The CPAC NeSSI GC Initiative by 7BvvE875

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									The CPAC NeSSI GC Initiative


     Brian Rohrback, Infometrix, Inc.




            IFPAC: February 23, 2006    1
          Poll of process users


1. Analytical failure prediction
2. Result validation
3. More process-specific information (timely,
   higher quality, more focused)
4. Simplification of procedures
5. Reduction in the cost of ownership



               IFPAC: February 23, 2006         2
       For chromatography specifically
• Reliability and maintainability (unattended
  operation)
• Method performance (discrimination, precision,
  accuracy)
• Reduction in cost of equipment and connections
• Speed of analysis
   – Continuous repetitive sampling, multistreaming
   – Shortening lag time
   – Reduction of downtime


                    IFPAC: February 23, 2006          3
                    Manpower crunch

August 2005 UBS newsletter, Investment Intelligence
  “Where have all the workers gone? Investing during the
  labor drought”
   – Over the next few years, millions of baby boom US workers will
     retire, creating a shortage that the follow-on generations have
     neither the numbers not the education and skills to fill.
   – Some older workers will choose not to retire completely, staying at
     the workplace at some level of activity, but these workers will be
     far too few in numbers to make up for the expected loss.


                  reference Geoff Dolbear Newsletter #59: September 2005


                         IFPAC: February 23, 2006                          4
             And in the oil industry…

US Oil sector shed more than 500,000 jobs between 1982 and
  2005
American Petroleum Institute surveyed and found a projected
  need for 30,000 engineers over the next 5 years
Total current enrollment in petroleum-related engineering
  programs in US universities stands at 1,500


                      http://www.ptsstaffing.com/ptsnews_05q3.pdf




                     IFPAC: February 23, 2006                       5
and from the Gulf Coast Conference (10-2005)

Bill Winniford, DOW: We have to make do with a lot fewer people and
   they will have more to do. We are entering a time we have never seen
   before.
    – Chief frustration is in data processing
    – We are not good at capturing knowledge from the experienced workforce
Randy Shearer, GE: We are faced with an ageing workforce worldwide
  and are graduating fewer scientists and engineers in the US. We need
  (among other things) quality standards for products, processes and
  services. We also need to apply the continuing improvements in
  computer technology.
    – Chromatography is a key area
    – In GC that means faster analysis (and comprehensive 2D GC)



                           IFPAC: February 23, 2006                           6
             In equation form

We want -
       d[control]
          dt      >0


      We have -
                   d[expertise]
                                <0
                       dt


                  IFPAC: February 23, 2006   7
                  Responsibilities of SAM

• Manage registration and collect data from the suite of NeSSI sensors
• Perform consistency checks to validate the data
• Incorporate rules and control automating sample flow and recalibration
• Correct multivariate signals (spectroscopic and chromatographic) to
  account for drift
• Apply automated pattern recognition algorithms to interpret corrected
  sensor input
• Communicate full data and interpretation to a process experience
  database; and
• Send a reduce data feed to the DCS using the process standard OPC
  mechanism.



                           IFPAC: February 23, 2006                        8
              Responsibilities of NeSSI GC

• Collect and store data from one or more GCs
• Perform consistency checks to validate the data
• Incorporate rules and control automating sample flow and recalibration
• Correct chromatographic signals to account for drift (i.e., changes in
  retention time
• Apply automated pattern recognition algorithms to interpret corrected
  chromatograms
• Communicate full data and interpretation to a process experience
  database; and
• Send a reduce data feed to the DCS using the process standard OPC
  mechanism.



                           IFPAC: February 23, 2006                        9
Run to run consistency

       Fast GC runs - 94 injections raw data




                  IFPAC: February 23, 2006     10
Run to run consistency

        Fast GC runs - 94 injections aligned




                                             Using LineUp™

                  IFPAC: February 23, 2006                   11
PCA scores before and after alignment

                           Alignment gives us:
                           • A means of eliminating the run-
                             to-run variability that affects all
                             forms of chromatography.
                           • Requires no internal standards
                             and no use (in the traditional
                             sense) of external standards, both
                             of which are impractical.




            IFPAC: February 23, 2006                               12
Instrument to instrument consistency

       3 instruments: C8 to C19 hydrocarbons




                  20                    40       60
                       Time (seconds)


                                             Raw data

                  IFPAC: February 23, 2006              13
Instrument to instrument consistency

       3 instruments: C8 to C19 hydrocarbons




                  20                    40        60
                       Time (seconds)


                                             Auto-Aligned

                  IFPAC: February 23, 2006                  14
      Focus on the C10 and C11 peaks




16     18      20     22                16         18      20     22
     Time (seconds)                              Time (seconds)




                      IFPAC: February 23, 2006                         15
               Alignment gives us ...

• A straightforward approach to transferring the calibration
  of one instrument to another.
• Compare to what we are forced to do in optical
  spectroscopy.




                      IFPAC: February 23, 2006                 16
                  System suitability
• With aligned chromatograms, we can statistically define a
  “sweet spot” in the overall instrument performance.
• Any deviation from the expected can be flagged
  immediately at the end of a run of the performance
  standard.
• If the problem has been seen before, it can be added as
  another class in the model and can be identified by name.
• Each task can be performed automatically.
• A model based on a single instrument appears to work for
  additional instruments running similar configurations.


                     IFPAC: February 23, 2006                 17
                 HRVOC specification
• Samples will need to be collected automatically at a specified
  frequency at minimum of one every 10 minutes (144/day,
  1008/week).
• Data collection will be unattended for extended periods of time;
  those on-site cannot be expected to do QC checks.
• Any QA check samples should be indicated and evaluated
  independently
• Processed data needs output restricted to location, time/date
  and target concentration or interpretation “without all the extra
  chromatographic info”.
                                                   ActiveX
                        IFPAC: February 23, 2006                      18
Fast GC data processing in action




                             When a sample is collected, the
                             quality metric is pulled from the
                             results of a multivariate analysis
                             interpreting the chromatogram.
                             The concentrations are presented
                             in ppb and are derived from the
                             instrument reports from one of
                             two columns

          IFPAC: February 23, 2006                                19
 The sample name/time is interactive




Sample names are organized
by date and time, most recent
at the top of the list. Each
name is a link to a picture of
the chromatograms.

                      IFPAC: February 23, 2006   20
One or more columns can be filled with
  automated interpretation results

                            In this case, the quality column
                            monitors the following conditions:
                            Background – normal (low)
                            background
                            Flame out – FID out
                            Calibration – determined to be a
                            calibration standard
                            Event – chromatogram shows some
                            abnormality
                            N/A – Fault in the data collection


             IFPAC: February 23, 2006                            21
One or more columns can be filled with
  automated interpretation results




                          The color of the total non-methane
                          hydrocarbons identifies the level:
                          Green – HRVOCs total 0-399 ppb
                          Orange – HRVOCs total 400-999 ppb
                          Fault – HRVOCs total 1 ppm or more



             IFPAC: February 23, 2006                          22
Feeding data forward




    IFPAC: February 23, 2006   23
                      Summary
• To use chromatography in a fast response mode, we need
  to process the data automatically.
• Retention time drift is a constant companion but can be
  corrected by software (LineUp) in a fully automated way.
• Alignment can unify data from more than one
  chromatograph yielding unattended assessment of samples,
  the instrument, and the process as a whole.
• The ActiveX approach is quick to implement and allows
  easier connection to commonly used software packages
  downstream.


                    IFPAC: February 23, 2006                 24

								
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