1 Terry Blevins Principal Technologist Emerson Process Management Austin, TX
2 QUALITY CONTROL 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.
3 PCA Principal Components Analysis Provides a concise overview of a data set. It is powerful for recognizing patterns in data: outliers, trends, groups, relationships, etc. PLS Projections to Latent Structures The aim is to establish relationships between input and output variables and developing predictive models of a process. PLS-DA PLS with Discriminant Analysis When coupled, is powerful for classification. The aim is to create predictive models of the process but where one can accurately classify future unknown samples.
4 Through the use of Principal Component Analysis (PCA) it will be possible to detect abnormal operations resulting from both measured and unmeasured faults. Measured disturbances may be quantified through the application of Hotelling s T2 statistic. Unmeasured disturbances The Q statistic, also known as the Squared Prediction Error (SPE), may be used. Faults are determined by comparing these calculated statistics to an upper limit An abnormal condition is indicated if the value exceeds the limit.
5 Integrated Order and Campaign Information / Visualization Web services and OPC On-line Data Analytics Batch Control and Historian Production Management Lab Info Mgmt Analyzer Management Process Automation & Asset Management Machinery Manager Busses Analyzers Instrumentation Machinery
6 Process holdups. Tools must account for operator and event initiated processing halts and restarts. Access to lab data. Lab results must be available to both the off-line and the online analytic toolsets. Variations in feedstock. The properties associated with each material shipment should be available for use in online analytic tools. Varying operating conditions. The analytic model must account for the batch being broken into multiple operations that span multiple units. Concurrent batches. The data collection and analysis toolset and online operation must take into account concurrent batches. Assembly and organization of the data. Efficient tools to access, correctly sequence, and organize a data set must be available to analyze the process and to move the results of that analysis online.
7 Batches Time Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 Batch 6 Batch.. b i X - Space On-line Process Measurements Batches all have variable length time durations Y - Space Quality Measurements
8 Before Dynamic Time Warping A key technology used in model generation Feature matches dissimilar length batches to a uniform length for analysis After
9 ISA S88.01 defines stage as: a part of a process that usually operates independently from other process stages and that usually results in a planned sequence of chemical or physical changes in the material being processed Analytic models are defined based on the batch stage consistent with ISA S88 model. The inputs and outputs used in analysis may be different for each stage.
10 When creating PCA and PLS models for a selected product, the X variables are defined by stages. Some X variables only apply to certain stages of manufacturing Define X variable per stage using stage selection
11 Form a multi-discipline team that includes plant operations Capture team input e.g. input-output data matrix Integrate Lab and Truck Shipment Data Identify Calculated Properties Instrumentation Survey, tune control loops Conduct Formal operator training
12 Process measurements, lab and Truck analysis over last year Analytic Process Models Operator Interface Predicted End of Batch Quality Tank Design 1 Tank Design 2 Tank Design 3 Storage Tank Design Calculated Feed Composition Evaluation process operation Fault Detection PC 208 PT 208 IT Vent 209 FC TC RSP TC LT FT Coolant Reagent supply e.g. Ammonia TT 206 TT 207 FC Coolant return 201 ph AT AC AT FT Bioreactor Feed AC Dissolved e.g. Glucose 205 Oxygen Process measurements Charge e.g. Media RSP FC 202 Air FT 202 To Harvest
13 Analytics Overview If a fault is indicted in the overview screen, then selecting the batch number will bring up the Fault Detection view. Fault Detection 1 Parameter Trend (s) 2 3 Quality Parameter Prediction Contribution 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
14 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
15 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.
16 The trend confirms that the media flow rate is ~ 2 liters/sec which is much lower than the normal flow rate of 4 liters/sec.
17 The prediction plot confirms that the low oil flow rate has no impact on the predicted product density.
18 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.
19 Development of a bridge in the salt bin will reduce the flow of salt to the screw feeder and thus will impact the final product concentration The reduced salt flow is reflected in the change in mixer level when the screw feeder is turned on.
20 The reduced salt flow when a bridge develops in the salt bin is detected as an unexplained deviation.
21 Reduced salt flow is reflected by a less than normal change in mixer level while the salt feeder turned on.
22 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.
23 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.
24 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.
25 Impact of the change in measurement bias is shown as an immediate change in ph.
26 Longer term the faulty ph measurement is reflected in an abnormally low reagent addition being used to maintain the indicated ph.
28 PT 208 IT Vent 209 FC TC RSP TC LT FT Coolant Reagent supply e.g. Ammonia TT 206 TT 207 FC Coolant return 201 ph AT AC AT FT Bioreactor Feed AC Dissolved e.g. Glucose 205 Oxygen Charge e.g. Media Air RSP FC 202 FT 202 PC 208 To Harvest PT 208 IT Vent 209 FC TC RSP TC LT FT Coolant Reagent supply e.g. Ammonia TT 206 TT 207 FC Coolant return 201 ph AT AC AT FT Bioreactor Feed AC Dissolved e.g. Glucose 205 Oxygen Charge e.g. Media Air RSP FC 202 FT 202 PC 208 To Harvest Inputs Unit 1 RM1 add - T setting 1 st RM2 add and heat 2 nd RM2 add and hold Inputs Unit 2 RM1 add - T setting RM2 add and hold Recover Filter/adjust Drying Cartridge filtration Outputs Transfer Outputs
30 The beta test showed through several batches an increasing deviation of the density measurement of a critical component. This phenomenon was linked to the start of plugging which was quickly solved by applying steam without time cycle impact. The on-line tool indicated a problem going on the cooling system of the reactor It detected that the component charge was being introduced too slowly and that the reactor temperature was running a little bit higher. The problem was solved on the Aero cooler. These problems were going unnoticed with the existing monitoring and control systems.
31 Problem identified with an up stream boiler negatively impacting operations. A quote from the operations personnel follows: thanks to the Beta. An equipment failure was discovered in advance and avoid losing 5 hours per batch for the batch in process and also for the following batches before discovering the problem with the traditional manner. Probably some days would have be necessary to discover that type of mechanical problem without the Beta. (Boiler combustion air controller located in a bad accessible zone and thermal oil leakage). (we would have) discovered this latter with the periodic update of the indicators of efficiency, but we saved time earlier thanks to the beta. Earlier is better than too late!
32 The use of on-line batch data analytics will cause people to think in entirely new ways and to address process improvement and operations with a better understanding of the process. Its use will allow operational personnel to identify and make wellinformed corrections before the end-of-batch, and help ensure that batches repeatedly hit pre-defined end-of-batch targets. Use of this methodology with allow engineers and other operations personnel to gain further insight into the relationships between process variables and their impact on product quality parameters. It also will provide additional information to help process control engineers pinpoint where process control needs to be improved. The results Greater understanding of the process An increase in quality consistency Increased throughput More good batches!
33 Published Papers Robert Wojewodka and Terry Blevins, Data Analytics in Batch Operations, Control, May 2008 Terry Blevins and James Beall, First Steps to Address PAT Initiative, Pharmaceutical Canada, Volume 8 Number 4, March-April, 2008 Conference Presentations Robert Wojewodka, Terry Blevins, Benefits Achieved Using On-Line Data Analytics. Emerson Exchange, 2009 Terry Blevins, Overview of PAT and Application of PAT for Product Development, Life Science Session, Emerson Exchange, 2008 Robert Wojewodka, Willy Wojsznis, Process Analytics In Depth, Emerson Exchange, 2008 Robert Wojewodka, Terry Blevins, The Application of Data Analytics in Batch Operations, Emerson Exchange, 2008 Michel Lefrancois, Randy Reiss, Tools for Online Analytics, Emerson Exchange, 2008
34 Conference Presentations (Cont) Terry Blevins, Michael Boudreau, Yan Zhang, Trish Benton, Application of PAT in Product Development, Interphex2008 Conference Presentation, March, 2008 Philippe Moro, Christopher Worek, Integrating SAP Software into DeltaV, Emerson Exchange, 2008 Robert Wojewodka, Philippe Moro, Terry Blevins, Coupling Process Control Systems and Process Analytics to Improve Batch Operations, Emerson Exchange Presentation, 2007 Video Video: Scott Broadley, Trish Benton, Terry Blevins, Emerson - Broadley James Beta: Video: Robert Wojewodka, Philippe Moro, Terry Blevins Emerson - Lubrizol Beta: Books Michael Boudreau and Gregory McMillan, New Directions in Bioprocess Modeling and Control, Chapter 8, ISA, 2006
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