What’s in the air for continuous emissions monitoring?

More attention to mercury and increased acceptance of predictive approaches is emerging. Such monitoring not only can keep plants on the right side of regulators but also can help provide insights for optimizing operation of equipment.

By Mike Spear, editor at large

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Established role

The general market for conventional CEMS’s, however, continues to be one of either steady replacement of aging systems, or installation of new systems driven by plant expansion both in the U.S. and, increasingly, in the rapidly growing areas of the Middle East and Asia. “Overall, CEMS’s have been around for decades, but they are still probably the most accurate way of measuring emissions. Typically, they are viewed as environmental compliance instruments, but they can also serve as process monitoring instruments, even though the stack is the last place you can measure in the process,” says Kriebel.

Apart from the mercury developments, Kriebel notes the EPA is “always pressing for new ways of measuring [pollutants].” From a CEMS perspective, he says the laser tuned diode — which measures in situ on the stack rather than taking samples for analysis — “seems to be making its presence felt for moisture, ammonia and other measurements that you can’t get very easily with CEMS.”

But whether your measurements are taken in situ or via samples sent down heated lines to the typical CEMS shelters and cabinets on the plant floor, the regulatory demands for constant on-going calibration and verification of the analytical instrumentation are always there. And with them, often quite literally hand in hand, is the demand for constant maintenance and monitoring of the systems themselves.

Maintenance is a factor,” agrees Kriebel, “and someone might be prepared to pay a little more for something that’s easier to maintain, rather than have someone climb a stack to maintain something a little cheaper. The drawback of CEMS’s is that you have to maintain them.”

Rosemount’s field-mountable MicroCEM system offers the choice of locating its analysis enclosure either right on the stack or in a ground-level enclosure. The goal of the “two-box unit” was to make it more compact to fit into expanding plants that may have limited ground space for shelters and cabinets, says Kreibel.

The CEMplicity monitoring system from Forney Corp., Carrolton, Texas, follows a similar approach. Dubbed a low-cost solution for CEMS and process monitoring, it provides the same cost savings as in situ type systems without the drawbacks associated with “in process” monitors, claims the company. Using EPA referenced method analyzers (chemiluminescent for NOx and paramagnetic for oxygen), CEMplicity is a close-coupled system packaged in a compact climate-controlled NEMA 4 enclosure.

Predictable interest

Meanwhile, an emissions monitoring technique that holds out the promise of little, if any, maintenance is attracting increasing interest. Evolving from work in the early 1990s on using neural networks to accurately predict NOx emissions from a natural-gas-fired boiler, Predictive Emissions Monitoring Systems (PEMS’s) are proving themselves on a wide range of applications in the chemical and petrochemical sectors.

Figure 1. Some systems based on models now include real-time sensor validation.

Figure 1. Some systems based on models now include real-time sensor validation. Courtesy: Pavilion Technologies

As the name suggests, PEMS’s are effectively model-based alternatives to the physical measurements required of CEMS’s. They predict emission levels from a knowledge of process and combustion plant parameters. More than 250 such systems have been installed worldwide and have been proven on many types of process units, combustion set-ups and fuel types, says Paul Reinermann, director of environmental solutions with PEMS pioneer Pavilion Technologies, Austin, Texas. “All of our customers will tell you that PEMS will cost less than a hardware-based CEMS — typically around 50% less both for total cost of ownership and initial capital cost,” he adds, while acknowledging that it can be “almost site dependent as to whether a company opts for CEMS or PEMS.”

The reasons behind that decision often relate directly to the work involved in creating the PEMS model. Like most systems on the market, Pavilion’s PEMS is statistically based and so requires running a series of experiments on the specific unit to generate data to correlate process operating conditions with emissions produced. Although this may only take 24 hours, some plants may not have the flexibility to run through the experimental operating ranges.

Reinermann points to refineries as a case in point: “When you ask them to vary the firing rates on a furnace or heater, they quickly add up how much crude they’re not going to be processing [while we are doing that]. Petrochemical plants on the other hand tend to have large banks of olefin furnaces, one or two of which will always be in flux, so it’s easier there.” In fact, he says Pavilion has recently had considerable success in U.S. ethanol plants. “I would estimate that a half of all new ethanol plants have our PEMS installed. The primary reason is that they don’t want to hire additional staff to maintain hardware-based CEM systems.”

Acquiring the data to create a PEMS model obviously also would be easier if the plant itself were equipped with a CEMS. While the plant’s data historian can provide the process data, emissions data in the U.S. has to come from an EPA-referenced source such as a CEMS. In practice though, as Reinermann points out, most PEMS’s are destined for units without a CEMS, so an outside contractor offering stack testing services might have to be called in for the experimental stage.

Creating the model though is definitely not for outsiders. “Even a simple boiler really requires a chemical engineer to build the right model,” he stresses. “It’s to do with the basics of what we learn about combustion as chemical engineers, all about what’s going inside that firebox or turbine. We have very good engineers that are expert at interviewing plant operators to acquire just that information and capture it in the model.”

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