1. Data Analytic and Spectral Analyzer

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. Information at the Operator Interface
Operator Interface
Process measurements, lab and Truck analysis over last year
Predicted End of Batch Quality
Analytic Process Models
Calculated Feed Composition
Evaluation process operation
Fault Detection
Process measurements
4. 2
3
1
3 Step Monitoring Procedure
Analytics Overview
If a fault is indicted in the analytics overview screen, then selecting the batch number will bring up the Fault Detection view.
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
Evaluate the parameter trends from process operation standpoint
-> take corrective action if necessary
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.
Fault Detection
Quality Parameter Prediction
Contribution
Parameter Trend (s)
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. 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. 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. 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. 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. Example pH Sensor Drift
TC
41-7
AC
41-4s1
AC
41-4s2
AC
41-1
AC
41-2
PC
41-3
VSD
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
VSD
Media
AT
41-15
PT
41-3
Vent
VSD
VSD
Inoculums
37 oC
TT
41-7
Glutamine
VSD
VSD
Glucose
Bicarbonate
7.0 pH
AT
41-4s1
Splitter
AY
41-1
AT
41-1
Glucose
2.0 g/L
pH
AT
41-4s2
0.002 g/L
AT
41-2
Glutamine
2.0 g/L
AT
41-6
Bioreactor
DO
Product Concentration
LT
41-14
MFC
CO2
AY
41-2
Level
VSD
Splitter
MFC
Air
Drain
MFC
O2
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. 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 controlleroutput can be obtained by clicking on media flow parameter in the contribution screen.
13. Example pH Sensor Drift
Impact of the change in measurement bias is show as an immediate change in pH.
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. 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. SpectralAnalyzers
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 developprincipal component analysis(PCA) and projection of latent structures (PLS) models.
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 accessedas 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.
3-D Plot of Spectral Data
Off-line PCA/ PLS Model Development
Historian
Application station
On-line Quality Parameter Prediction
Array/Data Set
NIR Analyzer
VIM Interface
Controller
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