Utility compliance specialists and policy analysts use iPOG™ to run numerous “What If?” scenarios across local and regional facilities. Ultimately, all these case studies could be synthesized into a strategy to achieve the greatest Hg emissions reductions for the lowest cost that are compatible with the company’s specific constraints on coal quality and gas cleaning configuration, and the timetable and depth of impending Hg emissions regulations. Environmental managers use it to estimate how modifications to a particular gas cleaning system will affect Hg emissions. The widespread installation of SCRs and FGDs currently underway in the USA and China is providing many opportunities to accurately estimate the reductions in Hg emissions rates due to retention of oxidized Hg in FGDs. Environmental and process engineers use iPOG™ to determine how variations in firing and gas cleaning conditions affect Hg emissions rates. Any adjustments to the firing conditions that significantly increase loss-on-ignition (LOI) levels, for example, may enhance Hg removals in the particle collection device. Fuel procurement specialists use it to estimate Hg emissions rates for the range of coal quality in their current and foreseeable operations. Project engineers use iPOG™ to ensure consistency with the backlog of data for similar gas cleaning configurations, and to understand where Hg is oxidized and removed along their gas cleaning system. OEMs for gas cleaning technology will use this package to estimate Hg emissions rates for their new installations. For example, FGD suppliers can easily estimate how much oxidized Hg is retained in the scrubber if they know the speciation at the FGD inlet. But that speciation is determined by the units upstream of the FGD, and iPOG™ estimates the upstream Hg transformations.

NEA assumed that iPOG™ users are generally familiar with the terminology and unit operations in modern utility gas cleaning systems. But those new to Hg control technologies have two important resources to support their iPOG™ calculations: First, the POG document cited at the beginning of this webpage is an excellent introduction to the principles of Hg emissions control, and to the optimization strategies incorporated into the iPOG™. Second, all data entries in iPOG™ can be made with default parameter specifications. Whereas entry-level iPOG™ users will heavily rely on the default specifications, expert users will take advantage of each input specification to tailor their simulation cases to match their existing and foreseen cleaning situations.

Since the calculations are cheap and fast, iPOG™ is a good tool to address “What if?” questions regarding variations in fuel quality, cleaning configurations, and external Hg controls. Will coal cleaning reduce Hg emissions by enough to meet the company’s target for next year and beyond? What if the coal-Cl level surges by a factor of three or more in the primary supply mine, as we have seen in the field test literature? What if we add SCRs and FGDs to two base-load plants and only FGDs to three smaller plants? What will happen when those SCRs are taken out of service during specified seasons of a year? What if we applied activated carbon injection (ACI) at those three smaller plants instead of FGD? The iPOG™ delivers quantitative answers to all these types of questions in no more than a few minutes of execution time. Even if your curiosity is endless, this tool will keep pace.

The iPOG™ is also a means to extrapolate from a limited set of test data to the full ranges of coal quality and gas cleaning conditions across utility operations of any size and complexity. It is too expensive for a sizeable company to test all the important combinations of fuel quality and gas cleaning conditions in their current and foreseen operations. And data from one system is hard to directly apply to other systems of similar configuration because, inevitably, some of the important cleaning conditions were different in the test than they will be in the other systems. So the most efficient strategy is to first use a limited amount of test data to ensure that the iPOG™ results are accurate for the gas cleaning conditions of interest, then rely on iPOG™ to estimate Hg emissions rates for all the other fuels and gas cleaning conditions that will come into play among the similar cleaning configurations. Since computerized calculations are so much faster and cheaper than field testing, users can easily evaluate much broader ranges of Hg control conditions than are represented in a field test database.

The iPOG™ does not estimate the costs for the various compliance options that are analyzed. But it nevertheless supports financial management strategies to minimize the costs of regulatory compliance by accurately estimating how much Hg can be removed for a broad range of inherent and external controls. By associating costs with their Hg control scenarios, users will be in a position to identify least-cost control options at the levels of individual plants as well as regional utility operations.

To support entry-level users, default parameter specifications are available for every required input value in iPOG™, although experienced users will appreciate the opportunities to specify their actual cleaning conditions in the calculations. Properties of the coal or coal blend (Rank, Moisture, Ash, S, HHV, Cl, Hg, Blend Percentages) are required to estimate the flue gas composition because there are no means to accurately estimate Hg- or Cl- contents in coals. Furnace conditions (Rating, Load, Gross Efficiency, Firing Configuration, Bottom Ash, LOI, Economizer O₂), including the firing configuration (Wall-, Corner-, or Cyclone-Firing) are also required to estimate a flue gas composition, and also to determine a flue gas flowrate. The partitioning of coal ash into bottom ash and flyash is also important because LOI is expressed as a percentage of the retained flyash only. Users must have a flow diagram from furnace exit to stack that shows all air pollution control devices and Hg controls, supplemented with select SCR conditions (Economizer NO Concentration & NO Reduction Efficiency), particulate removal conditions (an over-all Particulate Collection Efficiency), sulfur scrubbing conditions (SO₂ Capture Efficiency), and specifications on carbon sorbents (Conventional or Brominated Sorbent, Injection Position & Concentration) and halogenation agents (Wt. Percentage Halogen, Injection Position & Concentration). Both conventional and brominated carbon sorbents are supported.

Since the POG and, now, iPOG™ were developed for a broad user base, including people with no immediate experience in controlling Hg emissions, NEA definitely did not incorporate state-of-the-art calculation sequences to achieve the tightest quantitative accuracy on the calculation results. Tradeoffs were deliberately made to eliminate all but the most basic input requirements at the expense of quantitative accuracy for any particular utility gas cleaning system. Obviously, these tradeoffs limit how the estimates from the iPOG™ should be used. The most general limitation is that the iPOG™ estimates are, for the most part, based on regressions of field test data, rather than on validated chemical reaction mechanisms. The bulk of the field test data came from the extensive program directed by the National Energy Technology Laboratory of the US DoE. iPOG™ users must realize that the estimates from iPOG™ are certainly no more accurate than the qualified measurement uncertainties, which NEA estimates at 10 – 15 % of the total Hg inventory in each test. Differences among cases that are smaller than these tolerances should be ignored.

Another important limitation on the estimates is due to the need to omit all but the essential process characteristics from the input data requirements. Consequently, unlike MercuRator™, the estimates from iPOG™ cannot possibly depict the distinctive features of particular gas cleaning systems. Three particular instances of these system-specific omissions should be kept in mind. First, users do not specify the temperatures of their particulate control devices. In ACI applications, they also do not account for the variable performance of carbon sorbents from different vendors, due to differences in preparation techniques, loadings, and surface areas. Most important, the estimates for Hg capture on the unburned carbon in LOI and also on carbon sorbents does not account for interference by adsorbed SO₃. This interference can cut Hg removals in half on conventional and brominated carbon sorbents under the worst circumstances.

The second limitation from system-specific omissions pertains to the oxidation of elemental Hg vapor (Hg⁰) along SCR monoliths. The iPOG™ accounts for variations in the halogen concentration in the flue gas but it does not account for variations among the SCR design specifications and in the reactivities of the catalysts from different manufacturers and of different lifetimes in service. Collectively, the variations in the SCR design specifications are at least as important as the variations in the halogen concentrations in the flue gas, but these design specifications had to be omitted from the iPOG™ because they pertain to deeply technical and often proprietary information that many utility companies do not even have. The third limitation from system-specific omissions pertains to the retention of oxidized Hg vapor (Hg²⁺) in wet FGDs. In most FGD systems, essentially all the Hg²⁺ in the inlet flue gas is retained in the gypsum product  or, occasionally, in the scrubber wastewater. Rarely, however, significant fractions of the dissolved oxidized Hg are re-emitted as Hg⁰. Consequently, iPOG™ users should realize that the relatively high Hg removals estimated for cleaning systems with WFGDs will represent significant over-predictions for the unusual situations where re-emission comes into play.

Users who reach a point in their analyses with iPOG™ where these limitations are hindering their development work on Hg control strategies can consider more comprehensive simulation tools. NEA’s MercuRator™ is one such tool that requires system-specific input specifications and also uses field-test data to calibrate baseline predictions.

B. Krishnakumar, S. Niksa, L. Sloss, W. Jozewicz, and G. Futsaeter, “Interactive process optimization guidance for mercury emissions control,” Energy Fuels, 26(8):4624-34 (2012).

L. Sloss, S. Niksa, B. Krishnakumar, W. Jozewicz, and G. Futsaeter, “Preparing for the UNEP 2013 Global Mercury Treaty with the Process Optimisation Guidance Document (POG and iPOG™),” PowerGen, Las Vegas, NV, 2011.