Скачать презентацию Bridging the Lab Process Line Gap C Скачать презентацию Bridging the Lab Process Line Gap C

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Bridging the Lab – Process Line Gap C. Rechsteiner Chevron ETC, Richmond, CA B. Bridging the Lab – Process Line Gap C. Rechsteiner Chevron ETC, Richmond, CA B. Rohrback Infometrix Inc. , Bothell, WA © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Current State • In current control applications, there is a clear preference to obtain 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. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Future Trends What • Miniaturized instrumentation Why n Changing regulations n Changing products • Future Trends What • Miniaturized instrumentation Why n Changing regulations n Changing products • Fast instrumentation n Convergence of labs and • Remote access to sample points process analytics n Resource limitations • Abundant data rich analyzers l manpower, • More precise control needed l materials, … © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA l skill sets,

Future Solutions • To meet these challenges, we need: n to implement analyzers capable Future Solutions • To meet these challenges, we need: n to implement analyzers capable of measuring more critical process parameters; n to extract more information from those analyzers; n to better utilize computational advances; and n to put more smarts into our analyzers. This will make our jobs manageable! © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Implications • This means that: n Instruments must l be smarter l respond quicker Implications • This means that: n Instruments must l be smarter l respond quicker l make data rich measurements and l smartly reduce the data to a reasonable number of “model-able” parameters. n Instruments must heal themselves (or at least act as a diagnostician) Instruments should be the “same” in either the process or the laboratory environment © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The Needed Tools • Robust instruments • Fast spectral or chromatographic alignment • Fast 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 © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Steps Along the Way The Hardware Spanning the variety of gas chromatographic systems © Steps Along the Way The Hardware Spanning the variety of gas chromatographic systems © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Different GCs Giving Similar Results © Chevron 2008 Presented at Spring 2008 CPAC Meeting Different GCs Giving Similar Results © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Must Have… a Good … System © Chevron 2008 Presented at Spring 2008 CPAC Must Have… a Good … System © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Must Have… a Good Sampling System © Chevron 2008 Presented at Spring 2008 CPAC Must Have… a Good Sampling System © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Must Have… a Good Sampling-Control System © Chevron 2008 Presented at Spring 2008 CPAC Must Have… a Good Sampling-Control System © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Must Be Self Contained © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Must Be Self Contained © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Must Have… a Good Control Shed © Chevron 2008 Presented at Spring 2008 CPAC Must Have… a Good Control Shed © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Steps Along the Way The Alignment Advantage Seeing process detail that would otherwise be Steps Along the Way The Alignment Advantage Seeing process detail that would otherwise be missed. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

On-line Sim. Dis (Siemens Maxum II GC) 400 Samples Un-Aligned Same Samples Aligned © On-line Sim. Dis (Siemens Maxum II GC) 400 Samples Un-Aligned Same Samples Aligned © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

On-line simulated distillation The plot overlays 400 chromatograms collected over 6 days aligned © On-line simulated distillation The plot overlays 400 chromatograms collected over 6 days aligned © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Comparison of PCA scores Before alignment After alignment • 85% of all of the Comparison of PCA scores Before alignment After alignment • 85% of all of the variation in • Correcting for this shows us that the raw data is due to there are three different misaligned peaks. production regimes in these data. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Why Simulated Distillation (Sim. Dis)? • The primary refinery separation process is distillation. • Why Simulated Distillation (Sim. Dis)? • The primary refinery separation process is distillation. • Physical distillation can n take significant time, n requires a largish sample n skilled manpower, n and so-so reproducibility. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Why Simulated Distillation (Sim. Dis)? • Sim. Dis can be done n at/near-line with Why Simulated Distillation (Sim. Dis)? • Sim. Dis can be done n at/near-line with small samples, n good reproducibility in reasonable time, n and, if there are no problems, little manpower. • Sim. Dis retains the data-richness of chromatographic methods, which can be exploited. • Sim. Dis performance is fairly well understood and measurable. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The case for alignment - 1 Unaligned Chromatograms © Chevron 2008 Presented at Spring The case for alignment - 1 Unaligned Chromatograms © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The case for alignment - 2 Boiling Point Calibrated Chromatograms © Chevron 2008 Presented The case for alignment - 2 Boiling Point Calibrated Chromatograms © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The case for alignment - 3 Chemometric Aligned Chromatograms © Chevron 2008 Presented at The case for alignment - 3 Chemometric Aligned Chromatograms © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The case for alignment - 4 Yield curve comparison for the 12 runs BP The case for alignment - 4 Yield curve comparison for the 12 runs BP Calibrated Chemometric Alignment © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The case for alignment - 5 Data Bias © Chevron 2008 Presented at Spring The case for alignment - 5 Data Bias © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

The case for alignment - 6 Data Bias - Closeups © Chevron 2008 Presented The case for alignment - 6 Data Bias - Closeups © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Aligning Multiple Instruments Raw data 20 Time (seconds) 40 60 Auto. Aligned 20 © Aligning Multiple Instruments Raw data 20 Time (seconds) 40 60 Auto. Aligned 20 © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA Time (seconds) 40 60

Steps Along the Way Building the Knowledge Component Seeing process detail that would otherwise Steps Along the Way Building the Knowledge Component Seeing process detail that would otherwise be missed © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

PCA for Interpretation Alkylates Naphthas Reformates © Chevron 2008 Presented at Spring 2008 CPAC PCA for Interpretation Alkylates Naphthas Reformates © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

PCA of Aligned Chromatograms Alkylates Naphthas Reformates © Chevron 2008 Presented at Spring 2008 PCA of Aligned Chromatograms Alkylates Naphthas Reformates © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Steps Along the Way Working with Data-Rich Measurements Simulating in the Laboratory © Chevron Steps Along the Way Working with Data-Rich Measurements Simulating in the Laboratory © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Using DHA Reports as a Data -Rich Source Winter gasoline © Chevron 2008 Presented Using DHA Reports as a Data -Rich Source Winter gasoline © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Identifying the Big Problems Outliers: • Instrument problem? • Process upset? • Stream Error? Identifying the Big Problems Outliers: • Instrument problem? • Process upset? • Stream Error? 95% confidence interval © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Where Are We? The pieces are coming together! • We have made progress towards 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 Sim. Dis 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. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

Where Are We? The pieces are coming together! • Alignment can be automated for 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. © Chevron 2008 Presented at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA