Comparing Contaminant Removal Data

There are three things everyone should know when comparing contaminant removal data for different products:

• How was the data generated?
• How is removal reported?
• What is a significant difference among test data?

Comparing filter efficiencies is more than simply picking the highest number. 

How is the data generated?  To make “apples to apples” comparisons of competing products, the same standardized test procedure must be used for all data.  When looking at data from different sources, such as data from different suppliers, do not assume that all use the same test procedures.  Differences in test fluids and contaminants, calibration methods, or the type of test (single-pass, multi-pass, cyclic flow, etc.) can render comparisons between different procedures suspect.  To avoid this problem, it is recommended that only results obtained using industrial filter test standards developed by reputable organizations, such as ISO, ANSI, SAE, NFPA, and ASTM, be used.  Comparisons based on accepted industrial standards, such as ISO 16889 or SAE J1985, ensures a “level playing field” on which products were tested using the same test conditions.  These standards provide a common language for communicating results, reducing misunderstandings and avoiding misrepresentation.  How can you be sure that performance claims are legitimate?  The source of data should be able to cite the specific standard and revision used to evaluate the filter and, if questioned, be able to produce a test report verifying the veracity of the test data. 

How is removal reported?  Depending on the standard, removal may be reported as efficiency, filtration ratio, Beta Ratio, penetration, or even as a “micron rating.”  With the exception of “micron rating,” all of these are legitimate ways of communicating contaminant removal data that are defined by the corresponding filter test standard.  The key is to ensure that the same filter test standard and, by default, the same reporting method, are used to generate all the results.  In advertising, the term “micron rating” may be used to describe removal characteristics.  Supposedly, a smaller “micron rating” implies better removal.  In practice, this is not the case, as there is no accepted definition for a filter’s micron, absolute or nominal size rating.  Definitions and usage vary depending on the supplier.  A micron rating may refer to the particle size that is removed with anywhere from 50 to 100% efficiency, or it may simply be a number chosen because it is lower than that of a competitor.  In most industrial segments, the use of the terms micron, absolute and nominal size rating are strongly discouraged.  The only time these terms may be meaningful is when comparing filters from the same supplier.
 
What is a significant difference among test results?  Filter tests, like other measurement procedures, have some inherent level of uncertainty that needs to be considered.  The level of uncertainty depends upon the specific test standard used.  To provide an idea of what constitutes a significant difference, we will use an example based on the ISO 16889 multi-pass test for hydraulic filters.  This test is also being adapted for use with lube oil filters (ISO 4548-12) and fuel filters (ISO19438).  Referring to the figure, the first step is to plot particle Beta Ratio (or efficiency) as a function of particle size for each of the three filters.  Then, determine the level of removal upon which decisions will be based.  For hydraulic, diesel fuel, and lube filters, a Beta Ratio of 75 (efficiency of 98.7%) is a typical choice.  Next, we note the corresponding particle size for each filter that gives this Beta Ratio:  8.3 , 9.2 and 10.4 mm(c) for filters A, B, and C, respectively.  In general, if the difference in these sizes is greater than 20%, the results are statistically significant.  In the example, filter A has significantly higher removal than filter C.  On the other hand, differences less than 10-20% are within the statistical limits of the multi-pass test.  Thus, there is no statistically significant difference between filters B and C or between filters A and B. 

Comparing removal data is more than just picking the highest number.  Knowledgeable users will first ensure that the data can be compared and is valid, then will test the data for significance before making a decision.  This analysis can provide a stronger basis for selecting or purchasing a filter and allows for more accurate weighting of the importance of removal relative to other decision criteria, such as service life and cost.

Courtesy of Fleetguard Inc.

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