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Current State

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

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Current State

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  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. 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, … Future Trends What Why

  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 GivingSimilar 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) 400 Samples Un-Aligned Same Samples Aligned

  16. On-line simulated distillation The plot overlays 400 chromatograms collected over 6 days aligned

  17. 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. Comparison of PCA scores Before alignment After alignment

  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. Why Simulated Distillation (SimDis)? • 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 Unaligned Chromatograms

  21. The case for alignment - 2 Boiling Point Calibrated Chromatograms

  22. The case for alignment - 3 Chemometric Aligned Chromatograms

  23. The case for alignment - 4 Yield curve comparison for the 12 runs BP Calibrated Chemometric Alignment

  24. The case for alignment - 5 Data Bias

  25. The case for alignment - 6 Data Bias - Closeups

  26. Aligning Multiple Instruments Raw data AutoAligned 20 40 60 Time (seconds) 20 40 60 Time (seconds)

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

  28. PCA for Interpretation Alkylates Naphthas Reformates

  29. PCA of Aligned Chromatograms Alkylates Naphthas Reformates

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

  31. Using DHA Reports as a Data-Rich Source Winter gasoline

  32. Identifying the Big Problems • Outliers: • Instrument problem? • Process upset? • Stream Error? 95% confidence interval

  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.

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