Bridging the Lab Process Line Gap C

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 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 • 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 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 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 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 © 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 – Ne. SSI Session Seattle, WA

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 Meeting – Ne. SSI Session Seattle, WA

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 – Ne. SSI Session Seattle, WA

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 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 © 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 © 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 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. • 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 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 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

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 Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

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 - 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 © 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 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 Meeting – Ne. SSI Session Seattle, WA

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 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 at Spring 2008 CPAC Meeting – Ne. SSI Session Seattle, WA

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 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 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