Bridging the Lab Process Line Gap C. Rechsteiner Chevron ETC, Richmond, CA B. Rohrback Infometrix Inc., Bothell, WA

1. Bridging the Lab � Process Line GapC. RechsteinerChevron ETC, Richmond, CAB. RohrbackInfometrix Inc., Bothell, WA

2. Current State In current control applications, there is a clear preference to obtain the minimum number of process measurements that allow one to control the process. �I don�t want too many measurements, they make my model unstable.� For process GC applications, one either measures a few discrete components, or you need an assist from the local support laboratory.

3. Future Trends Miniaturized instrumentation Fast instrumentation Remote access to sample points Abundant data rich analyzers More precise control needed Changing regulations Changing products Convergence of labs and process analytics Resource limitations manpower, skill sets, materials, �

4. Future Solutions To meet these challenges, we need: to implement analyzers capable of measuring more critical process parameters; to extract more information from those analyzers; to better utilize computational advances; and to put more smarts into our analyzers. This will make our jobs manageable!

5. Implications This means that: Instruments must be smarter respond quicker make data rich measurements and smartly reduce the data to a reasonable number of �model-able� parameters. Instruments must heal themselves (or at least act as a diagnostician) Instruments should be the �same� in either the process or the laboratory environment

6. The Needed Tools Robust instruments Fast spectral or chromatographic alignment Fast pattern recognition with heuristics to determine how steady, steady-state is A knowledge base allows recognition of common instrument faults and communicates the corrective actions to the appropriate party Common platform that spans all data-rich analyzers

7. Steps Along the Way The Hardware Spanning the variety of gas chromatographic systems

8. Different GCs Giving Similar Results

9. Must Have�a Good � System

10. Must Have�a Good Sampling System

11. Must Have�a Good Sampling-Control System

12. Must Be Self Contained

13. Must Have�a Good Control Shed

14. Steps Along the Way The Alignment Advantage Seeing process detail that would otherwise be missed.

15. On-line SimDis (Siemens Maxum II GC)

16. On-line simulated distillation

17. Comparison of PCA scores 85% of all of the variation in the raw data is due to the misaligned peaks. Correcting for this shows us that there are three different production regimes in these data.

18. Why Simulated Distillation (SimDis)? The primary refinery separation process is distillation. Physical distillation can take significant time, requires a largish sample skilled manpower, and so-so reproducibility.

19. SimDis can be done at/near-line with small samples, good reproducibility in reasonable time, and, if there are no problems, little manpower. SimDis retains the data-richness of chromatographic methods, which can be exploited. SimDis performance is fairly well understood and measurable.

20. The case for alignment - 1

21. The case for alignment - 2

22. The case for alignment - 3

23. The case for alignment - 4

24. The case for alignment - 5

25. The case for alignment - 6

26. Aligning Multiple Instruments The above chromatogram shows runs of a C8 to C19 hydrocarbon mixture on three instruments. Although the run-to-run variability is small on a single instrument, there are differences among the three instruments. The above chromatogram shows runs of a C8 to C19 hydrocarbon mixture on three instruments. Although the run-to-run variability is small on a single instrument, there are differences among the three instruments.

27. Steps Along the Way Building the Knowledge Component Seeing process detail that would otherwise be missed

28. PCA for Interpretation

29. PCA of Aligned Chromatograms

30. Steps Along the Way Working with Data-Rich Measurements Simulating in the Laboratory

31. Using DHA Reports as a Data-Rich Source After the alignment and DHA steps have been completed, it may be useful to perform another multivariate prediction on the DHA report to confirm the original material identity.After the alignment and DHA steps have been completed, it may be useful to perform another multivariate prediction on the DHA report to confirm the original material identity.

32. Identifying the Big Problems Two additional samples were run and compared to the model. In this case, these gasolines show a pattern in the report table that is statistically-different from our expectations. The direction of the new points compared to the mass tells what peaks are responsible for this excursion.Two additional samples were run and compared to the model. In this case, these gasolines show a pattern in the report table that is statistically-different from our expectations. The direction of the new points compared to the mass tells what peaks are responsible for this excursion.

33. Where Are We? The pieces are coming together! We have made progress towards implementing a novel micro-GC for Simulated Distillation. The unique trapping approach of this system is compatible with the SimDis applications. Chemometric alignment will be essential for data-rich measurements to assure consistent data. Chemometric alignment has value even for a low resolution chromatographic techniques.

34. Where Are We? The pieces are coming together! Alignment can be automated for plant use. Alignment and chemometric identification techniques can provide effective analysis of complex data at the process line. These techniques can reduce the burden on highly skilled manpower to interpret complex data.