1. 1. MPC/PAT Advanced Closed Loop Control Continuous pharmaceutical tablet manufacturing processing PAT on-line spectral analysis
2. 2. Presenters Paul Brodbeck Control Associates, Inc. Emerson LBP Ravendra Singh Rutgers University Engineering Research Center Rohit Ramachandran Rutgers University Engineering Research Center
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4. 4. Engineering Research Center
5. 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. 6. 22 Research Structure
7. 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. 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. 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. 10. C-SOPS Industrial Value Chain Technology Suppliers Technology Integrators End Users
11. 11. Level 1 Members
12. 12. Level 2 Members
13. 13. TRISKELL Level 3 and 4 Members
14. 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. 15. Control system flowsheet model - gPROMS
16. 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. 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. 18. DeltaV Operate Graphic
19. 19. Control Scheme Flowchart(s)
20. 20. Design MPC: Hybrid MPC-PID (set point tracking) Note: Final actuator: Rotational speed of API feeder Slave controller: PID
21. 21. Design MPC: Hybrid MPC-PID (disturbances rejection) Note: Final actuator: Rotational speed of API feeder Slave controller: PID
22. 22. Performance evaluation (set point tracking) Control variable: Total flow rate from blender Cascade PID (scheme 1) Hybrid MPC-PID (scheme 3)
23. 23. Performance evaluation Control variable: API composition Control variable: RSD
24. 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. 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. 26. Control hardware and software integration Step 2 Step 4 Step 1 Step 3
27. 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. 28. Step 4. Overview
29. 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. 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. 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. 32. Step 1: Monitoring the process variable: spectrum Chute API composition Blender JDSU Micro NIR
33. 33. Step1: Monitoring the process variable: spectrum
34. 34. Step 1: Monitoring the process variable: spectrum
35. 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. 36. Step 2: Prediction model API Samples Excipients Pre-blend samples Blender How to acquire spectrum for model calibration used for continuous line?
37. 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. 38. Step 2: Model development: UnscramblerX
39. 39. Step 2: Prediction model validation for Closed-Loop Control
40. 40. Step2: Making online prediction from spectrum Prediction model developed in Unscrumbler X
41. 41. Step 2: Making prediction from spectrum
42. 42. Step 3: Communicating the measured signal with the DeltaV Via MATLAB
43. 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. 44. Step 3: Live display
45. 45. Step 3: Communicating the measured signal with the DeltaV
46. 46. Step 4: Creating control loop in DeltaV control studio
47. 47. Step 4: Implemented MPC strategy in DeltaV control studio
48. 48. Flexible control strategy PID MPC Smith predict or
49. 49. Step 4: Implemented MPC and PID strategies (flexible option)
50. 50. Communication of DeltaV with synTQ
51. 51. Step 4: Implemented MPC strategy in DeltaV control studio
52. 52. Step 4: Generation of linear model in DeltaV predict
53. 53. Step 4: MPC operating interface in MPC operate
54. 54. Step 4: User interface (DeltaV control system)
55. 55. Integration of gPROMS with DeltaV control system
56. 56. Closed-loop performance API composition RSD Set point
57. 57. QbD Collaboration Iteration
58. 58. Results Model Testing Static vs. Moving Powder Modeling Control Algorithm Testing PID Smith Predictor MPC Performance Results Path Forward
59. 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. 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. 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. 62. Where To Get More Information Advanced Process Control Foundation Optimal Web Site Camo Web Site C-SOPS website
63. 63. Thank You for Attending! Enjoy the rest of the conference.