1. MPC/PAT Advanced Closed Loop Control Continuous
pharmaceutical tablet manufacturing processing PAT on-line spectral
analysis
2. Presenters Paul Brodbeck Control Associates, Inc. Emerson
LBP Ravendra Singh Rutgers University Engineering Research Center
Rohit Ramachandran Rutgers University Engineering Research
Center
3. Photography & Video Recording Policy Photography and
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4. Engineering Research Center
5. Participants: Partner Schools: Rutgers (lead) Purdue NJIT
Univ. of Puerto Rico Team: 40 faculty, 80 students and postdocs, 40
companies, 120 industrial mentors ERC Overview C-SOPS Vision
National focal point for science-based development of structured
organic particle-based products and their manufacturing processes.
Lifetime Budget Highlights - 10 year program - Started 7/1/2006 -
$100 million total budget - $40 from NSF - $10 million from
universities - $1.5 cash memberships - $2 million in industrial
projects/yr - Other federal and state funding Distribution - 11%
Administration - 15% Education - 74% Research Participants Partner
Schools: FDA Rutgers (Lead) Industry: Purdue 40 Companies NJIT 120
Mentors U. of Puerto Rico Team: 40 Faculty 80 Students &
Post-Docs
6. 22 Research Structure
7. fromblendingtotableting Test Bed 1: Continuous tablet
manufacturing Impact Improved product quality Uniformity Reduction
of effects of segregation and agglomeration Better stability Cost
reduction Improved supply chain management Lower investment, raw
material & labor cost Simplified scale up Same equipment for
development and production
8. Testbed Singh, R., Boukouvala, F., Jayjock, E.,
Ramachandran, R. Ierapetritou, M., Muzzio, F. (2012). GMP news,
European Compliance Academic (ECE),
http://www.gmp-compliance.org/ecanl_503_0_news_3268_7248_n.html
9. Emerson Role w/ ERC Emerson provided DeltaV systems One at
Rutgers One at Purdue One at UPRM Optimal provided synTQ at Rutgers
Control Associates Mentor for TestBed 1 Continuous Tablet
Manufacturing Application support Control Modules synTQ
Orchestrations System integration Camo, Bruker, DeltaV, Matlab,
synTQ
10. C-SOPS Industrial Value Chain Technology Suppliers
Technology Integrators End Users
11. Level 1 Members
12. Level 2 Members
13. TRISKELL Level 3 and 4 Members
14. Introduction Test Bed 1 Continuous Direct Compaction Tablet
Manufacturing PAT synTQ, Camo Unscrambler X, Bruker Matrix &
JDSU NIRs Advanced Control Model Predictive Control (MPC)
Comparison of MPC, PID, & Smith Predictor FDA Guidelines QbD,
DoE FDA regulation Commercialization
15. Control system flowsheet model - gPROMS
16. Designed control system - gPROMS Singh, R., Ierapetritou,
M., Ramachandran, R. (2013). European Journal of Pharmaceutics and
Biopharmaceutics, http://dx.doi.org/10.1016/j.ejpb.2013.02.019
Singh, R., Ierapetritou, M., Ramachandran, R. (2012). International
Journal of Pharmaceutics, 438 (1-2), 307-326.
17. Model predictive control (MPC) 22 2 1 1 1 1 1 1 1 1 y u u n
n nP M M y set u u j j j j j j j j i j i j i j J w y k i y k i w u
k i w u k i u y: Controlled variable u: Actuator u: Predicted
adjustment manipulated variable deviations Controlled variable
deviations controller adjustments Singh, R., Ierapetritou, M.,
Ramachandran, R. (2013). European Journal of Pharmaceutics and
Biopharmaceutics, http://dx.doi.org/10.1016/j.ejpb.2013.02.019.
Tuning parameters 1. Output weights (w y j) 2. Rate weights ( )
3.Input weight ( ) 4. Prediction horizon 5. Control horizon u jw u
jw
18. DeltaV Operate Graphic
19. Control Scheme Flowchart(s)
20. Design MPC: Hybrid MPC-PID (set point tracking) Note: Final
actuator: Rotational speed of API feeder Slave controller: PID
21. Design MPC: Hybrid MPC-PID (disturbances rejection) Note:
Final actuator: Rotational speed of API feeder Slave controller:
PID
22. Performance evaluation (set point tracking) Control
variable: Total flow rate from blender Cascade PID (scheme 1)
Hybrid MPC-PID (scheme 3)
23. Performance evaluation Control variable: API composition
Control variable: RSD
24. Performance evaluation 0 2 4 6 8 10 12 14 16 18 20 22 0 200
400 600 800 1000 1200 1400 1600 1800 2000 Flowrate(kg/hr) Time (S)
Set point Closed-loop Open-loop Upper limit Lower limit 0.00014
0.00016 0.00018 0.0002 0.00022 0.00024 0.00026 0.00028 0.0003
0.00032 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Weight(kg)
Time (S) Set point Open-loop Closed-loop Upper limit Lower limit 0
500 1000 1500 2000 2500 3000 3500 4000 4500 5000 90 100 110 120 130
140 150 160 170 Time (S) Hardness(MPa) Set point Achieved
profile
25. Implementation of control system: Options Following control
strategies have been considered: Hybrid MPC-PID scheme PID Scheme
PID with Smith predictor Using the mathematical model in place of
plant and sensor for performance evaluation Following control plate
forms have been considered: Emersion DeltaV system and SynTQ
Emersion DeltaV system and MATLAB OPC
26. Control hardware and software integration Step 2 Step 4
Step 1 Step 3
27. Steps 1-3. Overview Prediction model Input folderInput
folder Output folderOutput folder Write to OPC DeltaV system DeltaV
system Write to DeltaV MATLAB OPC Tool Read from DeltaV JDSU micro
NIR user interface Unscrambler process pulse user interface
28. Step 4. Overview
29. Step1: Sensing Standard physical testing, while accurate,
is slow, labor- intensive and DESTRUCTIVE. Means are needed to make
rapid, multivariate measurements on solid materials. This suggests
spectroscopy. Most common industrial PAT tools are: Raman and NIR
Both technologies originate from bond vibration Basic Harmonic
Oscillator Why spectroscopy?
30. Step 1: Raman & NIR: Some Pharmaceutical Applications
Application Raman NIR Raw Material ID X* X Content Uniformity X X
Blend Uniformity X X Polymorph Studies XX X Particle Size X Density
X Moisture Content XX Reaction Monitoring X X Inorganics X XX
Excellent, X Good, Not Recommended
31. Step 1: NIR tools API Composition of powder blend Total API
content of a tablet MPA transmission NIR spectrometer NIR tools
generates spectrums
32. Step 1: Monitoring the process variable: spectrum Chute API
composition Blender JDSU Micro NIR
33. Step1: Monitoring the process variable: spectrum
34. Step 1: Monitoring the process variable: spectrum
35. Step2: Making prediction from spectrum Raw data is
meaningless. We need some form of analysis to generate a
statistically significant model in order to gain knowledge!!!! We
need model development tools We need real time prediction tool for
online monitoring SIMCA-QP
36. Step 2: Prediction model API Samples Excipients Pre-blend
samples Blender How to acquire spectrum for model calibration used
for continuous line?
37. Step 2: Building a prediction model Principle component
analysis (PCA) Exploratory data analysis (EDA) is an approach to
analyzing data sets to summarize their main characteristics Partial
Least Squares (PLS) Regression Quantitative regression method that
looks for correlations between spectral data (X-matrices) and the
independent variable of interest (Y- vector) Developing a weight
vector (link) between your samples and your variables Contains the
regression coefficients for the predicting equation Larger bs will
have a higher impact in our model y = bo + b1x1 + b2x2 + . +
b200x200
38. Step 2: Model development: UnscramblerX
39. Step 2: Prediction model validation for Closed-Loop
Control
40. Step2: Making online prediction from spectrum Prediction
model developed in Unscrumbler X
41. Step 2: Making prediction from spectrum
42. Step 3: Communicating the measured signal with the DeltaV
Via MATLAB
43. Step 3: Closing loop via synTQ Predicted data Data to
DeltaV (control variable, fitting parameters, alarms) Data to synTQ
(for batch reporting) Via synTQ
44. Step 3: Live display
45. Step 3: Communicating the measured signal with the
DeltaV
46. Step 4: Creating control loop in DeltaV control studio
47. Step 4: Implemented MPC strategy in DeltaV control
studio
48. Flexible control strategy PID MPC Smith predict or
49. Step 4: Implemented MPC and PID strategies (flexible
option)
50. Communication of DeltaV with synTQ
51. Step 4: Implemented MPC strategy in DeltaV control
studio
52. Step 4: Generation of linear model in DeltaV predict
53. Step 4: MPC operating interface in MPC operate
54. Step 4: User interface (DeltaV control system)
55. Integration of gPROMS with DeltaV control system
56. Closed-loop performance API composition RSD Set point
57. QbD Collaboration Iteration
58. Results Model Testing Static vs. Moving Powder Modeling
Control Algorithm Testing PID Smith Predictor MPC Performance
Results Path Forward
59. Summary A control system has been designed for flexible
multipurpose continuous tablet manufacturing process that include
direct compaction, wet granulation and dry granulation routes. The
control software and hardware integration has been completed via
SynTQ as well as via MATLAB The hybrid MPC-PID scheme, PID scheme
and PID with Smith predictor have been implemented to the Blender
and feeders using DeltaV control platform The performance of these
control schemes have been evaluated using the mathematical model
simulated in gPROMS The performance of these control system is
being evaluated in plant
60. References 1. Singh, R., Ierapetritou, M., Ramachandran, R.
(2012). An engineering study on the enhanced control and operation
of continuous manufacturing of pharmaceutical tablets via roller
compaction. International Journal of Pharmaceutics, 438 (1-2),
307-326. 2. Singh, R., Ierapetritou, M., Ramachandran, R. (2013).
System-wide hybrid model predictive control of a continuous
pharmaceutical tablet manufacturing process via direct compaction.
European Journal of Pharmaceutics and Biopharmaceutics,
http://dx.doi.org/10.1016/j.ejpb.2013.02.019. 3. Singh, R.,
Boukouvala, F., Jayjock, E., Ramachandran, R. Ierapetritou, M.,
Muzzio, F. (2012). Flexible Multipurpose Continuous Processing.
PharmPro Magazine, 28 June, 2012,
http://www.pharmpro.com/articles/2012/06/business-Flexible-Multipurpose-
Continuous-Processing/. 4. Singh, R., Boukouvala, F., Jayjock, E.,
Ramachandran, R. Ierapetritou, M., Muzzio, F. (2012). Flexible
Multipurpose Continuous Processing of Pharmaceutical Tablet
Manufacturing Process. GMP news, European Compliance Academic
(ECE),
http://www.gmp-compliance.org/ecanl_503_0_news_3268_7248_n.html 5.
Ramachandran, R., Arjunan, J., Chaudhury, A, Ierapetritou, M.
(2012). Model-Based Control Loop Performance Assessment of a
Continuous Direct Compaction Pharmaceutical Processes. J. Pharm.
Innov., 6(3), 249-263. 6. Ramachandran, R., Chaudhury, A. (2011).
Model-based design and control of continuous drum granulation
processes. Chemical Engineering Research & Design, 90(8),
1063-1073. 7. Sen, M., Singh, R., Vanarase, A., John, J.,
Ramachandran, R. (2012). Multi-dimensional population balance
modeling and experimental validation of continuous powder mixing
processes. Chemical Engineering Science, Volume 18, 349-360. 8.
Sen, M., Dubey, A., Singh, R., Ramachandran, R. (2013).
Mathematical Development and Comparison of a Hybrid PBM-DEM
description of a Continuous Powder Mixing Process. Journal of
Powder Technology, http://dx.doi.org/10.1155/2013/843784. 9. Singh,
R., Gernaey, K. V., Gani, R. (2010). ICAS-PAT: A Software for
Design, Analysis & Validation of PAT Systems. Computers &
Chemical Engineering, Volume 34, Issue 7, 1108-1136. 10. Hsu, S.,
Reklaitis, G.V., Venkatasubramanian, V. (2010). Modeling and
control of roller compaction for pharmaceutical manufacturing. Part
II: Control and system design. J. Pharm. Innov., 5(3), 24-36. 11.
Muzzio, F., Singh, R., Chaudhury, A., Rogers, A., Ramachandran, R.,
Marianthi Ierapetritou, M. (2013). Model-Predictive Design, Control
and Optimisation. Pharmaceutical Technology Europe, 31-33.
61. Acknowledgements This work is supported by the National
Science Foundation Engineering Research Center on Structured
Organic Particulate Systems (ERC-SOPS), through Grant NSF-ECC
0540855. ERC-SOPS colleagues for useful discussions. The authors
would also like to acknowledge Pieter Schmal (PSE) and Howard
Stomato (BMS)
62. Where To Get More Information Advanced Process Control
Foundation Optimal Web Site Camo Web Site C-SOPS website
63. Thank You for Attending! Enjoy the rest of the
conference.