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).