How the Purity of Sparged Carbon Dioxide Affects the Oxygen Concentration of Beer

 

My last blog post discussed the importance of carbon dioxide purity when using injected CO2 to increase the CO2 concentration of your beer. The thing we learned is that the purity of CO2 must be very high (99.99% or better) when using injection, or you will at the same time significantly increase your dissolved oxygen levels. However, injection isn’t the only method for adding CO2 to beer. Sparging, in which CO2 is bubbled through beer (usually in a tank with slight over-pressure) is another common practice for boosting CO2. So in this post we will explore how sparging a finished beer tank with carbon dioxide impacts the final oxygen concentration of your product.

First, let’s do a quick review of what happens when you inject CO2. When you inject gas (usually into a pipe,) you are forcing a given weight of gas into the liquid under pressure. All of the carbon dioxide, plus any trace oxygen and nitrogen, gets pushed into the beer and dissolves completely, allowing you to calculate the weight of gas used and extrapolate from there your various gas concentrations.

On the other hand, when you sparge gas into a liquid the dissolved concentration of gas will be bound by Henry’s Law. Henry’s Law tells us that the amount of gas that will dissolve in a liquid will be proportional – at a constant temperature – to the partial pressure of gas in equilibrium with the liquid.  This means that the gasses dissolved in your beer will never be more concentrated that the partial pressure of the gas you are using to sparge.

The consequence is that, with any given CO2 concentration outcome desired, you will have significantly lower oxygen concentrations for sparged beer then for injected beer. For example, in injected beer the oxygen pickup from injecting one volume of 99.95% CO2 (at 0oC) into the beer when the oxygen concentration is 0.01% is 143 ppb. But a theoretical sparging of that same CO2 into the beer at atmospheric pressure would follow Henry’s Law, and your oxygen pickup would be about 7 ppb.

In real brewing situations, however, most brewers use tank overpressure to help get sparged CO2 into solution, so you would probably be picking up about 2 times the above amount, or 14 ppb. The table below shows the expected oxygen pickup given varied percentages of O2 traces (in your CO2) when measured at sea level and at 0oC:

Sparged CO2 at 1 V/V

0.001% O2

0.005% O2

0.01% O2

<1

3

7

My final thought is that CO2 purity isn’t nearly as important if you are sparging rather than injecting, since the amount of gas that will dissolve into your liquid is much lower. This also applies to the purity of the gas you use to flush air from tanks before filling.

Brewery Inline Sensor Placement: Validation Techniques

 

My last couples of posts have dealt with sensor validation. I recently did some coaching on inline oxygen sensor placement, so I’d like to continue that discussion with those examples. One placement was pre-and-post centrifuge in a regional brewery and the other was in a process pipe flowing at about 500 gallons-per-minute. While the flow rate may make a difference in how far a sensor needs to be from pumps and other interferences, the same techniques can still be used to determine sensor placement.

The key to great dissolved gas measurement is making sure to measure in places where all of the gas is in solution. The same validation tips used for double-checking instrumentation can help you decide where to place an inline analyzer. In general we can say that if you measure close to a known – or at least probable — source of air ingress and yet can’t detect the full impact, but measure farther down the process and find higher values, then you probably want to place your sensor further away from that source.

Let’s talk first about the pre-centrifuge application. Since a centrifuge creates a small amount of vacuum, I don’t recommend putting a sensor immediately before it. Where there is any type of a vacuum, there is the possibility of a false low reading, especially if the vacuum can cause any foaming or bubbles forming in the beer due to degassing. If possible, I’d place a sensor where I’m fairly sure there is no suction on the beer.

You can get a false low reading post centrifuge as well, but for different reasons. The first is that any dO2 that ingresses via the centrifuge may not be fully dissolved in the beer in a short run to a sensor. The second is that any CO2 forced out of solution in the centrifuge may lower the dissolved O2. (The CO2 will carry O2 out with it, but then they will both reabsorb farther down the line.) In both cases, you can do better by placing the sensor at a distance post centrifuge.

Validation of inline sensor placement is easy if you use a portable instrument to help, even if you are measuring over a long distance. First take readings with both the inline and the portable at the farthest reasonable sample port from your Bright Beer Tank. If you get similar readings on both, you’ll know that your inline is correct and that you can trust both instruments. Then use your portable to take another sample at a port just before your bright beer tank. If there is more dO2 than you found at your inline placement, then you may have an ingress problem that needs correcting. (You may be tempted to test right in your BBT, but don’t. Tank filling nearly always results in pickup or loss of DO2, so measuring for that variable is a whole different matter.)

The advice I gave to the customer who was evaluating the 500 gallons-per-minute process pipe was similar, and can likewise be applied to most placement situations. Here’s a summary of my basic guidelines for the best inline probe locations:

–        As far as possible from the outlet of pumps

–        At least five pipe diameters from bends

–        As far as possible from CO2 or O2 injection

–        Never in a descending pipe

My final thought is to always validate an inline sensor location if possible. Remember to look for the places in your process where all of the gas in your beer is in solution: that’s usually the best place to put a sensor.

 

Using Basic Verification Techniques to Qualify Brewery Instrumentation

Any method of analysis — whether measuring pH, turbidity, sensory, dO2, TPO, or CO2 –will have some inherent error. I think that it’s always best to acknowledge problems up front and be ready to deal with them, so today I’d like to talk about understanding best practices and ways to anticipate and quantify errors, especially if you are in the process of qualifying a new instrument.

I’ve seen a lot of instrumentation improvement through the years, ranging from ease of use and maintenance to complexity of capability. However, even the most sophisticated instruments need the balance of statistical analysis and upfront testing to ensure reliable quality and minimal person-to-person variability. By learning the ways to test and verify an instrument during the demo period or just after purchase, we can learn the strengths and limitations of our analyzer, and know what best practices should be implemented before the data we gather is used in daily production.

Regardless of the type of analysis, the more statistical data we capture the higher the certainty that we understand our instrumentation, but even large amounts of data won’t help if we don’t understand the way our instruments fit into the context of the parameter being measured. For example, we may want to measure the turbidity of beer, but if we don’t understand that copious bubbles can throw off our measurements and that we need to either de-carbonate our product or keep it under pressure, then we can gather all the data points we want, but they won’t tell us what we need to know.

So understanding the context of the thing we want to measure is our first step, but assuming we have that part under control, how can we then experiment with the data from our new instrument, to ensure our measurements are meaningful and that we can trust our results? Here are a few ideas:

  • Take multiple measurements. If you’re using a portable instrument, measure multiple types of samples that are similar, but different, and measure multiple times. For example, with a dO2 meter choose three bright tanks that were recently filled and move between the tanks ten times. Record the readings and look at the variability. If this is done in a short amount of time on filtered beer, the readings shouldn’t decay much during testing.
  • Have three different people run the same tests, each rotating between the same samples. Record the values and see if there is user variability. Do this ten times per person and analyze the data. Is there a technique issue that yields an erroneous result? If you can correct it, then that information can be used to education future users.
  • Understand the variability and expectations of your process. Say you are evaluating a new TPO analyzer. The more you know about your filler, the easier it will be to evaluate your instrument, but statistics can help you regardless. For example, if your instrument is able to measure TPO and not just dissolved oxygen, understand whether it can also compare results on shaken and unshaken packages. Most of the new systems sold today can do both and should yield the same TPO concentration for packages, whether shaken or unshaken. If the results don’t match, understand why. It may point to a problem with the instrument.

My final thought is to use good statistics to drive your process control. Don’t base a decision on one data point. Whenever possible, validate your analyzers on a regular basis. I’ll have more on process and portable instrumentation validation in my next post.

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