The following tip is from the ISA book by Greg McMillan and Hunter Vegas titled 101 Tips for a Successful Automation Career, inspired by the ISA Mentor Program. This is Tip #89.

he best way to classify process simulators is to look at the original intent of the simulator software when it was first released, who the software developers were, who configures the models, and how the models are 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 an open loop 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. These models can be readily adapted to become step response models (experimental models) by the identification of the dynamics by an adaptive tuner and rapid modeler. The biases in the last equation in Tip #89 can be identified by the PV and AO at two operating points to provide a more accurate open loop gain based on deviation variables.

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. The simulation software is created by process research and development engineers and is primarily intended to be configured by process engineers.

The PFD simulator is often claimed to be a high fidelity model. While this is true from a physical property and equilibrium relationship viewpoint, it is not always true from a dynamics viewpoint. In particular, the PFD simulator is often missing deadtime due to a 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.

Concept: The prior job history of the developers and the design intent of the simulation software determine the ability of the software to meet different simulation objectives. The best tool for an automation engineer is a simulation that will accurately show the dynamic response for all scenarios, including start-up.

Details: Instead of the usual vague and subjective fidelity ratings of low, medium, and high, use the following fidelity ratings based on the functionality needed to meet operator and automation performance objectives. Physical fidelity achieved by a virtual plant (Tip #99) is assumed.

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

• Fidelity Level 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 Level 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 demonstrations.

• Fidelity Level 4: measurement dynamics (e.g., 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, and conducting control system research and development.

• Fidelity Level 5: process relationships and metrics (e.g., yield, raw material costs, energy costs, product quality, production rate, production revenue) and process optimums are sufficiently accurately 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.

Default tieback models automatically generated to match the configuration can achieve Level 1. Basic modeling objects, which consist of tiebacks and ramps with path logic, can achieve Level 2. Adapted tieback models with dynamics identified by the auto-tuner or a rapid modeler module (step response models) can achieve Level 3. Advanced modeling objects with first principle process models and dynamics of the automation system measurements and control valves can achieve Level 4. Adapted advanced modeling objects with parameters identified from design of experiments (DOE) or adapted by a model predictive controller (MPC) or a rapid modeler module can achieve Level 5.

Watch-outs: First principle models have a huge number of parameters. These must be visible and adjustable for improving model fidelity. The sensor deadtime may have to be increased to account for the additional deadtime observed in the process response.

Exceptions: First principle dynamic models developed for automation system design presently assume perfect mixing and identical bubble, cell, crystal, and particle size. In the future, subdivided volumes and population balances will be used to address these deficiencies.

Insight: Step response and first principle models can be adapted online to achieve much higher fidelity.

Rule of Thumb: Use tieback models, various levels of modeling objects, and online adaptation of dynamics and parameters to achieve the fidelity needed for your simulation application.

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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About the Author
Hunter Vegas, P.E., has worked as an instrument engineer, production engineer, instrumentation group leader, principal automation engineer, and unit production manager. In 2001, he entered the systems integration industry and is currently working for Wunderlich-Malec as an engineering project manager in Kernersville, N.C. Hunter has executed thousands of instrumentation and control projects over his career, with budgets ranging from a few thousand to millions of dollars. He is proficient in field instrumentation sizing and selection, safety interlock design, electrical design, advanced control strategy, and numerous control system hardware and software platforms. Hunter earned a B.S.E.E. degree from Tulane University and an M.B.A. from Wake Forest University.

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