South San Francisco plant cuts costs with energy analytics


South San Francisco Water Quality Control Plant (WQCP), based north of the San Francisco International Airport (SFIA) along the San Francisco Bay, is a facility that processes wastewater for the cities of South San Francisco, San Bruno, SFIA, Burlingame and Millbrae — collectively the North Bay Side Unit (NBSU) Partners. The WQCP provides primary, secondary and tertiary water treatment for South San Francisco and San Bruno wastewater, and tertiary treatment for the NBSU partners. These processes produce clean water which is discharged back into the San Francisco Bay.

The treatment of wastewater is an energy intensive process due to the various treatment steps and equipment used, namely: mechanical separation, aeration blowers and large motors for pumping.

The Challenge

The WQCP is an older plant and is operated using a relatively outdated Supervisory Control and Data Acquisition (SCADA) system. The management team was interested in determining how to reduce the energy costs associated with wastewater treatment, but the only visibility WQCP had into energy cost was the monthly utility bill. The WQCP struggled without access to key business metrics associated with energy cost to guide their wastewater energy cost reduction efforts.

An additional concern was how to measure and appropriately charge the North Bayside Partners for their share of the energy costs relating to their discharge flows of wastewater to the WQCP plant. WQCP accepts wastewater from Burlingame, Millbrae and SFIA for tertiary treatment and manually tracking flows and calculating actual energy costs is time consuming, subjective and prone to human error.

Step One – The Solution

Helio Energy Solutions installed discrete monitoring for the main utility feed, sub panels and cogeneration systems in order to capture energy, power and the total energy consumption by WQCP.

Step Two – Flow Monitoring

Flow monitoring was installed at each of the three remote plants: SFIA, Millbrae, and Burlingame. Flow and electric meters were also installed on each effluent discharge pump to measure how much electricity was being used to move processed water to the bay.


Finally, Helio Energy Solutions installed a new weather data logger and incorporated PredictEnergyTM analytics, utilizing WQCP’s existing SCADA data to support planning for rain events.

In aggregate, PredictEnergy analytics enabled WQCP to “visualize” their operations from a cost perspective by providing key business performance indicators (KPIs), including:

  • Kilowatt-hours per million gallons (MG) processed; energy use per unit output.
  • The cost of the energy based on the actual utility tariff which changes with time of day, day of week, season and peak energy demand; energy cost per unit output.
  • Comparison analytics against previous day, month or year’s performance.

PE-Screen 1

Step Three — Document Savings

With the enhanced visualization of PredictEnergy’s analytics, plant operators now have visibility into energy use and allocation as well as energy cost.  PredictEnergy combines energy analytics with applicable utility rates, enabling plant operators to accurately see current plant efficiency and production. As a result, SSF can now quantify actual energy savings, set new baselines and track efficiency improvements in minutes. PredictEnergy offers critical near real-time information for reducing energy costs at the operator level and calculates the NBSU partners’ energy use and energy cost for monthly billing and reconciliation.


With Effective visualization of the WQCP plant performance, PredictEnergy enables the processing of wastewater at the lowest cost and creates energy cost savings of nearly 10 percent. PredictEnergy’s analytics platform with near real-time data, provides a level of visibility into plant health never before available, The WQCP has also already identified multiple maintenance items prior to potential failure, that will further reduce costs and eliminate unnecessary downtime.



This article was contributed by HelioPower, an integrated energy solutions company that provided the implementation of the PredictEnergyTM analytics platform to help extend South San Francisco’s business intelligence capabilities, by providing plant operators deep visibility and precise control over energy and energy costs.

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