Wastewater Energy Cost: A Case Study

Wastewater Energy Cost Reduction

South San Francisco Water Quality Control Plant uses PredictEnergyTM analytics to reduce wastewater energy cost.

wastewater energy cost reduction at SSF

Situation

South San Francisco Water Quality Control Plant (WQCP) processes wastewater for several nearby major cities. The Wastewater Plant provides primary, secondary, and tertiary water treatment for two cities, and provides tertiary treatment for three other cities. These processes produce clean water which is discharged back into the ocean.

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.

Problem

South San Francisco WQCP is aging and operated using a relatively outdated SCADA (Supervisory Control and Data Acquisition) system. The management team was determined to reduce the energy costs associated with wastewater treatment, but the only visibility they had into energy cost was the monthly utility bill. They 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 other cities for their share of the energy costs related to their discharge flows of wastewater to the plant. The wastewater plant accepts partially treated wastewater from several cities and manually tracking flows and calculating actual energy costs is time consuming, subjective and prone to human error.

 

Solution

South San Francisco Wastewater energy cost reductionHelio 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 water plant.

Flow monitoring was installed at each of the three remote plants for those three specific cities. 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 weather data logging capabilities and incorporated PredictEnergy analytics, utilizing the plant's existing SCADA data to support planning for rain events.

In aggregate, PredictEnergy analytics enabled the wastewater plant 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.

 

The PredictEnergy Approach

wastewater energy cost reduction analytics from PredictEnergy

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, they 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 energy use and wastewater energy cost for monthly billing and reconciliation for the three cities.

Conclusion

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

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