interphex2009 advances in bioreactor modeling and control

Advances in Bioreactor Advances in Bioreactor Modeling and ControlModeling and Control

Greg McMillan, Trish Benton, and Michael BoudreauInterphex – March 17, 2009

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

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

http://www.modelingandcontrol.com/

[File Name or Event]Emerson Confidential27-Jun-01, Slide 2 Slide 2

CoauthorsCoauthorsCoauthors

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


AgendaAgendaAgenda

Mammalian Bioreactor ModelFlexible and Convenient KineticsVirtual Plant ConceptsTypes of Process ResponsesSingle Use Bioreactor (SUB) for Wireless TestsWirelessHART NetworkWireless PID FeaturesWireless SUB Results for pH and Temperature LoopsControl Studies of Wireless PID Control for pHControl Studies of Wireless PID Control for At-Line AnalyzersConclusionsSources for More Info on Modeling and Effect of Sample TimeReferences


Differences between Fungal or Bacterial and Mammalian Bioreactor ModelsDifferences between Fungal or Bacterial and Differences between Fungal or Bacterial and Mammalian Bioreactor ModelsMammalian Bioreactor Models

Kinetics– More than twice as many kinetic terms and parameters– Generalized Michaelis-Menten kinetic parameters– Slower product formation rate and batch cycle timeMass transfer

– Significantly less agitation and bubblesComponents

– Glutamine or glutamate utilization– Lactate and ammonia formationReagents

– Carbon dioxide – Sodium bicarbonateSparge

– Oxygen, carbon dioxide, and inert addition besides airOverlay

– Air, oxygen, carbon dioxide, and inert sweep– No manipulation of overhead pressure for dissolved oxygen control


Mammalian Growth and Product Formation RatesMammalian Growth and Product Formation RatesMammalian Growth and Product Formation Rates

Bioreactor models can handle any user expressions for kinetic rate factors

vTvHvOvbvavsvsvv rrrrrrr ∗∗∗∗∗∗∗= +2max 21µµ

Maximum Specific Growth Rate

(per hr) Growth Rate Factors (0-1)

glucose and glutamine substrates (rvs1) (rvs2), lactic acid (rva), ammonia base (rvb), dissolved oxygen (rvO2), pH (rvH+), and temperature (rvT)

TpHpOpspspp rrrrru ∗∗∗∗∗= +2max 21µ

Maximum Specific Product Formation Rate(g product/g cell per hr)

Product Formation Rate Factors (0-1)glucose and glutamine substrates (rps1) (rps2),

dissolved oxygen (rpO2), pH (rpH+), and temperature (rpT)


Flexible Michaelis-Menten KineticsFlexible MichaelisFlexible Michaelis--Menten KineticsMenten Kinetics

[ ] [ ]jii

i

jii

ji

KXX

KXK

jir21

1

++ ∗=Inhibition parameter Limitation parameter

ConcentrationGrowth or formation rate factor (0 - 1)

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, respectivelythe expected set point

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


Glucose Growth Rate FactorGlucose Growth Rate FactorGlucose Growth Rate Factor

Michaelis-Menten Cell Growth Rate Kinetics

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Glucose Concentration (g/Liter)

G

luco

se G

row

th R

ate

Fact

or


Convenient pH Model KineticsConvenient pH Model KineticsConvenient pH Model Kinetics

[ ]2maxmin

maxmin

)()()()()(

optpHpHpHpHpHpHpHpHpHpH

vHr

−−−∗−−∗−=+

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)


pH Growth Rate FactorpH Growth Rate FactorpH Growth Rate Factor

Cardinal pH Model Kinetics

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

6.00 6.20 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 8.00

pH

pH

Gro

wth

Rat

e Fa

ctor


Convenient Temperature Model KineticsConvenient Temperature Model KineticsConvenient Temperature Model Kinetics

[ ]])2()()()([)()()(

minmaxminmin

2minmax

TTTTTTTTTTTTTTT

vT optoptoptoptoptr ∗−+∗−−−∗−∗−

−∗−=

Tmax = maximum temperature for viable cells (45 oC)

Tmin = minimum temperature for viable cells (5 oC)

Topt = optimum temperature for product formation (37 oC)


Temperature Growth Rate FactorTemperature Growth Rate FactorTemperature Growth Rate Factor

Cardinal Temperature Model Kinetics

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00

Temperature

Te

mpe

ratu

re G

row

th R

ate

Fact

or


Virtual PlantVirtual PlantVirtual Plant

Advanced Control Modules

Process Model

Virtual PlantLaptop or Desktop

or Control System Station


Top Ten Reasons I Use a Virtual Plant Top Ten Reasons I Use a Virtual Plant 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


Virtual Plant Knowledge SynergyVirtual Plant Knowledge SynergyVirtual Plant Knowledge Synergy

Dynamic Process Model

OnlineData Analytics

Model PredictiveControl

Loop MonitoringAnd Tuning

DCS batch and loopconfiguration, displays,

and historian

Virtual PlantLaptop or DesktopPersonal Computer

OrDCS Application

Station or Controller

Embedded Advanced Control Tools

EmbeddedPAT Tools

Process Knowledge


Self-Regulating ProcessSelfSelf--Regulating ProcessRegulating Process

∆X

∆Y

θp τp

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

0.63∗∆Y

X

Y

ProcessDead Time

Self-Regulating Process Time Constant

Noise Band

New Steady State

Process Output (Y)& Process Input (X)

Time (t)

Response to change in process input with controller in manual

Most continuous processes have a self-regulating response (PV lines out in manual)


Integrating ProcessIntegrating ProcessIntegrating Process

Time (t)θp

Ki = { [ ∆Y2 / ∆t2 ] − [ ∆Y1 / ∆t1 ] } / ∆X(Integrating Process Gain)

∆X

ramp rate is∆Y1 / ∆t1

ramp rate is∆Y2 / ∆t2

X

Y

Process Output (Y)& Process Input (X)

ProcessDead Time


To prevent slow rollingoscillations and overshootfrom integral action, the product of the controller gain (Kc) and reset time (Ti) should satisfy the limit:Kc ∗ Ti > 4 / Ki

Most batch processes have an integrating response (PV ramps in manual)


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

∆X

θp

Kp = ∆Y / ∆X(Runaway Process Gain)

1.72∗∆YX

Y

ProcessDead Time

Runaway Process Time Constant

Time (t)τp’

∆Y

Noise Band

Acceleration

pH and exponential growth phase appear to have a runaway response (PV accelerates in manual)



Installation at Broadley JamesInstallation at Broadley JamesInstallation 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


WirelessHART Network TopologyWirelessHART Network TopologyWirelessHART Network Topology

Network Manager

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

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

ApplicationsNetwork 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


WirelessHART FeaturesWirelessHART FeaturesWirelessHART FeaturesWireless transmitters provide nonintrusive replacement and diagnosticsWireless transmitters automatically communicate alerts based on smart diagnostics without interrogation from an automated maintenance systemWireless transmitters eliminate the questions of wiring integrity and terminationWireless transmitters eliminate ground loops that are difficult to track downNetwork manager optimizes routing to maximize reliability and performanceNetwork manager maximizes signal strength and battery life by minimizing the number of hops and preferably using routers and main (line) powered devicesNetwork manager minimizes interference by channel hopping and blacklistingThe standard WirelessHART capability of exception reporting via a resolution setting helps to increase battery lifeWirelessHART 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 timePIDPLUS and new communication rules can reduce communications by 96%


Traditional and Wireless PID (PIDPLUS)Traditional and Wireless PID (PIDPLUS)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 valuePID reset and rate action are only computed when there is a new valuePID algorithm with enhanced reset and rate action is termed PIDPLUS


Automatically Identified SUB Temperature DynamicsAutomatically Identified SUB Temperature DynamicsAutomatically Identified SUB Temperature Dynamics


Wireless SUB Temperature Loop Test ResultsWireless SUB Temperature Loop Test ResultsWireless SUB Temperature Loop Test Results


Wireless SUB pH Loop Test ResultsWireless SUB pH Loop Test ResultsWireless SUB pH Loop Test Results


Elimination of Ground Noise Spikes by WirelessElimination of Ground Noise Spikes by WirelessElimination of Ground Noise Spikes by Wireless

Wired pH ground noise spike

Temperature compensated wireless pH controlling at 6.9 pH set point

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


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

Batch 1 Batch 2 Batch 1 Batch 2


Batches 1 and 2 have 0.00 pH resolution and standard PID

Feedforward Feedforward




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








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

Control Studies of pH Refresh Time and FeedforwardControl Studies of pH Refresh Time and Feedforward(Bioreactor batch running 500x real time)(Bioreactor batch running 500x real time)





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

Batches 11 and 12 have 30 sec x 500 refresh time and wireless PID


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

Continuous FF-NoStandard PID

Continuous FF-YesStandard PID

11 hr Sample FF-NoStandard PID

11 hr Sample FF-YesStandard PID

11 hr Sample FF-NoWireless PID

11 hr Sample FF-YesWireless PID

Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 Batch 6

Glucose Concentration

x1000

Batch 1: Glucose Probe (Continuous - No Delay) + Feed Forward - No + Standard PIDBatch 2: Glucose Probe (Continuous - No Delay) + Feed Forward - Yes + Standard PIDBatch 3: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - No + Standard PIDBatch 4: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Standard PIDBatch 5: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - No + Wireless PIDBatch 6: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Wireless PID


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

Reset Factor = 0.5

Standard PID Standard PID Standard PID

Reset Factor = 1.0 Reset Factor = 2.0

Wireless PID Wireless PID Wireless PID

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

Improvement in stability is significant for any integrating process with analyzer delay


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

Lambda Factor = 1.5



Lambda Factor = 2.0 Lambda Factor = 2.5

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

Improvement in stability is significant for any integrating process with analyzer delay


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





Improvement in stability and control is dramatic for any self-regulating process with analyzer delay


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





Improvement in stability and control is dramatic for any self-regulating process with analyzer delay


ConclusionsConclusionsConclusionsWireless PID and new communication rules can increase battery lifeWireless pH eliminates spikes form ground noiseWireless PID provides tight control for set point changesFeedforward of ammonia formation rate and oxygen uptake rate (OUR) offers significant improvement. OUR decouples interaction between pH and DO loopsWireless 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 processesIntegrating processes are much more sensitive than self-regulating processes to increases in sample time, decreases in reset time, and increases in gainDetuned 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


Top Ten Signs of a WirelessHART AddictionTop Ten Signs of a WirelessHART AddictionTop 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


For More on the Effect of Sample Time on PIDFor More on the Effect of Sample Time on PIDFor More on the Effect of Sample Time on PID

http://www.easydeltav.com/controlinsights/gm/AdvancedApplicationNote005.pdf

http://www.easydeltav.com/controlinsights/gm/AdvancedApplicationNote005.pdf


For More on Bioprocess Modeling and ControlFor More on Bioprocess Modeling and ControlFor More on Bioprocess Modeling and Control


ReferencesReferencesReferences

1. McMillan, Gregory, et. al., “PAT Tools for Accelerated Process Development and Improvement”, BioProcess International, Process Design Supplement, March, 2008

2. Blevins, Terry, and Beall, James, “Monitoring and Control Tools for Implementing PAT”, Pharmaceutical Technology, Monitoring, Automation , & Control, 2007

3. Boudreau, Michael and McMillan, Gregory, New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits, Instrumentation, Automations, and Systems (ISA), 2006

4. Boudreau, Michael, McMillan, Gregory, and Wilson, Grant, “Maximizing PAT Benefits from Bioprocess Modeling and Control”, Pharmaceutical Technology Supplement: Information Technology Innovations in the Pharmaceutical Industry, November 2006

5. McMillan, Gregory and Cameron, Robert, Advanced pH Measurement and Control, 3rd edition, ISA, 2005

6. Nixon, Chen, Blevins, and Mok, “Meeting Control Performance over a Wireless Mesh Network”, The 4th Annual IEEE Conference on Automation Science and Engineering (CASE 2008), August 23-26, 2008,, Washington DC, USA.

7. Chen, Nixon, Blevins, Wojsznis, Song, and Mok “Improving PID Control under Wireless Environments”, ISA EXPO2006, Houston, TX

8. Chen, Nixon, Aneweer, Mok, Shepard, Blevins, McMillan “Similarity-based Traffic Reduction to Increase Battery Life in a Wireless Process Control Network”, ISA EXPO2005, Houston, TX

interphex2009 advances in bioreactor modeling and control

Technology

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