High Fidelity

I was at a “Guess Who” concert at the “One World Theatre” here in Austin the “Live Music Capital” last night thinking wow the lead singer who obviously wasn’t born when the band had their greatest hits sounded the same but better than the original recordings. The tone and inflections were not only right-on but enhanced. While the singer I heard at the Austin “Bat Fest” who sounded more like Meatloaf than Meatloaf was impressive, this concert blew me away. I moved to the other side of my brain to the dynamic world of process control and pondered if “high fidelity” or in this case “hyper fidelity” is possible in dynamic models.

For the last 30 years I have been creating and using models of medium fidelity. When I got into bioreactor modeling, I moved into the realm of “high fidelity” as a result of necessity and opportunity.

For pharmaceutical processes, the process and control system design is set in the process research and development phase, often by the biochemist. By the process design and commercialization phase, the set points and control strategy or lack thereof is set in stone. If my modeling was going to be used for improving the product concentration and quality at the end of the batch or reduce the batch cycle time, I needed to move my model upstream from design into development. Also, standing with my bioreactor model demo next to the Broadley-James booth at Interphex 2007, Scott Broadley and I saw a synergist opportunity. Scott as a leading supplier of bench top systems completely automated with a full capability DCS and the latest technology in probes envisioned he could enhance the knowledge and system capability offered with a dynamic model of the process that could explore “what if” scenarios with a virtual batch running 1000x real time. I could see besides getting the needed process test data and characterization of the model, Broadley-James would be as interested as me in making the details public knowledge whereas pharmaceutical companies who expressed interested in participating in the model development would keep the results and conditions as closely guarded secrets. PATtools

Looking toward the future Emerson and Broadley-James (principally Trish Benton and Michael Boudreau) have ventured into the world of high fidelity by the parameterization of a bioreactor model in DeltaV Simulate Pro Control Studio based on cell culture runs to create a virtual plant.

So what if you are not into bioreactor or high fidelity modeling? There are plenty of uses and reasons for models of various levels of fidelity that can get you a virtual plant.

Top Ten Reasons for a Virtual Plant

10. You can’t freeze, restore, and replay an actual plant batch

9. No software 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 minutes instead of hours or days

3. Plant can be operated on a tropical beach

2. Last time I checked our wallet we didn’t have $1,000K for a plant to test

1. Actual plant doesn’t fit in my suitcase

For my own edification and possibly yours, I did the following core dump of uses and my assessment of the level of fidelity required on a scale of 1 to 10 where 1 is for tieback simulations where feedback by discrete values (e.g. valve limit switches and motor run contacts) go to the right status and analog values move in the right direction (e.g. loop process variables respond in the right direction to changes in controller output).

Typical Uses of Models and Levels of Fidelity Required

Process Development

Media or reactant optimization and identification of kinetics on the bench top – 10

Optimization of process conditions in pilot plant – 9

Agitation and mass transfer rates – 8*

Process scale-up – 8

* – assumes computational fluid dynamics (CFD) program provides necessary inputs

Process Design

Innovative reactor designs or single use bioreactors (SUB) – 7

Vessel, feed, and jacket system size and performance – 6

Automation Design

Real Time Optimization (RTO) – 7

Model Predictive Control (MPC) – 6

Controller tuning (PID) – 5

Control strategy development and prototyping – 4

Batch sequence (e.g. timing of feed schedules and set point shifts) – 3

Online Diagnostics

Root cause analysis – 5

Data analytics development and prototyping – 4

Operator Training Systems

Developing and maintaining troubleshooting skills – 4

Understanding process relationships – 3

Gaining familiarity with interface and functionality of automation system – 2

Configuration Checkout

Verifying configuration meets functional specification – 2

Verifying configuration has no incorrect or missing I/O, loops, or devices – 1

My world has been automation system design with some ventures in into process design for neutralization systems where pH controllability is so highly dependent on equipment and reagent injection dynamics but I am looking forward to the high fidelity experience.