Interphex2009 Advances In Bioreactor Modeling And Control

Advances in Bioreactor
Modeling and Control
Greg McMillan, Trish Benton, and
Michael Boudreau
Interphex – March 17, 2009

http://www.modelingandcontrol.com/
http://www.easydeltav.com/controlinsights/index.asp

Slide 1

Coauthors
Greg McMillan - Principal Consultant,
CDI Process and Industrial at Emerson
Trish Benton – Life Sciences Consultant,
Broadley-James Corporation
Mike Boudreau - Director of Bioreactor Manufacturing and Automation,
Broadley-James Corporation

[File Name or Event]
Emerson Confidential
27-Jun-01, Slide 2

Agenda
Mammalian Bioreactor Model
Flexible and Convenient Kinetics
Virtual Plant Concepts
Types of Process Responses
Single Use Bioreactor (SUB) for Wireless Tests
WirelessHART Network
Wireless PID Features
Wireless SUB Results for pH and Temperature Loops
Control Studies of Wireless PID Control for pH
Control Studies of Wireless PID Control for At-Line Analyzers
Conclusions
Sources for More Info on Modeling and Effect of Sample Time
References

27-Jun-01, Slide 3

Differences between Fungal or Bacterial and
Mammalian Bioreactor Models
Kinetics
– More than twice as many kinetic terms and parameters
– Generalized Michaelis-Menten kinetic parameters
– Slower product formation rate and batch cycle time
Mass transfer
– Significantly less agitation and bubbles
Components
– Glutamine or glutamate utilization
– Lactate and ammonia formation
Reagents
– Carbon dioxide
– Sodium bicarbonate
Sparge
– Oxygen, carbon dioxide, and inert addition besides air
Overlay
– Air, oxygen, carbon dioxide, and inert sweep
– No manipulation of overhead pressure for dissolved oxygen control

27-Jun-01, Slide 4

Mammalian Growth and Product Formation Rates

Bioreactor models can handle any user expressions for kinetic rate factors

µv = µ v max ∗r vs ∗r vs ∗r va ∗r vb ∗r vO 2 ∗r vH ∗r vT +
1 2

Maximum Specific
Growth Rate Factors (0-1)
Growth Rate
glucose and glutamine substrates (rvs1) (rvs2), lactic acid (rva), ammonia
(per hr) base (rvb), dissolved oxygen (rvO2), pH (rvH+), and temperature (rvT)

u p = µ p max ∗r ps1 ∗r ps 2 ∗r pO 2 ∗r pH + ∗r T
Maximum Specific Product Formation Rate Factors (0-1)
Product Formation Rate glucose and glutamine substrates (rps1) (rps2),
(g product/g cell per hr) dissolved oxygen (rpO2), pH (rpH+), and temperature (rpT)

27-Jun-01, Slide 5

Flexible Michaelis-Menten Kinetics
Michaelis-Menten

[ ] ∗[ ]
Concentration
Growth or formation
rate factor (0 - 1)

rji =
K1 ji Xi
Xi + K1ji Xi + K2 ji
Inhibition parameter Limitation parameter
Monod Equation

Initialization of kinetic parameters:

If the limitation or inhibition effect is significant the limitation
and inhibition parameters are set to 0.1x and 10x, respectively
the expected set point

If the limitation or inhibition effect is negligible the limitation
and inhibition parameters are set to 0 and 100, respectively

27-Jun-01, Slide 6

Glucose Growth Rate Factor

Michaelis-Menten Cell Growth Rate Kinetics

1.0000
0.9000
Glucose Growth Rate Factor

0.8000
0.7000
0.6000
0.5000
0.4000
0.3000
0.2000
0.1000
0.0000
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Glucose Concentration (g/Liter)

27-Jun-01, Slide 7

Convenient pH Model Kinetics

[ ]
( pH − pH min )∗( pH − pH max )
rvH + = ( pH − pH min )∗( pH − pH max ) −( pH − pH opt ) 2

pHmax = maximum pH for viable cells (8 pH)
pHmin = minimum pH for viable cells (6 pH)
pHopt = optimum pH for viable cell growth (6.8 pH)

27-Jun-01, Slide 8

pH Growth Rate Factor

Cardinal pH Model Kinetics

1.0000

0.9000
0.8000
pH Growth Rate Factor

0.7000
0.6000

0.5000
0.4000

0.3000
0.2000

0.1000
0.0000
6.00 6.20 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 8.00
pH

27-Jun-01, Slide 9

Convenient Temperature Model Kinetics

[ ]
( T −Tmax )∗( T −Tmin ) 2
rvT = ( Topt −Tmin ) ∗ [ ( Topt −Tmin )∗( T −Topt ) − ( Topt −Tmax )∗( Topt + Tmin − 2∗T ) ]

Tmax = maximum temperature for viable cells (45 oC)
Tmin = minimum temperature for viable cells (5 oC)
Topt = optimum temperature for product formation (37 oC)

27-Jun-01, Slide 10

Temperature Growth Rate Factor

Cardinal Temperature Model Kinetics

1.0000
0.9000
Temperature Growth Rate Factor

0.8000
0.7000
0.6000
0.5000
0.4000
0.3000
0.2000
0.1000
0.0000
5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00
Temperature

27-Jun-01, Slide 11

Virtual Plant

Virtual Plant
Laptop or Desktop
or Control System Station

Advanced Control Modules

Process Model

27-Jun-01, Slide 12

Top Ten Reasons I Use a Virtual Plant

(10) You can’t freeze, restore, and replay an actual plant batch
(9) No separate programs to learn, install, interface, and support
(8) No waiting on lab analysis
(7) No raw materials
(6) No environmental waste
(5) Virtual instead of actual problems
(4) Batches are done in 14 minutes instead of 14 days
(3) Plant can be operated on a tropical beach
(2) Last time I checked my wallet I didn’t have $100,000K
(1) Actual plant doesn’t fit in our suitcase

27-Jun-01, Slide 13

Virtual Plant Knowledge Synergy

DCS batch and loop
configuration, displays,
and historian
Embedded
Embedded
Advanced Control Tools
PAT Tools

Dynamic Loop Monitoring
Virtual Plant
Process Model And Tuning
Laptop or Desktop
Personal Computer
Or
DCS Application
Station or Controller
Online Model Predictive
Data Analytics Control

Process Knowledge

27-Jun-01, Slide 14

Self-Regulating Process
Self-Regulating
Response to change in process input with controller in manual
Process Output (Y)
& Process Input (X)
New Steady State

Y
Kp = ∆Y / ∆X
(Self-Regulating Process Gain)

X

∆Y

0.63∗∆Y
∆X

Noise Band

Time (t)
τp
θp

Process Self-Regulating Process
Dead Time Time Constant

Most continuous processes have a self-regulating response (PV lines out in manual)
27-Jun-01, Slide 15

Integrating Process
Process Output (Y)
& Process Input (X)
Y

To prevent slow rolling
Ki = { [ ∆Y2 / ∆t2 ] − [ ∆Y1 / ∆t1 ] } / ∆X
(Integrating Process Gain)
oscillations and overshoot
X
from integral action, the
product of the controller
gain (Kc) and reset time (Ti)
should satisfy the limit:
∆X
Kc ∗ Ti > 4 / Ki
ramp rate is
ramp rate is
∆Y2 / ∆t2
∆Y1 / ∆t1

Time (t)
θp

Process
Dead Time

Most batch processes have an integrating response (PV ramps in manual)
27-Jun-01, Slide 16

Runaway Process
Process Output (Y)
& Process Input (X)

Y
Kp = ∆Y / ∆X Acceleration
(Runaway Process Gain)

1.72∗∆Y
X

∆Y
∆X

Noise Band

Time (t)
τp’
θp

Process Runaway Process
Dead Time Time Constant

[File Name and exponential
pH or Event] growth phase appear to have a runaway response (PV accelerates in manual)
27-Jun-01, Slide 17

Installation at Broadley James

Hyclone 100 liter
Single Use
Bioreactor (SUB)
Rosemount
WirelessHART
gateway and
transmitters for
measurement
and control of
pH and
temperature.
(pressure
monitored)
BioNet lab
optimized
control system
based on DeltaV

27-Jun-01, Slide 18

WirelessHART Network Topology
Wireless Field Devices
– Relatively simple - Obeys Network Manager
– All devices are full-function (e.g., must route)
Adapters
– Provide access to existing HART-enabled Field
Devices
– Fully Documented, well defined requirements
Gateway and Access Points
– Allows access to WirelessHART Network from
Network Manager
the Process Automation Network
– Gateways can offer multiple Access Points for
increased Bandwidth and Reliability
– Caches measurement and control values
– Directly Supports WirelessHART Adapters
– Seamless access from existing HART
Applications
Network Manager
– Manages communication bandwidth and
routing
– Redundant Network Managers supported
– Often embedded in Gateway
– Critical to performance of the network
Handheld
– Supports direct communication to field device
– For security, one hop only communication

27-Jun-01, Slide 19

WirelessHART Features
Wireless transmitters provide nonintrusive replacement and diagnostics
Wireless transmitters automatically communicate alerts based on smart
diagnostics without interrogation from an automated maintenance system
Wireless transmitters eliminate the questions of wiring integrity and termination
Wireless transmitters eliminate ground loops that are difficult to track down
Network manager optimizes routing to maximize reliability and performance
Network manager maximizes signal strength and battery life by minimizing the
number of hops and preferably using routers and main (line) powered devices
Network manager minimizes interference by channel hopping and blacklisting
The standard WirelessHART capability of exception reporting via a resolution
setting helps to increase battery life
WirelessHART control solution, keeps control execution times fast but a new
value is communicated as scheduled only if the change in the measurement
exceeds the resolution or the elapsed time exceeds the refresh time
PIDPLUS and new communication rules can reduce communications by 96%

27-Jun-01, Slide 20

Traditional and Wireless PID (PIDPLUS)
PID integral mode is
restructured to provide
integral action to match the
process response in the
elapsed time (reset time is
set equal to process time
constant)
PID derivative mode is
modified to compute a rate
of change over the elapsed
time from the last new
measurement value
PID reset and rate action
are only computed when
there is a new value
PID algorithm with
enhanced reset and rate
action is termed PIDPLUS

27-Jun-01, Slide 21

Automatically Identified SUB Temperature Dynamics

27-Jun-01, Slide 22

Wireless SUB Temperature Loop Test Results

27-Jun-01, Slide 23

Wireless SUB pH Loop Test Results

27-Jun-01, Slide 24

Elimination of Ground Noise Spikes by Wireless

Incredibly tight pH control via 0.001 pH wireless resolution
setting still reduced the number of communications by 60%

Temperature compensated wireless pH controlling at 6.9 pH set point

Wired pH ground noise spike

27-Jun-01, Slide 25

Control Studies of pH Resolution and Feedforward
(Bioreactor batch running 500x real time)

Feedforward Feedforward
Batch 1 Batch 2 Batch 1 Batch 2

Batches 1 and 2 have 0.00 pH resolution and standard PID

Feedforward
Feedforward
Batch 3 Batch 4
Batch 3 Batch 4

[File Name or Event] Batches 3 and 4 have 0.01 pH resolution and standard PID
27-Jun-01, Slide 26

Control Studies of pH Resolution and Feedforward

Batch 5 Batch 6
Batch 5 Batch 6

Batches 5 and 6 have 0.02 pH resolution and standard PID

Feedforward
Feedforward
Batch 7 Batch 8
Batch 7 Batch 8

[File Name or Event] Batches 7 and 8 have 0.04 pH resolution and standard PID
27-Jun-01, Slide 27

Control Studies of pH Refresh Time and Feedforward

Batch 9 Batch 10 Batch 9 Batch 10

Batches 9 and 10 have 30 sec x 500 refresh time and standard PID

Batch 11 Batch 11
Batch 12 Batch 12

Batches 11 and 12 have 30 sec x 500 refresh time and wireless PID
27-Jun-01, Slide 28

Control Studies of Glucose Sample Time and
Feedforward (Bioreactor batch running 1000x real time)

Glucose
Concentration

Batch 3 Batch 6
Batch 1 Batch 2 Batch 5
Batch 4
11 hr Sample FF-No 11 hr Sample FF-Yes
11 hr Sample FF-Yes
Continuous FF-No Continuous FF-Yes 11 hr Sample FF-No
Wireless PID
Standard PID
Standard PID Standard PID Wireless PID
Standard PID

x1000

Batch 1: Glucose Probe (Continuous - No Delay) + Feed Forward - No + Standard PID
Batch 2: Glucose Probe (Continuous - No Delay) + Feed Forward - Yes + Standard PID
Batch 3: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - No + Standard PID
Batch 4: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Standard PID
Batch 5: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - No + Wireless PID
Batch 6: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Wireless PID
27-Jun-01, Slide 29

Control Studies of Reset Factor & Wireless PID for
Real Time Integrating Process (20 sec analyzer sample time)
(20 sec analyzer sample time)

Standard PID Standard PID Standard PID

Reset Factor = 0.5 Reset Factor = 2.0
Reset Factor = 1.0

Wireless PID Wireless PID
Wireless PID

Reset Factor = 0.5

Improvement in stability is significant for any integrating process with analyzer delay
27-Jun-01, Slide 30

Control Studies of Lambda Factor & Wireless PID for
Real Time Integrating Process (20 sec analyzer sample time)
(20 sec analyzer sample time)


Lambda Factor = 2.5
Lambda Factor = 2.0
Lambda Factor = 1.5

Wireless PID

Lambda Factor = 2.5
Lambda Factor = 2.0
Lambda Factor = 1.5

Improvement in stability is significant for any integrating process with analyzer delay
27-Jun-01, Slide 31

Control Studies of Reset Factor & Wireless PID for
Real Time Self-Regulating Process (40 sec analyzer sample time)
Self-Regulating (40 sec analyzer sample time)


Reset Factor = 1.0

Wireless PID

Reset Factor = 0.5

Improvement in stability and control is dramatic for any self-regulating process with analyzer delay
27-Jun-01, Slide 32

Control Studies of Lambda Factor & Wireless PID for
Real Time Self-Regulating Process (40 sec analyzer sample time)
Self-Regulating (40 sec analyzer sample time)

Standard PID
Standard PID Standard PID

Lambda Factor = 2.0 Lambda Factor = 2.5
Lambda Factor = 1.5

Wireless PID

Lambda Factor = 1.5 Lambda Factor = 2.0 Lambda Factor = 2.5

Improvement in stability and control is dramatic for any self-regulating process with analyzer delay
27-Jun-01, Slide 33

Conclusions
Wireless PID and new communication rules can increase battery life
Wireless pH eliminates spikes form ground noise
Wireless PID provides tight control for set point changes
Feedforward of ammonia formation rate and oxygen uptake rate (OUR) offers
significant improvement. OUR decouples interaction between pH and DO loops
Wireless PIDPLUS dramatically improves the control and stability of any self-
regulating process with large measurement delay (sample delay). The wireless
PID is a technological breakthrough for the use at-line analyzers for control
– The Wireless PIDPLUS set point overshoot is negligible for self-regulating processes
with large sample delays if controller gain is less than the inverse of process gain
Wireless PIDPLUS is stable for self-regulating process with large sample delay if
controller gain is less than twice the inverse of the process gain
– As the analyzer sample time decreases and approaches the module execution time, it is
expected that the wireless PID behaves more like a standard PID
Wireless PIDPLUS significantly reduces the oscillations of integrating processes
but the improvement is not as dramatic as for self-regulating processes
Integrating processes are much more sensitive than self-regulating processes to
increases in sample time, decreases in reset time, and increases in gain
Detuned controllers (large Lambda Factors), makes loops less sensitive to
sample time (see Advanced Application Note 005 “Effect of Sample Time ….”)
If the controller gain is increased or the wireless resolution setting is made finer,
the PIDPLUS can provide tighter control. For a loss of communication, the
PIDPLUS offers significantly better performance than a wired traditional PID
particularly when rate action and actuator feedback (readback) is used

27-Jun-01, Slide 34

Top Ten Signs of a WirelessHART Addiction

(10) You try to use the network manager to schedule the activities of your children
(9) You attempt to use RF patterns to explain your last performance review
(8) You use so much resource allocation in your network manager, you eat before
you are hungry
(7) You propose your wireless device for the “Miss USA” contest
(6) You develop performance monitoring indices for your spouse
(5) You implement network management on your stock portfolio
(4) You carry pictures of your wireless device in your wallet
(3) You apply mesh redundancy and call three taxis to make sure you get home
from your party
(2) You recommend a survivor show where consultants are placed in a plant with
no staff or budget and are asked to add wireless to increase plant efficiency
(1) Your spouse has to lure you to bed by offering “expert options” for scheduling

27-Jun-01, Slide 35

For More on the Effect of Sample Time on PID

http://www.easydeltav.com/controlinsights/gm/AdvancedApplicationNote005.pdf
27-Jun-01, Slide 36

For More on Bioprocess Modeling and Control

27-Jun-01, Slide 37

References
McMillan, Gregory, et. al., “PAT Tools for Accelerated Process Development
1.
and Improvement”, BioProcess International, Process Design Supplement,
March, 2008
Blevins, Terry, and Beall, James, “Monitoring and Control Tools for
2.
Implementing PAT”, Pharmaceutical Technology, Monitoring, Automation , &
Control, 2007
Boudreau, Michael and McMillan, Gregory, New Directions in Bioprocess
3.
Modeling and Control: Maximizing Process Analytical Technology Benefits,
Instrumentation, Automations, and Systems (ISA), 2006
Boudreau, Michael, McMillan, Gregory, and Wilson, Grant, “Maximizing PAT
4.
Benefits from Bioprocess Modeling and Control”, Pharmaceutical Technology
Supplement: Information Technology Innovations in the Pharmaceutical
Industry, November 2006
McMillan, Gregory and Cameron, Robert, Advanced pH Measurement and
5.
Control, 3rd edition, ISA, 2005
Nixon, Chen, Blevins, and Mok, “Meeting Control Performance over a Wireless
6.
Mesh Network”, The 4th Annual IEEE Conference on Automation Science and
Engineering (CASE 2008), August 23-26, 2008,, Washington DC, USA.
Chen, Nixon, Blevins, Wojsznis, Song, and Mok “Improving PID Control under
7.
Wireless Environments”, ISA EXPO2006, Houston, TX
Chen, Nixon, Aneweer, Mok, Shepard, Blevins, McMillan “Similarity-based
8.
Traffic Reduction to Increase Battery Life in a Wireless Process Control
Network”, ISA EXPO2005, Houston, TX

27-Jun-01, Slide 38

Interphex2009 Advances In Bioreactor Modeling And Control

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Interphex2009 Advances In Bioreactor Modeling And Control