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Demand Controlled Filtration

Demand Controlled Filtration. David Faulkner, MS, PE Indoor Environment Department Lawrence Berkeley National Lab. Demand Controlled Filtration. What it is, what it is not Background Previous work Energy savings potential. What it is, what it is not.

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Demand Controlled Filtration

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  1. Demand Controlled Filtration David Faulkner, MS, PE Indoor Environment Department Lawrence Berkeley National Lab

  2. Demand Controlled Filtration • What it is, what it is not • Background • Previous work • Energy savings potential

  3. What it is, what it is not • Controls particle concentration, via the recirculation fans, not controlling ventilation (amount of outside air) • As particle counts increase, then recirculation fans increase speed

  4. Previous Study • Class 100 cleanroom • Area of 300 ft2 • Intermittent use • Energy reduction of 60-80% • Reference: Faulkner, D., W.J. Fisk, and J.T Walton, “Energy Savings in Cleanrooms from Demand-Controlled Filtration,”Journal of the Institute of Environmental Sciences 39 (6): 21-27, 1996. LBNL-38869.

  5. Current Study • Industrial Cleanroom in San Francisco Bay Area • 4 separate regions with different cleanliness ratings • Experiments performed in Class 10,000 room • 16 FFU • Floor Area 606 ft2

  6. Other Industrial Settings • Southern CA industrial facility is saving energy by night-time setback • Large Southern CA manufacturer is in the process of implementing DCF • Large university in the East recently retrofitted cleanroom facilities and is ready to implement DCF

  7. Case study – recirculation setback • Setback based solely on time clock, 8:00 PM-6:00 AM • No reported process problems or concerns from process engineers • 60% – 70% power reduction on turndown

  8. Facility Existing Procedures • All fans controlled by PC running proprietary software • Software capable of scheduling fan speeds • Fans controlled from 100% during day to 50% at night and weekends?? • 100% from 6:00 am to 10:00 pm

  9. Study Phases • Background measurements • DCF • Occupancy sensor

  10. DCF • Used a MetOne particle counter • Continued night and weekend setback

  11. Occupancy Sensors • Stopped weekend and night setback • 6 wireless occupancy sensors • 30 minute delay after last occupancy detection

  12. The Savings

  13. Conclusions • Facility was already saving (?) 28% by night and weekend setback as compared to 24/7 • Additional savings possible of 40% with DCF • Smaller but still significant savings of 37% with Occupancy Sensors

  14. Acknowledgements • Paul Rogensack and Tony Wong • Larry Chu and Peter Rumsey • Dennis DiBartolomeo, Duo Wang, and Bill Tschudi • Industry contacts • Pacific Energy Center

  15. Future Work & Questions • Is increase recirculation fan speed optimum? • Simple interface for control

  16. DAVID FAULKNER • D_Faulkner@lbl.gov • 510.486.7326

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