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AAPS Advanced Controls Uploaded 2
- 1. Advanced Process Controland Continuous Processing in Pharmaceutical Manufacturing: What Can We Learn from Other Industries Paul Brodbeck/Control Associates Inc.
- 2. What isProcess Control? How? Why? Benefits? Why Advanced Process Control? Advanced Controls ◦ MPC ◦ Kalman Filter ◦ Neural Networks ◦ LP Optimization
- 3. Controlling processvariable to a desired SP. ◦ Reactor Temperature ◦ Heat Exchanger Flow Rate ◦ Boiler Pressure ◦ OTC Tablet (API) Concentration. ◦ Dryer Moisture Content ◦ House Temperature ◦ Car Speed ◦ Distillation Column Production Rate Controller
- 4. First FeedBack Controller – Humans Closed Loop Control – Level, Press, Flow, API Concentration (%) Vary output – Valve, Pump, Agitator, Fan
- 5. WHY? Manualvs. Automatic Easy Quality – Temperature Variability Temperature Cycling ◦ Poor Quality ◦ Inefficient ◦ Wear and Tear on Heater and Parts Auto change SP at day/night Cost Savings Control Improves Quality & Reduces Costs
- 6. WHY? Manualvs. Automatic Quality – Constant Speed Speed Cycling ◦ Poor QualityInefficient ◦ Wear and Tear on Car and Parts Get there faster! ◦ Set Speed closer to speed limit ◦ Less Risk/Less Speeding Tickets Control Improves Quality & Reduces Costs
- 7. WHY? Manualvs. Automatic Production Yields Profit Reduces Costs ◦ Labor ◦ Energy Safer Lower Risk
- 8. Improves Quality Reduces Costs Increases Production
- 9. Reduce Variability! ◦Almost at end in itself. Edward Deming – Quality Program Founder Japan Post WWWII Better Quality ◦ Autos, Semi-Conductors, Steel… 1980’s American Manufacturing Poor Quality Statistical Process Control Introduced into US Get Process under Control (Statistically) ◦ Control Charts Reduce Variability Increase Quality
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- 12. Improve Quality Increase Yields Increase Production Reduce off-spec Reduce Bad Batches Reduce Energy Costs Reduce Production Costs Improve Safety Reduce Risk Increase Profitability
- 13. Optimal Control Better Control Control Poor Control Manual Control
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- 15. Tuning Constants: 1. Proportional(P) 2. Integral (I) 3. Derivative (D)
- 16. Applications Car CruiseControl Home Heating/AC, Distillation Columns Chemical Reactors Bioreactors Crystallization Chromatography Industries Chemical Pharmaceutical Petroleum Automotive Robots Aerospace Boilers Missile Guidance
- 17. Applications Distillation Columns Robotics Drones Aerospace Robots Missile Guidance Stock Market, Operations Research Economics Scheduling Industries Chemical Pharmaceutical Petroleum Automotive Robots Aerospace Boilers Missile Guidance
- 18. 1. ModelPredictive Control (MPC) ◦ Distillation Columns, Robotics, Drones, Aerospace… 2. Kalman Filter ◦ Robots, Aerospace, Missile Guidance… 3. Neural Networks (NN) ◦ Pattern Recognition, Stock Market, Genetics… 4. Linear Programming (LP) Optimization ◦ Operations Research, Economics, Scheduling
- 19. Machine Learning ◦Computer Science & Statistics ◦ Real World Problem Prediction/Optimization Search Engines Stock Market Prediction Pattern Recognition (OCR) Robotics Recommender Systems DNA Sequencing Chemometrics
- 20. Numerical Methods Least Squares Statistics Modeling Analytics Linear Programming Optimization MPC Neural Networks MVA Tools MLR PCA PLS Kalman Filter Multivariate SPC
- 21. Optimal Control Slow Processes Large Dead Times Multiple Loops (50x25) Complex Dynamics Strongly Correlated Loops
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- 24. 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 (wy j) 2. Rate weights ( ) 3.Input weight ( ) 4. Prediction horizon 5. Control horizon u jw u jw
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- 33. Filtered NIR signalCV (API composition) Actuato r Ratio SP NIR signal
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- 35. Statistically OptimalEstimator Numerous Applications ◦ De facto Standard Robotics ◦ Aerospace ◦ Missile Guidance ◦ Economics ◦ Signal Processing State Prediction based on: ◦ Noisy Data ◦ Physical Model (Error increases w/ Time)
- 36. Takes astatistical average of: ◦ Measured Variable ◦ Model Acts Recursively to continuously predict most probable state. First used by NASA to predict location of rockets ◦ Uncertain GPS Signal ◦ Physical Model error increases with time Use measurement signal to correct errors with model. Use model to validate measured values.
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- 39. E-mail Spam Internet Browser Recommender systems Pattern Recognition ◦ Bar coders ◦ Facial identification ◦ Robotics Pharma ◦ Soft Sensors ◦ Non-Linear Control
- 40. Non-Linear DataModeling Combination of Linear Regressions Build up a series of linear models (regressions) to create a non-linear model
- 41. 0 2 4 6 8 10 12 14 1 2 34 5 6 7 8 9 10 11 Fit Raw Data 0 100 200 300 400 500 600 700 1 3 5 7 9 11 13 15 17 19 21 23 25 0 100 200 300 400 500 600 700 1 3 5 7 9 11 13 15 17 19 21 23 25 1 2 3
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- 47. Linear Programming A mathematical/computer optimization technique – Simplex Method Solve a system of linear equations Can be used to find the minimum and maximum states of process control Can be made subject to multiple constraints
- 48. Pusher Function Maximize Flowrate subject to constraints
- 50. Introduction ofTechnology ◦ PAT ◦ Continuous Manufacturing Introduce Advanced Controls ◦ MPC ◦ Kalman Filter ◦ Neural Net ◦ LP Optimization ◦ MultiVariable SPC