Simulation, Expertise, and Knowledge Transfer

If you’re involved with simulation in your plant, you may be familiar with Mynah’s Martin Berutti and his blog. In one of his posts, he shares his thoughts on the business benefits, requirements, and steps for building a Virtual DeltaV system with a virtual plant and I/O.

I thought I’d share some of his thoughts from the presentation, which are not specific to the automation system. You may find yourself needing to capture your plant’s experienced operators and operations personnel through the use of simulation, before they all retire to warmer, sunnier locales.

Martin shared some U.S. demographics that the average age of an energy industry worker is over 50 and that half the current work force (more than 500,000 workers) will retire in the next 5-10 years. Some of these retirements were accelerated by the global economic slump and ironically may accelerate again when the equity markets recover for those whose retirement funds dwindled.

As shared in a post last week, regulations and government oversight continues to grow, increasing the load on the plant’s operations team. These regulations combined with global supply pressures on financial margins add to the operations burden.

The paradox that the era of plant automation has ushered in is that operator error is the highest cause of loss, followed by design error, process upset, and mechanical failure. The first two are directly related to experience and skill level. This regulatory environment has changed over the years where now operations management can be held liable for their actions or inactions on operations issues. Martin observes, “They didn’t tell us in engineering school we could go to jail for something we did or didn’t do!”

If you’re considering simulation as a way to capture the operations team’s expertise for the next wave of operators, maintenance techs, and plant engineers, Martin suggests four simulation approaches to avoid:

  • The first is not to use process design models since they do not have the real-time performance or range of operating conditions of the dynamic simulations required for operator training systems.
  • The second is not to emulate the automation system. If you’re going to build skills and gain experience on the operator graphics, alarms, and controls, these items should be identical to the real system–not an approximation.
  • Third, Martin councils to avoid adding simulation to the control system configuration. This increases opportunity for errors, adds complexity, and ups the risk of design errors. Adding simulation to the control system configuration also makes the process of keeping the operator training system consistent with the on-line control system difficult if not impossible.
  • The final caution is to avoid starting the simulation development too late in the project cycle. These efforts are usually rushed and don’t provide the depth of training that operators and other operations personnel need to acquire the skills and confidence to operate the process after it is commissioned.

The proper approach is to have a virtual control system, which is an exact replica of the plant automation system. The operator graphics, alarms and controls are identical to the running system. Also, the virtual system can be the testing grounds for new and modified control strategies.

Connected with the virtual control system is a virtual process/dynamic simulation. The fidelity of this model can range from simple I/O signal modeling and device tiebacks, to mass and heat balance models, all the way to complete mass balance, rigorous heat balance, reaction kinetics and associated thermodynamic properties. The level of model complexity depends on the initial business objectives, amount of knowledge capture, and skill level sought.

The virtual control system combined with the virtual process forms, in the words of ModelingAndControl.com‘s Greg McMillan, the virtual plant.

Knowledge transfer requires explicit learning–what the operating procedure says, implicit learning–how things really work, and tacit learning–how decisions made affect the whole process. Properly done, simulations provide the hands-on training for these three types of learning.

Defining your objectives clearly up front and following some of the guidance shared by Martin, can help reduce the errors and associated liabilities/risk, reduce operating costs through less unscheduled downtime, improve product quality, and increase time to market by reducing startup time.

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