Paul Kimpton, technologist, process control for Shell U.K. Oil Products, Ellesmere Port, England, foresees more and more plants using models embedded in a distributed control system (DCS) for predictive maintenance. That will require some significant changes, though, cautions Earl Ziegler, engineering technologist/specialist for DuPont in Camden, S.C. Niche players provide much of the specialized predictive-maintenance equipment and many of these vendors are not taking advantage of developments in fieldbus. Incompatibilities remain a problem. Lack of integration is a major issue, he says.
“The real challenge is to integrate information from diverse field instruments in a cost-effective manner, to pull together process and equipment data, to get a fuller picture,” says NOVA’s Durham. He does see some progress in vendors appreciating and addressing this.
Addressing abnormal situations
Unexpected incidents, such as those caused by a pump failure or a plugged heat exchanger, can not only compromise the health of equipment, but also process performance and safety. So, advances in predicting when such situations can occur promise to have implications for both equipment maintenance and process control.
According to an analysis of previous incidents, Emerson’s White reckons that improved process measurements and real-time analysis could have prevented or at least substantially reduced the damage in 25-40% of cases. Pre-empting such abnormal situations can lead to big savings for plant operators, says Ogden-Smith. Early analysis suggests benefits on the same order as those for advanced process control, he notes.
Smart devices can play a significant role in predicting abnormal situations, White believes, since they can provide diagnostics data (Figure 3). Process anomalies generally cause a predictable pattern in data that can be categorized into five distinct patterns <em dash>— representing drift, bias, noise, spike and stuck. These patterns are common for all sensor types, notes White, and can be detected by Fieldbus Foundation devices with built-in signal-processing capabilities without the need for redundant measurements, a mathematical model of the process or a mathematical model of the sensor. The high-speed sampling can spot problems that cannot be detected by traditional means, such as analysis of historian data, he adds.
The technology now has been tested at two refineries, he notes. In one case, it identified when a fluid-catalytic-cracker catalyst bed was about to lose fluidization 30 minutes before conventional measurements spotted the problem. The second trial focused on detecting flame instability in a process heater.
Both tests relied on high-speed data acquisition, statistical analysis and simple rules for classification, adds White.
Challenges in control
Model predictive control (MPC) has been around for ages, but many companies reamain leery of it. Cost historically has been a major factor. Thus, many implementations have focused on high-volume process equipment, such as at some refining units. However, MPC now makes strong economic sense for much smaller applications, particularly ones that would benefit from tighter or better product quality, says Paul Bonner, Houston-based senior director for chemicals and polymers at AspenTech. Invensys’s Fitzgerald points to recent use of MPC on multiple-effect evaporators and spray dryers.
“Before, there was a big ante to get in the game — money, time, people; now you don’t need the skilled experts,” says White. MPC today is easier and cheaper to implement, with better engineering tools for plant staff, adds Ogden-Swift.
Results can be impressive. According to White, standard deviation reduction routinely reaches 30-70% compared to regular proportional-integral-derivative (PID) control, and payback periods of a few months are common.
Improved computational speed also enhances applicability. MPC now can handle applications that it couldn’t before, such as surge control for compressors with multiple feeds and cracking-furnace temperature control, says White.
“There’s certainly a drive for more use of MPC because of advances in modeling technology, computing platforms and information technology infrastructure,” says Dow’s Gipson. The company now is in the latter stages of a rollout to broadly deploy MPC.
At refineries, the trend is to deploy MPC across related operations, notes Ogden-Swift. At Shell, says Kimpton, the latest thrust is to have MPC handle whole trains, not just units, thanks to today’s computational power.
NOVA Chemicals now is implementing MPC at plants that ordinarily might not have been able to justify it, says Tom Alloway, advanced process control leader in Moon Township, Pa. These plants are benefiting from a program that started in March 2004 to roll out MPC across the company. Already eight controllers (five at polymer units and three at petrochemical operations) have been turned on and 34 more are in various stages of implementation.
The decision to go company-wide changed the economics, says Alloway, because the corporation, not the individual site, covers a number of the major expenses.
A real turn off
But economics aren’t the only concern.
“Some companies have been scared off because of [MPC] sustainability fears,” says Yiannis Dimitratos, technical manager for process control in the Engineering Technology Center of DuPont, Wilmington, Del. “This has been an issue that has kept companies from more widely adopting MPC.”
The track record supports those fears. “The real issue for MPC is keeping operators from turning it off. Over 50% of all predictive controllers are in manual,” says Peter Martin, vice president of performance measurement and management for Invensys.
“Just putting in technology doesn’t create long-term value. There must be a plan to maintain gains,” says Gipson. “Sustainability should involve assigned responsibilities, resources and tools.” And, he cautions, “You can get into trouble if you don’t consider management of change and culture issues.”