Optimizing Wastewater Asset Management through Predictive Analytics

Freya Systems developed an algorithm to reduce operational costs in wastewater treatment.

Need

Identify a data-driven optimization within the treatment process.

Background

Wastewater treatment is a process that removes contaminants from wastewater to produce an effluent suitable for reentry into the water cycle. To generate such an effluent, wastewater treatment plants often employ automated systems designed to treat waste. Freya Systems partnered with a prominent wastewater treatment plant to identify data-driven optimization opportunities within the treatment process. This particular plant moves wastewater through its treatment system until the waste reaches a series of tanks, which serves as one of the major stages of effluent conversion. In these tanks, the amount of dissolved oxygen (D.O.) present is carefully measured and maintained by a series of automated blowers and valves.

Approach

A random forest algorithm was trained to predict full blower use.

The Objective

Predict whether all blowers will be needed within the next 30 minutes to enable the implementation of cost-saving interventions.

The Approach

The client provided approximately three years of historical data that measured all major components of the treatment process at a rate of once per minute. In addition to measuring the D.O. in each tank, the data included measurements of the openings of valves, blower activity, pressure, airflow, and other aspects of the automated system. The data also encompassed other information pertinent to the treatment process, like weather and the pH of the wastewater soon after entering the treatment facility. A random forest algorithm was then trained using features capturing the state of the treatment system both at each minute and in the recent past to predict whether all blowers were required 30 minutes at each point in time.

Results

Among all the days in which all blowers were used concurrently, the algorithm successfully predicted the occurrence beforehand every time.

The Result

Among all the days in which all blowers were used concurrently, the algorithm successfully predicted the occurrence beforehand every time.

Following this monitoring period, the client deemed the algorithm a success and is actively working to fully integrate the algorithm’s predictions into the automated treatment process.

Consistent, Actionable Insights on Blower Usage

Precision & Recall

The algorithm demonstrated strong initial performance, leading to on-site implementation and multi-month monitoring.

Two-Thirds Success Rate

Among all days where the algorithm predicted full blower usage, blowers were indeed used concurrently on approximately two out of three days.

Zero Missed Events

Whenever all blowers did run concurrently, the algorithm successfully predicted the occurrence every time.

With these reliable predictions, operators gained the ability to adjust schedules and usage patterns before all blowers were triggered, improving energy efficiency and extending equipment life.

“The whole experience with Freya was great! We have been trending their control algorithm data daily and are pleasantly surprised by its predictions.”

– Clint Swope, DELCORA

Conclusion

After six months of real-world operation, Freya Systems analyzed the post-implementation data and found that the algorithm could reduce energy consumption on blower functions by 6.4%. This is especially significant given that blower systems account for roughly 40% of overall energy use in wastewater processes. By integrating predictive analytics into the automated workflow, the facility could save on operational costs and also prolong equipment life—an important step toward enhancing both sustainability and cost efficiency in modern wastewater treatment.

Freya Systems remains committed to helping utilities harness the power of advanced analytics, ensuring that every insight gained delivers measurable value and supports the long-term health of critical infrastructure.