Applying Batch Data Principles to Continuous Manufacturing AIChE Final

Applying Batch Data Principles
to Continuous Manufacturing
for the Purposes of Data
Management, Batch Reporting,
Analytics and Traceability
Paul Brodbeck
Chief Technologist
QbD Process Technologies, Inc.
Bob Engel
Vice President
Informetric Systems Inc.
Ravendra Singh
Chemical and Biochemical Engineering
Department, Rutgers University

2 QbD Process Technologies
ContinuousPlant™ Software Suite
Background
• Many pharmaceutical processes that have been implemented using traditional batch
techniques are now evolving towards continuous manufacturing in order to improve
manufacturing efficiency and product uniformity
• There is intense research and development associated with applying continuous process
technology to drug manufacturing that was previously batch oriented
• Since there is not discrete separation of materials at various stages of the process, it can
be difficult or impossible to link incoming raw and intermediate materials to final product
• The regulatory and traceability considerations are particularly critical in this area
• Residence Time Distribution (RTD) models represent a possible solution for product
traceability that is required with respect to regulatory and safety guidelines, as well as
Current Good Manufacturing Practice (cGMP)
• RTD models enable batch reporting, analysis and material traceability of continuous
processes

Continuous Plant for OSD
Rutgers
Engineering Research Center
for Structured Organic Particulate Systems (ERC-SOPS)
Singh, R., Boukouvala, F., Jayjock, E., Ramachandran, R. Ierapetritou, M., Muzzio, F. (2012). Flexible Multipurpose Continuous
Processing. PharmPro Magazine, 28 June, 2012,

Experimental Setup and Instrumentation
• NIR sensor has been integrated with process
for real time monitoring and feedback control
• Chemometric tools have been used for real
time NIR spectrum analysis in order to
acquire a concentration input
• OPC communication protocol has been used
to communicate the data across different
software tools
• Control loops have been implemented in
distributed process control system
• Controller outputs have been sent to the
actuators through fieldbus devices

Residence Time Distribution (RTD)
• Models relationship between input and
output concentrations
• Input pulse dissipates through unit
operation as function of time
Time
OutletConc.
τ
Time
Inletconc.
t0
0
( )
(t)
( )
out
out
C t
E
C t dt



0
( )MRT t E t dt

  
Mean residence time
RTD
• Output concentration is characterized
by Mean Residence Time (MRT) and
distribution
• Maximum output concentration
amplitude represents maximum
possible response to input disturbance

Continuous Plant / RTD Models
Feed Frame &
Tablet Press
Loss-in-Weight
Feeders
Comil
Mixer
M
M
EX
M
M
API
M
M
MgSt
Loss-in-Weight
Feeders
Comil/Mixer
Mixer
Feed Frame &
Tablet Press
Flowsheet Continuous Line RTD Models
(… N …)
Full line RTD can be achieved by integrating the individual unit RTDs

Residence Time Distribution (RTD)
Feeder
1
Feeder
2
Feeder
5
Feeder
4
Feeder
3
BLENDERCOMIL
TABLET
PRESS
TABLET
E(t) – Probability Ratio of Drum ID
For example 10/90kg Refill Strategy
90% Drumn
9% Drumn-1
.9% Drumn-2
.09% Drumn-3
.009% Drumn- 4
E(t)d1
Drum
Drum
Drum
Drum
Drum
Barcode
E(t)d5
E(t)d4
E(t)d3
E(t)d2
E(t)dn E(t)dc E(t)db
Blend Uniformity RSD
API %
E(t)dp
tdc
tdb tdp
Probability Convolution Formulas
E(t)tablet,drum = E(t)dn + tdc + E(t)dc + tdb + E(t)db + tdp + E(t)dp + ttab
E(t)tablet,RSD = tdp + E(t)dp + ttab
E(t)tablet,API = tdp + E(t)dp + ttab
ttab
Direct Compression RSD Formulas
Ideal CSTR has an exponential
residence time distribution: )
Residence Time (tc)
E(t) = 1/tc * (e-t/tc
)

RTD Modeling for Material Tracing
Lot A
Raw
Material
Drums
Feeder
RTD
Mill
RTD
Blender
RTD
Tablet
Press
RTD
Tablet
Drums
E(tF) E(tM) E(tB) E(tTP)tDF tDTtDTPtDBtDMt0 tF+ + + + + + ++ + =
RTD: E(t) = 1/tc * (e-t/tc
)
Deadtime: tD
t1
t2
t3
t4
t5
t6
t7
t8
t9
t10
t1
t2
t3
t4
t5
t6
t7
t8
t9
t10
t11
t12
TRACE
BACK
TRACE
FORWARD
Material Tracing in DeltaV
RAW
MATL
DRUMS
TABLET
DRUMS
Lot B
Lot C

Reporting Architecture

Reporting Stages
• Data Collection
– Report server collects raw data from disparate data sources according to specified
context model
• Aggregation
– Report server aligns data
– Report server performs intermediate calculations
• Rendering
– Report server renders formatted report

Contextualization
• Traditional Batch
– S88 Procedural Model
(recipe driven)
• Continuous
– RTD Model Driven
Material Tracing
Model based

Example Report – Tablet API Tracing

Summary
• The evolution of traditionally batch oriented processes to continuous manufacturing
introduces challenges related to product traceability
• Residence Time Distribution (RTD) models enable the application of traditional batch
reporting and analysis techniques to continuous manufacturing processes
• Contact Information:
– Paul Brodbeck, QbD Process Technologies, Inc.,
paul.brodbeck@qbdprocess.com
– Bob Engel, Informetric Systems Inc., bengel@informetric.com
– Ravendra Singh, Rutgers University, rs1034@scarletmail.Rutgers.edu
– References:
• “Modeling of Residence Time Distribution in Continuous Solid Oral Dose Pharmaceutical Manufacturing Processes”. AIChE
Annual Meeting. M. Sebastian Escotet-Espinoza, Amanda Rogers, Fernando J. Muzzio and Marianthi Ierapetritou. Department
of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

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