Is it good enough? Is it too good? Do you even know? Should you care?Well yes, you probably should care. Most level processes are non-self-regulating or integrating processes. Everything you probably learned about tuning PID self-regulating loops like flow, pressure, and temperature does not work quite the same on integrating processes. So it is quite common for level loops to be tuned “by the seat of the pants” or trial and error. Furthermore, most level loops are tuned to achieve good setpoint response and yet most level loops have one setpoint (typically 50% of the tank height) and rarely is the setpoint ever changed. It is usually more important to consider the response to load disturbances. Even if and sometimes especially when the level is tightly controlled, regardless of how it was tuned, it is likely that the underlying disturbance and resulting variability is amplified rather than attenuated. That is never a good thing.
Lou notes that control loops are meant to control low-frequency disturbances. High frequency variability such as some flow loops can be reduced through the use of surge tanks or other vessels in the process. Lou writes:
To take advantage of the surge capacity, it is necessary to know the potential variability or worst case disturbance of the wild flow and the allowable limits on the level. Then we can tune the level control to be able to respond the worst case while keeping the level in bounds.
He describes the need for a tuning methodology and that Emerson Control Performance consultants prefer to use Lambda tuning. We’ve touched on Lambda tuning in numerous posts here on the blog. Lou describes it:
Lambda is the closed loop time constant and defines the speed of response of the loop under control. Interacting self-regulating loops can be dynamically decoupled by making the lambda of one loop sufficiently larger than the other. There is a minimum lambda that can be defined to avoid unstable or oscillatory response under closed loop control. But in the context of level control, the selection of lambda defines the speed of response which is related to the arrest time and deviation for a disturbance. Lambda tuning of integrating processes reduces the variability of the manipulated flow and takes maximum advantage of the surge capacity in the vessel without risking loss of containment.
Lou shares several process example where he’s applied Lambda tuning for level control. Here’s one example on the bottom level control on a distillation column:
It is quite common to see the base level controller tuned for very tight, aggressive control. The result is that the bottom flow can be quite variable and in the extreme can see the bottom flow oscillating between high flow and no flow as fast as the control valve can move. This is obviously not good for the control valve, but it can be detrimental to the process as well. The bottom of the column is at a high temperature and often is beneficial to recover some of the heat before that stream is sent to the next step in the process. If the heat recovery is used to preheat the column feed, for example, you can see how that will introduce variability into the feed of the column and be disruptive. I have found this to be quite common on fractionator columns in refinery crude units. It would be much better to reduce and minimize the variability in bottom product flow, even if the level varies a bit in the base of the column.
Read the rest of Lou’s post for the other examples he shares and how other control strategies including feed forward control and model predictive control may be required to achieve satisfactory control.