Interphex2009 Advances In Bioreactor Modeling And Control
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Presentation of kinetics, beta test results of wireless pH and temperature transmitters, and virtual plant study results on the effect of measurement resolution and time delay for bioreactor control

Presentation of kinetics, beta test results of wireless pH and temperature transmitters, and virtual plant study results on the effect of measurement resolution and time delay for bioreactor control

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Interphex2009 Advances In Bioreactor Modeling And Control Presentation Transcript

  • 1. 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
  • 2. 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
  • 3. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 3
  • 4. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 4
  • 5. 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) [File Name or Event] Emerson Confidential 27-Jun-01, Slide 5
  • 6. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 6
  • 7. 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) [File Name or Event] Emerson Confidential 27-Jun-01, Slide 7
  • 8. 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) [File Name or Event] Emerson Confidential 27-Jun-01, Slide 8
  • 9. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 9
  • 10. 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) [File Name or Event] Emerson Confidential 27-Jun-01, Slide 10
  • 11. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 11
  • 12. Virtual Plant Virtual Plant Laptop or Desktop or Control System Station Advanced Control Modules Process Model [File Name or Event] Emerson Confidential 27-Jun-01, Slide 12
  • 13. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 13
  • 14. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 14
  • 15. 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) [File Name or Event] Emerson Confidential 27-Jun-01, Slide 15
  • 16. Integrating Process Response to change in process input with controller in manual 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) [File Name or Event] Emerson Confidential 27-Jun-01, Slide 16
  • 17. Runaway Process Response to change in process input with controller in manual 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) Emerson Confidential 27-Jun-01, Slide 17
  • 18. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 18
  • 19. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 19
  • 20. 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% [File Name or Event] Emerson Confidential 27-Jun-01, Slide 20
  • 21. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 21
  • 22. Automatically Identified SUB Temperature Dynamics [File Name or Event] Emerson Confidential 27-Jun-01, Slide 22
  • 23. Wireless SUB Temperature Loop Test Results [File Name or Event] Emerson Confidential 27-Jun-01, Slide 23
  • 24. Wireless SUB pH Loop Test Results [File Name or Event] Emerson Confidential 27-Jun-01, Slide 24
  • 25. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 25
  • 26. 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 Emerson Confidential 27-Jun-01, Slide 26
  • 27. Control Studies of pH Resolution and Feedforward (Bioreactor batch running 500x real time) Feedforward 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 Emerson Confidential 27-Jun-01, Slide 27
  • 28. Control Studies of pH Refresh Time and Feedforward (Bioreactor batch running 500x real time) Feedforward Feedforward Batch 9 Batch 10 Batch 9 Batch 10 Batches 9 and 10 have 30 sec x 500 refresh time and standard PID Feedforward Feedforward Batch 11 Batch 11 Batch 12 Batch 12 [File Name or Event] Batches 11 and 12 have 30 sec x 500 refresh time and wireless PID Emerson Confidential 27-Jun-01, Slide 28
  • 29. 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 [File Name or Event] Batch 6: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Wireless PID Emerson Confidential 27-Jun-01, Slide 29
  • 30. 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 = 1.0 Reset Factor = 2.0 Reset Factor = 0.5 Improvement in stability is significant for any integrating process with analyzer delay [File Name or Event] Emerson Confidential 27-Jun-01, Slide 30
  • 31. Control Studies of Lambda Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time) (20 sec analyzer sample time) Standard PID Standard PID Standard PID Lambda Factor = 2.5 Lambda Factor = 2.0 Lambda Factor = 1.5 Wireless PID Wireless PID 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 31
  • 32. 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) 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 = 1.0 Reset Factor = 2.0 Reset Factor = 0.5 Improvement in stability and control is dramatic for any self-regulating process with analyzer delay [File Name or Event] Emerson Confidential 27-Jun-01, Slide 32
  • 33. 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 Wireless PID 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 33
  • 34. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 34
  • 35. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 35
  • 36. For More on the Effect of Sample Time on PID http://www.easydeltav.com/controlinsights/gm/AdvancedApplicationNote005.pdf [File Name or Event] Emerson Confidential 27-Jun-01, Slide 36
  • 37. For More on Bioprocess Modeling and Control [File Name or Event] Emerson Confidential 27-Jun-01, Slide 37
  • 38. 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 [File Name or Event] Emerson Confidential 27-Jun-01, Slide 38