The Evolution of NIR Technology and Manufacturing Processes- Through Decades of NIR Deployments

Transcription

1 The Evolution of NIR Technology and Manufacturing Processes- Through Decades of NIR Deployments Charles Miller, Nathan Pixley, Bruce Thompson, Manoharan Ramasamy, John Higgins Merck Sharp and Dohme, West Point PA IFPAC 2016

2 Outline Background Rationale, Scope, and History (NDIR, NIR) Five Case studies Case 1: At-line, Food/Ag Case 2: In-line FTNIR Reaction Monitoring Case 3: At-line RTRT Assay Case 4: On-line BU Monitoring Case 5: At-line IPC Spray Coating Summary and Conclusion 2

3 Rationale, and Scope QbD: Reciprocal Learning : NIR Process: timely, relevant information Process NIR: technology shake-down Challenges: Hardware and Method Robustness Distrust in NIR Observer effect, Uncertainty Principle Reference Cell Light Sources Chopper Filter Cells Sample Cell 3 OK, but..: Process Photometry: since 1930s-1950s (NDIR) Process NIR Spectroscopy: since 1960s Durable, profitable applications Presentation Scope: What has been learned from process NIR applications in industry?.. About NIR, and About the process Radiation absorber Diaphragm capacitor Amplifier Radiation absorber Indicator Pneumatic detector REF: Joseph W. (Bill) Worthington, 60 YEARS OF CO ANALYSIS BY NDIR GAS ANALYZERS

4 NIR Theory (Yes- it really does exist!) Electrical and Mechanical Anharmonicity 1960s: Physical chemistry, solvation states of ions NIR band features Frequency: mass, force constants Shape/Width: dephasing mechanisms Intensity: electrical and mech. anharmonicity Sensitivity to composition and environmental conditions M. L. Myrick, Existing Models of Pressure and Temperature Effects on NIR Spectra, IDRC C.E. Miller, Near Infrared Spectroscopy of Synthetic and Industrial Samples, in The Handbook of Vibrational Spectroscopy, Volume I: Theory and Instrumentation, John Wiley and Sons, Chichester, U.K., 2002, pp

5 History- NIR Process Analytics 1950s 70s 5 Technologies: Non-dispersive: IR, vis, UV 1962: DuPont Model 400 Photometer Ratio of two wavelengths Over 5000 manufactured! Model 800: rotating filter wheel photometer Vis, NIR, polarized vis, mid-ir Applications: in-line gases, mostly Referencing built-in to systems Food/Ag: Testing at-line NIR solids reflectance Grating, integrating sphere

6 Reflections : NIR/NDIR 1970s Mostly in-house development Applications mainly in-line gases for chemical industry But NIR for Ag/food starting Filter photometry dominated Dispersive, Interferometry developing Deliverables for the Process: Safety! Control of demanding processes Open up process dynamics Deliverables for NIR: Referencing built into systems! Robust hardware: no moving parts Methods: Simple (1 st principles, empirical MLR), hardwired Not perfect, but useful ( What s Chemometrics?...) 6 Karl Norris (Cary 14)

7 Case 1: Soymeal At-line NIR 1974: first filter NIR 1987: Network of 6 tilting filter NIRs 1989: first dispersive NIR 1989 Calibration data still used today (2016)! 1992: First usage of PLS models, w/dispersive NIR 2009: Platform conversion started Robustness testing: Eight Constituents for Five Products Tested using Three Platforms: Foss 6500, XDS, Diode Array Reference method changes Crop year effects Select a Minimum Diversity of Samples ( ) to Represent the Population Model refresh strategy: Eliminate newest crop years, or eliminate oldest crop years? Dickey John GAC III Isolate Protein Crop Year Distribution Isolate PAI 7 From: David J. Ryan, Learning How to Break, So You Will Not Break, Your Calibrations: A Study of Crop Year Effects, IDRC 2014

8 Case 1, Learnings and Deliverables Learnings: multi-year robust calibrations do not fall apart overnight, given an ever evolving landscape of variables, models still require routine updates Models including 5-6 crop years work best Cal samples older than ~8 yr add less value to models, due to response bias and non-random distribution Food/ag DBs require ~300 samples to enable good transfer to new platform Process/Product Deliverables: Crop year effects on CQAs: mean and distribution NIR Deliverables: Proactive Model Robustness Testing ~3x actual model update frequency Diagnostics: Not just SEP! T2, Q-residual, and score scatter Optimal refresh scheme for cal samples Impact of crop year on NIR methods Method transfers between platforms 8 From: David J. Ryan, Learning How to Break, So You Will Not Break, Your Calibrations: A Study of Crop Year Effects, IDRC Eliminate Newest: Bias IsoPAI IsoDM WFPDB WFNSI LecAI SMCF DAPDB DAFAT

9 Case 2: In-line FTNIR Reaction Monitoring Purpose: Reactor control in a continuous process Redundant with on-line GC, process model 4 production units across 2 plant sites Hundreds of product grades, each with different composition space 31 PLS models total 1989: Instruments and Sample System installed 1992: PLS models deployed, Outlier diagnostics 1994 Sampling: Slip stream transmittance, 5000 PSI, flammable fluid, with entrained wax Phase separation risk T, P and flow sensors on sampling system 20 sec. analysis frequency Instrument: Analect wedge FT-NIR NEMA enclosure, in shaky location 1996: High Pressure Calibrator: injected DOE standards for calibration development Custom real-time chemometrics Still running today! FEED ANALYZER REACTOR PRODUCT 9 C. Miller, et al, Multivariate Outlier Diagnostics: A Critical Component of NIR/PAT Method QA, IDRC2014

10 Case 2: Multivariate Diagnostics- The Story 1992: Highly divergent feed composition resulted in a runaway reaction! No injuries, but >5 days downtime Root cause difficult to determine..but many blamed the NIR Model diagnostics: Custom coded: leverage ratio and residual ratio Support at least 3 functions: Model maintenance Process control (disable NIR PV usage) System reliability Non-specific, but can infer issues upstream (instrument, process) T 2 n = K 2 n, k t k = 1 ( t kt k) /( N t 1) 10 C. Miller, et al, Multivariate Outlier Diagnostics: A Critical Component of NIR/PAT Method QA, IDRC2014

11 Case 2: Other Diagnostics Instrument diagnostics: Custom card: enclosure T, power supply voltages, currents, etc. Proactive maintenance, reliability Sampling System diagnostics: P, T, flow sensors on the sampling system Univariate Fouling Factor Remote access (PCAW) With access protocols 11 C. Miller, et al, Multivariate Outlier Diagnostics: A Critical Component of NIR/PAT Method QA, IDRC2014

12 Case 2: Deliverables Process Deliverables: Redundancy support for closed-loop composition controller Window into process dynamics, product transitions System Safety and Utility NIR Deliverables: NIR H/W: Improved robustness to sampling, and environment Cal sample sets: can mix DOE and on-line samples effectively Real-time Diagnostics: For Model, Sampling and Instrument monitoring Supports process control, system robustness and reliability Especially critical for continuous process Criticality of IT: software and networking COPA, PAT-IT Developed Lifecycle Management systems: Model monitoring, administration, change 12 management

13 Case 4: On-line Blend Uniformity Monitoring In-line NIR diffuse reflectance Thermo Target NIR Scans synched with rotation 2D/SNV, then spectral std. dev. Some batches produced irregular blend profiles (periodicity) Data Variables 13

14 Case 4: Diagnostics from Preprocessing Raw spectra raw spectra, in subrange Preprocessed spectra after derivative and SNV- in subrange RSD moving block of D/SNV RSD RSD moving block number SNV mean (offset) SNV std.dev. (multiplicative)

15 Case 4: Batch Comparison batch 1 batch 2 batch 3 RSD SNV offset 1.4 x 10-4 SNV multiplicative batch 1 batch 2 batch 3 Coefficients from preprocessing can be useful diagnostics! batch 1 batch 2 batch

16 Case 4, Deliverables Process Deliverables: Insight into blending dynamics Effect of process parameters, blend composition,.. Conditions for window fouling NIR Deliverables: Methods: Challenge of separating physical vs. chemical NIR information Optimal spectral preprocessing schemes Useful diagnostics from preprocessing! 16

17 Case 5: At-line IPC- Spray Coating API Spray coating process 3 NIR methods (1 per dosage) Thermo Antaris II NIR At-Line PLS regression (Y= HPLC assay) Relevant Y range limited to valid range for control SIPAT PAT-IT Solution CAMO (UNSC) Models 17

18 Case 5: NIR Model NIR Model (PLS): Using Mixed Calibration Population: Full scale development Stability studies Using GA-selected variables Model Robustness Assessment: X: NIR spectra of tablets at same fixed levels of spray coating Y: Spray coating process parameter Fit?: Infers some sensitivity of NIR spectrum to process parameter Y CV Predicted 5 Air:Water Ratio [CF/g] Air/water ratio Samples/Scores Plot of MK0431AXR_CUMASTER_ FSS FSD BB Y Measured 5 Air:Water Ratio [CF/g] FSD4 1:1 Y CV Predicted 9 Bed Temperature [ C] Samples/Scores Plot of MK0431AXR_CUMASTER_ R 2 = Latent Variables RMSEC = RMSECV = Bias = 0 CV Bias = Bed T FSS FSD3 BB FSD4 1:1 fit 95% Confidence Level Y Measured 9 Bed Temperature [ C] 18

19 Case 5: At-line IPC- Spray Coating Risk-based %CLs on ODs (using theoretical method) Outlier alarm triggers investigation NIR Predictions from flagged samples disqualified for NIR control application Outlier Diagnostics aid in: NIR usage decisions, NIR model QA, and Addressing NIR sampling issues Hotelling T2: 12 batches 19

20 Case 5, Deliverables Process Deliverables: Efficient, cost-effective control Process insight: Subtle (in-spec) composition variations correlated to process changes NIR Deliverables: Sensitivity of NIR methods to process attributes ODs for on-line screening Careful tuning of limits Limited use of PLS loadings, residuals in investigations Customized diagnostics? Use process space to influence Cal sample set design 20

21 Take-Home Process NIR has been successfully leveraged for decades! Technology has not regressed since then Theory has been understood for even longer Hardware was robust in the 90 s! Real-time Diagnostics (ODs): Be Resourceful, Creative! Exploit data- It s being collected anyway Can support process, sampling and method monitoring Also support lifecycle management NIR in Continuous Processing: Redundant PVs for control an expectation, not an anomaly Empirical Models (Chemometrics): Can be sensitive to environmental conditions, process state Likely require intermittent updating Always limited in application range Effectively monitored via ODs 21

22 Acknowledgements and References Acknowledgements: Merck: Gianmaria Ghisoni, Luis Torres, Brandye Smith-Goettler DuPont: David A. Russell, Carter Bidwell, Kunle Ogunde, John Steichen David J. Ryan Gert Thurau References: David J. Ryan, Learning How to Break, So You Will Not Break, Your Calibrations: A Study of Crop Year Effects, IDRC 2014 C. Miller, Chemometrics in Process Analytical Technology (PAT), in Process Analytical Technology, 2nd edition, K. Bakeev, editor, Wiley, Chichester UK, 2010, pp M. L. Myrick, Existing Models of Pressure and Temperature Effects on NIR Spectra, IDRC C.E. Miller, Near Infrared Spectroscopy of Synthetic and Industrial Samples, in The Handbook of Vibrational Spectroscopy, Volume I: Theory and Instrumentation, John Wiley and Sons, Chichester, U.K., 2002, pp C. Miller, et al, Multivariate Outlier Diagnostics: A Critical Component of NIR/PAT Method QA, IDRC