Achievability of Liquid Oil

- Fired MACT Limits (revised June 9, 2011)

In section V. “Rationale for this Proposed NESHAP”, sub

- section D. “How did EPA determine the MACT floors for existing EGUs?” EPA outlines their methodology to calculate the floor values

. This comment addresses the achievability of the proposed limits.

Summary At Federal Register / Vol. 76, No. 85 / Tuesday, May 3, 2011 page 25044 EPA states: Test method measurement imprecision can also be a component of data variability. At very low emi ssions levels, as encountered in some of the data used to support this proposed rule, the inherent imprecision in the pollutant measurement method has a large influence on the reliability of the data underlying the regulatory floor or beyond

the

- floor emis sions limit. Variability of data due to measurement imprecision is inherently and reasonably addressed in calculating the floor emissions limit when the data distribution, which would include the results of all tests, is significantly above the MDL.

EPA’ s claim that measurement imprecision is “inherently and reasonably addressed” and the methodology related to the monitor detection limit misses the point of these imprecise measurements. The ultimate question that must be addressed is whether the ICR data set for oil

- fired units provides adequate information so that controls can be expected to reduce stack concentrations to meet the proposed limits, i.e. “, these standards must reflect the maximum degree of reduction in the HAP emissions that is achievable .”

To answer that question it is appropriate to review the data to determine if there is a reason

– a measureable driver

-- for the stack emission concentrations setting the MACT floor level to be lower relative to the rest of the measured distribution. Consider a simple pollutant example: in the example of the correlation between fuel sulfur concentration and SO2 stack measurements, the best performing 12% units would reflect either lower sulfur in the fuel, a particular control technology, a specific boiler type or combination of the three. In this simple sulfur example, for units with no back

- end controls, the amount of SO2 emitted is directly correlated to the amount of sulfur in the fuel.

Similarly the amount of HAP emitted should also positively correlate with the hazardous pollutant concentration in the fuel. If true then affected sources could limit emissions by fuel specifications that limit fuel concentrations. EPA agrees that a fuel standard is a potential control methodology because Feder al Register /Vol. 76, No. 85 /Tuesday, May 3, 2011 page 25037 states :This proposed rule contains an option to monitor inlet chlorine, fluorine, non

- Hg metal,

and Hg content in the liquid oil to meet outlet emission rate limitations. This is reasonable bec ause oil

- fired units may choose to remove these fuelborne HAP from the oil before combustion in lieu of installing air pollution control devices. This option can only be done on a mass basis by liquid oil

- fired EGUs.

However, this comment will demonstrate that the ICR data used in this analysis are not sufficiently accurate and precise to meet two conditions necessary to demonstrate an “achievable” MACT limit. The first condition is to show a positive correlation between parameter fuel content and paramet er stack emission rate and the second condition is to demonstrate that the majority of the observed data indicate that the derived limit could be achieved. There is no statistically significant relationship between fuel content and stack emissions in the greatest part of the database . For those parameters where there is an acceptable relationship the tests results indicate that there are a large fraction of test results that indicate that the potential control strategy would not meet the proposed limit. Therefore,

we conclude that the database is not sufficiently robust to set a MACT floor that has any relationship to what is “achievable” in a real

- world scenario.

Methodology The most obvious control option for HAP emissions is to reduce the concentratio n of the pollutant in the fuel. The ICR data set contains both fuel and stack measured values. If there is a statistically significant positive relationship between the measured concentration in the fuel oil and the concentration measured in an uncontrol led stack then it is possible to specify a MACT floor limit that can be achieved. However, if the data don’t show that relationship then the measurements are inadequate to define an achievable limit

. For this comment the data in the EPA spreadsheet floor_ analysis_oil_03x1611.xls was analyzed to determine if the test results at oil

- fired units indicated that the data show a relationship between the oil measurements and the stack measurements. In other words, was there a statistically significant relationsh ip between the fuel and stack measurements? The statistical program Statgraphics © ( http://www.statgraphics.com/ ) was used to analyze these data. Statgraphics is an interactive statistical analysis program tha t provides the tools that can be used to verify stack and CEMS data. It has over 160 statistical procedures for exploratory data analysis and statistical modeling. It includes features that are particularly useful for emissions monitoring data. All the statistical applications can be “transformed”, that is to say the data used can be limited so that, for example, just results from sources that do not have pollution control equipment are considered. Also, there is a “Stat Advisor” that describes the stati stical methodology and the implications of the results.The first step in the analysis was to revise the spreadsheet for use with the statistical program.

Edits included the following. Missing data were set to

- 9999 instead of blank values. The data set was edited such that the variable control_group_1 listed units with PM control and control_group_2 listed units with NOX control. It turns out that every stack in the database that had NOX control also had PM control. A variable was added for the total m etals concentration of the fuel by summing the fuel concentrations of each parameter used to calculate the total metals in stack concentration. (SG Floor Analysis Input Data 031611.xls)
For this analysis, the Statgraphics simple regression analysis procedu re was used as follows. The independent variable was the parameter (metal or acid gas) content of the fuel (e.g., Cadmium_Fuel_lb/MMBtu) and the dependent variable was the stack rate (e.g., Cadmium_Min) where both variables used the lb/mmBtu results. Usin g the pollution control information the analysis was limited to those stacks that had no pollution controls. For each parameter, the regression model (among the 27 models available) that provided the highest correlation coefficient and also calculated ANO VA results was chosen to represent the relationship for the parameter. The Statgraphics regression results (Attachment 1) include the following description of the analyses (in this example the text provided is for the antimony results):
The output shows th e results of fitting a logarithmic

- Y squared

- X model to describe the relationship between Antimony

- min and Antimony_Fuel_lb/MMBtu. The equation of the fitted model is

Antimony

- min = exp(

- 11.094

- 2.9955E6*Antimony_Fuel_lb/MMBtu^2)

Since the P

- val ue in the ANOVA table is greater or equal to 0.05, there is not a statistically significant relationship between Antimony

- min and Antimony_Fuel_lb/MMBtu at the 95.0% or higher confidence level.

The R

- Squared statistic indicates that the model as fitted explains 11.2513% of the variability in Antimony

- min. The correlation coefficient equals

- 0.33543, indicating a relatively weak relationship between the variables. The standard error of the estimate shows the standard deviation of the residuals to be 2 .66669. This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu.

The mean absolute error (MAE) of 1.97289 is the average value of the residuals. The Durbin

- Watson (DW) statistic tes ts the residuals to determine if there is any significantcorrelation based on the order in which they occur in your data file. Since the P

- value is

less than 0.05, there is an indication of possible serial correlation at the 95.0% confidence level. Plot the residuals versus row order to see if there is any pattern that can be seen. Attachment 1 lists the simple regression results, the ANOVA table including the Stat Advisor description and a plot of the stack and fuel data with the best fit regression l ine.

It is also necessary to review the plot of the observed values and the statistical model relative to the proposed limit. Reviewing the data in this way will show whether the tests used to generate the limit result in an achievable standard. The foll owing results section shows graphs for total metals in the stack and fuel for all the tests, for the tests at stacks with no pollution control equipment and at stacks with pollution control equipment. Plots of total metals vs. sulfur and PM concentrations are also shown.

Results There is one result of this work that must be addressed. There are stack test results from Eagle Valley Units 1&2 and Eddystone Units 3&4 that are considered separate tests in the EPA analyses. However, the stack concentrations measured in both tests are identical and must represent a single test. For example, the Eagle Valley 1 and 2 Nickel

– min measurements are both 1.18876E

- 06 . As a result they must not be considered separate tests in the MACT floor analysis. However, Eagl e Valley results are two of the seven tests used for the antimony, cadmium, chromium, manganese, nickel, selenium, and total metals MACT floors.

Whatever the rationale was to allow these two stations to claim identical stacks would have identical test resu lts is discredited upon review of the data. Fortunately, there were 14 pairs of stack tests at the same station: 11 stations with tests on two units and one station with tests on three units. These data are evaluated in tab paired metals in the Total Met als HF HCL limits analysis spreadsheet. The average difference in the stack total metals concentration measurements was 1.02E

- 4 or 34% of the proposed limit

. The maximum difference in the stack total metals concentration measurements was 2.28E

- 4 or 76% o f the proposed limit

. The minimum difference in the stack total metals concentration measurements was 2.32E

- 6 or 1% of the proposed limit . With such a limited data set it is inappropriate to try to guess how the MACT floor result would change had two stat ions not tried to save money but clearly there would be a change. Absent having them test another unit the prudent thing to do is to count the tests as only one for the purposes of the MACT floor analysis. For this analysis the ANOVA table, the R

- Squared statistic and the correlation coefficient are of interest. However, the ANOVA table is most important because it is used to determine whether there is a statistically significant relationship between the measured oil and stack concentrations. For an unco ntrolled stack there should be a significant relationship between the fuel concentration and the stack concentration. If not, then the measurement methodologies are insufficiently precise and accurate to determine an achievable MACT floorvalue. Note tha t we also have to check whether an increased fuel oil concentration also

increases the measured stack concentration. If there is not a positive increase in stack concentrations then either the fuel or stack measurements are ins ufficiently precise or

accur ate.

There are only f ive parameters that have a statistically significant relationship at the 95.0% or higher confidence level: nickel, HF, HCL, total metals, and cadmium. However, cadmium does not have a positive relationship between the observed concen tration in the fuel and that measured in the stack. In other words based on the EPA ICR data set the projected control methodology would be to increase the amount of cadmium in the fuel. Therefore it is only appropriate to consider developing a MACT floor for total metals, nickel , HCl, and HF because they are the only pollutants that we expect could be controlled by reducing concentrations in the fuel oil.
In order to have an achievable MACT limit the first condition is a statistically significant positive relationship between the HAP fuel concentration and stack concentration. The second condition is that tests with similar characteristics also meet the limit. The following figures plot stack concentrations and fuel concentrations to test the second cond ition.

Figure 1 is a plot of the total metals in the fuel (sum of all the fuel metal measurements) and the stack tests results for total metals for units and shows the limit in red. The statistical tests (included in Attachment 1) indicate significant re lationships between the fuel and the stack concentrations and that the regression model adequately represents the observed data 1

. However, reviewing the plot of the data indicates that e ven when the statistical tests show significant relationships there s till are fundamental concerns whether these test results indicate that the proposed to tal HAP metals limit of 0.00003 lbs/mmBtu is achievable. This plot shows that none of the seven test results that make up the MACT floor 12% average are within the confi dence intervals for the relationship and that there are many other test results outside the confidence intervals on the high side. In all the results shown there is a very large range to the outer bounds or the prediction limits shown on the graph

.
In ord er for the limit to be achievable there has to be a control methodology available. In the absence of control equipment setting a fuel standard would be the control approach. The

1 In statist ical terms: Because the P

- value in the ANOVA table for the simple regression analysis is less than 0.05, there is a statistically significant relationship between Total Metals_Min and Total Metals_Fuel at the 95.0% confidence level and because the P

- value for lack

- of

- fit in the ANOVA with lack

- of

- fit table is greater or equal to 0.05, the model appears to be adequate for the observed data at the 95.0% confidence level.statistical tests (included in Attachment 1) indicate significant relationsh ips between the fuel

and the stack concentrations and that the regression model adequately represents the observed data for this data set. Figure 2 lists results at stacks where no control equipment is used. In addition to the red line denoting the propos ed total metals limit there is a green vertical line (0.7 * 0.001) that represents a possible fuel standard specification. There are five test results on the following graph that meet the MACT limit and were less than the fuel specification

. However note the fact that there are also five test results above the limit that also met the fuel specification. T he fuel concentrations are lower (including the two lowest measurements) than the specification set by the MACT floor tests but the stack test numbers fa r exceed the proposed limit

. This indicates that setting a fuel standard would only have a fifty

- fifty chance of successfully meeting the proposed limit.
In the next plot (Figure 3) the same methodology was applied to data from stacks with controls. Th e analysis of the total metals in the fuel (sum of all the fuel metal measurements) and the stack tests results for total metals for units with pollution controls does not indicate significant relationships between the fuel and the stack concentrations or that the regression model adequately represents the observed data. In other words the first condition for data supporting an achievable limit is not met. A detailed look at the plotted data shows that the second condition for these data is even worse . T here are two stack tests on the following graph that meet the limit. There are six stack test results that have lower Total Metal s_Fuel concentrations but do not meet the proposed limit. Note that all the stack test results above the blue prediction line are stacks where multiple cyclones were the control device and the rest of the results are for stacks with electrostatic precipitators. There is less than a 50% chance that electrostatic precipitators can be used to meet the proposed limit.

Another poss ible control alternative for oil

- fired units is to limit the sulfur content on the presumption that higher sulfur oil indicates higher concentrations of HAPs. The plot shown in Figure 4 shows a similar pattern as the previous stack test results. There are as many or more test results at the same or lower sulfur measured concentrations that do not meet the proposed limit . Figure 5 limits the analysis to those stacks that do not have pollution control equipment and the results are similar. Therefore, stack owners do not have confidence that a sulfur specification will insure compliance with the proposed total metals limit.

Finally, Figure 6 plots the stack HAPs concentrations against the stack PM concentrations . This plot show s an example of how this kin d of data plot should look for an “achievable” limit

. In particular, all the data are within the prediction limits and the data in the tail that makes up tests that would be used to set the limit are bunched together with only one test outside the range o f PM tests. Note, that these data indicate that if the stack PM concentrations meet thelimit shown that the stack owner could meet the MACT limit because there are no results above

the limit with low PM concentrations. Unfortunately, there isn’t a fuel specification that insures these PM concentrations. On the other hand the potential to develop a limit based on PM as a surrogate for HAPs emissions is supported by this graph.

Conclusion EPA’s claim that measurement imprecision is “inherently and reasona bly addressed” and the methodology relat ed to monitor detection limits does not insure that the limits derived are “achievable”

. The ICR data set for oil

- fired units does not provide adequate information to specify controls that can be expected to reduce stack concentrations to meet th e proposed limits. Because the majority of parameter s do not have a positive relationship between fuel and concentration measurements and those that meet that criterion have a large fraction of results that could not meet th e MACT limit using a fuel specification , the proposed standards do not reflect the maximum degree of reduction in the HAP emissions that is achievable . The ICR measurements of fuel and stack concentrations at the oil

- fired units are not precise enough to support the proposed limits. These data support EPA ’s statement in the ICI boiler MACT that inherent variability and unpredictability of the non

- mercury metal HAP compositions and amounts in fuel has a material effect on the composition and amount of non

- mercury metal HAP in the emissions from the boiler

.

### Figure 1 : ICR Oil

- Fired Stack Tests Plot

- Fuel vs. Stack with simple regression model and confidence limits and MACT limit shown

All t he regression plots in this document incl ude the following : The line of best fit or prediction equation (blue line) which is the equation that would be used to predict values of the dependent variable Y given values of the independent variable X. Confidence intervals (green lines) for the
mean re sponse at X. These are the inner bounds in the above plot and describe how well the location of the line has been estimated g iven the available data sample. Prediction limits (grey lines) for new observations. These are the outer bounds in the above plot a nd describe how precisely one could predict where a single new observation would lie

.

### Figure 2 : ICR Oil

- Fired Stack Tests Plot at Stacks Without Pollution Controls

- Fuel vs. Stack with simple regression model and confidence limi ts, MACT limit & Fuel Spec shown . Note that the Y scale on the graph is in powers of ten

Green vertical line represents a possible fuel specification standard of 0.0007

### Figure 3 : ICR Oil

- Fired St ack Tests Plot at Stacks With Pollution Controls

- Fuel vs. Stack with simple regression model and confidence limits, MACT limit & Fuel Spec shown . Note that the Y scale on the graph is in powers of ten

Green vertical line represents a possible fuel specification standard of 0.00134

### Figure 4 : ICR Oil

- Fired Stack Tests Plot

- Stack HAPS vs Stack SO2 concentration with simple regression model and confidence limits and MACT limit shown . Note that the Y scale on the graph is in powers of ten

### Figure 5 : ICR Oil

- Fired Stack Tests Plot Limited to Stacks with No Pollution Controls

- Stack HAPS vs Stack SO2 concentration with simple regression model and confidence limits and MACT limit shown . Note that the Y scale on the graph is in powers of ten

### Figure 6 : ICR Oil

- Fired Stack Tests Plot

- Stack HAPS vs Stack PM concentration with simple regression model and confidence limits and MACT limit shown . Note that the Y scale on the graph is in powers of ten