1 CPAC: November 10, 2005 NeSSIfying GC (a roadmap for SAM) Brian Rohrback, Infometrix, Inc.

1CPAC: November 10, 2005

NeSSIfying GC(a roadmap for SAM)

Brian Rohrback, Infometrix, Inc.


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


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


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)


In equation form

< 0

> 0d [control]

dt

We want -

We have -

d [expertise]dt


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.


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.


Fast GC runs - 94 injections raw data

Run to run consistency


Fast GC runs - 94 injections aligned

Run to run consistency

Using LineUp™


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.


3 instruments: C8 to C19 hydrocarbons

Raw data

20 40 60Time (seconds)

Instrument to instrument consistency


3 instruments: C8 to C19 hydrocarbons

Auto-Aligned

20 40 60Time (seconds)

Instrument to instrument consistency


Focus on the C10 and C11 peaks

16 18 20 22Time (seconds)

16 18 20 22Time (seconds)


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.


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.


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”.


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


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.


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


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


Summary

• To use chromatography in a fast response mode, we need to process the data automatically.

• Retention time drift is a constant companion that flummoxes the analysis but can be corrected by software (LineUp) in a fully automated way.

• The result of alignment is better run-to-run peak identification and can unify data from more than one chromatograph.

• With aligned data, interpretation is both more sensitive and more reliable, allowing unattended assessment of samples, the instrument and the process as a whole.

1 CPAC: November 10, 2005 NeSSIfying GC (a roadmap for SAM) Brian Rohrback, Infometrix, Inc.

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