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Addressing Variability in Mercury Stack Emissions

This study builds on EPRI's Part III mercury ICR data analysis, using chlorine in coal as the primary variable. It utilizes Part II coal ICR data to predict mercury emissions and percent removal. Computational steps involve regression models for Spray Dryer/Fabric Filter to calculate mercury removal percentages. Limitations include data quantity variations among sites but show consistency in achievable emission rates.

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Addressing Variability in Mercury Stack Emissions

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  1. Addressing Variability in Mercury Stack Emissions MACT Working Meeting Washington, DC December 18, 2001

  2. Method of Analysis • Builds on EPRI’s analysis of the Part III (stack) mercury ICR data. • Regression based - uses chlorine in coal as primary explanatory variable. • Makes use of the Part II (coal) ICR data.

  3. ICR Part II Coal Data Hg, Cl, S EPRI ICR Report Equations Predicted Hg Emissions ICR Part III Test Data, % removal

  4. Computational Steps • Using the appropriate EPRI regression model, compute a Hg emission factor for each Part II coal analysis. • Rank order the factors in ascending order. • Compute percent  each given value and plot results.

  5. Regression Model -Spray Dryer/Fabric Filter • Percent mercury removed is given by: %R = 0.2854*Ln(Cl2) - 1.1302 Where: %R = percent of mercury removed relative to that contained in the coal Cl2 = concentration of chlorine in coal.

  6. Regression Model -Fabric Filter • Percent mercury removal is given by: %R = 0.1816*Ln(Cl2) - 0.4287 Where: %R = percent of mercury removed relative to coal concentration Cl2 = concentration of chlorine in coal.

  7. Limitations and Observations • The regression model is not a perfect fit. • Quantity of data is uneven (e.g., 418 samples for Sammis and 41 for Mecklenburg). • However, much consistency among the sites (i.e., the emission rate that can be achieved 90% of the time is much higher than the one that can be achieved 50% of the time).

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