This is an excerpt from the March/April 2013 InTech by R. Russell Rhinehart. To read the full article, please see the link at the bottom of this post.

Nonlinear control techniques can be used for more efficient process operation

Often, nonlinearity is the primary problem for single-input-single-output (SISO) chemical process control. One solution is to design the process for PID control success – for linear responses, or with large inventories to reduce interaction and temper upsets. However, design is an act of balancing multiple objectives; other desirable issues, such as capital cost, flexibility, resource use, energy integration, and sustainability are usually sacrificed to accommodate process control. Practicable techniques for nonlinear control can ease design OLYMPUS DIGITAL CAMERAconstraints and permit the operation of more competitive processes.

For nonlinear processes, gain scheduling is a conventional solution to accommodate nonlinearity. In gain scheduling, the controller coefficients are changed to reflect the operating region; the engineer uses process knowledge to create tuning values, which are either placed in a look-up table or expressed in equations.

However, if the engineer’s process knowledge is expressed as a dynamic nonlinear model, it is almost as easy to implement a process model-based controller (PMBC) as it is gain scheduling. Several process model-based control approaches have found success in industrial applications. There are several commercial products for relatively simple nonlinear process model-based controllers, engineers have implemented versions in-house, and control integrators and service providers have been implementing model-based controllers for decades.

Process-model based control (PMBC) has several advantages over either classic PID or gain-scheduled PID, even with ratio and feedforward enhancements of advanced regulatory control (ARC). PMBC has a single-tuning parameter, has nonlinear compensation throughout the entire operating range, preserves process knowledge, and provides continuous monitoring of the process – for health, predictive maintenance, and constraint recognition, and economic optimization of setpoints.

PMBC can also be used within a horizon-predictive, constraint-handling framework. And, for multi-input-multi-output (MIMO) processes, PMBC can additionally decouple nonlinear interaction, balance deviations from setpoints when manipulated variable (MV) constraints are hit, and determine economic optimum MV values when there are extra degrees of freedom.

However, this article addresses only the multi-input-single-output (MISO), single step-ahead control approach. This approach can solve many problems and can be implemented by a process engineer.

The references describe PMBC applications on commercial-scale, pilot-scale, and lab-scale processes. The references cite SISO and MISO applications for control of fluid flow rate, heat exchanger temperature, distillation bottoms composition, plasma reactor pressure, and pH. MIMO applications include distillation dual-end composition control and fluidized bed gasification.

To read the full article on simple, model-based process control, click here.

R. Russell Rhinehart

About the Author
R. Russell Rhinehart, Ph.D., is a professor in the School of Chemical Engineering at Oklahoma State University. He holds the Amoco Endowed Chair and has experience in both industry (13 years) and academe (26 years). He was head of the school from 1997 to 2008 and interim head from 2011 to 2012). Russell is president of the American Automatic Control Council, a Fellow of ISA (2001), a Control Automation Hall of Fame inductee, and received the 2009 ISA Distinguished Service Award. He was editor-in-chief of ISA Transactions from 1998–2011. His 1968 B.S. in chemical engineering and subsequent M.S. in nuclear engineering are both from the University of Maryland. His 1985 Ph.D. in chemical engineering is from North Carolina State University. Contact Russell at:

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