This guest post was written by Greg McMillan, industry consultant, author of numerous process control books, 2010 ISA Life Achievement Award recipient and retired Senior Fellow from Solutia Inc. (now Eastman Chemical).

 

In the ISA Mentor Program, I am providing guidance for extremely talented individuals from Argentina, Brazil, Malaysia, Mexico, Saudi Arabia, and the USA. This question is from Madhawa Somasiri in the USA:

What are the types of dynamic simulators and the pros and cons? This information will help me on how to approach the opportunity and the management.

The best way to classify simulators is to look at the original intent of the simulator software when first released, who are the software developers, who configures the models, and how are the models used.

Simulators originally designed for system acceptance testing (SAT), operator training systems (OTS), and process control improvement (PCI) focus on automation system details, such as the inputs, outputs, and dynamic relationships, that affect control system and operator performance. These OTS, SAT, and PCI simulations can be as simple as tieback models that take analog output (AO) from a control system, pass the signal through a gain, deadtime, and time constant, and connect the result as the analog input (AI) signal that becomes the process variable (PV) used by the control system and seen by the operator.  Increasingly, first principle models of process and equipment relationships including reaction kinetics and mass and heat transfer rates are being offered to improve the simulation of process dynamics. A graphical studio similar to what is employed for control system configuration is used to develop basic and advanced process modeling objects. The simulation software is developed by control system engineers and is primarily intended to be configured by automation engineers and technicians.

Simulators originally designed to create a process flow diagram (PFD) focus on process details, such as physical properties and equilibrium relationships that affect process design. These simulators start out as simulators of continuous process operation at a steady state.  Increasingly the capability of going from a “steady state” to a “dynamic” mode is offered by adding equipment volumes, flow coefficients or flow resistances for valves, piping, and equipment, and pump curves. Equilibrium relationships still often rule with flash blocks that immediately put a vapor phase in equilibrium with a liquid phase making the simulation of startup and batch operation dynamics problematic. A graphical flow sheet is used to define streams and equipment. The simulation software is developed by process research, development, and design engineers and is primarily intended to be configured by process engineers. The original concept of being able to use a model developed for process design for SAT-OTS-PCI purposes by simply switching from the steady state to dynamic mode was not realized in practices. I found out the hard way in the 1990s. The transition for equilibrium to dynamic response was typically unstable with erroneous flows particularly from incorrect flow resistances. If you dig into who is doing what you find that control system engineers would build a dynamic model from scratch. The user ends up with a steady state built by a process engineer and dynamic model built by a control system engineer unsure of the differences. In actuality, both process and control system design skills are needed but this expertise is largely relegated to consultants, and technology departments of large chemical, pharmaceutical, and oil companies. Even here, the merger of skills is fading due to maturing of processes and attrition.

The use of operator training essential for all types of process control systems is particularly important for operator buy-in and understanding of advanced process control. The supportive participation of specialists in the control room is critical as the inevitable questions arise as to what the advanced process control (APC) or model is doing. An APC system may appear to be taking actions that seem wrong to the operators due to human limitations in understanding complex relationships, delayed effects, and trajectories. A trusting and active relationship between operators and specialists is the key to success.

The SAT-OTS-PCI simulator and the PFD simulator are usually both capable of being part of a virtual plant (VP) where a virtual version of the actual control system configuration and graphics including historian and advanced control tools is interfaced to the simulator are running in a personal computer. The use of a virtualized rather than an emulated control system is necessary so the operators, process engineers, and automation engineers and technicians are using the same graphics, trends, and configuration as the actual installation. This “physical” fidelity is essential. Even the emulation of a PID controller is problematic because of the numerous and powerful proprietary features, such as anti-reset windup, bumpless transfer, external-reset feedback, and feedforward. Even universities and technical training schools besides plant training programs should use virtualized and actual industrial control systems so the students learn how to work with and interface the systems that they will use on the job.

When a PFD simulator is used, an interface needs to be configured and the coordination of control system and process model speeds needs to be setup. If Object Link Embedding (OLE) for process control (OPC) is used for the interface, the minimum communication update time is 1 second, which is too slow for fast dynamics, such as pressure and flow. For speed up of the model to handle slow dynamics of columns and large vessels, the OPC update time can also be too slow for temperature and composition response in small volumes.

Scenario development, running, capture, playback, and grading for both the control system and model is often already integrated in a SAT-OTS-PCI VP. The setup to do these scenario functions in a PFD simulator is typically not as detailed and predefined. Part of this is the difference in thinking between a process engineer and control system engineer.

The PFD simulator is often stated to be a high-fidelity model. While this is true from a physical property and equilibrium relationship viewpoint, this is not typically true from a dynamics viewpoint. In particular, the PFD simulator is often missing deadtime due to lack of pure and equivalent deadtime from process transportation and mixing and from automation system deadband, update time, filters, resolution, threshold sensitivity, sensors lags, and wireless systems.  As noted in Appendix C for the ISA InTech article “PID Tuning Rules,” the controller tuning and performance primarily depends on deadtime.

There are many ratings stated for simulation fidelity with low, medium, and high being the most common. The criteria are often vague or not control system oriented. I prefer to use the following criteria based on functionality needed to maximize operator and automation system performance.

Fidelity 1: measurements can match setpoints and respond in the proper direction to loop outputs for operator training.

Fidelity 2: measurements can match setpoints and respond in the proper direction when control and block valves open and close and prime movers (e.g., pumps, fans, and compressors) start and stop for operator training.

Fidelity 3: loop dynamics (e.g., process gain, time constant, and deadtime) are sufficiently accurate to tune loops, prototype process control improvements, and see process interactions for basic process control demonstration.

Fidelity 4: measurement dynamics (response to valves, prime movers, and disturbances) are sufficiently accurate to track down and analyze process variability and quantitatively assess control system capability and improvement opportunities for rating control system capability, conducting control system research and development, and assisting empirical model development.

Fidelity 5: process relationships and metrics (e.g., yield, raw material costs, energy costs, product quality, production rate, production revenue) and process optimums are sufficiently accurate modeled for the design and implementation of advanced control, such as model predictive control (MPC) and real time optimization (RTO), and in some cases virtual experimentation.

The more advanced SAT-OTS-PCI simulation software offers the following modeling capability: 

Default Tieback Models automatically generated to match the configuration can achieve
Fidelity 1

Basic Modeling Objects which consist if tiebacks and ramps with path logic can achieve Fidelity 2

Adapted Tieback Models with dynamics identified by the auto tuner or a rapid modeler module (step response models) can achieve Fidelity 3

Advanced Modeling Objects with first principle process models and dynamics of the automation system measurements and control valves can achieve Fidelity 4

Adapted Advanced Modeling Objects with parameters identified from design of experiments (DOE) or parameters adapted by a MPC or rapid modeler module can achieve Fidelity 5

 

About the Author
Gregory K. McMillan, CAP, is a retired Senior Fellow from Solutia/Monsanto where he worked in engineering technology on process control improvement. Greg was also an affiliate professor for Washington University in Saint Louis. Greg is an ISA Fellow and received the ISA Kermit Fischer Environmental Award for pH control in 1991, the Control magazine Engineer of the Year award for the process industry in 1994, was inducted into the Control magazine Process Automation Hall of Fame in 2001, was honored by InTech magazine in 2003 as one of the most influential innovators in automation, and received the ISA Life Achievement Award in 2010. Greg is the author of numerous books on process control, including Advances in Reactor Measurement and Control and Essentials of Modern Measurements and Final Elements in the Process Industry. Greg has been the monthly “Control Talk” columnist for Control magazine since 2002. Presently, Greg is a part time modeling and control consultant in Technology for Process Simulation for Emerson Automation Solutions specializing in the use of the virtual plant for exploring new opportunities. He spends most of his time writing, teaching and leading the ISA Mentor Program he founded in 2011.

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