Simulation was such an integral part of my job it is difficult for me to visualize a process control career without models. I was asked to join Engineering Technology (ET) at Monsanto in 1976 because I had developed a dynamic compressor model as the lead Instrument and Electrical engineer for what was the largest Acrylonitrile plant in the world. I developed the model in order to understand more about the incredible surge phenomena where reversals of flow could occur in less than 0.01 seconds leading as a minimum to a loss in efficiency and in some cases to the damage of shafts and seals of large and expensive compressors from the extreme momentum swings and vibration. In most plants the ability to initiate and explore abnormal situations is severely limited or not allowed. A dynamic model allows you to readily and quickly try out "What if Scenarios" whose only limit is your imagination.

ET developed FLOWTRAN, a process simulator that was directed by the government to be sold to Aspen institute. Several key specialists left with the FLOWTRAN to develop the process modeling software that eventually was the state of the art process design modeling software by AspenTech. In the ET process control groups, we used FLOWTRAN to get the process gains and then used IBM's Continuous System Modeling Programs (CSMP) followed by Raytheon's Advanced Continuous Simulation Language (ACSL), and ultimately HYSYS Plant for dynamic simulations. After retirement from my career in ET, I focused on using the DCS as a Virtual Plant for simulation and control. The graphical configuration environment where function blocks are equipment and wires are streams (e.g. DeltaV Control Studio and MiMiC) allows the development of dynamic process models in the same familiar way as the configuration of control strategies.

My vision of a virtual plant has a simple first principle model that starts with one component (e.g. water and air) that is corrected by an experimental model automatically generated by a simple test that takes less 10 minutes to execute for most loops. The result is a plant wide simulator. As more information is available and desired, the process knowledge embedded in the model grows but the fundamental basis is the same. No re-write is required. The opportunities and associated fidelity needed are as follows:

1. Control system set point optimization - Fidelity 5

2. Control strategy analysis and R&D - Fidelity 4

3. Root cause analysis and data analytics R&D - Fidelity 4

4. Operator training for abnormal situation management - Fidelity 4

5. Controller tuning and PID structure and options analysis - Fidelity 3

6. Batch configuration checkout and operator training for system familiarization - Fidelity 2

7. Loop configuration checkout - Fidelity 1

Fidelity 1: loop process variables respond in the proper direction to their loop output

Fidelity 2: measurements 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.

Fidelity 3: loop dynamics (e.g. process gain, time constant, and deadtime) are sufficiently accurate (e.g. 50%) to tune loops and see process interactions

Fidelity 4: measurement dynamics (response to valves, prime movers, and disturbances) are sufficiently accurate (e.g. 25%) to track down and analyze disturbances

Fidelity 5: process metrics (e.g. yield, raw material costs, energy costs, product quality, production rate, production revenue) are sufficiently accurate (e.g. 5%) to find optimums

In the ISA New Orleans section short course I am teaching on March 3 and 4 titled: "Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements", I will use a virtual plant suitable for process control research, development, and education. I will demonstrate how a user can perform a 10 minute test of a manipulated process flow to provide a fidelity level 3 and 4 model. The contact for the course is Robert Deeb (ISA New Orleans section education chairman).

In the InTech Jan-Feb 2010 Web Exclusive "Advances in Flow and Level Measurements Enable Dramatic Improvements in Process Knowledge and Control", the following perspective was offered on the importance of flows for many types of process models including the following:

• Projection to Latent Structure or Partial Least Squares (PLS)
• Model Predictive Control (MPC)
• PID Adaptive Controller Tuning
• Neural Network
• First Principle

Flows determine what is going on in a process. If you don't get the flows right, not much else matters. Because of valve backlash, stick-slip, nonlinearities, and variable pressure drop, all types of process models have suffered from the use of valve positions rather than flow measurements. PLS, MPC, and PID performance assumes dynamics that are linear and independent of direction and size, all bad assumptions when valve positions rather than flows are used as inputs. Additionally, the valve nonlinearity from the installed characteristic varies with pressures at the inlet and outlet of the valve.

Pioneering advances in dynamic modeling by Alex Muravyev offer a next generation of pressure-flow solvers that will be robust and flexible enough to provide flows from valve positions. The solver is expected to handle complex piping networks and the discontinuities from batch and startup sequences (AdvancedSimulationPressureFlowSolver.pdf). The ability to consistently and comprehensively compute flows for all streams will enable dynamic models to reach the highest levels of fidelity required for research, development, and design of automation systems for nearly all applications. Presently, models can only move up in fidelity when flow control loops are installed on the key streams so that feedback action removes the nonlinearity and unknowns of the valve and piping system. New pressure-flow solvers can eliminate this precondition. A side benefit will be the demonstration by these models of the improvement in process performance that can be gained from cascade, feedforward, and ratio control. The quantifiable benefits from demonstrable test cases can justify new flow devices to provide missing flow measurements or improve the accuracy of existing flow measurements.

Startups, grade transitions, and abnormal conditions are the most difficult, operator intensive, hazardous, and inefficient periods of plant operation. Operators often believe these conditions require operator evaluation and action. The conditions are thought to be too special and the response too situation dependent to automate. The operators are right in saying these periods of operation require the best in operator expertise. However, case histories show that the power of the PID can be used to automate the best operator responses and build on them to provide faster, safer, and more efficient plant operation during these difficult process conditions. For some specific examples dealing with compressors and reactors check out the two chapters "Wally and the Beave Automate Reactor Startups" and "Wally and the Beave Return to Automate Another Reactor Startup" in my E-book on this website A Funny Thing Happened on the Way to the Control Room. For impressive examples for chemical, mining, and pulp and paper operations, check out the Control Talk columns "Show Me the Money - Part 1" (November 2009) and "Show Me the Money - Part 2" (December 2009) in Control magazine.

An extensive interview of the operators and process engineers is necessary to capture the best responses for a preliminary functional description of the control system. There are often a lot of surprises hidden by the diversity of actions that are inevitable from human responses. Free will implies these decisions are basically unpredictable. The operator actions consistent with first principles and process knowledge offer a good starting point but not the final strategy. During the commissioning of the control system, the plant response must be carefully observed and the best operator actions verified and improved by the use of the many options built into a PID loop to deal with rampant problems as the plant goes from zero to full rate, or vice versa. For example, output tracking, dynamic reset limiting, set point ramping, PID structure, gain scheduling, adaptive control, and override control can be used to deal with the problems at low rates such as noisy or inaccurate flow signals, excessive valve stick-slip near the closed position, larger transportation delays, and unrepresentative measurements. One of the common solutions is to head start (initialize) the controller output via output tracking to the best valve position for startup, transition, or abnormal situation. The initial position can be a "Full Throttle" position for fastest set point response. When the set point approaches the set point, the controller output can be momentarily set to a resting value based on experience or average position captured from a representative operating point from the last run. For fast loops such as flow and pressure, the resting value can be used as the "head start". One of the common mistakes is for process engineers to get carried away with trying to sequence the PID controller output too much or hold the controller output in the track mode for too long. For shutdown, the output must normally be held but otherwise the PID controller should be returned to automatic as soon as possible to deal with disturbances, unknown process effects, raw material variability, and nonlinearities. The process is not known or measured well enough to sequence flows without feedback control. It is particularly important to return pressure loops to automatic as fast as possible. Smart techniques for startup, transitions, and abnormal situations that take full advantage of the flexibility of the PID controller have been the source of the most impressive benefits in process control improvement. In general, these were also "quick hits" in that they were implemented in a matter of a couple of weeks by just configuration changes and controller tuning.