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Process Analytics: Improving Measurement Capability in your Plant

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  1. Process Analytics: Improving Measurement Capability in your Plant AIChE Meeting: Nov. 17, 2009 Steve Wright Process & Environmental Analytics Eastman Chemical Company

  2. Overview of Presentation • Introduction • Sampling Theory – Why Analyzers? • Process Analyzers and Sensors • Sampling Systems • Ownership and Maintenance • Summary and Q&A

  3. Introduction • Process Analytics is the • Analytical Measurement of • Chemical Composition • Chemical Properties of a Chemical Production Stream • Using one, or more, of four approaches • In-Situ / In-Line • Extractive • At-Line • Ambient Detection 100 ppm 100 ppm

  4. Eastman Process Analytics: Kingsport • Almost 2000 Process Analyzers and Chemical Sensors • Personnel: 41 chemists, engineers, techs and analysts. • Support: 24x7 where needed. • Responsibilities include: • Analyzer consultation/ specification • Analyzer system design/purchase • Sample system construction • Installation/checkout • Preventative maintenance • Reactive maintenance • Analyzer succession planning

  5. Why Analyzers?

  6. Measurement & Control MEASUREMENT CONTROL Can’t do one without the other..

  7. Majority of Process Measurement Tasks Can be Done Using Simple Sensors or Lab Analyses • Pressure, Temperature, Flow, Mass • Lab measurements – slow and steady processes.. • For the exceptions – process analytics..

  8. Traditional Reactor Sampling Path Sample Point Ye Olde Bucket/Spigot Wait for Truck Enter Sample Order Wait for Truck II Insert Lunch Results! Insert Break Create Report GC Results Lab Queue

  9. Under-Sampling (Aliasing) + SP - Time, Minutes GARBAGE ZONE + SP - Time, Minutes

  10. Meeting Nyquist.. Just barely Sampling Okay – but it’s breaking the bank.. + Comfort Zone – >4F to “over-sampling” SP - Time, Minutes GARBAGE ZONE + LAB $$$$$$$$$$$$$$$$$$$ SP - Time, Minutes

  11. Good Measurement can Lead to Great Processes Transitioning from Product A to Product B How long can we continue making “Good A” And when can we call “Good B”? 100% ? Good A Good B ?? 0% Time

  12. Sample Time-Stamping Errors Process trend Target Time Input Lead/Lag Control Complexities…. Variability Complicates it even More… Crew 1 is timely.. Crew 2 isn’t.. Early Early Error Lead Process Variable Late Error Lag Late Late Early Time “Just trying to keep up” “While we’re here, let’s save time & take the next sample”

  13. Good Measurement can Lead to Great Processes The Reaction Continues… % Completion 60% 80% Two Hours 20 deg C 70 deg C A  B at 70 deg C 0-100% in 20 minutes A  B at 20 deg C 0-100% in 640 minutes While waiting 2 hours for analysis  20% change!!!

  14. Models • Ultimate process understanding “victory” • Control process with lean measurements, T, P, flow, etc. • “As good as their input data” • Model response surface must be well-defined • Models tend to perform best in “known territory” • Prediction weakness can occur during critical times: • Upsets • Start-Ups/Shut-Downs • Product transitions • Direct measurement benefits • Often easier to set up and maintain than complex models • Full process interaction / understanding is not required to measure • Output can help build better models!

  15. Value Proposition “The Wall” “The Wall” Tight measurement & control Loose measurement & control – broad process performance Run Closer… “Wall” = > Impurities, lower value product, permits, safety issues, etc.

  16. Process Analyzers & Sensors

  17. Fixed or Dedicated Systems • Transmitter Style • Simple to install • Low cost • Can be used as “cheap” analyzers • If high accuracy not required • Inferential • Extractive sampling of processes • - Addition of flow cells and sample system • Direct Insertion Alan Hensley, 2009

  18. Ambient / Area Point Monitoring • Personnel Protection • Leaks or Spills • Electrochemical • Toxic Gases • Hydrogen sulfide • Chlorine dioxide • Carbon monoxide • Oxygen • % levels • Oxygen deficiency • High oxygen in processes Alan Hensley, 2009

  19. Area / Point Monitoring • Combustible Gases • Normally report values in terms of % of lower explosive limit (LEL) • Not specific to gas – detect hydrocarbons • Catalytic sensor • Combust the sample • Require oxygen • Infrared sensor • Can be used in oxygen deficient or inert environments • Where “poisoning” of catalytic sensor is of concern Alan Hensley, 2009

  20. Liquid Analytical • pH & Conductivity • Sumps / Pure Water / Condensate & Discharges • Material release • Quality • Contamination • Process • Inferential composition measurement • pH control for reactions / batch processes • Dissolved Oxygen • Wastewater Treatment

  21. Oxygen • Fuel Cell • % and ppm level measurements • Paramagnetic • % level measurements • Oxygen level in nitrogen convey systems • Zirconium Oxide • Stack monitoring • Handle dirty environments • High temperature operation Alan Hensley, 2009

  22. Physical Property • Density (not just for mass flow) • Inferential Composition Measurement • Depends on process stream • Turbidity • Contamination • Viscosity • Process Control Alan Hensley, 2009

  23. Fixed or Dedicated Systems • Traditional Style • Analyzer is remote from area • Extractive sampling with sample system • Higher cost • Higher complexity • Require more care and feeding • Stream Complexity • Accuracy • Specific

  24. Photometric Methods • Photometers • UV/NIR/Non-dispersive IR • Use specific wavelengths = 1 or 2 components • Solids: non-contact • % moisture • Gases • CO, CO2, NOx, SO2 • Liquids • % water, % organic acids Alan Hensley, 2009

  25. Process Analyzer Availability • If it can be done in the lab, it can installed on-line. • Up-Front cost issues / ROI • Sample handling issues • Our group will build it if we cannot buy it. • Integration tasks, sample handling systems • Panel Shop in B-359A FRONT VIEW

  26. Sampling Systems Maintenance

  27. Goals • Representative Sampling / Minimal Handling • Want sample to mirror process content • Minimal interaction with sample • Minimal sampling delays • Sample Compatibility with Analyzer Specifications • Temperature • Pressure • Flow • Viscosity • Particulates / Bubbles • Materials Compatibility

  28. In-situ Measurement • No sampling system • Pipeline/tank/line insertion • No delay – real time results • Probe design • Probe can be removed for cleaning – usually. • Exceptions would be high pressure / temperature applications • Representative! • Passive? Yes.

  29. Extractive Sampling Systems Flow Integrity Monitor Sample Loop Rapid Bypass Loop Analyzer 0-10 gpm 0-0.5 gpm DP Filter

  30. Extractive Sampling Systems • Sample stream removed from main process line • Advantages • Allows isolation from process (cleaning/calibration) • Filtration, dilution, P/T manipulation • Improved safety – block/bleed • Difficulties • Must have dP across sample loop - or • Sample pumping • Delays • Returning altered material to process or waste • Filtration maintenance – when needed

  31. Process Analyzer Maintenance Ownership Cycle..

  32. Maintenance is major cost of analyzer installation – process GC example Development Replacement Purchase Installation Improvement Maintenance Start-Up 70-90% Cost of Ownership

  33. Reliability  Maintenance Approach • Reliability – in degrees.. • Ideal • Lasts forever, accurate and precise – cheap to own too. • Reality (March to Entropy) • Machine components wear out • Unusual, unexpected events happen • Goal • Want capable function whenever machine is needed • High Availability Uptime. • Want ownership at lowest possible cost. • Reliability-Centered Maintenance Approach

  34. Maintenance Categories • Reactive (RTF) • Appropriate for ultra-high reliability, low criticality systems • Cheapest / Most Expensive approach – feeling lucky? • Preventative (PrM) • Process analytics use PrM • Shewhart control charts (+/- 3 sigma, run of eight) • Scheduled benchmarking visits • Predictive (PdM) • Maximum system availability at minimum cost. • Relies on obtaining detailed history at component levels. • We now have tools in-place to transition to PdM where needed.

  35. Analyzer Benchmarking +3s pH 4.1 • Apply standard of known concentration to analyzer • Read analyzer response • Compare response to standard response • Within control limits: • Note response, add to control chart • Walk away, just walk away….. • Outside of control limits, or eight either side of average: • Note response, add to control chart • Calibrate analyzer / determine cause / log • Avoids human tendency to over-control, chasing system noise. Much better for process stability. Target pH 4.0 pH 3.9 -3s pH 4.15 pH 4.00 pH 3.95 pH X.XX pH 4 pH 4 pH 4 Standard Buffer Solution

  36. Process Analyzer Maintenance • Effective PrM has greatly improved reliability of our analyzers • High availability up-times • Analyzer data can be trusted for monitoring & control • Productivity (analyzers/analyst) has greatly improved over the last 20 years • Better analyzer technology • Better diagnostics • Scheduled PrM • Improved tools such as OSI PI

  37. Questions & Answers • Thanks! • Steve Wright • Senior Development Associate • Process and Environmental Analytics • Bldg 359A • Eastman Chemical Company • Phone: 423-229-4060 • Email: sfwright@eastman.com