ISA FPID Symposium Advanced Program

Opportunities for PAT and Advanced Process Controls for Optimization in Continuous Manufacturing

Paul Brodbeck/Control Associates Inc. Emerson Local Business Partner

Benefits of Continuous Manufacturing

State of Continuous Manufacturing

Capital Growth Continuous Manufacturing

Advanced Control Opportunities

1. Model Predictive Control - MPC

2. Neural Networks - NN

3. Linear Programming Optimization - LP

4. Kalman Filter - KF

Agenda

Improved Product Quality • Better Quality Control• Meaningful PAT In-Process Control • Real Time Release• Prevents Segregation and Agglomeration• Reduced Off-Spec Batches & Materials

Cost Reduction• Smaller Equipment • No Scale Up• Faster Development• Less API used in Development• Lower Cost of Quality Assurance• Reduced Processing Time,

Continuous Manufacturing

Less than 20% Time

To produce a product order Continuously versus Batch

2 Days CM versus 2 weeks Batch

Less than 20% Wasted Material

During Development

100 kgs CM versus 5000 kgs Batch

Less than 20% Installed Costs

$20 mil CM versus $200 mil Batch

CM Benefits

“At a factory in Puerto Rico, J&J has built a line that could manufacture the HIV/AIDS medicine Prezista starting in 2016 using the new techniques if regulators approve. The main ingredients will still be made elsewhere, but J&J aims to manufacture 70% of its highest-volume products” using the processes within eight years”, Mr. McKenzie said.

“As a result of such benefits, companies will save an estimated 30% or more in operating costs, according to Bernhardt Trout, director of the Novartis-MIT Center for Continuous Manufacturing, which has been developing the new technologies with funding from Novartis.” – WSJ

Hayden Thomas, a Vertex manufacturing official. If the company’s new cystic-fibrosis drug gets approved, the facility would make 100,000 tablets in an hour, rather than the four to six weeks that would be needed to make a batch at a traditional plant.

WSJ Article – Feb. 8, 2015

Novartis-MIT Center for Continuous Manufacturing $65MM

GlaxoSmithKline Continuous Plant in Singapore $29MM

PfizerCollaboration with GEA & G-CON

Patheon – CMO/CDO Eli Lilly “By early next year, Eli Lilly and Company will have installed

and demonstrated four different continuous-processing platforms. Currently, almost all of our potential medicines that are in development have continuous-processing steps in place.” B. Huff - Executive Director of Chemical Development September 2013

CM – Capital Growth

1. Model Predictive Control - MPCRefineries, Robotics, Drones, Aerospace…

2. Linear Programming - LP OptimizationOperations Research, Economics, Scheduling…

3. Neural Networks - NNPattern Recognition, Stock Market, Genetics…

4. Kalman Filter - KFRobots, Aerospace, Missile Guidance…

APC ‘State-of-Art’ Other Industries

1.Model Predictive Control

Feed Forward to Tablet Press

API Concentration Feedback

2. LP Optimization

Feedrate Optimization

3. Neural Net

Soft Sensor

4. Kalman Filter

PAT Signals i.e. BioReactors

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CM- Opportunities

Model Based Closed-Loop Control

Multivariable Inputs and Outputs

Decouples interacting loops

Good with difficult dynamics

Deadtime compensated automatically

Feed Forward Implicit

Optimal Constraint Control

Robust and Proven in Industry

Predictive – Not waiting for an error.Anticipates the best strategy

Planning Ahead – like Humans

1. Model Predictive Control (MPC)

Model Predictive Control (MPC)

22 2

1 1 1 1 1 1

1 1y u u

n n nP M My 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 variableu: Actuator△u: Predicted adjustment

ManipulatedVariable

Controlled Variable Deviations (SP-PV)

Controller Adjustments(Output Change)

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

u

jw

u

jw

The Objective Function J

minimizes the Error &

Changes for the sum of

all three internal

functions.

MPC BioReactor Perfusion

Multi-Loop PID

Multi-Loop Control

Multi-Loop MPC

BioReactorTemperature, pH, DOGlucose, Nutrient…

Feed Back Closed Loop API %

Feed Forward Density to Tablet Press

2. LP Optimization

Linear Programming

Discrete Optimization

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

Common Algorithm

Simplex Method

Pusher Function

Maximize Flowrate subject to constraints

LP Optimization of Feedrate

Moving Horizon Based Dynamic Real Time Optimization - MHDRTO

Objective: The objective of this work is to integrate the Moving

Horizon Based Dynamic Real Time Optimization (MHDRTO) with a

well controlled continuous tablet manufacturing process.

Economically driven Dynamic Optimization

MHDRTO is Maximizing Profit. The outcome of optimization is the Production Rate. Constraints: Equipment operating limitations/ CQA’s. Control: Tablet weight, Hardness, Level, API composition.

MATLAB/gPROMS vs. COTS Research by Dr. Ravendra Singh/Assistant Research Professor – C-

SOPS NSF Engineering Research Center at Rutgers University

Non-Linear Data Modeling

Combination of Linear Regressions

Build up a series of linear models (regressions) to create a non-linear model

3. Neural Network Algorithm

E-mail Spam

Internet Browser

Recommender systems

Pattern RecognitionBar codersFacial identificationRobotics

PharmaSoft SensorsNon-Linear Control

Neural Networks

Linear Regression

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Combine Multiple Linear FitsRegress Against Y Data

Built a Neural Net Model against Process Data and Hard Sensor values to Build Soft Sensor

Cross Validate the Soft Sensor against Hard Sensor

Can be used as a check against the Hard Sensor for alarming

Soft Sensor used when Hard Sensor is offline.

Neural Net – Pharma Soft

Statistically Optimal State Estimator Used to filter noisy data when you have a model

you can compare it against Uses a weighted average of the measurement

model prediction Weighted average is automatically updated every

new measurement to find the optimal value Numerous ApplicationsDe facto Standard RoboticsAerospaceMissile GuidanceEconomics Signal Processing

4. Kalman Filter

Takes a statistical average of:Measured VariableModel

Acts Recursively to continuously predict most probable state.

First used by NASA to predict location of rocketsUncertain GPS SignalPhysical 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 GrowthBioReactor Application

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

PAT Data Noisy

Often models are available to predict the state

Kalman Filter can use both the find the OPTIMAL value

Statistically KF is the Best Guess

Good for control

Lower lags than typical first-order smoothing functions. Moving Block, Sovitzky-Golay

Kalman Filter in Pharma

Difficult to Introduce New Technology New Industry InitiativesPATContinuous Manufacturing

Now is time to introduce Advanced Controls! MPCKalman FilterNeural NetLP OptimizationMSPC-Multivariate Statistical Process Control Batch and Continuous Analytics

Fuzzy Logic

Opportunity

Thanks!