Data analysis keeps your refrigeration running cool

Data analysis produces a refrigeration health metric.

By Stephen Puryear

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As an engineer in a maintenance group within a multinational pharmaceutical manufacturer, I see many conditions to which most plant professionals can relate. We’re under pressure to reduce both costs and staff while increasing reliability. Maintenance people, like most of the production support staff, have an extra twist added to our challenge. We don’t directly add value to the product our company sells. At best, what we deliver to our business-management team is reduced costs and increased reliability. Ironically, the management team adds no direct value to the product, either. So a big question looms: How can maintenance people find a way to convey their value?


Instinct and feeling are personal assets that experienced maintenance people cultivate over years but use every day. In the name of reliability, maintenance technicians and supervisors need to know more about individual system performance data that many either take for granted or aggregate into larger groups of data.

A critical strength of any system health metric is that it reveals whether a system is stealing money. Energy costs aren’t going to stop rising. There’s no point spending finite resources to detect a system failure if the ever-increasing energy bills have already put us out of business. Fortunately, diminishing efficiency often is a warning about equipment failure, so looking upstream in process time makes a lot of sense. Stockholders and upper management sometimes appreciate this sort of attention to the way money is spent.

Data is everywhere, and more arrives every day. Fortunately, the kind of data that you’ll need to generate a health metric is available from any system that controls equipment, distributes power or monitors a building. And these data already have been gathered, delivered, validated and paid for.

Also, our global neighbors in the European Union, India and China are in the same boat as far as this analytical challenge is concerned. Everyone must abide by the same laws of physics. Nor is this challenge an instance in which inexpensive labor can provide any advantage.

Experiment and data

That’s enough “thinking globally.” What about the “acting locally?” Several years ago, a happy set of circumstances allowed development of a modest experiment to explore some actual tools that might lead toward such a “health metric.”

The experiment is now a project that explores what it might take to develop a useful refrigeration system efficiency or “health” measurement. Some maintenance people might recognize this approach under the name “condition monitoring.” Marrying it to a valid statistical framework would allow business managers to make better decisions about refrigeration systems.

I established an experimental platform using a household freezer from Best Buy and attached to it a data-collection system cobbled together from surplus parts. This freezer has been subjected to many of the same conditions that its larger cousins would encounter in industry.

Table 1 shows some interim data from this platform. It lists a variety of conditions under study and the load placed in the freezer. Varying the load was simple. It consisted of adding or subtracting half-gallon jugs of antifreeze. An EPA license allowed me to transfer the R-134a refrigerant in or out of the system using a recovery pump and a tank as a reservoir.



        Est'd annual
Charge  Load  Duty Power draw
(grams) (jugs) cycle (gigajoules)
150 0 0.5551 3.74
143 0.5880 3.84
133 0 0.5434   3.52
130  0 0.5593 3.64
130  2 0.6745  4.28
130  0.6548  4.19
130 4  0.6020  3.85
130 2 0.5883  3.77
130 4  0.6346  4.10
122 0  0.6727 4.33
114 0  0.6659  4.13
114 0  0.6813 4.22
113 0 0.6736   4.18
Table 1. This chart shows the relationship between input variables and the energy cost.


The quantity of R-134a refrigerant was varied from the 129-gram nominal charge. The middle column shows the duty cycle associated with each of the controlled conditions. Duty cycle is the fraction of the total possible run time that the compressor must operate to handle the imposed load. Capturing the current draw during each duty cycle permits forecasting the corresponding annual energy consumption.

The first piece of information to be gleaned from these data is that even near the nominal factory-standard refrigerant charge, there’s a lot of noise. By this I mean that repeated runs at similar or identical conditions produced somewhat variable results. One reason is that the system is sensitive to ambient conditions (Table 1). This is true of all refrigeration systems.

Had these data followed a normal distribution, regression analysis would help remove the effect of ambient temperature. It would then be straightforward to compare different charges and the corresponding system response. Many researchers are attacking this aspect of the problem to solve other challenges.

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