Process modeling, feedback controllers
Advanced regulatory control (ARC) can resolve many industrial control problems, but there’s a common misconception held by nearly all current practicing control engineers regarding the features and capabilities of ARC.
During the 1970s, and with the advent of digital control systems and process control computers, those involved in the evolution of ARC developed two powerful ARC techniques, referred to as feedforward control and decoupling control. Feedback control, regardless of how sophisticated, by itself was insufficient for keeping important control variables close to setpoint when disturbances occurred.
Feedforward control was developed initially as the name suggests—to make adjustments to independent manipulated variables (MVs) to keep important dependent control variables (CVs) close to setpoint when the feed rate to a unit operation changed.
How was this done? With models, of course! Open-loop response data was first gathered to observe the response of the CVs to changes in the feed rate. This response was typically fit with a deadtime and lag model. The next step was to "invert" this model so as to develop the feedforward pattern needed to adjust the MV used to control the CV so as to keep the CV close to setpoint during the feed rate change.
So, for example, on a distillation column where the overhead temperature is being controlled with reflux flow, the challenge is to apply deadtime and lead/lag compensation to the feed rate, passing the incremental trajectory changes (with proper gain) to the reflux flow so that the reflux is adjusted in coordinated fashion, and the disturbance is cancelled out. If the feedforward compensation is perfect, no feedback correction is required at all!
Model-predictive control (MPC) provides more or less identical "model-predictive" control action for feedforward variables (FFVs). ARC feedforward control action can actually be made superior to MPC feedforward control action. The proper gain, or the amount the MV must be adjusted to keep the CV on setpoint, is not a constant ratio. For example, the reflux/feed ratio required to keep the top temperature on setpoint may differ today from what it was yesterday, due to feed composition changes, etc. ARC can incorporate dynamic, adaptive feedforward gain compensation to account for process changes. MPC cannot.
The other model-based control feature of ARC, decoupling control, was developed for similar reasons and initially for distillation columns. On many fractionators where high purity products are being produced, the heat and material balances are highly coupled, such that moves made on one end of the column also affect the other end. We recognized this phenomenon early on and realized that simultaneous decoupling could be applied to both ends of the column so as to minimize heat and material balance upsets. For example, if the reflux needs to be increased to maximize the purity of the overhead product, then a similar decoupling move needs to be made to the reboiler heat so as not to disturb the bottom product purity. The decoupling moves are determined from a model developed in a manner similar to the one described earlier.
ARC techniques developed over 40 years ago use a model to predict what will happen and react to avoid the event, which is to refute the misconception that ARC is not MPC. It certainly is predictive as implemented today and has been for the last 40 years.
In addition, ARC can be made multivariable; that is, feedforward and decoupling control action can be implemented simultaneously for several FFVs and DCVs acting on several MVs to control several CVs. In general, ARC is cheaper to implement, is more durable, is more easily understood by operators, and requires less maintenance when compared to MPC. It should always be considered first as the go-to technology for solving most common industrial control problems.
Advance control in three ways
Advanced process control (APC) concepts can be implemented a little bit at a time but still make big steps toward improving processes. With the increase in processor power, the number of APC algorithms available can help improve operations. In some older plants, a cascade or feedforward control loop could be considered advanced control. What constitutes advanced control? The usual suspects involve fuzzy logic, neural networks, and predictive modeling, but other opportunities can improve control without re-sorting that may be difficult for some to understand and use.
APC has three basic elements: deadtime compensation, adaptive tuning, and decoupling.
1. Deadtime compensation
Deadtime compensation is an advanced control concept that is not very well understood. Many think that simply inserting a deadtime function block into the input to the loop is adequate but doing so neglects the effect of process disturbances to the output of the proportional-integral-derivative (PID) controller.
Using a Smith predictor allows the control loop to adjust the model bias according to the magnitude of the disturbance. A modified Smith predictor also allows the controller to adjust the gain as well as the bias depending on the disturbance. A heat exchanger that has a disturbance in its feed temperature requires a bias change to hold the outlet temperature, while a heat exchanger that has a disturbance in its feed rate requires an adjustment to the gain to maintain outlet temperature. The Smith predictor can be constructed using standard function blocks, but some distributed control system (DCS) manufacturers may include a control module template to speed implementation.
2. Adaptive tuning, gain scheduling
A second advanced control element that is implemented using standard function blocks is adaptive tuning, sometimes called gain scheduling. The classic controlled variable that can make use of this is pH, though any loop that exhibits changes in the process response that are essentially linear for that portion of the operating range is a candidate. The implementation of this isn’t difficult, but the user needs to know how many control regions there are and where the changes occur in the response of the loop. The user will need to use a tuning software package to determine the responses in each of the linear regions and some method to determine the width of the transition area between regions.
3. Decoupling network
The third element recognizes that some control loops interact and can even fight each other, therefore building a decoupling network is another useful function block tool. A good example of this is a lime kiln where the temperature needs controlling at both ends of the kiln. The cold end is controlled by the induced draft fan speed, while the hot end is controlled by the fuel flow.
By holding fuel flow constant and increasing fan speed, the hot-end temperature drops, and the cold-end temperature increases.
By holding fan speed constant and increasing fuel flow, both ends’ temperatures increase, but the hot end increases much more than the cold end.
So it’s easy to see that if both controllers are in automatic, they will fight each other. To break this coupling, the outputs of the control loops are taken through deadtime and lead/lag function blocks in a series and summed with the output of the other control loop. Tuning the network requires knowing the two time constants of each of the loops involved, the deadtime, and the response time.
Taking these relatively simple APC instances to logical extremes requires use of special algorithms like model predict or fuzzy logic, but starting with deadtime compensation, adaptive tuning, and decoupling can improve operations with the tools at hand.
– Jim Ford is a senior consultant, and Bruce Brandt, PE, is DeltaV technology leader at Maverick Technologies, a system integrator for the process industries. Edited by Mark T. Hoske, content manager, CFE Media, Control Engineering, email@example.com.
- Advanced process control and advanced regulatory control offer benefits with complexity of more advanced control methods.
- Examples show how ARC can help improve control.
- Deadtime compensation, adaptive tuning, and decoupling offer control benefits.
What advanced process control techniques could improve your operational effectiveness?