Now, the WASD’s Information Technology team and WASD staff can pull data from their network for a holistic view of their infrastructure, as well as a more accurate profile of each pumping station, its maintenance history, and projected future performance. WASD staff can also extract data from specific years and better visualize those metrics. Data can be stored as necessary to better identify key details, and planners and engineers can make data-driven decisions in near real time.
