Variable Air Volume (VAV) fume hoods typically have flow rates between 180 and 1000 CFM, between 0% open and 60% open, respectively. This variable rate not only is important to create a more energy efficient system, but to keep the operator safe by maintaining a constant linear air velocity while the height of the hood changes. A rate of 180 CFM and 1000 CFM correlate to a total ventilation cost of between $900 to $5000 per year. As large research institutions have many hundreds of fume hoods, being able to quickly identify energy discrepancies in specific hoods for maintenance is a financial and associated carbon concern.
The linear relationship between sash height and exhaust flow can be modeled by the equation y=11x+136 where y=CFM and x=(sash height %*100), with an r²=0.92 – This was found by sampling 10 hoods over 150,000 data points over a year.
Building automation systems (BAS) can track and record data on many parameters of VAV fume hoods; sash height (%) and exhaust rate (CFM) are the two I will be focusing on in this post.
From 28 VAVs in multiple teaching and research labs, data from the BAS on the parameters of Exhaust CFM & Sash Height from the month of October 2018. Now that we know the relationship between the two should be linear, quickly visualizing many hoods at once will give a quick insight into problem hoods.
Teaching Lab 1302 shows strong relationships like we would expect. Teaching Lab 1326 mostly shows the expected relationship, with Hoods D and H showing slightly off-center results. The teaching labs show expected results for hoods A:D while the remainder indicates serious issues with the hoods and should be prioritized in any annual inspections, or sooner for energy savings. Hood C in the faculty hood sheet shows that the hood is routinely opened past 60% which is the max height for safe science; this hood is a great target a personal lab visit to reiterate the safety and energy concerns of keeping fume hoods completely open for extended time periods.
This framework provides an opportunity to identify misperforming fume hoods on a mass scale. The next step in this would be to create a script in R or Python which would run the linear regression for all fume hoods on a regular basis, and flag hoods which deviate in the known relationship by a set amount. This could be beneficial for anyone in the roles of a Campus Energy Manager, Green Labs Manager, Research Safety, etc.
Known issues with this framework could include:
- BAS was set up to retrieve information incorrectly; the exhaust CFM could be set for the wrong hood, aligning incorrectly.
- The linear relationship may not be the same for all types of VAVs and air-balanced spaces.