Thomas Steiner of Emerson 'Addressing Controls Challenges in Flexible, Continuous Bio Manufacturing

3rd International Symposium on Continuous Manufacturing of Pharmaceuticals

Addressing Control Challenges in Flexible, Continuous Bio Manufacturing Thomas Steiner Emerson Automation Solutions Global Strategic Account Director – Life Science

Delivering Quality in Continuous Manufacturing Requires Advanced Automation Satisfy Critical Quality Attributes Quality by design for new unit operations equipment Inline, at-line, and real time monitoring necessary

Controlling Disturbances, Dynamics, Constraints Direct measurement Simulation Predictive Modeling

Effective Start-up / Shutdown Procedures

Material Traceability

Effective, repeatable start-up to minimize ramp up time to an “in control” state

Detailed reporting of material genealogy

Effective, repeatable shutdown procedures to minimize waste

Time and process based tracking of materials through the process

Feedback vs Feedforward Process parameters manipulated in response to disturbances to maintain quality

“In Control” vs Steady State

New Strategies for Unit Operations Simple, low level controls Integrated control across production

Maximizing Uptime

Adjusting for variability in materials

Reduced time for diagnostic or maintenance work

Adjusting for variability in the process and disturbances

High equipment utilization necessary

Downstream and upstream interactions International Symposium on Continuous Manufacturing of Pharmaceuticals, MIT May 20-21, 2014 White Paper 6: Control Systems Engineering in Continuous Pharmaceutical Manufacturing Allen S. Myerson, Markus Krumme, Moheb Nasr, Hayden Thomas, Richard D. Braatz

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Process Analytical Technology Required for Continuous Manufacturing FDA Definition: PAT is a system for designing, analyzing and controlling manufacturing through… critical quality attributes … with the goal to ensure final product quality.

Quality Prediction (Critical Quality Attributes (CQA))

Fault Detection

Process Data CQA

CQA

CQA

Spectral Analyzer and Chemometric Models

In-line Property Analyzers (multiple)

Process Data CQA

Predicted Quality (soft sensors)

Models

Abnormal Fault Detection (PCA)

Models

Lab Data

Chromatography • • • •

NIR / RAMAN sensors

Conductivity, Dissolved O2

Biomass concentration pH Growth rate Distillation point

Control & Optimization Process Data, CQA

Manipulated Variables (MVs)

MPC Optimization Models

• • • • • •

Analyzer Validation Sensor Failure PV Drift Equipment Malfunction Abnormal yields Etc.

• Dissolved Oxygen • pH • Crystallization particle size • Concentration • Etc.

Typical “Layered� PAT Applications Challenges (PAIN)

HMI Models

Spectral History Data and Chemometric Models

L3

L2

Predicted Quality (soft sensors)

Abnormal Fault Detection (PCA)

MPC Optimize

OPC Client In DCS / PLC

- Fragile architecture - Difficult to implement - Difficult to maintain - Perceived as Less Secure - Multiple operator interfaces - Difficult to validate process Example Large Molecule Continuous Manufacturing: Merck Vision for Biologics Agile Supply

Controller

DCS

L1 In-line Property Analyzers (multiple)

NIR / RAMAN sensors Emerson Confidential

Dissolved O2

Chromatography

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Future Solution Architecture for PAT In Flight‌ -

Models

Off-line Chemometric Models

L3 L2

OPC-UA

On-line Spectral Data & Models

L1

Spectral Data History

Predicted Quality (soft sensors)

Abnormal Fault Detection (PCA)

OPC-UA Client

Robust architecture Easy to implement Easy to maintain More Secure Single operator interface Traditional DCS validation effectiveness

MVDA Optimize

Controller

DCS

OPC-UA

In-line Property Analyzers (multiple)

NIR / RAMAN sensors Emerson Confidential

Dissolved O2

Chromatography

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Continuous Biologics Processing Challenges / Solutions: Integrate Islands of Automation • Unit operations are not really islands but interdependent • Bags for process and variability in process – pH change requirements between steps – Step 3 slows -> Need to slow Step 2

• Coordination of unit operations • Comprehensive batch report • Material consumption including single use components and material generation tracking • • • • • • Emerson Confidential

Operators have normalized interface Small OEM to DeltaV interface Small effort compared to coordinating many skid vendors Skid suppliers focus on their strengths Suppliers are not charging for control outside their unit op DeltaV produces a coordinated batch report at the end 6

Continuous Biologics Processing Challenges / Solutions: Track Materials via Residence Time Distribution Across the Process • • • •

Models relationship between input and output concentrations Input pulse dissipates through unit operation as function of time Output concentration is characterized by Mean Residence Time (MRT) and distribution Maximum output concentration amplitude represents maximum possible response to input disturbance

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Continuous Biologics Processing Challenges / Solutions: Requires a Two Stage Qualification / Validation Strategy

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Single Use Enabled Automated Continuous mAb Production Nuno Pinto Ph.D., Life Sciences Symposium Mar. 28-30, 2017 8

Unlocking the Value of Flexible and Continuous Manufacturing Requires Planning Flexible and Continuous Manufacturing in Life Sciences are progressing Assess people, processes and technologies within your organization to determine how to get the benefits Emerson Confidential

Approach requires re-thinking your manufacturing processes and business Technology barriers are being addressed Secondary manufacturing is moving the fastest but primary is also progressing Requires people with a combination of process and automation / data technology skills Requires flexible and adaptable automation and data management platforms 9