We conclude with the following myths from Appendix D in my guide Models Unleashed – Virtual Plant and Model Predictive Control Applications published by ISA, 2004.
(21) You need an advanced degree to do advanced control. Not so anymore. New software packages used to form a virtual plant automate much of the expertise needed and eliminate the need for special interfaces. The user can now focus mostly on the application and the goal.
(22) Dynamic simulations and model based control are only applicable to continuous processes. Since most of the applications are in the continuous industry, this is a common misconception. While it is true that a steady state simulation is not valid since there is by definition no steady state in batch, dynamic simulation can follow a batch as long as the software can handle zero flows and empty vessels. Model predictive control (MPC), which looks at trajectories, is suitable for the optimization of fed batch processes during particularly important points in the batch cycle. The opportunities to improve a process’s efficiency by MPC are about 25% for batch compared to 5% for continuous operations.
(23) You need consultants to maintain models and advanced control systems. No longer necessarily true. The ease of use of new software allows the user to get much more involved, which is critical to make sure the plant gets the most value out of the models. Previously, the benefits started to drop as soon as the consultant left the job site. Now the user should be able to tune, troubleshoot, and update the models.
(24) You don’t need good operator displays and training for well designed advanced control systems. The operators are the biggest constraint in most plants. Even if the models used for real time optimization and model based control are perfect, operators will take these systems offline if they don’t understand them. The new guy in town is always suspect, so the first time there is an operational problem and there is no one around to answer questions, MPC systems are turned off even if they are doing the right thing. Training sessions and displays should show the individual contribution of the trajectories of each controlled, disturbance, and constraint variable to the observed changes in the manipulated and optimization variables.
(25) You need to know your process before you start a model based control system application. This would be nice, but often the benefits from a model stems from the knowledge discovery during the systematic building and identification procedures. Frequently, the understanding gained from developing models leads to immediate benefits in terms of better set points and instruments. The commissioning of the MPC is the icing on the cake and locks in benefits for varying plant conditions.