Process Data, Dynamic Modeling and a Recipe for Profitable Control
It is best practice to follow a formal procedure or "recipe" when designing and tuning a PID (proportional-integral-derivative) controller. A recipe-based approach is the fastest method for moving a controller into operation. And perhaps most important, the performance of the controller will be superior to a controller tuned using a guess-and-test or trial-and-error method.
Additionally, a recipe-based approach overcomes many of the concerns that make control projects challenging in a commercial operating environment. Specifically, the recipe-based method causes less disruption to the production schedule, wastes less raw material and utilities, requires less personnel time, and generates less off-spec product.
The recipe for success is short:
- Establish the design level of operation (DLO), defined as the expected values for set point and major disturbances during normal operation
- Bump the process and collect controller output (CO) to process variable (PV) dynamic process data around this design level
- Approximate the process data behavior with a first order plus dead time (FOPDT) dynamic model
- Use the model parameters from step 3 in rules and correlations to complete the controller design and tuning.
Step 2: Bumping Our Process and Collecting CO to PV Data
From a controller's view, a complete control loop goes from wire out to wire in as shown below. Whenever we mention controller output (CO) or process variable (PV) data anywhere on this site, we are specifically referring to the data signals exiting and entering our controller at the wire termination interface.
To generate CO to PV data, we bump our process. That is, we step or pulse the CO (or the set point if in automatic mode as discussed here) and record PV data as the process responds. Here are three basic rules we follow in all of our examples:
- Start with the process at steady state and record everything
- Make sure the PV response dominates the process noise
- The disturbances should be quiet during the bump test
Data that has been corrupted by unmeasured disturbances is of little value for controller design and tuning. The model (see below) will then incorrectly describe the CO to PV cause and effect relationship. And as a result, the controller will not perform correctly. If we are concerned that a disturbance event has corrupted test data, it is conservative to rerun the test.
Step 4: Using Model Parameters For Design and Tuning
The final step of the recipe states that once we have obtained model parameters that approximate the dynamic behavior of our process, we can complete the design and tuning of our PID controller.
We look ahead at this last step because this is where the payoff of the recipe-based approach is clear. To establish the merit, we assume for now that we have determined the design level of operation for our process (step 1), we have collected a proper data set rich in dynamic process information around this design level (step 2), and we have approximated the behavior revealed in the process data with a first order plus dead time (FOPDT) dynamic model (step 3).
Thankfully, we do not need to know what a FOPDT model is or even what it looks like. But we do need to know about the three model parameters that result when we fit this approximating model to process data.
The FOPDT (first order plus dead time) model parameters, listed below, tell us important information about the measured process variable (PV) behavior whenever there is a change in the controller output (CO) signal:
- process gain, Kp (tells the direction and how far PV will travel)
- process time constant, Tp (tells how fast PV moves after it begins its response)
- process dead time, Өp (tells how much delay before PV first begins to respond)
Where:
CO(t - Өp) = controller output signal as a function of time and shifted by Өp Өp = process dead time t = time
We study what these three model parameters are and how to compute them in other articles, but here is why process gain, Kp, process time constant, Tp, and process dead time, Өp, are all important:
- Tuning
- Controller Action
- Loop Sample Time, T
- Dead Time Problems
- Model Based Control
Fundamental to Success
With tuning values, loop specifications, performance diagnostics and advanced control all dependent on knowledge of a dynamic model, we begin to see that process gain, Kp; process time constant, Tp; and process dead time, Өp; are parameters of fundamental importance to success in process control.
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Anonymous says:
Generally, most of people may instantly think the jacketed reactor when people refer to the vacuum glass distillation. From: www.toption-china.com/reactors-and-instruments/chemical-synthesis-reactor-series. Although this reactor is different from other reactors, it is equipped with its unique characteristics.