Guest Column | August 25, 2017

How To Use Big Data To Plan For Sewer Assets

Peter Chawaga - editor

By Peter Chawaga, Associate Editor, Water Online

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As wastewater systems have access to more data than ever, questions remain about how to put it all to good use. A new tool uses analytics to predict the needs of sewer lines over a 20-year span, helping utilities make cost-effective choices and protect their infrastructure.

We are living in a world of Big Data.

Everything from shopping preferences to lifestyle habits is being recorded on some level, with that information collected and put to use in all kinds of ways. It’s a trend that may make many of us uncomfortable and others feel better served, but it is one that is not going away. It’s something that the country’s water and wastewater systems have to work to embrace.

The 2017 Water Industry Report from global engineering and consulting firm Black & Veatch reveals the treatment industry has done their part to collect data but has been too slow to put it to good use.

“Water and wastewater systems are increasingly outfitted with data-producing instruments, but that information is too often siloed or out of sight, like much of the infrastructure itself,” Black & Veatch reported.

Putting Big Data To Work
It’s an issue that engineering firm AECOM is helping wastewater systems address. Working with software provider SEAMS, it has introduced an Enterprise Decision Analytics (EDA) tool that can model the future condition of sewer lines, predicting necessary interventions, costs, and performance over a 20-year period against a number of business and regulatory drivers. It’s a way for utilities to make better use of the data that is collected through instrumentation and in the field.

“Effectively, it uses data trends from asset condition, failures, and consequences to predict consecutive years of failures,” said Andy Gibson, AECOM’s technical practice leader for wastewater networks planning in Australia and New Zealand.

The software can provide proactive intervention options, characterized by their predicted effects, costs, and applicability for different assets. EDA uses various algorithms to find the most cost-effective combination of these interventions. It provides utilities with a range of options, allowing them to choose the long-term strategy that most closely aligns with customer, regulatory, and business drivers.

“It is all about planning future operational, maintenance, and capital budgets and then figuring out how to maximize the level of service provided by the sewers and minimizing risk of failure,” Gibson said. “There is lots of risk with deferring work because you can generate an expenditure peak in the future through many assets reaching the end of their life (and failing) at the same time. There is some truth in the saying, ‘a stitch in time saves nine.’ It is much better to manage critical assets effectively, through proactive intervention, and understand the relationship between pipe material, age, and pipe failure.”

The tool was developed to help utilities in the U.K. meet a national regulatory requirement that calls for precise budget determinations well before anything actually breaks. To help them predict budgetary requirements as accurately as possible, a better method for predicting pipe rehabilitation was needed.

“Underground wastewater infrastructure is always a challenge to manage, given that we can’t see it, and surveying pipe condition is expensive,” said Gibson. “Therefore, we need these tools to help us optimize the ‘where, when, and how’ in terms of pipe rehab and replacement. Our tools maximize cost-efficiency and minimize risk at each time step for each asset.”

EDA is just one powerful instance of what drinking water and wastewater systems could do if they focused on using the data available to them to improve operations.

“Big Data is not new for water utilities. They have been gathering data at 1-minute intervals for at least the past 30 years in some instances,” Gibson said. “As leaders in the industry, we have access to innovative technology practices and, combining this with available data and a strong technical team, can support our clients in making optimized, data-driven decisions in infrastructure investment.”

‘Iconic’ Data Actualization
The benefits of EDA were recently realized by Icon Water, a public drinking water and wastewater utility in Australia. It used EDA to predict pipe condition transition over time, improving blockage rates and engaging customers on the relationship between service and pricing. “The entire system was assessed and a 10-year tactical plan developed,” said Gibson. “I can say that [savings] are significant when compared to total expenditure.”

When asked how other utilities could make best use of the tool and analytical projections in general, Gibson offers a few tips. Don’t get hung up on data, he said, because it will never be perfect. Rather, start looking at analytics and use what you see to drive and improve the collection process. He also recommended looking at large data sets from other regions, as these can shed some insight into a local issue if calibrated the right way. Finally, he urged systems to walk before they run, establishing a sound framework around Big Data collection and its use before attempting an ambitious project.

“Analytics will find relationships between data that humans can’t,” Gibson said. “We are biased in our decision-making. Math isn’t.”


About The Author
Peter Chawaga is the associate editor for Water Online. He creates and manages engaging and relevant content on a variety of water and wastewater industry topics. Chawaga has worked as a reporter and editor in newsrooms throughout the country and holds a bachelor’s degree in English and a minor in journalism. He can be reached at pchawaga@wateronline.com.