This document describes a project to improve the reliability of gas chromatography (GC) analysis for quality control of custom refrigerant blends produced by Honeywell. The GC was found to have poor repeatability and a wide range of signals for sample compositions. Root cause analysis identified errors in the weighing procedure for preparing standard mixtures. Improving the standard operating procedure for weighing mixtures validated more accurate composition percentages. While the GC issues were not fully resolved, the project provided insight into prioritizing analytical chemistry work for a refrigeration department.

1. LoweringTaktTime for
Production of Customer
Requested Heat
Transfer Fluid Blends
Gauge R&R of Gas Chromatography Analysis on Hydrofluoroolefin
Mixtures
Kurtis Colwell MS
Timothy Clapp - North Carolina State University

2. 2
DEFINE: Refrigerant Sample Production for Honeywell Research Partners and
Potential Customers
Supplier Input Process Output Customer
Arkema,
Honeywell
Raw materials
received for
mixture
production
Raw material
analysis ahead
of utilization in
production
Quality Control
data from the
Analytical
department
Refrigeration
Department
Refrigeration
Department
Analyzed Raw
materials
received for
mixture
production
Preparing
Mixtures for
Honeywell
Customers
Completed
Mixtures are
sent to analytical
for QC testing
Honeywell
Customers and
research
partners
Analytical
Department
Analytical
Department
supplies data to
ensure quality of
ordered material
HW Ref. Dept.
picks up in spec.
samples and
prepares them
to ship
Shipping paper
work,
Certificates of
analysis and
labelling are
attached,
samples are
shipped
Honeywell
Customers and
research
partners
Supplier, Input, Process, Output, Customer (SIPOC)
Project: Refrigerant Sample Production
Team: Refrigeration Department
Date Revised: 4/23/2015

3. 3
Recieve Raw
Materials from
distributor
Yes - Raw
Materials
Blended into
Refrigerant
Refrigerant
Mixtures sentto
Analytical for QC
Raw
material
In Spec?
Raw Materials
Analyzed for
Quality in
Analytical
No - Send
material
back to
Refrigeraant
Mixture in
Spec?
No, sample
slightly out of
spec - Adjust the
sample to put
parameters in
spec
No, Sample to far
out of Spec -
Prepare New
Mixture and send
to Analytical
Yes - Ship
Mixture sample
to Customer
How far out
of spec is the
Mixture ?

4. Measure : Experimental Set Up
• Experimental Procedure
•Refrigerant Gas Mixture contains 3 chemical components
•Three weight standards were prepared by weight percent on an analytical scale
•The GC produced a signal for each component for each refrigerant gas standard
mixture
•The GC instrument was then “told” the actual weight percentage to which each
signal corresponded
•The standards were rerun as unknowns to evaluate the GC’s ability reproduce
the signal that matches the known weight percent
•Note: The blend specifications are 30%, 30%, 40% component A, B, and C
respectively
•THE INSTRUMENT “KNOWSTHE ANSWER” can it reproduce the answer when
the same samples are introduced as unknowns?
•If not then we cannot rely on it to tell us if an unknown is within specifications
4

5. Measure – Check For Normality
• Data Sets for Components A, B, and C are not Normality
Distributed AcrossThree Standards
• Normal Quantile plot shows poor normality character
• For all three components the spread of data by the bar graphs
shows an 80000 ( component B) to as much 150000 (component
C) area count spread in the range of samples
• This range needs to be smaller for investigators to have
confidence in the GCY output/signal
5

6. Component A
6

7. Component B
7

8. Component C
8

9. MEASURE: Key Performance
Indicator –Y = % Area
• The GC Operational Definition of the KPI is the Area % signal
• Area % Signal needs to match known Weight Percent of analyzed
standards within 0.5%
• These standards were used to write the calibration methods on the
GC software
9

10. MEASURE:Variability of GC Signal of Component A versus %Weight of
Component A
10

11. Variability Chart for GC Signal B
11

12. Variability Chart for GC Signal C
12

13. Measure – Summary of Results
• Component A in Standard 1 experienced wide variability about the mean (slide 10)
• Component C in both standards 2 and 3 experienced wide variability about the mean
(slides 11 and 12)
• Component B seems to be the most consistent measurement within the 3 standards
• Example: The GC is not measuring the same signal level for a 28% weight percent of
component A in standard 1
• Therefore the GC cannot measure three components consistently enough to tell us if
unknowns are in spec
13

14. ANALYZE: Identify root cause(s)
• Data range for each component is too wide
• Percent Weight of Composition and Percent Area FitY by X
• Linear Regression analysis to further demonstrate the wide range in
data
• Supporting Evidence that the GC does not produce consistent data
• Possible Root Causes
• Sampling Error
• Each standard was sampled by two separate samplings and GC was
inconsistent across all samplings
• Machine in need of repair/recalibration by outside contractor
14

15. Bivariate Fit of GC Signal of Component
A versusWeight Percent
15

16. Bivariate Fit of GC Signal B By %Weight
Component B
16

17. Bivariate Fit of GC Signal C By %
Weight Component C
17

18. Analyze – Poor Repeatability Leads to Large
Range in SampleAnalysis
• Area counts should line up tightly on the trend line with weight
percent and they do not in this analysis
• Example :
• The first Standard with 28% A should have all signal
measurements relatively close
• Instead there is a spreading of the data from 160000 area counts
to 280000 area counts
• This is to much…Which is the correct area count for the known
weight quantity?
• This trend is repeated for the other 2 components
• Poor reproducibility also occurs
• Cannot produce accurate data that is produces by other
operators on other machines (data not shown)
18

19. ANALYZE: Root Cause Summary
•Failure mode analysis was used to determine possible root
causes:
• Sampling error
• Error in standard preparation
• Scale drift/error in weight standard preparation
• Component fractionation by vapor pressure
difference
19

20. IMPROVE: Solutions Identification
andValidation
• Weighing errors made at the time of blending mixtures on the
scale was identified as a primary source of error
• Flow into the filling cylinder was faster than the rate the scale
could measure the weight changes
• The technician would observe erroneous weight change and
base standard % weight calculations off of the incorrect scale
reading
• Altering the Standard Operating procedure for cylinder filling
while preparing standards provided more accurate and
validated results
• Validation by analytical department, not in house Gas
Chromatograph
20

21. CONTROL Phase
• Control – “maintain the gain” of a functional instrument
• Run calibration checks during down time
• Keep up on routine maintenance of instrument hardware
• Keep a log book of usage to ensure constant upkeep of the
instrument and a record of samples analyzed
• While the focus of this study was the GC, the error was found on
the scale that assisted in determining standard % weight
composition
• Consistently evaluate SOP’s for the presents of failure modes and
improve SOP’s accordingly
21

22. Conclusions
• This project has stalled in the Improve phase asValidating
the root cause was determined to be a lower priority than
other work
• Prioritization of the blend operations would eventually be
lowered
• Operation of the GC by the department was ended
• Standards for blend compositions were also lowered to
provide room for more error in blend preparation
• DMAIC in this case provided perspective
• i.e.A department whose scope of work is in mechanical refrigeration
should not place high priority on analytical chemistry
22