Control systems assume a constant linear process. Unfortunately, all process variables and control valves are nonlinear to some degree. The response to a given change in the controller output shifts with time, throughput, operating point and plant conditions. The lack of consistency in this response has significant implications for the performance of the process, not only in the tuning of controllers but also in recognizing degradations and achieving optimums.

To put this into context, it is important to realize that controllers are tuned, consciously or subconsciously, based on a tradeoff between performance and robustness. The capability to tightly control at an operating point is inversely proportional to the ability to weather changes in the behavior of the plant without becoming oscillatory. The operating environment for most loops is stormy and the last thing you want is for a control loop to introduce more variability. Consequently, all controllers are detuned (backed off from maximum performance) to some degree to provide a smooth response despite the inevitable changes in the process dynamics. An industrial controller approaches turns cautiously because it doesn’;t know what lies ahead.

Polymerization Control

Figure 1. In plant trials, reactor feedback and feedforward loops were based on pH.

The roadmap
In practice, three parameters are used to provide a first-order-plus-dead-time model of the process dynamics and to capture the essence of the process response. The most important of these is loop dead time, which is the time delay between a change in a manipulated or disturbance variable (process input) and the resultant shift in a controlled variable (process output). If the dead time were zero and the measurement and valve resolution were unlimited, tuning would not be an issue and perfect control would be possible. But dead time always exists and encompasses the inherent delays from plug flow (transportation delays), valves (dead band and stick-slip) and digital devices (scan and execution time intervals) and the secondary lags from mixing, heat transfer, actuator, sensors and volumes in series [1].

The next most important parameter is process gain, which is the final change in a process output for a given shift in a process input. A high process gain (sensitivity) is desirable because it improves the inference of a process condition from a measurement. A highly sensitive column, evaporator or reactor temperature measurement is important to recognize and control changes in composition. However, a high process gain amplifies the stick-slip in valves and the noise from non-ideal mixing.

The third parameter is the process time constant, which is the time to reach 63% of the final change in a controlled variable after the process has started to move (after the dead time). In plants, this time constant rarely is constant. For a large back-mixed volume, it essentially is the residence time if the secondary lags take a back seat [2].

Controller tuning settings can be computed from this first-order-plus-dead-time model. The shifts in these parameters reveal changes in the operation, process, equipment, valves and sensors. The size, direction and characteristics of these movements can provide a roadmap, knowledge of the terrain, and a full throttle controller to reach the destination of maximum process efficiency and minimum downtime.

Today’;s speed bump
Nearly all adaptive controllers presently used at chemical plants take a relatively long time to observe changes in the process variable before adjusting tuning settings. The tuning rules are embedded and usually unknown to the user. Today’;s most common adaptive controller relies on pattern recognition and will, if necessary, increase the controller gain to induce oscillations so it can get a better handle on the maneuverability of the process. The size of the transients or oscillations and the time required for identification can translate into significant process variability and lead to an adaptation rate slower than the rate of change of the process parameters. For example, it may take four or more oscillations each with a period of four hours (thus two shifts) for these adaptive controllers to search and find the best tuning settings for the temperature controller on a distillation column.

Most adaptive controllers are playing catch-up even if they have seen the same situation a thousand times before. At best, these controllers provide a snapshot of the current tuning requirements and no real insight as to where the process has been or where it is going. Also, sudden unexplained shifts in the tuning settings or bursts of oscillations reduce the operator’;s confidence and thus lower prospects the controllers will run in the adaptive mode and be used in future applications.

In contrast, the next generation of adaptive controllers will identify a process model quickly and automatically and provide process model parameters that can be displayed, trended and diagnosed. The availability of a suite of tuning methods will enable selection of the method that best matches the process and the plant objectives. Furthermore, these controllers will remember the results for similar conditions, eliminate repetitious identification and take the initiative.

Such a controller, which now has been demonstrated in plant tests, can identify the dead time, process gain and time constant for both manipulated and disturbance variables and save these as a function of a key variable. The user can apply the recommended tuning method or choose an alternative to compute the tuning settings for the current and memorized conditions. When the key variable indicates the process has changed, the tuning then is scheduled based on the process model saved in the operating region. The controller remembers the results from previous excursions and does not wait to recognize old territory. For example, for loops with nonlinear installed valve characteristics and nonlinear controlled variables such as pH, the model and tuning is scheduled based on the controller output and input, respectively.