I just completed today a World Batch Forum (WBF) Webcast on Process Analytical Technology (PAT). The Webcast was fun and I can see where it opens up a whole new avenue of education even though the technology is not quite as far along as I expected. The Webcast went well thanks to the help of Deb Franke and Ed Guinn at Emerson Process Management and Mike McEnery (committee chairman for the WBF Webcast & Education Committee). I would encourage anyone doing this for the first time to do a pretest and trial run with the same equipment and in the same room used for the Webcast with two PCs and the help of audio and Web people. One PC is in the normal view to see the Q&A pane and provide faster navigation between slides. The other PC is in the full screen presentation mode. It is also important to realize that custom animation is not yet consistently feasible for a webcast due to variations in internet connection speeds and there are compatibility issues between Internet Explorer 7.0 and “Live Meeting.”
This WBF PAT Webcast was based on the book New Directions in Bioprocess Modeling and Control. You can check out a review of the book by Control magazine editor Walt Boyes in his March 13 blog http://waltboyes.livejournal.com/207809.html
The following questions and answers may be instructive:
What are some examples of “near” and true integrators for batch operation?
The classic true integrator is level where the rate of liquid mass accumulation in a batch (level ramp rate) is proportional to the feed rates.
Gas pressure in the head space is a “near” integrator when the change in drop across the vent valve from a change in head space pressure is small compared to the normal pressure drop. Also, since the process constant is much larger than the process dead time, the open loop response looks like a ramp in the control region. If the drop across the vent valve becomes critical, the gas pressure becomes a true integrator because a change in pressure does not cause a change in vent flow.
For liquid temperature and composition, there is no discharge flow during the part of the batch of interest. Consequently, there is a loss of self-regulation inherent in continuous processes. For a change in temperature, there is change in temperature drop across the heat transfer surfaces (e.g. jacket), but this is small compared to the normal drop. Like the gas pressure loop, the process constant is much larger than the process dead time for the temperature loop. Finally, the magnitude besides the relative size of the process time constant is very large making any steady state beyond the time frame of interest.
For pH and substrate control, as the reagent or substrate is consumed (e.g. ammonia and glucose), the response is a “near” integrator from its large process time constant although the nonlinearity of the titration curve may cause the response to accelerate or decelerate for increases and decreases in pH, respectively.
Normally there is a peak in the plot of biomass growth rate or product formation rate versus pH, substrate, or temperature. Deviations from the optimum operating conditions can alter the metabolic rates enough to change the reagent demand and cause a delayed and very slow secondary effect in the same or opposite direction of the initial change.
How much wireless communication delay can I have before I see degradation in fermenter control?
Let’s assume there are no aliasing or jitter issues communication delay so we can focus on the effect of an increase in lop dead time on loop performance.
Dead time dominant loops do not have as much leeway as loops where the process time constant is greater than the dead time but a communication delay that is less than 20% of the existing dead time is normally within the variation already seen from the many sources dead time so this is a reasonable rule of thumb. This allowable additional dead time is quite small for secondary flow and speed loops and depends heavily upon the module execution time and final element resolution.
For “near’ or true integrators you can introduce an interval up to 50% of the existing dead time for a controller that has a Lambda factor of one (Lambda equal to the process time constant). This permissible additional delay is quite large for the slow primary fermenter loops.
When should a batch MPC for production rate optimization be turned on?
The MPC should be turned on when the concentration and rate of change of the concentration becomes significant. In the example given, the MPC was turned at about the peak in the product formation rate so the set point track PV feature could capture the best rate for the batch and try to hold it until the end point was reached. There could be a separate MPC to first maximize biomass growth and then to maximize product formation rate.
What are some other examples of MPC used for bioreactors?
Amgen at the 2004 Emerson Exchange and Rutgers in the Control magazine August 2004 issue showed the use of MPC for pH and DO. In this blog site we discussed the setup of an MPC to eliminate split ranged controller outputs and the associated limit cycling around the split range point. The MPC is documented in the Advanced Application Note 002 titled “MPC Implementation Methods for the Optimization of the Responses of Control Valves” http://www.modelingandcontrol.com/repository/AdvancedApplicationNote002.pdf
How can I get enough batch data for batch analytics?
Best bet is to run bench top trials that have an industrial DCS and data historian with automated lab entry. Match up the virtual plant to these profiles and then use the virtual plant to generate more data.
How can I predict batch end points?
You could run a virtual plant faster than real time out to completion of the batch. If you have MPC helping to maintain the slope of the batch profile, after the peak in the product formation rate you can multiply the slope by the remaining batch time and add it to the product concentration from the last sample. You can keep updating this prediction after each lab sample. If the slope is variable, you could do the prediction piece wise based on a reference profile. If none of this is possible, you could simply bias the predicted batch profile by the difference between it and the current profile much like the simple feedback correction of the future process trajectory for MPC. A prediction is generally not viable until the concentration and rate of change of the concentration are both significant.