APC9_rev1 S Craven

8/10/2019 APC9_rev1 S Craven

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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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Setpoint OutputProcess

P

I

D

)(tKe

t

i

de

T

K

0

)(

dt

tdeKT

d

)(

+

-


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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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Output

Setpoint

Process

ModelObjective

Function

Optimisation

Feed

Advanced Contro l

MPC Con trol ler


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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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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 simulation

- PID 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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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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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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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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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 real time application

- PID 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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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



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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

APC9_rev1 S Craven

Documents

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