Back to Basics: Relearning terms, concepts for process control
Process control terms vary according to engineering discipline and industry. A common vocabulary and better understanding of process control fundamentals make teams and communications more efficient and effective.
Good functioning regulatory process control is critical for safety and profitability of operating process units. For the past several years, chemical process industry (CPI) has seen a worldwide reduction in capital spending on existing operations. The main focus of CPI operators has been on getting the most from existing units. As a result, the process control resources on-site have been spread thin. Better and more precise communication about regulatory control issues among operators, process engineers, and control professionals is one solution that might alleviate some demands on limited process control resources. A common vocabulary and better understanding of process control fundamentals will make teams and communications more efficient and effective. Classifying and explaining the underlying cognitive difficulties faced by novices in the field of process control provides value to practicing engineers.
Process control terms vary
In process control there are different ways to identify a system. Chemical engineers traditionally are taught to write and test first principles-based models in the time domain. Electrical engineers are trained to use frequency analysis as a way to understand the dynamic behavior of a system. Laplace transform is a mathematical device used to make a round trip from the time to the frequency-dependent complex variable(s) domain and back. State space is another way of representing a set of differential equations. It can be used for representing multiple input and multiple output systems, is ideal for performing matrix operations, and can provide more insight on a system’s dynamic behavior than the input-output based models. State space also seems to be a preferred method for representing mathematical models among the scientists across the pond. Perspectives of a system and its dynamics vary based on whether one works with hardware or software. This underlying stratum of domains, representations, and disciplines makes the study of process control challenging.
Process control is a topic of interest and utility for engineers, scientists, economists, and even philosophers. As a stand-alone discipline, just like chemical reactor engineering or electrodynamics, one expects subject-specific jargon to exist, such as proportional-integral-derivative (PID) controller, setpoint, cascade control, etc. However, since the subject seems to have been developed independently by each discipline as well as evolved separately as a discipline in recent years, in addition to its own language it has terminology from other disciplines. Working on process control related projects poses a communication challenge because of tricky terminology. Here we present examples that illustrate four types of labeling problems.
Phase: A word or a phrase, such as phase, may mean different things to different people and differ in meaning depending on discipline. Other examples of words that differ include span, range, and critical points.
Question: What comes to mind when I say the word phase? Answers:
Chemical engineer: Solid, liquid, vapor
Electrical engineer: Phase lead or lag in ac currents
Mechanical engineer: Planning, execution, auditing
Excitation: Two or more labels or words may represent the same idea depending on the discipline. An example is excitation, which describes a change made to an input signal to study the dynamic response of a system. The label and the corresponding discipline are listed in the table, Same Idea, Different Labels. Other examples include the words autonomous/time-invariant and the words actuator/final and control element/control valve.
Same Idea, Different Labels
Command signal change
DC gain: Sometimes a term from one discipline begins to be used in other disciplines but another term describing a similar idea already exists in that discipline—dc gain, for example. Several chemical engineers were asked to explain the meaning of the term dc gain. Responses ranged from “No idea” to “I guess it has something to do with direct current.”
DC gain is the steady-state response of a system to a unit step input . Some popular textbooks [2, 3] use the phrase dc gain but do not state what dc stands for. DC gain is the value of a transfer function of a system as the “s” variable in the Laplace domain approaches zero or as the input frequency goes to zero. Since dc current has zero frequency or is constant when plotted against time, the result of that calculation is called the dc gain. The term originated from electrical engineering and is seldom used by chemical engineering students. It appears to be a carryover from early frequency response analysis work done using dc motors.
Its popularity in newer textbooks may be partially attributed to its being used as an internal variable name in The Mathworks Inc. Control System Toolbox software. The equivalent idea in the time domain is called the steady-state gain, which is the ratio of the output value of a system response to a unit step change in the input as time goes to infinity. It is the gain of a system when time or when the input excitation is not changing with time. The terms dc gain and steady-state gain are at the intersection of two disciplines and also at the intersection of two domains: Laplace variable(s) and time.
Sensor, transducer: Some terms clearly have different meanings that have blurred over time, such as sensor and transducer. A transducer  is any device that converts one form of energy into another. A microphone converts sound energy into electrical energy. A loudspeaker converts electrical energy into sound energy. A light bulb converts electrical energy into light energy. An electric motor converts electrical energy into mechanical energy. A sensor is a device that receives and responds to a signal . In instrumentation language, a sensor is a device that responds to a signal and converts it to an output that can be easily read and understood. Transducers do not quantify the energy converted. A transducer usually is a part of a sensor, but a transducer is not a sensor.
So, why would someone use those terms interchangeably in practice or be confused about them? The reason is that early in the development of new devices, each physical device typically fulfills one function. Over time, due to innovation, physical devices begin to usurp other nearby functions. So, one physical manifestation begins to embody multiple logical perspectives. Today a smart sensor may include a sensor, transducer, and even a transmitter. And, that may be a source of misuse of the terminology. The terms sensitivity and resolution provide another example. Technically the two have different meanings, but the conversion from analog to digital measurements has increased the potential for mixing up the two terms.
The next category of difficulty is related to what Krathwohl, Bloom, and Masia  define as the comprehension and application levels of cognitive processing. As is true in many disciplines, some concepts are intrinsically difficult to grasp. These concepts embody many underlying facts and concepts. The next two examples show that comprehension of all the embedded ideas is needed to understand such concepts.
Anti reset-windup: Consider the phrase anti reset-windup. The prefix anti is unambiguous but it is unclear to what the phrase is referring. When students were asked to explain what the term reset means, many were unclear about the concept. Reset mode is another name for integral (I) part in a proportional-integral (PI) controller. Why another name? Just like proportional band (PB) is a parameter used in the field to adjust the proportional gain, the term reset controller came from people working with the applications of PI controllers in the field. A proportional-only mode leaves a steady-state offset. The integral action is added to the proportional mode to reset the “offset” to zero. It resets the output value to the setpoint. In the absence of the “I” mode, one would accomplish the same objective in a P-only controller by resetting the offset to zero by manually adjusting the bias.
During situations such as activation of an override mode or nonsurge part of a surge controller operation, or a long start-up of a process, the I mode continues to reset the bias and keeps moving the output signal past the valve saturation . In case of a control system that keeps sending output signal past the valve saturation, it has to first unwind from the windup before it can begin to have any effect on the process being controlled. A method used to prevent occurrence of the windup is called anti reset windup. One way to prevent the override problem is, for example, to stop the integration action when the output saturates. The prefix anti then applies to the windup caused by the reset mode. So it is not anti-reset windup but anti reset-windup. Smith  presents an easy-to-comprehend example showing the effect of reset windup.
Nonminimum phase, nonminimum phase zero
Even without a background in process control, it is clear that the term nonminimum phase zero is information (or “Shannon’s entropy” ) rich. The phrase is like the phrases space time curvature or Maxwell’s demons used by quantum physicists. Understanding these terms is important because they are useful in predicting the stability and controllability of dynamic systems. Some degree of confusion is likely to stem from the use of the word phase. Phase denotes the particular point in the cycle of a waveform, measured as an angle in degrees with reference to 0 degrees. The phase in nonminimum phase really should be called phase shift or phase change because it is the difference in the phases of the output and the input for a frequency response for a given frequency.
The drawback of using phase-shift instead of phase is that the expression will become longer than it already is. To understand these terms, one must start with a topic from high school algebra called rational functions. Both poles and zeros are properties of rational functions. Knowing poles and zeros, one can reconstruct the input-output differential equation for the LTI [linear time invariant] system. But poles have been emphasized more than zeros because they have a direct impact on the stability of a system.
Question: Why did the Polish airliner crash? Answer: Because there were too many poles in the right half plane.
Teacher: “Division by zero is undefined in mathematics.” Student: “But, you just defined it!”
A pole-zero graph shows the poles and zeros on a complex plane whose axes are the real and imaginary parts of the Laplace domain variable “s.” Most textbooks state that an inverse response of a system appears as a nonminimum phase in the “pole-zero” graph. However, what is left out is that the vice-versa is not always true. A nonminimum phase does not always imply an inverse response. Nonminimum phase can also refer to an unstable process response or a dead time response. The addition of zero to non-minimum phase makes the term even more interesting. An inverse response representation on the “pole-zero” graph has at least one nonminimum phase zero. But, a nonminimum phase zero may also appear when a dead time response is approximated by a rational function.
From a learning standpoint, co-evolution of process control as a separate field as well as a part of other disciplines makes it interesting and challenging. For simple ideas, cross-discipline communication can be a challenge because each discipline or domain has developed its own vocabulary that at times is redundant or at other times inconsistent.
“Identifying similarities and differences” is a well-established teaching strategy  and is appropriate for overcoming terminology related problems. However, the emphasis of the strategy needs to be on discerning subtle rather than gross differences between two labels or two phrases. Some concepts are intrinsically complex because they tie together many other concepts. Good practical examples serve as helpful learning aids for complex concepts. Finding such examples is not easy.
- Saidas M. Ranade is manager of product innovation, General Physics Corporation, Houston, Texas; Hector Salazar is a process engineer, Dyprotec, Ltd., S.A., Bogotá, Colombia; and Luis Andres Rodriguez is APC Engineer for Toppings Units at the Barrancabermeja refinery of Ecopetrol, S.A., Barrancabermeja, Colombia. Reach the authors via firstname.lastname@example.org. Edited by Mark T. Hoske, CFE Media, Control Engineering, www.controleng.com/tutorials.
Note: a shorter version of this article was adapted for the print and digital edition, September 2011. To see that version, go to www.controleng.com, click on the digital edition, and see the Back to Basics column for September 2011, after the issue has posted.
More author information
Saidas M. “Sai” Ranade (email@example.com) is the Manager of Product Innovation for General Physics Corporation’s Oil and Gas practice in Houston, Texas. He earned his PhD in chemical engineering from the University of Houston, where he also taught Thermodynamics. He has published over 25 papers on topics such as mathematical modeling, organizational excellence, and competency development. Dr. Ranade has expertise in instructional design and delivery, competency modeling and management, process simulation and design, and business and IT assessments.
Héctor Andrés Salazar is a process engineer for Dyprotec LTD (Bogotá, Colombia). He holds a BS and a MS in Chemical Engineering from The National University of Colombia. In his present role, he provides technical support for gas treatment plants. Prior to joining Dyprotec, Héctor worked for GP (or RWD). At GP, he developed Matlab simulations for the course mentioned in this article and also taught some parts of the course.
Luis Andrés Rodriguez is the APC Engineer for Toppings Units at the Barrancabermeja refinery of Ecopetrol S.A., Colombia. He earned his BS in Chemical Engineering from the National University of Colombia, Bogotá, 1999, and his ME in Industrial Automation, 2006. He got a Diploma in Industrial Informatics from University Distrital Francisco Jose de Caldas, Bogotá, 2002. He has also been a part-time instructor at several universities in Colombia. His current interests are industrial automation, optimization, and process control.
Authors developed and delivered five instances of a course on fundamentals of process control to about 80 engineers and operators working for the refining unit of Ecopetrol, S.A. The authors used the classroom as a laboratory to analyze the muddiest points and share findings in this article. Author opinions are their own and do not represent the views of any other entity mentioned in this article. The authors thank General Physics Corp. and Ecopetrol, S.A., for support.
 J.J. DiStefano, A.R. Stubberud, I.J. Williams, Feedback and Control Systems, McGraw-Hill Book Co., New York, NY, 1995, p. 132.
 N.F. Macia, G.J. Thaler, Modeling and Control of Dynamic Systems, Thomson Delmar Learning, Clifton Park, New York, 2005, p. 222.
 G.F. Franklin, J.D. Powell, A. Emami-Naeini, Feedback Control of Dynamic Systems, sixth ed., Prentice-Hall, Inc., Upper Saddle River, NJ, 1994, p. 94.
 The University of New Mexico, Southwest Center for Microsystems Education, “Introduction to Transducers, Sensors and Actuators,” Participant Guide, Int_Dvices_PK10_PG_041510.
 D.R. Krathwohl, B.S. Bloom, B. B. Masia, Taxonomy of Educational Objectives, The Classification of Educational Goals. Handbook II: Affective Domain, David McKay Co., New York, NY, 1973.
 B.G. Liptak, B.G. Lipták (Eds.), Instrument Engineer’s Handbook: Process Control and Optimization, fourth ed., Taylor and Francis, Boca Raton, Florida, 2005, p. 118.
 C.L. Smith, Practical Process Control: Tuning and Troubleshooting, John Wiley & Sons, Inc., Hoboken, NJ, 2009, p. 203.
 J. Gleick, The Information, Pantheon Books, New York, NY, 2011, p. 229.
 R.J. Marzano, D.J. Pickering, J.E. Pollock, Classroom Instruction That Works: Research-Based Strategies for Increasing Student Achievement, ASCD, Alexandria, VA, 2001, p. 16.
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