Appendix D

Life Cycle Costing Resources

D.1 Energy Project Ranking

To assess the potential to make energy improvements, the following types of data should be collected on the assets. One set of data is from historical sources. The other set is based on field data collection. Appendix A contains an inventory sheet that can be used to collect energy data on assets. This worksheet also contains a column to calculate energy use of the asset.

Historical Data Collection for Energy Using Assets
  • Minimum of 1 year of energy use data - existing accounting system, utility billing records, SCADA system records, O&M records, equipment/motor lists with horsepower and load information (Note: SCADA - add as many data loggers as possible at different phases of the process to determine if any of the equipment has large energy spikes)
  • Understand energy rate structure - the price of energy and the energy rate structure affect energy costs
  • Hydraulic loading data - flow data should be compared to energy use data.
  • Utilize graphs to report the data.
Field Data Collection for Energy Using Assets
  • Equipment Name
  • Location
  • Age
  • Nameplate horsepower
  • Type of Energy Used
  • Current Energy Use & data source - if available
  • Current Energy Costs & data source - if available
  • Operating status and practices
  • Maintenance practices/history
  • Engineering/design limitations, compare design specifications with actual performance
  • Motor sizes
  • Current drawn (amperage) - using a current meter if necessary
  • Average equipment run times and hours of operation per year
  • Measured power consumption
  • Total kilowatt-hours (kWh) of electrical consumption per year
  • Sub-metering system data if available
  • Calculate: kWh/MG treated - benchmark
  • Calculate: horsepower * 0.746 = kilowatts
To determine which projects might be the most advantageous to pursue, an energy decision matrix such as the one included here can be used. The rankings for each criteria from 1 to 5 are meant as a qualitative judgment and a relative ranking of one project to another. The scores can then be added across the rows to get a total score. The total scores can be compared to see which projects are the most favorable to complete. The projects with the highest scores are the most likely to result in the largest energy efficiency improvements.