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By Bob Sperber
Missing project milestones was not an option. Never mind that His Royal Highness The Duke of York, would be visiting at start-up; process expansions and control-room updates were a critical piece of a £390 million “Project Genesis” modernization program to turn Ineos ChlorVinyls' Runcorn, Cheshire, U.K., site into a showpiece for eco-friendly chemical processing. However, with a year to go, process control manager Philip Masding couldn't find a supplier who could model his plant in time to test how his control scheme would handle his process and cope with dynamic process disturbances. It's a lot harder to find experts in chloralkalide process modeling than, for example, more common petrochemical modeling.
“We looked at a number of possible suppliers and their delivery dates were always going to be short of time,” says Masding. Knowing time was short no matter who did the work, he took the work in-house, linking his control system to models he built himself. “We started with the most critical areas, and we would at least train operators on the most critical areas of the plant” using a general-purpose simulator to model, test and ultimately train operators, starting with gas pressure controls.
And while the Duke—Prince Andrew, second son of Queen Elizabeth II and an international trade official—was rightly pleased upon his July 2006 visit, Masding didn't rest on his laurels. He has since “drastically reduced the number of large-scale excursions [or disturbances] that we've had by applying the model to the control scheme,” using updated operating data.
Dynamic models that feed simulated process data to offline test versions of control systems have become an indispensable tool for process control engineers. The term operator training simulator (OTS) that generally applies to them is a bit of a misnomer because these types of systems address ongoing needs, from control system factory acceptance testing (FAT) through start-up and ongoing training and system improvements. While lower levels of fidelity, or detail, characterize most OTS systems, high-fidelity systems are still mainly used for new process designs. As those high-end simulators once relegated to steady-state engineering designs add dynamic modeling, the costs and benefits of such systems are as varied as a plant’s needs.
Martin Berutti, business director of Mynah Technologies, says OTS benefits include reduced “operations-induced unscheduled downtime,” faster time to market for control systems, product quality, operating costs and mitigation of risk. He cites savings from $100,000 to $500,000 per day when OTS speeds commissioning and validation; $500,000 to $1 million per production run based on extensive software testing to reduce off-spec product; and $500,000 to $100,000 per hour through better-trained operations staff and reduced automation system errors for cost-avoidance of $50,000 to $1 million per incident.
“The cost of running a full series of tests on-site with live equipment are considerable, versus the cost of running a full series of multiple tests, multiple times, with multiple scenarios in the back of our office,” agrees Andrew Robinson, project engineer with control systems integration firm Avid Solutions in Apex, N.C. The firm works at several pharmaceutical plants where FDA validation is stringent. He cites a recent job where one engineer spent two weeks in a plant starting up a batch-process DCS. Without thorough off-site testing, he says the start-up “would have probably taken two or three times as long and two or maybe three people to get the job done right.”
DCS vendors use OTS on their own staging floors before shipping a system, which lets them test and update the customer’s configuration to “save weeks or months of DCS commissioning time,” says Tobias Scheele, vice president of advanced applications for Invensys Process Systems. This reduces configuration errors in the field. He adds that the value to the plant depends on “the revenue associated with the additional days of early production, which usually dwarfs the cost of the control checkout effort.”
Systems can be updated several times, even daily, on a DCS vendor’s staging floor. Configuration bugs found in the morning will be fixed in the afternoon, and the process can be repeated many times. Once the system is shipped, the plant’s personnel will then run through their own checkout or full FAT cycles, testing the physical, online system as they follow with start-up and commissioning. However, training strategies must start before that because the need is critical—and expensive.
OTS systems address the critical need for training as soon as models and the DCS configuration can be simulated (or “emulated,” which applies to some control simulation).
Software costs can run in tens of thousands of dollars up to several million dollars; Invensys’ largest OTS had more than 80,000 I/O points. And there’s more to a system than process models. The control system needs to offer its own simulation (or emulation) function, to which the customer’s control configuration and graphics must be added.
A full solution also includes training courseware and a training curriculum. Such a system can cost $135,000 at the low end, says Mark O'Rosky, OTS operator training solutions group leader at Emerson Process Management. He adds that Emerson’s entry-level OTS Express occupies a sweet spot for his OTS customers in the $250,000 to $450,000 range, which covers an application “about the size of a hydro-treating unit or a crude unit at a refinery, typically a one-process unit with two operator training seats.”
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