Ask Russ Rhinehart - How does One Calibrate First-Principles Models?

Russ' Response:

Yes!  It is very useful for some economic, operational, and control evaluations. Environmental vagaries would include continually drifting raw material composition, changing ambient heat loads on outside equipment, changes in fouling and friction factors, even measurement calibration drift.  Operational vagaries would be issues such as rerouting piping flow patterns for maintenance, loss of a pump, switching units for regeneration, etc.

Then there is noise on process measurements that confound control algorithms.

Simple stochastic (random perturbation) models can be included in either a dynamic or steady state simulator to generate measurement noise and environmental or operational disturbances.  Then the simulator will reveal the process output variation. 

This can help in process design, for instance sizing tanks to adequately blend material, or choosing the number of trays in a distillation column to ensure on-spec product despite input vagaries. 

This can help in control system design, for instance to determine the set point deviation from specification or constraint that is required to minimize violations. 

Instead of modeling stochastic input values and events, one could choose a range of values for each input then do a factorial experiment running the simulator for each high, mid, and low value of each input.  But, the factorial design has the same probability of each set of conditions, which may misrepresent the probability of independent influences causing an issue.  Letting random drifts provide the input patterns has a higher likelihood of providing accurate distributions of the key process metrics. 

An extended version of this pod with details and examples can be accessed under the MODELING/SIMULATION Menu on my web site, www.r3eda.com. For more information on this topic email Russ Rhinehart at [email protected].