2014 AAPSAdvanced Process Control and Continuous Processing in

Pharmaceutical Manufacturing: What Can We Learn from Other Industries

Paul Brodbeck/Control Associates Inc.

What is Process Control? How? Why? Benefits? Why Advanced Process Control? Advanced Controls

◦ MPC◦ Kalman Filter◦ Neural Networks◦ LP Optimization

Agenda

Controlling process variable 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

What is Process Control?

How is Process Control done? First Feed Back Controller – Humans Closed Loop Control – Level, Press, Flow, API Concentration (%) Vary output – Valve, Pump, Agitator, Fan

WHY? Manual vs. 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

House Temperature Control

WHY? Manual vs. 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

Car Cruise Control

Distillation Columns/Refinery

WHY? Manual vs. Automatic Production Yields Profit Reduces Costs

◦ Labor◦ Energy

Safer Lower Risk

Improves Quality Reduces Costs Increases Production

Control Generalization

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

How does it improve Quality?

Variable Parameters

Variable Quality

Controlled Parameters

Fixed Quality

Statistical Process Control

Pharma Poor Manufacturing Techniques

Why Process Control?

Improve Quality Increase Yields Increase Production Reduce off-spec Reduce Bad Batches Reduce Energy Costs Reduce Production

Costs Improve Safety Reduce Risk Increase Profitability

Optimal Control Better Control Control Poor Control Manual Control

Process Control Benefits

Basic vs. Advanced Control

BasicPID

Advanced PID

AdvancedControl

NoControl

OptimalControl

Optimization

Control

Basic Process Control = PID

Tuning Constants:1. Proportional (P)2. Integral (I)3. Derivative (D)

Applications Car Cruise Control Home Heating/AC, Distillation Columns Chemical Reactors Bioreactors Crystallization Chromatography

Basic Control (PID) - Ubiquitous

Industries Chemical Pharmaceutical Petroleum Automotive Robots Aerospace Boilers Missile Guidance

Applications Distillation Columns Robotics Drones Aerospace Robots Missile Guidance Stock Market, Operations Research Economics Scheduling

Advanced Control - Ubiquitous

Industries Chemical Pharmaceutical Petroleum Automotive Robots Aerospace Boilers Missile Guidance

1. Model Predictive 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

What are Advanced Controls?

Machine Learning◦ Computer Science & Statistics ◦ Real World Problem Prediction/Optimization

Search Engines Stock Market Prediction Pattern Recognition (OCR) Robotics Recommender Systems DNA Sequencing Chemometrics

Big Data/Data MiningAPC Subset

Numerical Methods Least Squares Statistics Modeling Analytics Linear Programming Optimization

Machine LearningUnderlying Technology

MPC Neural Networks MVA Tools

MLR PCA PLS

Kalman Filter Multivariate SPC

Optimal Control Slow Processes Large Dead Times Multiple Loops (50x25) Complex Dynamics Strongly Correlated Loops

Model Predictive Control (MPC)

MPC Batch Reactor

Multi-Loop Control

Multi-Loop PID Multi-Loop MPC

Model predictive control (MPC)

22 2

1 1 1 1 1 1

1 1y u un n nP M M

y set u uj 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 variableu: 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 parameters1. Output weights (wy

j) 2. Rate weights ( ) 3.Input weight ( ) 4. Prediction horizon5. Control horizon

ujw

ujw

Rutgers Engineering Research CenterContinuous Manufacturing

Single Loop MPC

MPC – API Controller

MPC Model Builder

Actuator Control variable

Linear Model for MPC

Linear Model for MPC

MPC Operator Interface

Actuator

Control variable

MPC Performance - Simulation

MPC Performance – Pilot Plant

Filtered NIR signal CV(API composition)

Actuator Ratio SP

NIR signal

MPC Performance v. RobustnessPenalty on Move/Penalty on Error

3

1

2

Statistically Optimal Estimator Numerous Applications

◦ De facto Standard Robotics◦ Aerospace◦ Missile Guidance◦ Economics ◦ Signal Processing

State Prediction based on:◦ Noisy Data◦ Physical Model (Error increases w/ Time)

Kalman Filter

Takes a statistical 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.

Kalman Filter

Kalman Filter Cannonball

Kalman Filter – Logistic Growth

dF(x)/dx = f(x)*(1-f(x))

E-mail Spam Internet Browser Recommender systems Pattern Recognition

◦ Bar coders◦ Facial identification◦ Robotics

Pharma ◦ Soft Sensors◦ Non-Linear Control

Neural Networks

Non-Linear Data Modeling Combination of Linear Regressions Build up a series of linear models

(regressions) to create a non-linear model

Neural Network Algorithm

Linear Regression

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FitRaw Data

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1 3 5 7 9 11 13 15 17 19 21 23 250

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Combine Multiple Linear FitsRegress Against Y Data

Neural Net ExampleContinuous Digester Degree of delignification indicated by Kappa number.

Neural Net Parameters

Build Neural Net

BUILD MODEL

PREDICTVALUE !

Neural Net Configuration

LP Optimization 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

Pusher Function Maximize Flowrate subject to constraints

LP Optimization w/MPC

Complexity PID vs. APC

Introduction of Technology◦ PAT◦ Continuous Manufacturing

Introduce Advanced Controls ◦ MPC◦ Kalman Filter◦ Neural Net◦ LP Optimization◦ MultiVariable SPC

Opportunity