Modeling and Control

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 available on http://www.easydeltav.com/ControlInsights/

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.

When Monsanto was making the transition to a life science company, I had the opportunity to work on fermenter measurement and control for various genetically engineered products. Important opportunities identified then such as the application of mass spectrometers, dissolved carbon dioxide probes, and inferential measurements of metabolic processes have come to fruition today opening the door to more advanced process analysis and control techniques. Additionally the applications gave me a chance to apply my expertise in pH measurement and control in new ways and dig into the practical aspects of dissolved oxygen measurement and control.

These opportunities and practical considerations were documented in a book Biochemical Measurement and Control, which is now available free electronically via the link

http://www.easydeltav.com/controlinsights/biochemicalmeasurement/default.asp

This E-book also offers an introduction to importance of biotechnology and a perspective of the future from the past. My expression of gratitude in the Acknowledgement to KSHE Sweetmeats for the inspiration has initiated speculations over the years that are more interesting than the explanation that Sweetmeats is the mascot for a Saint Louis classic rock radio station that I listened to while writing the book.

My latest book Bioprocess Modeling and Control - Maximizing Process Analytical Technology Benefits published by ISA in 2006 provides an updated view and details on new tools for batch modeling, analysis, and control. This ISA book includes the development of neural network inferential measurements of dryer moisture by Washington University in Saint Louis and my first principle dynamic fermentor models for the National Corn to Ethanol Research Center. The book concludes with an excellent review of new technology for batch analytics by the University of Texas.

It is interesting how the past plays into the future. I how have the privilege to participate in a beta test of new PAT tools with Broadley-James for fermenter modeling, analysis, and control of BioNet systems. The beta tests offers a synergistic environment for combining the expertise of Dr Thomas Edgar and Yang Zhang at the University of Texas and many key people at Broadley-James and in DeltaV’s Future Architecture team including my coauthor Michael Boudreau. Another really neat thing about this beta test is that we can extensively share the details of the results.

In analyzing loops in the control room, the story for me was more in the controller output. Yet data analytics tend to focus mostly on primary process variables.

The clues to the significance of the controller output as a source of information are in its job and its action. The job of a feedback controller is to transfer variability and offsets in the controlled variable (process variable) to its manipulated variable (controller output) whether they originate in the sensor, process, or valve. The PID controller output is the result of proportional, integral, and possibly derivative action. Thus the trend of the PID output can contain information on the duration of a shift, approach to set point, and the rate of change of the process variable. Several examples help illustrate this concept.

A trend of a pressure controller’s output showed it varied significant from day to night. It was later confirmed that there was a day to night temperature induced shift in the calibration of the transmitter.

The shifts in the steady state value of the reflux to feed ratio manipulated by a column temperature controller and the reagent to influent feed ratio manipulated by a pH controller were found to coincide with the replacement of the sensors.

For a batch reactor, a larger and earlier dip in manipulated jacket inlet temperature and peak in manipulated vent flow corresponded to a higher heat release and secondary product vapor flow from a more concentrated reactant. In other cases, a higher makeup coolant flow manipulated by the jacket temperature controller coincided with a higher cooling tower temperature.

For a continuous reactor, a larger variability in the vent valve position at higher rates was discovered to be caused by a significant flattening of the installed characteristic of the butterfly valve above 50% open.

Sustained equal amplitude saw tooth and sinusoidal oscillation in the controller output were deduced to be indicative of a limit cycle from stick-slip in a self-regulating loop and deadband in an integrating loop, respectively.

A study of the control of reactor feed flow showed that an inadvertent change in the time interval used for the calculation of the loss in weight flow measurement created a shift in the feed controller output (manipulated speed of the positive displacement pump).

If the controller gain is higher than one or rate action is used, noise in the process variable will be amplified in the controller output. If the peak to peak noise in the controller output exceeds the dead band and resolution of the valve, the controller is inflicting disturbances upon itself or other loops.

Noise also makes it more difficult to see the change in the pattern of the controller output due top changes in process inputs. Thus, whether the analysis of the controller output is done visually or by multivariate statistical process control, the reduction of noise by better doing tuning and filtering is important for batch and continuous analytics.

When there are set point changes, there is also significant information in the pattern of the process variable (e.g. approach, overshoot, and settling of reactor temperature). In a way this consideration is consistent with the above concept in that the set point and thus the process variable is being manipulated by a batch or startup sequence. Similarly, the process variables of loops manipulated by cascade or model predictive control are important.

Unfortunately most of the examples in literature for batch analytics are for process variables of manual or missing controllers. For example, a significant downward trend in dissolved oxygen (DO) is often shown for batch analytics of a fermentor when in fact DO would be controlled at a set point and the story would be in the manipulated air flow.

The concept of transfer of variability from controlled variables to manipulated variables for analysis of batch profiles is emphasized in the book New Directions in Bioprocess Modeling and Control (ISA, 2006).