Data Analytics Spectral Analyzer

1,452 views

Published on

This is a presentation given by Terry Blevins, Emerson, as part of a roundtable in the Life Science Industry Forum at Emerson Exchange 2010

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,452
On SlideShare
0
From Embeds
0
Number of Embeds
64
Actions
Shares
0
Downloads
27
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Data Analytics Spectral Analyzer

  1. 1. Data Analytic and Spectral Analyzer
  2. 2. Bridging the Gap with On-line Analytics  On-line Decision Support for Operations Personnel – Product quality predictions – Early process fault detection  Embedded On-line Analytics brings quality information, fault detection, and abnormal situation knowledge to the operator – bridging the gap between quality and control.  The PAT Guidelines issued by the FDA emphasized the use of multivariate analytics as a means of reducing cost, improving product quality in the pharmaceutical industry.  On-line Data Analytics is targeted for DeltaV v12. QUALITY CONTROL
  3. 3. Information at the Operator Interface Analytic Process Models Evaluation process operation Process measurements, lab and Truck analysis over last year Calculated Feed Composition Process measurements Operator Interface Predicted End of Batch Quality Fault Detection TT 207 TC 207 TT 206 TC 206 Coolant return Bioreactor RSP AT 205AT 204 FC 203 FC 201 FT 201 Feed e.g. Glucose AC 204 Reagent e.g. Ammonia FC 202 FT 202 Air pH AC 205 Dissolved Oxygen Vent PT 208 PC 208 RSP Charge e.g. Media FT 203 Coolant supply IT 209 LT 210 To Harvest TT 207 TC 207 TC 207 TT 206 TC 206 TC 206 Coolant return Bioreactor RSP AT 205AT 204 FC 203 FC 203 FC 201 FC 201 FT 201 Feed e.g. Glucose AC 204 AC 204 Reagent e.g. Ammonia FC 202 FC 202 FT 202 Air pH AC 205 AC 205 Dissolved Oxygen Vent PT 208 PC 208 PC 208 RSP Charge e.g. Media FT 203 Coolant supply IT 209 LT 210 To Harvest Storage Tank Design Tank Design 2 Tank Design 1 Tank Design 3 Storage Tank Design Tank Design 2 Tank Design 1 Tank Design 3
  4. 4. 3 Step Monitoring Procedure 1. If either Fault Detection plot exceeds or approaches the upper control limit of 1.0, click on that point in the trend and -> Select the Parameter in the lower corner of the screen that contributed to the fault 2. Evaluate the parameter trends from process operation standpoint -> take corrective action if necessary 3. Inspect impact of fault on quality prediction plot to find out how quality may be affected Note: Use Up arrow to return to the Analytics Overview. If a fault is indicted in the analytics overview screen, then selecting the batch number will bring up the Fault Detection view. Analytics Overview Quality Parameter Prediction ContributionParameter Trend (s) 2 Fault Detection 31
  5. 5. Example – Low Hot Oil Flow Rate  When the hot oil valve is opened, the flow rate is much lower than normal  The lower flow rate impacts the time needed for the mixer to reach target temperature – extending batch time
  6. 6. Example – Low Hot Oil Flow Rate  Fault shows up in Indicator 2 deviating above 1.  To find the cause of the fault, select the point of maximum deviation and then choose the Contribution Tab or select the parameters that contribute most to the fault - shown in the lower corner of the screen.
  7. 7. Example – Low Hot Oil Flow Rate  The trend confirms that the media flow rate is ~ 2 liters/sec which is much lower than the normal flow rate of 4 liters/second.
  8. 8. Example – Low Hot Oil Flow Rate  The prediction plot confirms that the low oil flow rate has no impact on the predicted product density
  9. 9. Prediction of Product Density  For the Saline process, the prediction of product density has proven to be very accurate even though variations in the salt bin level are a major source of variation in the processing conditions.
  10. 10. Example – pH Sensor Drift O2 Bioreactor VSD VSD TC 41-7 AT 41-4s2 AT 41-4s1 AT 41-2 TT 41-7 AT 41-6 LT 41-14 Glucose Glutamine pH DO Product Concentration VSD VSD AC 41-4s1 AC 41-4s2 Media Glucose Glutamine VSD Bicarbonate AY 41-1 AC 41-1 Splitter AC 41-2 AY 41-2 Splitter CO2 Air Level Drain 0.002 g/L 7.0 pH 2.0 g/L 2.0 g/L 37 o C VSD Inoculums VSD PT 41-3 Vent MFC MFC MFC PC 41-3 AT 41-15  Coating of the sensor may introduce a bias into the pH measurement - resulting in a shift of the pH maintained in the reactor.  May impact cell growth rate and product formation AT 41-1
  11. 11. Example – pH Sensor Drift  Fault shows up as an explained and unexplained change – deviation above 1.  To find the cause of the fault, select the point of maximum deviation and then choose the Contribution Tab.
  12. 12. Example – pH Sensor Drift  Drift in the pH measurement is reflected in the pH measurement and controller output.  A trend of the pH and pH controller output can be obtained by clicking on media flow parameter in the contribution screen.
  13. 13. Example – pH Sensor Drift  Impact of the change in measurement bias is show as an immediate change in pH.
  14. 14. Example – pH Sensor Drift  Longer term the faulty pH measurement is reflected in an abnormally low reagent addition being used to maintain the indicated pH.
  15. 15. Learning More A workshop is being offered at Emerson Exchange on data analytics and field trail at Lubrizol, Rouen. The schedule for this workshop is: 08-167 Batch Process Analytics (PA) – An In Depth Update – Tuesday, Room 206B, 10:00 AM – Thursday, Room 206A, 11:00 AM
  16. 16. Spectral Analyzers  Spectral analyzers may be used at critical points throughout the process. – Pharmaceutical - inspection of feedstock, blend uniformity, granulation, drying and coating and particle size analysis. Online QA/QC tool for production. – Chemical - acid value, adhesive content, cure, melt index, and polymer processes - reaction monitoring – Refinery, petrochemical - fuel production monitoring  A wide variety of commercial on-line, at-line, and laboratory spectral analyzers are available.  Calibration of an NIR analyzer is based on use of spectral data to develop principal component analysis(PCA) and projection of latent structures (PLS) models.
  17. 17. Example: NIR Analyzers  Careful development of a set of calibration samples and their use in PCA/PLS model development is the basis for near-infrared analytical methods.  For purposes of analysis, the spectral data for a sample should be saved and accessed as one set of data e.g. an array.  3-D plotting of spectral data can be helpful in screening samples and in analyzing on-line use of spectral data. Off-line PCA/ PLS Model Development On-line Quality Parameter Prediction Historian Array/Data Set Application station NIR Analyzer Controller VIM Interface 3-D Plot of Spectral Data
  18. 18. Learning More  The technical feasibility of providing 3-D plotting and historian collection of array data has been explore and the value of such a capability demonstrated in at a field trail conducted on an absorber and stripper process unit at UT Pickle Research Center, Austin, TX.  Two presentations on this field trail are scheduled for Emerson Exchange. 04-132 Process Analysis Using 3D plots – Tuesday, 3:00:00 PM, Room 207B – Thursday, 3:15:00 PM, Room 201

×