Pinpointing potential risks in recycled water through data analytics
William J. Raseman uses data science and systems optimization to find solutions for a wide range of water sector issues, including potable reuse challenges.
At a Glance
- Through a Water Research Foundation project with Trussell Technologies, Hazen built a data analytics program that can rapidly spot potential problems in potable reuse systems—helping operators address them before they could threaten public health or violate regulations.
- The program can quickly process tens of thousands of data points to identify a wide range of potential issues, from equipment and monitoring failures to chemical spikes in the source water.
- The program can include sophisticated commands that don’t require coding experience to modify, and the project team is also developing a guide for how best to share and visualize its analyses.
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“Critical control points are all the treatment processes designed to protect public health. It’s crucial for them to be working properly and for operators to have accurate data about them. We created this program to focus on the most important CCP data, so it can catch problems before they become crises.”
~ Billy Raseman, PhD, PE, Engineer, Hazen
Potable reuse is the process of treating wastewater until it’s safe to drink. Indirect potable reuse (IPR) involves sending the recycled water to an underground aquifer or large reservoir upstream of a drinking water plant. Days—even months—can pass before the water travels through the plant and into people’s faucets.
Direct potable reuse (DPR) systems have no such buffer: The recycled water is piped straight from the reuse facility to a drinking water plant or distribution system, sometimes within hours. For that reason, it’s critical for DPR system operators to spot and address potential problems immediately.
That’s easier said than done. On top of the treatment needed to make wastewater clean enough to put back into rivers, streams, and other natural areas, potable reuse systems use multiple advanced processes, from reverse osmosis to UV disinfection. That means massive amounts of data to monitor—and, in DPR systems, little time to analyze it.
Hazen and Trussell Technologies built a computer program that can quickly analyze data from potable reuse systems—both DPR and IPR—to spot potential public health issues. It’s a way to cut through the fog of data, zero in on what's most important, and address potential problems early.
How the program works:
- Data is frequently stored on devices across the entire potable reuse system, then shared with the program.
- The program screens irrelevant data for times when parts of the system are offline, undergoing maintenance, or starting up.
- Using customized commands and statistical tests, it flags data points that could indicate problems.
- The flags help the program identify potential events, from malfunctioning equipment to changes in upstream water quality, then classify them by type and rank them by level of urgency.
The program can even use the data to pinpoint specific devices that might need attention—for example, one bank of membranes out of thousands in a large reverse osmosis system. It also has flexibility for adjustments. If something changes what it should be looking for, like a new regulation, system operators can revise the parameters and commands in Excel without having to touch any code.
What we used to create it:
- Extensive technical knowledge of potable reuse systems, which helped us identify the most important potable reuse data points to monitor in order to protect public health
- Python, an open-source programming language
- Pecos, an open-source Python package designed to automate quality control and performance monitoring to enable operators to quickly detect issues
- Several months of data from a pilot potable reuse system at a partner utility, which we used to train and test the program
What’s next:
The team is fine-tuning the computer script. Because potable reuse data analysis requires experts spanning multiple disciplines, from plant operations to data visualization, we’re also crafting guidelines for how they can all can work together to get the most out of their data.
