Efficient Control of Continuous Pharmaceutical ... · Basic engineering of plant layout ... Method PI NIRS NIRS FBRM CI OCT ... - first principles model - data driven model

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Efficient Control of Continuous Pharmaceutical Manufacturing

Processes

Stephan Sacher

evon up2date, 21.6.2016

Overview

21.6.2017 evon up2date Slide 2

Traditional vs advanced pharmaceutical manufacturing

Advanced pharmaceutical control concept

Process Analytical Technology

Active process control

PharmControl

Batch vs Continuous Manufacturing


Traditional batch-based manufacturnig

Continuous manufacturing based on a sequence of unit operations

Continuous manufacturing with an integrated continuous process

QA, material handling

and storage

Blending step in batch

and continuous mode

Benefits of Continuous Manufacturing


Reduce variability

Improve robustness

Better utilization of capacities

Standardized development effort

Shorten supply chains

Smaller footprint

Less working capital

Reduced complexity of logistics

Individualize manufacturing

Cost effectiveness

Expertise for Advanced Pharmaceutical Manufacturing

21.6.2017

Manufacturing Science

RTRT

CM Control

Strategies

Mass-flow Under -standing

Process Economics

evon up2date Slide 5

Concepts for 100% quality control through

manufacturing line

Development and adaptation of novel PAT tools

Soft sensor concepts

Basic engineering of plant layout (interfaces,

mass transport, buffers, etc.)

Test runs to determine the system dynamics

Evaluation of RTDs (experimental and modeling)

Development of process models

Flow sheet modeling and sensitivity analysis

Model based controller design and in silico

implementation

Techno-economic profiling of manufacturing

routes

Advanced Control Concept including PAT Strategy and OOS Handling


Risk based process analysis

Definition of a QTPP and associated CQAs

Assessment of relation between single unit operations and CQAs

Criticality assessment of process parameters and definition of CPPs

Development of a PAT strategy in order to

monitor quality in-line

handle out of spec material

initiate process control actions


Development of a hierarchical control strategy

Design of model based controllers

Implementation of a closed loop control

No active process control, just

reaction on events

Discharge of OOS material

(intermediates, product)

Closed loop control, e.g. model

predictive control

Mitigation of OOS material

Risk-based Process Analysis - Influence Factors on Final Product Quality


Definition of a QTPP for a model drug product and

associated CQAs

Assessment of relation between single unit

operations and CQAs of intermediates and final

product

Criticality assessment of process parameters and

definition of CPPs

For PPs marked in red, in-process control actions are

considered

Feeding-Blending step as example

CQA final DF: content uniformity

CQAs FBU: blend homogeneity, blend ratio

CPPs FBU: feeder refill, feed rate, blender

speed, fill volume, blender set-up (e.g. weir)

Process Analytical Technology - Motivation

evon up2date 21.6.2017 Slide 8

Fixed and approved process

Any change might be dangerous

0

0,2

0,4

0,6

0,8

1

1,2

0 5 10 15 20

Product Quality

Sources of variability Yet, variable product quality

Responsibility for people

Process Analytical Technology - Motivation


Process is understood

and product quality ensured for a design space

0

0,2

0,4

0,6

0,8

1

1,2

0 5 10 15 20

Product Quality

Sources of variability

are understood and the

process controlled

Quality is ensured during production

Responsibility for people

Generating process knowledge

In-line monitoring

Active process control

Real-time release testing

Full Process Analytical Technology Concept Steps in drug manufacturing call for different analyses and analytical technologies


Production

Step

Crystallization Drying Blending Granulation Compaction Coating

Material Crystals / Solvent Slurry Granular Material Agglomerates Compacted Powder Tablets / Pellets

CMA/CQA Crystal Size Water Content Homogeneity Particle Size Content Uniformity Coating Thickness

Method PI NIRS NIRS FBRM CI OCT

Control Temp. Gradient Air Flow Blender Speed Pressure Quality Check Spray Time

Data Layer for data archiving, process understanding, and multi-unit control actions

Soft-Sensors

Concept:

Computation of process parameters via

mathematical models (soft-sensor)

in order to

replace expensive sensors

supply redundant information,

e.g., for sensor failure detection

compute non-measurable process parameters

Example: fluid bed dryer

compute the residual moisture from available

measurement data


Process Control Out of Spec Handling and Residence Time Distribution


NIR Controller

Time delay due to

Acquisition rate

Data treatment, e.g. chemometrics and prediction

Reaction time of discharge unit

OOS

In Spec Intermediate

Knowledge about RTD is crucial for

Correct discharge of OOS material, release of entire batch

Material tracking and batch definition

RTD Measurements and Modeling for a Blender

Addition of tracer impulse at the

inlet of the blender

Acquisition of impulse response

using a camera on top of

conveyor belt

Data processing: extraction of

color values (tracer intensity)

using Matlab

Fit of RTD model with

experimental data


Time in s

Tra

cer

Inte

nsity

Camera

Time in s

Tra

cer

Inte

nsity

Residence Time Distribution of Blender

t=0

Process Control Model Predictive Control (MPC) for a Feeding Blending Unit


Actuating signals are computed by solving an optimization problem

Constraints (e.g. minimum/maximum mass hold-up) are considered

Multi-input, multi-output systems can be handled straightforward

Setup:

Block diagram:

Idea of MPC:

Mathematical plant

model, e.g.:

- first principles model

- data driven model

Process Control, Simulation Results


PharmControl


Project Name: Efficient Control of Continuous Pharmaceutical Production

Project duration: 1.1.2017-31.12.2018

Project type: FFG funded project

Project partners:

RCPE GmbH

evon GmbH

Institute of Automation and Control, Graz University of Technology

Merck KGaA & Co. Spittal

Operative Project Goals


Realisation of continuous manufacturing line in RCPE technical lab

Direct compaction line

DC + HME pelletization


Development of control strategies

OOS handling

Model predictive control

Realization of in-line process monitoring (PAT) and real time process data acquisition

Development and design of control hierarchy and controllers

Development of observer / soft sensor concepts

Implementation of control concept in manufacturing line and execution of runs

Including Start and Stop

Events, long term runs

Layout of Continuous Model Process


Feeding

Feeding

Hot Melt Extrusion

Blending

Tableting

Cooling Track

Strand Pelletizer

Powders

Blend

Strands

Tablets

Pellets

Powders

Unit o

pera

tions

Raw

mate

rials

, in

term

edia

tes,

pro

ducts

Definition of CQAs and CPPs


CQAs to be monitored

Hoppers: fill level (no CQA)

Extrusion: temperature, API homogeneity

Cooling track: temperature, quality, diameter

Pelletizer: PSD

Blender: homogeneity

Tablet press: weight, thickness, hardness

CPPs to be controlled

Feeders: rotation, mass flow rate, total mass

Extrusion: barrel tempertures, rotation,

pressure, torque

Cooling track: cooling air

Pelletizer: intake speed, knife speed

Blender: rotation, mass flow/ hold up

Tablet press: diverse (pressures, turret speed,

etc.)

Definition of Controlled and Manipulated Variables


Stellgröße / Manipulated variable Regelgröße / Controlled variable

turret speed tablet press level hopper tablet press

feeder set-points

feed frame speed tablet press Variation compaction pressure

blender speed NIR probe after blender (blend

homogeneity)

feeder set-points

level hopper pellet feeder

NIR probe after blender / extruder (API

concentration)

feeder refill intervall / refill time

(pneumatic transport unit) level hopper pellet feeder

intake speed strand pelletizer,

knife speed pelletizer PSD after pelletizer

temperature extruder strand temperature

Development of Process Models


Unit operation Modelled parameters Description

Feeder Mass flow rate of feeder,

including feed disturbances

Blender RTD model, Fokker-Planck

Extruder RTD model, Fokker-Planck

1D/3D SPH model

LoLiMoT

Soft sensor model

Strand cutter Pellet length, PSD, pellet

diameter

Mechanistic model based on

intake and cut velocity

Tablet Press Mass flow

Hopper fill level

Weight, thickness, hardness

Dependent on turret speed

RTD model, Fokker-Planck

Soft sensors based on

process parameters

Control Concept for Homogeneity at Feeding/Blending

Based on concentration/homogeneity measurement adaption of

Feeder set-points

Blender speed

If material cannot be kept in-spec OOS handling (discharge flap)

21.6.2017 Slide 22 evon up2date

Process Example Trigger of a Control Action Based on Spectroscopic Data

21.6.2017 evon up2date

Feeding/blending unit

Blend is transported via

conveyor belt

NIR for monitoring of

concentration

Discharge of out of spec

material by means of a

vacuum cleaner

M M

API

EXC

QRC 001 Waste Product

Good Product

M

M

SHL

Slide 23

Detailed Experimental Set-up

evon up2date 21.6.2017

API Feeder EXC Feeder

Conveyer Belt

Blender NIR probe Vacuum

Wall

Spectrometer Control PC

Container

Separate control units

for feeders and blender

Connected to the

control software

Nominal concentration

of 5 % API

API feed rate 5 kg/h

Intentional increase of

API concentration

Slide 24

Information Flow


Feeder set points and

actual values (mass) are

accessible via OPC

Blender set point and

actual value (rotation

speed) is accessible by

Modbus

Fiber optic connection

between NIR probe and

spectrometer

The recorded spectrum is

transferred to the OPC

server at the control PC

The control PC performs

the spectral interpretation

and triggers the vacuum

OPC

TCP/IP

Light

Modbus

SentroRemoteOPCServer

XAMControl

SentroSuite

SentroOPCDriver

Slide 25

Supervised Test Run


API feeder is set to 10 kg/h

API level is increasing, spectra

outside of the specified

concentration range are

measured

API concentration reaches

trigger level, relay is set active

(note the purple rectangle in the

SCADA) and the vacuum is

turned on

Vacuum is running. Note the

black line on the conveyer belt,

where powder is cleared by the

vacuum

Slide 26

Supervised Test Run


The API feeder is set back to

the 5% target level

The concentration of the blend

reaches the nominal value again

and the vacuum is turned off

Process is in regular state again

Slide 27

Acknowledgement


Thank you for

your attention!

RCPE:

Julia Kruisz

Jakob Rehrl

Otto Scheibelhofer

Documents

Efficient Control of Continuous Pharmaceutical ... · Basic engineering of plant layout ... Method PI NIRS NIRS FBRM CI OCT ... - first principles model - data driven model