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Comparative Analysis of Controller
Technologies for a CHO320 Fed BatchFermentation
Stephen Craven
19th September 2011
APC9 2011
York
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Presentation Overview
-Bioprocess Control
- Overview of apPAT Project
- Controller Strategy Simulations
- PID Control
- MPC Control
- Experimental Verification
- Conclusions
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Bioprocess Modelling and Control
Challenges
-Their dynamic behaviour is inherentlynonlinear
-Accurate process models are rarely
available due to the complexity of theunderlying biochemical processes
- Reliable biosensors to measureintercellular activities are rarely
available, making the process statesvery difficult to characterise
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apPAT Project
Industry led research prog ram funded by Enterpr ise
Ireland which aims to develop a PAT enabled mammalian
cel l b iopro cess
Modelling and
Prediction
Process
Control
Online
Sampling
Analytics,
Sensors
Real time
monitoring
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PAT Enabled Fed-batch Bioprocess
G
L
Q
A
Sin
F
ProcessController
Raman Probe
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Process Control
Process
Objective:
Maximize
Biomass/Productivity
Control
Objective:
Maintain a low
Glucose/Glutamine level
in order to avoid a buildup of Lactate/Ammonia
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Control Strategy Development, Simulation & Application
Control strategy development
- PID Control
- Non-linear MPC (Model Predictive Control)
Configure
& Tune
Simulate
Real
Time
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Control Strategy Development
Based on the ISA control law consisting of three controlparameters (K, Td, Ti)
Control parameters tuned via Ziegler Nichols (Closedloop) relay based tuning
Class ical Contro l
PID Con tro l ler
t
d
i dt
tdeTdeT
teKtu
0
)()(1)()(
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Control Strategy Development
Setpoint OutputProcess
P
I
D
)(tKe
t
i
de
T
K
0
)(
dt
tdeKT
d
)(
+
-
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Control Strategy Development
Class ical Contr ol
PID Contro l ler Tuning
Ziegler Nicho ls clos ed loop w ith relay ident i f icat ion techn ique.
7.1UK
PID: K =
Ti=
Td=
2
UP
8
UP
= 0.010558
= 0.4
= 0.1
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Control Strategy Development
Output
Setpoint
Process
ModelObjective
Function
Optimisation
Feed
Advanced Contro l
MPC Con trol ler
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Control Strategy Development
Indus tr ial MPC Formulat ion
M
i
ikik
P
i
ik
tutu
tutRtyMkk 1
2
1
2
1)(),......,(
)()((min1
Subject to,
maxmin
maxmin
uuu
yyy
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Bioprocess Model
Unstructured, non-segregated bioprocess model
- System of coupled ODEs, mass balance and rate equations
- Contains approximately 16 model parameters
- Used a differential evolution parameter optimisationalgorithm to fit the parameter values with the possible rangesconstrained based on published values
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Control Strategy Development
Advanced Contro l
MPC Tun ing
4 Tuning parameters:
P =Prediction horizon
M =Control horizon
= Output weight matrix
= Input weight matrix
Off l ine Tuning Rules
P =Increase until no further effect
M =1 (>1, leads to more aggressive control)
= scale on control variables = 1
= Increase to avoid aggressive CO
Tuning results
P= 10
M= 1
= 1
= 0
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Control Strategy Development, Simulation & Application
Control strategy simulation
- PID Control
- Non-linear MPC (Model Predictive Control)
Configure
& Tune
Simulate
Real
Time
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Controller Performance Simulation Results
Stepped Set-point Tracking
Disturbance Compensation
Propagation of Measurement Noise
Perform ance Off-Lin e Testing Criteria:
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PID Set-point Step Tracking
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentratio
n(mM) Cglc
Set-point
0
0.01
0.02
0.03
0.04
0.05
0.06
0 50 100 150 200 250 300Time (h)
Feed-rate(L/h)
Controller Output
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MPC Set-point Step Tracking
0
5
10
1520
25
30
0 50 100 150 200 250 300Time (h)
Concentration(mM)
Cglc
Set-point
0
0.01
0.02
0.03
0.04
0.05
0.06
0 50 100 150 200 250 300Time (h)
Feedrate
(L/h)
Controller Output
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Controller Performance Simulation Results
Stepped Set-point Tracking
Disturbance Compensation
Propagation of Measurement Noise
Perform ance Off-Lin e Testing Criteria:
PID Di b C i
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PID Disturbance Compensation
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentratio
n(mM) Cglc
Set-point
0
0.02
0.04
0.06
0.08
0.1
0.12
0 50 100 150 200 250 300Time (h)
Feed-rate
(L/h)
Controller Output
Disturbance
applied
MPC Di t b C ti
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MPC Disturbance Compensation
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentratio
n(mM) Cglc
Set-point
0
0.02
0.04
0.06
0.08
0.1
0.12
0 50 100 150 200 250 300Time (h)
Feedrate
(L/h)
Controller Output
Disturbance
applied
C ll P f Si l i R l
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Controller Performance Simulation Results
Stepped Set-point Tracking
Disturbance Compensation
Propagation of Measurement Noise
Perform ance Off-Lin e Testing Criteria:
PID P i f M N i
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PID Propagation of Measurement Noise
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentratio
n(mM) Cglc
Set-point
0
0.01
0.02
0.030.04
0.05
0.06
0.07
0 50 100 150 200 250 300Time (h)
Feed-rate(L/h)
Controller Output
Fast on-off
Control
PI P ti f M t N i
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PI Propagation of Measurement Noise
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentration
(mM) Cglc
Set-point
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 50 100 150 200 250 300Time (h)
Feed-rate
(L/h)
Controller Output
Removal of
D term leads
to less abrupt
control
Better set-point
tracking
MPC P ti f M t N i
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MPC Propagation of Measurement Noise
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentratio
n(mM) Cglc
Set-point
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 50 100 150 200 250 300Time (h)
Feed-rate
(L/h)
Controller Output
P = 10
M = 1
= 1
= 0
MPC P ti f M t N i
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MPC Propagation of Measurement Noise
0
5
10
15
20
25
30
0 50 100 150 200 250 300Time (h)
Concentration(mM)
Cglc
Set-point
00.010.020.030.040.050.060.07
0 50 100 150 200 250 300
Time (h)
Feed-ra
te
(L/h)
Controller Output
P = 40
M = 1
= 1
= 0
MPC P ti f M t N i
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MPC Propagation of Measurement Noise
0
5
10
1520
25
30
0 50 100 150 200 250 300Time (h)
Concentration(mM) Cglc
Set-point
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 50 100 150 200 250 300Time (h)
Feed-rate
(L/h)
Controller Output
P = 40
M = 1
= 1
= 10000
Si l ti St d R lt
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Simulation Study Results
Reference tracking:MPC starts adjusting the control signal
ahead of reference changes, while PID cannot start before.
MPC gives less control error.
Disturbance compensation:MPC better than PID. MPC
can deal with plant-model mismatch.
Measurement noise:Less propagation through tuned MPCthan tuned PID. By increasing the penalty weight on the input,
the control action in the presence of measurement noise can
be dampened to a more smooth control signal.
SISO/MIMO: PID can only handle one input and one output.
MPC is a multivariable control strategy.
Control Strategy Development Simulation & Application
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Control Strategy Development, Simulation & Application
Control strategy real time application
- PID Control
- Non-linear MPC (Model Predictive Control)
Configure
& Tune
Simulate
Real
Time
3 L Bioreactor Experimental Set up
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3 L Bioreactor Experimental Set-up
Real Time Controller Application
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Real Time Controller Application
Raman
Probe
Spectral
acquisition
software
Spectral
file
Calibration
model &
real-time engine
Control
model &
real-time
engine
Feed Pump
USBText file
read/write
File Directory
Text file
read/write
Text file
read/write
Serial
15 L Bioreactor Experimental Set up
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15 L Bioreactor Experimental Set-up
Real Time Controller Application
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Real Time Controller Application
Raman
Probe
Spectral
acquisition
software
Spectral
file
Calibration
model &
real-time engine
Control
model &
real-time
engine
ABB
800xa
control
system
USB TCP/IP
ABB xPAT Data
Manager
OPCOPC
OPC
Experimental Results
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Experimental Results
Communication and controller configuration for real time
application complete.
Plan to run MPC and PID control on both the 3 L and 15 L
bioreactor setups with the Raman probe for real time monitoring of
glucose concentration.
Over the next few months, preliminary experimental data for the
closed loop feedback control fed-batch runs will be gathered.
3 L Bioreactor Experimental Results
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3 L Bioreactor Experimental Results
0
5
10
15
20
25
30
0 20 40 60 80 100 120 140Time (h)
Concentration
(mM)
Glucose
Set-point
0
0.001
0.002
0.003
0.004
0.005
0.006
0 20 40 60 80 100 120 140Time (h)
Feed-rate
(L/h)
Controller Output
=50000 =0=20000 =0
Conclusions and Future Work
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Conclusions and Future Work
Fed-batch model has been incorporated into the simulation of
PID and MPC controller technologies.
MPC performed best for set-point tracking both in thepresence of noise and disturbances from the simulation study.
Configured and simulated control strategies are to be appliedin real time with online and at-line PAT on two differentbioreactor scales.
Apply MIMO MPC for glucose and glutamine control.
Real time results are to be compared to the results gatheredon a simulation basis.
Acknowledgements
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Acknowledgements
Prof. Brian Glennon
Dr. Jessica Whelan
apPAT Research Team
Enterprise Ireland