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

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Presentation transcript:

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

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

- Slide 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

- Slide 4 Previous Study Class 100 cleanroom Area of 300 ft 2 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, LBNL

- Slide 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 ft 2

- Slide 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

- Slide 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

- Slide 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

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- Slide 13 Study Phases Background measurements DCF Occupancy sensor

- Slide 14 DCF Used a MetOne particle counter Continued night and weekend setback

- Slide 15 Occupancy Sensors Stopped weekend and night setback 6 wireless occupancy sensors 30 minute delay after last occupancy detection

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- Slide 22 The Savings ConfigurationkW-h Percent of reference 24/7 151 Reference Night & Weekend Setback 10972% Reference Occupancy Sensor 9663%88% DCF 9160%83% Half Speed 7046%64%

- Slide 23 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

- Slide 24 Acknowledgements Paul Rogensack and Tony Wong Larry Chu and Peter Rumsey Dennis DiBartolomeo, Duo Wang, and Bill Tschudi Industry contacts Pacific Energy Center

- Slide 25 Future Work & Questions Is increase recirculation fan speed optimum? Simple interface for control

- Slide 26 DAVID FAULKNER