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

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Presentation on theme: "Process Analytics: Improving Measurement Capability in your Plant AIChE Meeting: Nov. 17, 2009 Steve Wright Process & Environmental Analytics Eastman Chemical."— Presentation transcript:

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 Insert Break Lab Queue Enter Sample Order Ye Olde Bucket/Spigot Wait for Truck Wait for Truck II GC Results Sample Point Create Report Results! Insert Lunch

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

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

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

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

13 Good Measurement can Lead to Great Processes 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!!! Two Hours 70 deg C 20 deg C % Completion 60% 80% The Reaction Continues…

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

16 Process Analyzers & Sensors

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

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

19 Area / Point Monitoring  Combustible Gases o Normally report values in terms of % of lower explosive limit (LEL) o Not specific to gas – detect hydrocarbons o Catalytic sensor Combust the sample Require oxygen o 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 o Sumps / Pure Water / Condensate & Discharges Material release Quality Contamination o Process Inferential composition measurement pH control for reactions / batch processes  Dissolved Oxygen o Wastewater Treatment

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

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

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

24 Photometric Methods Photometers  UV/NIR/Non-dispersive IR  Use specific wavelengths = 1 or 2 components  Solids: non-contact o % moisture  Gases o CO, CO2, NOx, SO2  Liquids o % 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 0-10 gpm gpm Rapid Bypass Loop Filter PP Flow Integrity Monitor Analyzer Sample Loop

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

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 pH 4 pH X.XX pH 3.95 pH 4.15 Analyzer Benchmarking  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. pH 4.0 pH 3.9 pH  -3  Target pH 4.00 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:

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