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 tieback models can readily adapted by the identification of the dynamics by an adaptive tuner and rapid modeler to become step response models (experimental models).
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. The best tool for an automation engineer is a simulation that will accurately show the dynamic response for all scenarios including startup.
Instead of the vague and subjective fidelity ratings of low, medium, and high, consider the following fidelity rating based on functionality needed to meet operator and automation performance objectives. Physical fidelity achieved by a virtual plant is assumed a given for all of the fidelity levels.
- 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.
Default tieback models automatically generated to match the configuration can achieve Fidelity 1. Basic modeling objects which consist of 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 adapted by a model predictive controller (MPC) or a rapid modeler module can achieve Fidelity 5.
Simulators for system acceptance testing (SAT), operator training systems (OTS), and process control improvement (PCI) need to be part of a virtual plant 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. The emulation of the PID block is problematic because of the numerous and powerful proprietary features, such as anti-reset windup and external-reset feedback. 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.
The following is a checklist to help trigger the right thought processes to get a simulation to meet your objectives. Questions 18 through 30 are for first principle models. In general, you should start with a tieback model, increase the tieback model fidelity by step response models, and develop a first principle model with valve, measurement, and process dynamics adjusted to match step response model. Note that dead band, resolution, and threshold sensitivity will affect the open loop gain but not the dead time in step response models. See the ISA book 101 Tips for a Successful Automation Career for more details.
- Are the actual displays and trend charts used in the control room loaded and the actual configuration downloaded into the virtual plant?
- For SAT does the simulation write and read to the actual I/O assignments?
- Do you have the Process Flow Diagrams (PFD) and Process & Instrument Diagrams (P&ID)?
- Do you have the operating procedures and control definitions?
- What are the simulation fidelities needed for SAT, OTS, and PCI?
- What are the most important loops to achieve the needed fidelity?
- Do you have scenarios to test the ability to deal with abnormal situations?
- Can you playback the scenarios?
- Do you have an automatic grading system for the scenarios?
- Can you speed up dynamics so scenarios are completed in less than an hour?
- Have you retuned the controller for the new time constant to dead time from speed up?
- Have you provided a way of resetting compositions, levels, pressures, and temperatures?
- For the important loops, do you have the open loop gain, dead time, and open loop time constant identified from an adaptive tuner or rapid modeler for a step response model?
- For the important loops, do you have two operating points to provide the biases for the controller output and process variable to enable an open loop gain based on deviations?
- For the important loops, have you modeled the control or variable frequency drive deadband, delay, lag, dead time, installed characteristic, rate limiting, resolution, and threshold sensitivity?
- For the important loops, have you modeled the measurement delay, lag, noise, resolution, and threshold sensitivity including wireless default update rate and analyzer cycle time?
- For DCS simulate mode have you interfaced model outputs to AI block simulate inputs and AO block outputs to model inputs (interfacing PID will by pass AI filter and AO valve action)?
- Do you have the chemical name, formula, and physical properties (e.g. molecular weight, density, vapor pressure, phase enthalpies, and boiling point for liquids) for each component?
- Do you have the stoichiometric equations, yield, and kinetic equations for reactions?
- Do you have the seed, growth, and attrition kinetic equations for crystal and cells?
- Do you have the volumes for equipment and pump and compressor curves?
- Do you have the cross sectional areas for levels
- Can you use an existing library of advanced modeling objects for unit operations, piping, final control elements (e.g. control valves and variable speed drives) and measurements?
- Do you need to write modeling objects for missing unit operations in CALC blocks using:
a. differential equations for material, energy, and component balances for all phases as exemplified in the ISA book Advanced Temperature Measurement and Contro
Appendix F - First-Principle-Process-Gains-Deadtimes-and-Time-Constants
b. charge balance equation of acids and bases for pH as exemplified in slides 53-56 in ISA-Saint-Louis-Advanced-pH-Short-Course-Day-1.pdf with dissolved carbon dioxide added as moderator of slope between 4 and 8 pH
c. mixing and injection delays as exemplified in slides 65-69 in ISA-Saint-Louis-Advanced-pH-Short-Course-Day-1.pdf
d. driving force equations for heat and mass transfer with heat and mass transfer coefficients and areas and equilibrium relationships between vapors and liquids and between gases and dissolved gases
e. equation of state for gas pressure (e.g. ideal gas law with compressibility factor)
f. equations for final control element and measurement dynamics
g. equations for kinetics and population balances
h. equations for momentum balance for compressor surge control
i. speedup factors for differential equations and kinetic equations - Have you added flow control loops as necessary to reduce the dependence on a pressure-flow solver for flows in virtual plant to match flows in actual plant?
- If the controls cannot be speeded up in unison with process model, have you scaled flows in proportion to kinetic rate and mass transfer rate speedup, kept the dead time about the same, and decreased the controller gain by the differential equation speed up factor?
- Have you commissioned and tuned level and pressure loops to keep inventories in bounds?
- Have you commissioned and tuned remaining loops with setpoints to match PFD?
- Have you adjusted model parameters to match manipulated flows on PFD or use model predictive control (MPC) to automatically adapt parameters to make manipulated flows in a virtual plant running in sync with same setpoints as real plant as described in Control magazine November 2007 article "Virtual Control of Real pH"?
- Have you adjusted model parameters to match process gains (e.g. slope of pH titration curve) as described in Chemical Processing magazine article "Virtual Plant Provides Real Insights"?
