Intro:

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.

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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.

 

Data:

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. Dashboard 2Dashboard 1Dashboard 3

 

Observations:

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.

 

Conclusions:

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.