Advances in GC with Alignment for Real-Time Decision Making Brian Rohrback and John Crandall

Transcription

1 Advances in GC with Alignment for Real-Time Decision Making Brian Rohrback and John Crandall October 31, 2010 CPAC Workshop

2 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. Elimination of analytical discrepancies 6. Reduction in the lifecycle cost (cost of ownership) Driven in part by dwindling manpower, skill sets and capabilities! 2

3 Rethinking Gas Chromatography Lifecycle Cost Price paid Installation Size Simplicity Maintenance Power Resources( supplies, people) Modularity Rugged/Reliable Application Efficiency Flexibility/Ease of Use Multiple applications Lab or Line Speed of analysis Seconds or a small number of minutes Automated interpretation 3

4 Greener Analysis A 6890 GC consumes between 2250 and 2950 kw at peak draw. Air conditioning requires 40% of that energy to remove the waste heat. Contract analytical services company in California Environmental, remediation, hazmat 18,000 foot 2 facility, 19 GC or GC/MS instruments AC year-round, capacity is 62 tons, accounts for 50% of electrical use. Summer electric use 5,000 kwh/day Air conditioning is our biggest maintenance problem. Mike Brech & David Tsubota of BSK Analytical

5 Power delivery is 33% efficient I assume the 6890 averages 1kWh and include air conditioning at 0.4 kwh, so, A conventional GC in the lab requires 4.2 kwh of production at the power plant, peak at 12.6 kw! 5

6 Delivering Information Just having the measurements does not translate into control Remember, there are not enough skilled technicians to handle even the current workload. Chemometrics solves the information processing problem with 2 technologies: Alignment enables us to sell instruments that have vastly-lower calibration requirements. Interpretation algorithms automates the generation and the qualification of the information derived from the raw data. And if we can make all of our instruments look as much alike as possible. Interchangeability common interpretive base 6

7 Response Raw process chromatograms Full Data; Time Index (E +03) 7

8 Response The same chromatograms after alignment Full Data; Time Index (E +03) 8

9 Chemometrics for instrumentation: the value proposition All this results in the ability to make the most of the data you are collecting and enables Continuous validation of the instrument, possibly the entire process A vast improvement in the ability to automatically interpret the stream of data, leading to better feed-back and feed-forward control A better ability to maintain the process, the instrument and the multivariate model. Software Goal: continuous validation of the system performance and guarantee the quality of the data. 9

10 The data to information transition 10

11 Raw data in the C 15 to C 19 region for ten oils run in duplicate

12 C 15 to C 19 region after retention time alignment

13 C 15 to C 19 region after area normalization and alignment

14 QC of x-ray contrast agents Original HPLC data Aligned HPLC data 14

15 QC of x-ray contrast agents 15

16 QC of x-ray contrast agents 16

17 Amino acid analysis 17

18 Capillary Electrophoresis 18

19 Capillary Electropherograms 19

20 Gating Problem 20

21 Gating Problem Solved 21

22 Biodegraded crude oil

23 Chromatographic Alignment 3 instruments Time (seconds) Raw data 23

24 Chromatographic Alignment 3 instruments Time (seconds) Auto-Aligned 24

25 Automated alignment works 5 year period, 6 GCs 25

26 Automated alignment works 5 year period, 6 GCs aligned 26

27 Chemometrics for instrumentation: the value proposition Anything you can do to improve precision of the multivariate measurements collected by the instrument will allow you to tighten the control essentially for free. One way is to construct an application-specific, objective evaluation system: Experimental design Exploratory data analysis Leading to Multivariate modeling (qualitative and quantitative analysis) Just as key is the signal processing aspect of chemometrics to reduce instrument-derived variability Within an instrument (e.g., noise reduction) Between instruments (i.e., transfer of calibration) 27

28 Interpretation Data, data and more data Human qualities Good at seeing small differences Bad at quantifying small differences Fair at recollection Poor at seeing patterns in tables of numbers Objectives Quantify a property or attribute? Characterize the sample? 28

29 54 samples of M. intracellulare (1 lab) 29

30 38 samples of M. simiae (5 labs) 30

31 Variation in M. asiaticum 31

32 PCA Scores Plot After Alignment Samples of the same species cluster, some species to a greater extent than others. Also, species known to be similar express similar chromatographic profiles and cluster near each other in this factor space. 32

33 Use of decision points, hierarchical models Journal of Chromatographic Science vol

34 Continuous validation of a multivariate instrument We can correct retention times to match an applicationspecific relevant sample This eliminates the transfer of calibration problem in chromatography Common regression and classification algorithms can be applied automatically to infer physical properties or characteristics This allows us to bring more complex analyses into on-line use 34