Process control: what to teach?

Thomas F. Edgar, University of Texas -- Control Engineering, 5/1/2006

There is an ongoing debate in chemical engineering departments on what should be taught in an undergraduate process control course. How can we better prepare students for what they will see in industry upon graduation?

Topics covered in a typical 15-week university control course include dynamic behavior, with one week on Laplace transforms and analytical solutions to differential equations, physical and empirical modeling, computer simulation, measurement and control hardware technology, basic feedback and feedforward control concepts, and advanced control strategies.

I have recently received feedback from practicing engineers about the importance of these topics. While the need to understand Laplace transforms, frequency domain analysis, or relative gain arrays may not be widely applicable, the knowledge of how to control processes using measurement feedback is applicable to almost every job a young graduate may encounter. It should be considered a basic building block of their education.

New engineers should also understand that process control is a natural extension of material and energy balances in process plants, and that dynamic loops are needed to maintain these balances. Practical aspects of process control—such as understanding control objectives, how a control strategy fulfills these objectives, how to tune control loops, and understanding dynamic interactions among process variables—are often learned on the job, after graduation. The disturbing fact is that many recent graduates feel shortchanged when they learn how critical process control is to their job effectiveness, and how little they understand about it from their undergraduate education.

To further illuminate the skills and concepts that industrial employers find important in a chemical engineering graduate, an informal survey was conducted of 34 industrial practitioners who represent the biotechnology, pharmaceutical, petroleum and petrochemical, chemical, consumer product, and process control consulting business areas. Each of these individuals was asked to rank-order a list of 10 skills and concepts, with 10 being the most important (average ranking is in parentheses):

1. Optimization of a process or operation (8.6);
2. Statistical analysis of data and design of experiments (7.2);
3. Physical dynamic process models (7.0);
4. Statistical or empirical dynamic process models (6.9);
5. Multivariable interactions and multivariable system analysis (6.6);
6. Statistical process control and process monitoring (5.3);
7. Design and tuning of PID loops (5.1);
8. Nonlinear dynamics and analysis of nonlinear systems (3.9);
9. Frequency domain analysis (2.4); and
10. Expert systems and artificial intelligence (1.9).