TABLE OF CONTENTS Process control systems assume a constant linear process. Unfortunately, all process variables and control valves are nonlinear to some degree: the process response to a given change in the controller output changes with batch time, seed culture, and bioreactor operating conditions. The lack of consistency in the process response has significant implications for the process's performance not only in terms of tuning controllers but of recognizing degradations and achieving optimums [3-12].
Consciously or subconsciously, tuning controllers involves a tradeoff between performance and robustness. The controller's ability to tightly control at an operating point is inversely proportional to its ability to weather changes in the plant's behavior without become 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. A PID controller approaches turns cautiously since it doesn't know what lies ahead [3-12].
| Note | PID controllers are backed off from best performance because of the uncertainty of tuning settings. |
Controller tuning settings can be computed from a first-order or an integrating-plus-dead-time process model. The changes in the model parameters reveal changes in the cells, process conditions, equipment, final elements, and sensors. The size, direction, and characteristics of these changes can provide a road map and knowledge of the terrain
