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Digital Copper Corrosion Measurement Vs. Visual Rating _ Incorporating New Technologies To Method Development
Aaron Mendez Ph.D. and Juan Ayala Ayalytical Instruments Inc.2787 W Fulton St, Chicago, IL 60612 USAwww.ayalytical.com
Galveston, TX October 16 - 17, 2018
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ASTM D130• Corrosiveness to Copper from Petroleum Products by Copper Strip Test
A polished copper strip is immersed in a specific volumeof the sample being tested and heated under conditions oftemperature and time that are specific to the class of materialbeing tested. At the end of the heating period, the copper stripis removed, washed and the color and tarnish level assessedagainst the ASTM Copper Strip Corrosion Standard.
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*Claret Red
Multicolor Lavender or silver or both overlaid on claret red
Multicolor red and green showing peacock, but not gray
Magenta overcast on brassy strip
Moderate Tarnish Dark Tarnish CorrosionSlight Tarnish
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Reasons for Development• One of the last…
• Remove BIAS from Operator to Operator
• Digital Recording of Images
• LIMS Integration
• Standardization on Light Source
• Magnification of Sample Strip
• Strip Size Determination
• Current Chart – NOT REAL
• Fixed corrosion criteria
• Automatic Measurement…Not assessment
• More reliable
• Repeatable and Reproducible
• A better rating…
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THE DESIGN
*Patent Pending
Digital High Resolution CCD Camera
NFX Strip Holder*
Pogo Pin Connectors
Spring LoadedStrip Holder
D130 Copper Test StripCuDDI Analyzer*
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Vision Algorithm
• Define Strip Area – Automatically
• Inspection Area Determination
• 8x8 Pixel scan, ~96,000 points total
• Accumulate Results
• Calculate Percentage of each Rating
• Pass values through Support Vector Machine and Algorithm for Categorizing
• Ability to “Train” and Adopt Model
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• Lighting
• Fingerprints
• Dimensions
• Proper Polishing
• Holes in D1838
• Water Spots – LPG Samples
• 2a to 3a Logic Handling
• 2c to 3b Logic Handling
D130 Critical Points
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WK62219 Revision of D130-18Revision of D130 - 12 Standard Test Method for Corrosiveness to Copper from Petroleum Products by Copper Strip Test
Rationale:D130-18 is the standard method for the determination of the degree of corrosiveness ofpetroleum products with a vapor pressure up to 124kPa (37.8 °C), such as natural,automotive and aviation gasoline, aviation turbine fuel, kerosene, diesel fuel, distillate fueloil, cleaners solvent (Stoddard), lubricant oils and other hydrocarbon fractions fallingwithin the specified vapor pressure limit. A polished copper strip is immersed and heatedfor a determined time in a predetermined volume of the sample under test. Heatingconditions and time of exposure are specific of the class of material being tested. After theheating, the strip is removed washed and the degree of corrosiveness of the sample isassessed by visual comparison of the level of tarnish and color against an appropriateASTM Copper Strip Corrosion Standard. The proposed revision improves the rating processby a Digital Detection Imaging analyzer removing the subjective visual rating of D130,strictly adhering the classification specified in Table 1. Exact levels of corrosion are digitallyand integrally identified in a 4-step automated vision algorithm process using a high-resolution CCD camera with optical intelligence.
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BALLOT ITEM D02.05 (18-04)
WK #62219 Revision of D130-18
Date: 05/07/2018
To: Subcommittee D.02.05 on Properties of Fuels, Petroleum Coke and Carbon Material
Tech Contact: Aaron Mendez, [email protected]/ 281 984 7319
Work Item #: 62219
Ballot Action: Revision of D130-18/ Standard Test Method for Corrosiveness to Copper from Petroleum Products by Copper Strip
Test
Rationale: ASTM D130 is the standard method for the determination of the degree of corrosiveness of petroleum
products that exhibit a vapor pressure up to 124kPa (37.8 °C). A polished copper strip is immersed and heated for a
determined time in a predetermined volume of the sample being tested. After the heating period the strip is removed
washed and the degree of corrosiveness of the sample is assessed manually by visual comparison of the level of
tarnish and color against an appropriate ASTM Copper Strip Corrosion Standard.
This ballot proposes to add a new automated rating procedure of the copper strip, using a high-resolution CCD
camera with optical intelligence and digital image analysis technique.
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BALLOT ITEM D02.05 (18-04)
WK #62219 Revision of D130-18
1. Most product specifications that have a copper strip corrosion requirement
set the limit at No. 1 maximum. So the critical rating issue is whether the
copper strip is 1a / 1b or if there is any hint of rose color for No. 2 or higher
rating. Whether the strip is No. 2b or higher is not very relevant – it is a
failure. An ILS should concentrate on validating the new optical reader versus
visual rating at the No. 1b to No. 2a level.
2. While D975, the diesel fuel specification, allows a No. 3 maximum copper
strip rating, I doubt that many ULSD fuels exceed No. 1b.
3. At this point, I would not recommend the automatic reader as there has not be
a full ILS with published precision and bias data.
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BALLOT ITEM D02.05 (18-04)
WK #62219 Revision of D130-18
4. When adding a second tube rating technique, the Precision and Bias is
critical to ensure consistency in test results. In this case, the ballot is providing
an alternative solution to reading the test coupons; visual reading vs automatic
reading done with an algorithm. A full ILS in accordance with ASTM D6708,
Standard Practice for Statistical Assessment and Improvement of Expected
Agreement Between Two Test Methods that Purport to Measure the Same
Property of a Material, needs to be completed.
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Display of results and rating in Automatic mode
EXPERIMENTAL RESULTS
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Display of results and rating in Automatic mode
EXPERIMENTAL RESULTS
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EXPERIMENTAL RESULTS
A compiled data set of the 13 Copper strip samples run in three instruments by three operators is
presented as Table 2 below:
Internal Repeatability Study:
Manual Referee rating=> 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
RIG # Operator coup1 coup2 coup3 coup4 coup5 coup6 coup7 coup8 coup9 coup10 coup11 coup12 coup13
Instrument1 Oper.1 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
Instrument1 Oper.2 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
Instrument1 Oper.3
Instrument2 Oper.1 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
Instrument2 Oper.2 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
Instrument2 Oper.3
Instrument3 Oper.1 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
Instrument3 Oper.2 1a 1a 1b 1b 2b 2b 2a 3a 2d 2d 3a 4a 4a
Instrument3 Oper.3
Table 2. Internal comparative data
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EXPERIMENTAL RESULTSSample ID D130 Visual CuDDI
1 1b 1b
2 3a 2b
3 2a2a (Highest)
3b (Most Corrosive)
4 1a 1a
5 2e2e (Highest)
4a (Most Corrosive)
6 4c 4c
7 2b 4a
8 1b1b (Highest)
2a (Most Corrosive)
9 2c 2c
10 4a 4a
11 3b 3b
12 4c 4a
13 2a 2a
14 2c2c (Highest)
4a (Most Corrosive)
Table 3. Internal Correlative Study
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EXPERIMENTAL RESULTS
Combined Stream DateD130 Visual Rating
CuDDI Automatic
Rating
5/8/2018 2C 4A
6/5/2018 3A 2D
6/12/2018 1A 2C
6/3/2018 1B 2A
Combined Stream DateD130 Visual Rating
CuDDI Automatic
Rating
5/9/2018 1B 1A
5/10/2018 1B 1A
5/11/2018 1A 1A
5/12/2018 1B 1A
5/13/2018 1A 1A
5/14/2018 1B 1B
5/15/2018 1B 1B
5/16/2018 1B 1A
5/17/2018 1B 1B
5/18/2018 1A 1A
5/19/2018 1A 1B
5/20/2018 1A 1A
5/26/2018 1A 1A
5/26/2018 1A 1A
5/26/2018 1A 1A
5/29/2018 1A 1A
6/2/2018 1A 1A
6/4/2018 1B 1B
6/5/2018 1A 1A
6/5/2018 1A 1A
6/6/2018 1B 1B
6/7/2018 1B 1B
6/9/2018 1B 1A
6/10/2018 1B 1A
6/13/2018 1B 1A
6/19/2018 1A 1A
6/21/2018 1A 1A
Table 7. External CuDDI vs. D130 Data
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EXPERIMENTAL RESULTS
Table 6. External CuDDI vs. D130 Data
Sample DateD130 Visual
Rating
CuDDI Automatic
Rating
Mid Stream 5/5/2018 2E 2E
5/8/2018 2B 2B
5/15/2018 2B 2A
5/16/2018 2A 2B
5/18/2018 2A 2C
5/26/2018 2C 2C
5/27/2018 2B 2B
5/12/2018 3B 2B
5/13/2018 3B 2C
5/14/2018 3B 2C
5/17/2018 3B 2C
5/29/2018 2B 2B
5/29/2018 2A 2B
6/2/2018 2C 2C
6/4/2018 2A 2A
6/9/2018 2C 2D
5/29/2018 2B 2B
5/29/2018 2A 2B
6/2/2018 2C 2C
6/4/2018 2A 2A
6/12/2018 2C 2D
6/13/2018 2C 2C
6/19/2018 2D 2D
6/21/2018 2D 2D
6/8/2018 3B 2C
Sample DateD130 Visual
Rating
CuDDI Automatic
Rating
Mid Stream 5/9/2018 1B 1A
5/10/2018 1B 2A
5/11/2018 1B 2A
5/20/2018 1B 1A
5/29/2018 1B 1B
Sample DateD130 Visual
Rating
CuDDI Automatic
Rating
Mid Stream 5/22/2018 3B 4A
6/6/2018 3B 4A
6/4/2018 3B 4A
6/5/2018 3B 4A
6/7/2018 3B 4A
6/10/2018 3B 4A
6/20/2018 4A 4A
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EXPERIMENTAL RESULTS
Table 4. External CuDDI vs. D130 Data
Sample DateD130 Visual Rating
CuDDI Automatic
Rating
Feed # 2 5/10/2018 2A 2A
5/11/2018 3B 2A
5/12/2018 1B 2A
5/14/2018 1B 2A
5/15/2018 1B 2A
6/3/2018 1B 2A
6/4/2018 1B 2B
Sample DateD130 Visual Rating
CuDDI Automatic
Rating
Feed # 1 5/10/2018 2A 2A
5/11/2018 3B 2A
5/12/2018 1B 2A
5/14/2018 1B 2A
5/15/2018 1B 2A
6/3/2018 1B 2A
6/4/2018 1B 2B
6/9/2018 1B 2A
6/3/2018 1B 2D
5/5/2018 2C 2C
6/4/2018 1B 2D
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EXPERIMENTAL RESULTS
Table 5. External CuDDI vs. D130 Data
Sample DateD130 Visual Rating
CuDDI Automatic
Rating
Feed # 4 5/4/2018 3B 4A
5/8/2018 3C 4A
5/13/2018 3B 4A
5/16/2018 3B 4A
5/17/2018 4A 4B
5/18/2018 3A 4A
5/20/2018 4A 4A
5/26/2018 4A 4A
5/27/2018 4A 4A
5/29/2018 2B 4A
6/5/2018 3B 4A
6/5/2018 2C 4A
6/6/2018 2C 4A
6/7/2018 2C 4A
6/8/2018 3B 4A
6/8/2018 4A 4A
6/10/2018 4A 4A
Sample DateD130 Visual Rating
CuDDI Automatic
Rating
Feed # 3 5/9/2018 3B 3A
5/10/2018 3B 3B
5/11/2018 3B 3B
5/13/2018 2C 3B
6/17/2018 3A 3A
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ASTM Standard Designation Standard Specifications for:
D396-18 Fuel Oils
D975-18 Diesel Fuel Oils
D1655-18a Aviation Turbine Fuels
D2880-18 Gas Turbine Fuel Oils
D4806-18Denatured Fuel Ethanol for blending with gasoline for use as Automotive Spark-Ignition Engine Fuels
D4814-18 Automotive Spark Ignition Engine Fuels
D5798-15Ethanol Fuel Blends for Flexible-Fuel Automotive Spark-Ignition Engines
D6615-15a Jet B Wide-Cut Aviation Turbine Fuel
D6751-15 ce1 Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels
D7467-18 Diesel Fuel Oils & Biodiesel Blends (B6 to B20)
D7566-18 Aviation Turbine Fuels containing Synthesized Hydrocarbons
D7960-17Unleaded Aviation Gasoline Test Fuels containing non-hydrocarbon components
D8011-17Natural Gasoline as a Blend Stock in Ethanol Fuel Blends or as a Denaturant for Fuel Ethanol
D8076-18 100 RON Test Fuels for Automotive Spark-Ignition Engines
Table X. Product span for D130-18
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D130-18 Precision Statement
Results by this test method are nonnumerical and involve multi-categoric rating classifications therefore conventional D02 statistical techniques, such as Practice D6300, are not suitable for determining precision.Instead, a statistical simulation approach was used to mathematically assess the “degree of disagreement” under “r” and “R” conditions for the ILS data set. Based on the statistical approach followed, no statistically significant difference in disagreement percentages between r and R conditions was determined for rating classifications in category 1, 2, and 3. For rating classification 4 however, samples showed more within-classification disagreement.Based on this information, the statistician determined that in the long run, the precision of the analysis (that is, both “r” and “R”) is that there is nominally a 5% chance that a difference between any two ratings will exceed the criteria in D130 Table 2.
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D130-18 Revision Plan
D6708-16b _ Standard Practice for Statistical Assessment and Improvement of Expected Agreement Between Two Test Methods that Purport to Measure the Same Property of a Material
• The interlaboratory study must be conducted on at least ten materials that span the intersecting scopes (See Table X) of the test methods and results must be obtained from at least six laboratories using each method.
• Similar cases were automatic procedures with its own precision were added to the standard, specifically D1322-18_ Smoke Point and D3241_ Thermal Oxidation Stability of Jet Fuels (JFTOT).
• A common set of representative coupons previously rated according to D130 can be circulated among cooperators to determine the automatic rating. The minimum number of samples and the degrees of freedom involved is something that must be determined with ASTM D02.94 statisticians.
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CONCLUSIONS
• CuDDI possesses a tight controlled analyzer that guarantees high consistency
• High resolution CCD camera provides precise machining and motors to reproducibly rotate the strip
• The novel Pongo Holder eliminates unwanted markings that might affect the measurement
• Ratings proceed by accurately measuring the degree of corrosion eliminatingoperator bias in the visual subjective assessment
• Low data dispersion between tests and operators/instruments
• CuDDI presents unique advantages in avoiding distortions aroundfringing areas and in determining the real copper strip size
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CONCLUSIONS
• In the case of pass/fail data, no generally accepted method for determining precision is currently available, except for gasoline sample types (see D130-18, 14.1.1 and 14.1.2) analyzed using the test tube procedure stated in D130-18 11.3.1.
• Bias has not been determined for this test method.
• A full fledged Round Robin Study covering all types of fuels is being prepared before resubmitting this work item for approval.
