10/ 2 4 /2011 1 DRAFT

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### MEMORANDUM

TO: Conrad Chin, U.S. Environmental Protection Agency, OAQPS

FROM: Beth Friedman, Jeff Harris, and Brad Nelson, EC/R

DATE : October 2 4 , 2011

SUBJECT: Draft MACT Floor Analysis for the Ferroalloys Production Source Category

This memorandum presents the methodology and results of the maximum achievable control technology

(MACT) floor determinations for particulate matter ( PM), h ydrochloric a cid (HCl), t otal p olycyclic

a romatic h ydrocarbons (PAH), m ercury (Hg), and f ormaldehyde (HCHO) from e lectric a rc f urnaces

(EAF) , and PM from p roduct s izing operations for the Ferroalloys Production source category.

We determined the MACT floors using em i ssions test data obtained from the 2010 Information

Collection Request (ICR). We ranked emissions test averages for each of the sources, calculated MACT

floor averages, and conducted emissions test data variability analysis to estimate these best performin g

sources’ emissions performance over time. We determined the MACT floors based on the results of

these analyses. We also conducted the beyond the floor analyses required by section 112 (d)(2), to

evaluate possible standards that would be more stringent th an the MACT floor. The organization of this

memo is as follows:

1.0 Summary of Existing Limits

2.0 Background on MACT Floor Methodology

3.0 MACT Floor Determination Methodology

4.0 Beyond the Floor

5.0 Summary of MACT Floor Analysis

Appendix A: Detailed Emissions Test Data10/ 2 4 /2011 2 DRAFT

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### 1.0 SUMMARY OF EXISTING MACT LIMITS

The existing National Emissions Standard for Hazardous Air Pollutants (NESHAP) for the Ferroalloys

Production source category (40 CFR part 63, subpart X XX ) established emissions limits for PM (as a

surrogate for metal hazardous air pollutants (HAP)). Table 1

- 1 summarizes the emissions limits in the

existing NESHAP.

Table 1

- 1. Emissions Limits in the Ferroalloys NESHAP.

New or Reconstructed or Existing So urce Affected Source Applicable PM Emission

Standards

New or reconstructed Submerged arc furnace 0.23 kilograms per hour per megawatt (kg/hr/MW) (0.51 pounds per hour per megawatt (lb/hr/MW) or 35 milligrams per dry standard cubic meter (mg/dscm) (0.015 grains per dry standard cubic foot (gr/dscf)

Existing Open submerged arc furnace producing ferromanganese and operating at a furnace power input of 22 megawatts (MW) or less 9.8 kg/hr (21.7 lb/hr)

Existing Open submerged arc furnace producing ferromanganese and operating at a furnace power input greater than 22 MW 13.5 kg/hr (29.8 lb/hr)

Existing Open submerged arc furnace producing silicomanganese and operating at a furnace power input greater than 25 MW 16.3 kg/hr (35.9 lb/hr )

Existing Open submerged arc furnace producing silicomanganese and operating at a furnace power input of 25 MW or less 12.3 kg/hr (27.2 lb/hr)

Existing Semi

- sealed submerged arc furnace (primary, tapping, and vent stacks) producing ferromanganese 11.2 kg/hr (24.7 lb/hr)

New, reconstructed, or existing Metal oxygen refining process 69 mg/dscm (0.03 gr/dscf)

New or reconstructed Individual equipment associated with the product crushing and screening operation 50 mg/dscm (0.022 gr/dscf)

Existing Individual equipment associated with the product crushing and screening operation 69 mg/dscm (0.03 gr/dscf)10/ 2 4 /2011 3 DRAFT

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### As shown in Table 1

- 1, the current NESHAP does not specify emissions limits for pollutants other than

PM. Therefore, pursuant to sections 112(d)(2) and (3) of the Clean Air Act (CAA), we have calculated

MACT floors and conducte d beyond the floor analyses for Hg

, PAH, formaldehyde, an d HCl. Because

MACT has already been established for PM (metal HAP), we con ducted a technology review for those

emissions. However, we used the MACT floor methodology to evaluate potential revised emissions

limits based on the recent test program. For addtional detail, see Draft Technology Review for the

Ferroalloys Production So urce Category

.

2.0 OVERVIEW OF MACT STANDARD DEVELOPMENT METHODOLOGY

2.1 MACT Determination

Section 112(d) of the CAA requires EPA to set emissions standards for HAP emitted by major stationary

sources. The MACT standards for existing sources must be at least as stringent as the average emissions

limitation achieved by the best performing 12 percent of existing sources (for which the Administrator

has emissions information) or the five best

- performing sources for source categories with less than 30

sourc es (CAA section 112(d)(3)(A) and (B)). The Ferroalloy Production source category consists of

fewer than 30 sources. Where, as here, there are five or fewer sources, we base the MACT floor limit on

the average emissions limitation achieved by those sources for which we have data. This level of

minimum stringency is called the MACT floor. For new sources, MACT standards must be at least as

stringent as the control level achieved in practice by the best controlled similar source (CAA section

112(d)(3)). These requirements constitute the MACT floor for new and existing sources. EPA may not

consider costs or other impacts in determining the MACT floors. EPA must consider cost, non

- air

quality health and environmental impacts, and energy requirements in connection with any standards

that are more stringent than the MACT floor (beyond

- the

- floor controls).

MACT analyses involve an assessment of the emissions from the best

- performing units in a source

category. The assessment can be based on actual emissions data, on knowledge of the air pollution

control in place in combination with actual emissions data, or on state regulatory requirements that may

enable EPA to estimate the actual performance of the regulated units and other relevant emissions

information. For each source category, the assessment involves a review of actual emissions data with

an appropriate accounting for emissions variability. Other methods of estimating emissions can be used

provided that the methods can be shown to provide reasonable estimates o f the actual emissions

performance of a source or sources. In a March 2007 court decision (Sierra Club v. EPA, 479 F. 3d 87510/ 2 4 /2011 4 DRAFT

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### [DC Cir. 2007]) (Brick MACT), EPA was directed to set MACT floors conforming to the statute, with

these specific directives:

Floor s for existing sources must reflect the average emissions limitation achieved by the best

performing 12 percent of existing sources, not levels EPA considers to be achievable by all

sources (479 F. 3d at 880

– 81).

EPA cannot set floors of “no control.” The court reiterated its prior holdings, confirming that

EPA must set floor standards for all HAP emitted by the major source, including those HAP that

are not controlled by at

- the

- stack control devices (479 F. 3d at 883).

EPA cannot ignore non

- technology fac tors that reduce HAP emissions. Specifically, the court

held that “EPA’s decision to base floors exclusively on technology even though non

- technology

factors affect emissions violates the Act” (479 F. 3d at 883).

EPA may not use emissions levels of the wo rst performers to estimate variability of the best

performers without a demonstrated relationship between the two (479 F. 3d at 882).

2.2 Considering Variability in Establishing Emissions Limits

Section 112(d)(3)(B) of the CAA requires that EPA determine the emissions control that is achieved in

practice by the average of the best

- controlled top five sources when establishing the MACT floor for

units in a source category with 30 or fewer units. EPA must exercise its judgment, based on an

valuation of the relevant factors and available data, to determine the level of emissions control that has

been achieved by the average of the best

- performing top five units under variable conditions. The Court

has recognized that EPA may consider variability in estimatin g the degree of emissions reduction

achieved by best

- performing sources and in setting MACT floors (see Mossville Envt’l Action Now v.

EPA, 370 F.3d 1232, 1241

- 42 D.C. Cir 2004), holding that EPA may consider emissions variability in

estimating performance achieved by best

- performing sources and may set the floor at a level that best

performing sources can expect to meet “every day and under all operating conditions.”

The MACT and other technology

- based standards are necessarily derived from short term emissions test

data, but such data are not representative of the range of operating conditions that the best

- performing

facilities face on a day

- to

- day basis. In statistical terms, each test produces a limited data sample, not a

complete enumeration of the available data for performance of the unit over a long period of time. EPA,

therefore, often needs to adjust the short

- term data to account for these varying conditions in order to10/ 2 4 /2011 5 DRAFT

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### properly estimate these sources’ performance over time. The types of vari ability that EPA attempts to

account for include operational distinctions between and within tests at the same unit.

“Between

- test variability” can occur even where conditions appear to be the same when two or more

tests are conducted. Variations in emiss ions may be caused by different settings for emissions testing

equipment, different field teams conducting the testing, differences in sample handling or different

laboratories analyzing the results. Identifying an achieved emissions level for best perform ing sources

needs to account for these differences between tests for “a uniform standard [to] be capable of being met

under most adverse conditions which can reasonably be expected to recur[.]” (See National lime Ass’n

v. EPA, 627 F.2d at 431, n. 46.; see also v. Ruckelshaus, 486 F.2d at 396 noting industry point that “a

single test offered a weak basis” for inferring that plants could meet the standards.)

The same types of differences leading to between

- test variability also cause variations in results be tween

various runs comprising a single test, or “within

- test variability.” A single test at a unit usually includes

at least three separate test runs. (See 40 CFR 63.7(e)(3) for MACT standards under CAA Section 112.)

Each data point should be viewed as a s napshot of actual performance. Along with an understanding of

the factors that may affect performance, each of these snapshots gives information about the normal, and

unavoidable, variation in emissions that would be expected to recur over time.

Variability can be accounted for using different statistical methods. For example, recent rules have used

the upper limit (UL) or the upper prediction limit (UPL) to evaluate variability as part of the process for

determining the MACT floor emissions limit s. A UL is based on the distribution of the available

emissions observations (e.g., test runs), and does not embody the predictive aspect that a UPL does. A

prediction interval (e.g., a UPL) for a future observation is an interval that will, with a specifi ed degree

of confidence, contain the next (or some other prespecified) randomly selected observation from a

population. In other words, the prediction interval estimates what future values will be, based on present

or past measurements. Given this definiti on, the UPL represents the value that the mean of three futu re

test run observations (three

- run average) can be expected to fall below, based on the results of the

independent sample of size (n) from the same population. Therefore, should a future test con dition be

selected randomly from any of these sources (i.e., average of three runs), one can be 99 percent

confident that the reported level will fall below a MACT floor emissions limit calculated using a UPL,

assuming representativeness of the underlying data set. For these reasons, we used the UPL to account10/ 2 4 /2011 6 DRAFT

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### for variability in our MACT floor calculations. 1 

### W e did, however, employ alternative statistical methods

to account for variability when calculating MACT floors for acid gases and formaldehyde due to limited

emissions data, as discussed further in section 3.2 of this memorandum.

3.0 MACT DETERMINATION FOR FERROALLOYS SOURCE CATEGORY

The following sections outline the steps taken in determining the MACT floors for the Ferroalloys

Production source category. We calculate d emissions test averages and rank ed the top sources (section

3.1), perform ed statistical analyses to account for variability in emissions data (section 3.2), perform ed

analyses to assess the impact of below detection level (BDL) emis sions data (section 3.3), and calculate d

the 99% UPL for each pollutant (section 3.4)

3.1 Ranking the Best Performing Sources

The emissions test data used to calculate the MACT floors were obtained using an ICR survey sent to

the two existing ferroallo ys facilities in April 2010. The data used in our MACT floor calculations,

including individual emissions test runs, is presented in Appendix A

.

The M ACT floor emissions limits for Hg , PAH, formaldehyde, and HCl were calculated for furnace

emissions from both facilities. A MACT floor emission limit was al s o calculated for PM emissions

from product sizing/crushing operations at both facilities.

To determine the MACT floor emissions limits, the average pollutant concentration was calculated for

each affected source for which emissions data was available

. T he MACT floors for each pollutant were

calculated using the units shown in Table 3

- 1.

Because we had emissions test d ata for less than five sources (there are o nly two sources in the source

category and each provided emissions data for a single furnace and a single product sizing operation)

, the average of the best

- performing sources was determined as the average of all available emissions test

data for each poll utant. One plant provided test data for a unique emission source, metal oxygen refining

(MOR).

1 Because a source must demonstrate compliance with the MACT floor using the average of a three

- run test, the appropriate test condition to use to assess variability is three. If a source had to demonstrate compliance by showing that each individu al

test ru n was below the MACT floor emissions limit, it would be appropriate to use a future test condition of one.10/ 2 4 /2011 7 DRAFT

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### Table 3

- 1. Units of MACT Floor for Ferroalloys NESHAP.

Pollutant Units of MACT Floor

Total PM (Furnace) mg /dsc m @ 2% CO 2



### HCl (Furnace) μ g/dscm @ 2% CO 2 

### Hg (Furnace) μ g/dscm @ 2% CO 2



### Total PAH (Furnace) μ g/dscm @ 2% CO 2


### Formaldehyde (Furnace) μ g/dscm @ 2% CO 2



### Total PM (MOR) m g/dscm @ 2% CO 2


### Total PM (Product Sizing) mg /dsc m

3.2 Incorporating Data Variability

Data variability was incorporated after ranking the emission s points . Each test run from the top sources

was used in the variability analysis. By including multiple emissions tests from facilities, EPA can

evaluate intra

- unit variability of emissions tests over time, considering variability in c ontrol device

performance and unit operations. As discussed previously, the UPL was used for the Ferroalloys

Production MACT floor variability analysis.

The first step included a determination of whether the concentration data from the best performing

so urces were normally or log

- normally distributed. If the data were normally distributed (e.g., similar to

a typical bell curve), further variability analyses were conducted on the data set. If the data were not

normally distributed (for example if the data were asymmetric or skewed to the right or left), the type of

distribution (e.g., log

- normal) was determined and a data transformation (e.g., taking the natural log of

the data) was performed to normalize the data prior to conducting the variability analysi s. Two statistical

measures, skewness and kurtosis, were examined to determine if the data were normally or log

- normally

distributed.

The skewness statistic (S) characterizes the degree of asymmetry of a given data distribution. Normally

distributed data have a skewness of zero. A non

- zero S value indicates that the data are asymmetrically

distributed. A positive S value means that the data distribution has a right tail extending toward positive

values. A negative S value means that the data distribution h as a left tail extending toward negative10/ 2 4 /2011 8 DRAFT

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### values. Further assessment of data normality can be made using the standard error of the skewness

statistic (SES) shown in Equation 1:

(Eq. 1)

Where:

N = the sample size.

According to the small sample skewness hypothesis test, if S is greater than two times the SES, the data

distribution can be considered non

- normal.

The kurtosis statistic (K) characterizes the degree of peakedness or flatness of a given data distribution

in comparison to a norm al distribution. Normally distributed data have a kurtosis of zero. A K value that

is greater than zero indicates a relatively peaked distribution while a K value less than zero indicates a

relatively flat distribution. The normality of the data can be fur ther evaluated using the standard error of

the kurtosis statistic (SEK) shown in Equation 2:

(Eq. 2 )

Where:

N = the sample size.

According to the small sample kurtosis hypothesis test, if K is greater than two times the SEK, the data

di stribution is typically considered to be non

- normal.

The skewness and kurtosis hypothesis tests were applied to both the reported test values and (where

necessary) the lognormal values of the reported test values. If the S and K statistics of the reported data

set were both less than twice the SES and SEK, respectively, the data set was classified as normally

distributed. If neither of the S and K statistics or only one of these statistics was less than twice the SES

or SEK, respectively, skewness and kurt osis hypothesis tests were conducted for the natural log

transformed data. The distribution most similar to a normal distribution was selected as the basis for10/ 2 4 /2011 9 DRAFT

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### calculating the UPL. If both the reported values and the natural

- log transformed reported values had S

and K statistics that were greater than twice the SES or SEK, respectively, the normally distributed data

set was selected as the basis of the floor to be protective of the environment. If the results of the

skewness and kurtosis hypothesis tests we re mixed for the reported values and the natural log

transformed reported values, the normal distribution was chosen to be protective of the environment.

After determining the distribution of each dataset, a student’s t

- test statistic was determined to ca lculate

the UPL value. The t

- statistic test, which can be found in standard statistical reference tables or by using

the Excel function “TINV,” requires the degrees of freedom and the desired confidence interval. The

degrees of freedom was calculated as on e less than th e number of test runs for the two facilities and the

confidence interval used was 99 percent.

Following the determination of the student’s t

- test statistic, the 99 percent UPL values for each pollutant

were calculated using the test run data for both facilities. Because compliance with the MACT floor

emissions limit is based on the average of a 3

- run test, the UPL was calculated using the following

equation:

(Eq. 3)

Where:

n = the number of test runs (i.e., sample size),

m = the number of test runs in the compliance average,

s = standard deviation of the emissions test data,

= mean, i.e., average of the emissions test data, and

t (0.99, n

– 1) = t

- statistic for 99% significance and a sample si ze of n

- 1

.

Equation 3 was applied to the normally distributed data sets. However, for the transformed data, the

average and the standard deviation of the test runs were replaced with the average and the standard

deviation of the transformed data. The expo nential function of Equation 3 was calculated to obtain the

99 percent UPL value.10/ 2 4 /2011 10 DRAFT

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### The 99 percent UPL represents the value that one can expect the mean of future 3

- run performance tests

from the best

- performing facilities to fall below with 99 percent conf idence, based upon the results of

the independent sample of observations from the same best performing sources. Imprecision due to non

detect values was investigated after determining the 99 percent UPL values.

Because only one facility provided useful test data for furnace PAH (the other facility provided PAH

data based on the analysis of baghouse dust) and only a single facility performs the MOR process

, these

UPL s w ere calculated using the three test runs available.

3.3 Use of Non

- Detect V alues

At very low emissions levels where emissions tests result in non

- detect ( ND ) values, the inherent

imprecision in the pollutant measurement method has a large influence on the reliability of the data

underlying the regulatory floor emissions limit. Be cause of sample and emissions matrix effects,

laboratory techniques, sample size and other factors, method detection levels normally vary from test to

test for any specific test method and pollutant measurement. The confidence level that a value measured

a t the detection level is greater than zero is about 99 percent. The expected measurement imprecision for

an emissions value occurring at or near the method detection level is about 40 to 50 percent. Pollutant

measurement imprecision decreases to a consistent level of 10 to 15 percent for values measured at a

level about 3 times the method detection level.

We applied the following procedures to account for the effect of measurement imprecision associated

with a database that includes ND values , and to address the concerns associated with use of a small data

set, such as the Ferroalloys Production data set for acid gases. As a first step, we reviewed an HCL

emissions data set for the industrial boilers rule, which represents several hundred emissions tests used

in the floor calculations (i.e., best performers) for the boilers rule. We presume that data for the best

performing units also reflect the capabilities of high quality testing companies and laboratories. Further,

the method detection levels cal culated from larger data sets are more representative of the inherent

measurement variability both within and between testing companies than the limited Ferroalloys

Production dataset is. We believe that emissions tests conducted with these methods for mos t

combustion operations ( e.g. , fossil fuel, biomass, and waste fired units; brick and clay kilns; Portland

cement kilns), including ferroalloys production, should produce method detection levels very similar to

the level of 6 0 μg/dscm that is the result of this review.10/ 2 4 /2011 11 DRAFT

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### Accordingly, we used this value (6 0 μg/dscm ) as the representative method detection level (RDL) for

HCl.

Because no such data was available for formaldehyde, we used the following methodology to determine

the RDL. We identified all of the available reported pollutant specific method detection levels and

calculated the arithmetic mean value. We deemed the resulting mean of the method detection levels to

be the RDL.

The second step in the process is to calculate th ree times the RDL and compare that value to the

calculated floor or emissions limit. If three times the representa tive method detection level was less than

the calculated floor or emissions limit calculated from the UPL, we would conclude that measurement

variability was adequately addressed. The calculated floor or emissions limit would need no adjustment.

If, on the other hand, the value equal to three times the representative method detection level w as greater

than the UPL, we would conclude that the cal culated floor or emissions limit does not account entirely

for measurement variability. If indicated, we substituted the value equal to three times the representative

method detection level to apply as the adjusted floor or emissions limit. This adjusted v alue would

ensure measurement variability is adequately addressed in the floor or the emissions limit.

3.4 MACT Floor and Variability Analysis Results

3.4.1 Existing Sources

Tables 3

- 2 through 3

- 8 present the results of the existing source MACT floor and variability analyses

. As described in section 3.3, because of data reported below the detection limit, three times the RDL for

HC l , and formaldehyde was compared to the calculated UPL. For HCl, the calculated UPL was higher

than three times the RDL, and thus was selected as the MACT floor for existing sources. For

formaldehyde, the calculated UPL was less than three times the RDL, and thus three times the RDL was

selected as the MACT floor for exis ting sources. Because only a single facility performs MOR

operations, the UPL is calculated based on the three test runs from that facility.

The test data for PAH w ere obtained from a n outlet stack test from one of the facilities and a baghouse

dust anal ysis from the other facility. The outlet PAH concentration s for the stack test w ere calculated

using the analytical results and the calculated stack test parameters. The outlet PAH concentration s for10/ 2 4 /2011 12 DRAFT

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### the baghouse dust data w ere calculated using a mass balance around the baghouse by assuming the

control efficiency of the PAH capture. Because of the uncertainty of the calculated PAH outlet

concentration s from the baghouse dust data , only the outlet stack test data w ere used to ca lculate the

UPL for PAH

.

Table 3

- 2

. Existing Source MACT Floor and Variability Analysis Results for Furnace PM Total PM (Furnace) Total Facility Count 2 # Facilities

Ave Top 2

- All Runs 8.0 (mg/dscm @ 2% CO2)

Min Run in Top 2 1.2 (mg/dscm @ 2% CO2)

Max Run in Top 2 16.8 (mg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.29
Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES 0.29

normal S/SES<2

Kurtosis (K)

- 2.14

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values) K/SEK

- 1.07

normal K/SEK<2

99% UPL (normal distribution) 23.44 (mg/dscm @ 2% CO2)



### Table 3

- 3. Existing Source MACT Floor and Variability Analysis Results for Furnace HCl

HCl (Furnace) Total Facility Count 2 # Facilities

Ave Top 2

- All Runs 495.8 (μg/dscm @ 2% CO2)

Min Run in Top 2 341.34 (μg/dscm @ 2% CO2)

Max Run in Top 2 659.26 (μg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs
Sample Size (n) 6 # of runs

Skewness (S) 0.05
Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES 0.05

normal S/SES<2
Kurtosis (K)

- 2.47
Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values) K/SEK

- 1.24

normal K/SEK<2

99% UPL (normal distribution) 808.65 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 60

3x RDL 18010/ 2 4 /2011 13 DRAFT

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### Table 3

- 4

. Existing Source MACT Floor and Variability Analysis Results for Furnace Hg

Hg (Furnace) Total Facility Count 2 # Facilities

Ave Top 2

- All Runs 24.33 (μg/dscm @ 2% CO2)

Min Run in Top 2 0.52 (μg/dscm @ 2% CO2)

Max Run in Top 2 48.06 (μg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.02
Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES 0.02

normal S/SES<2

Kurtosis (K)

- 3.20

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values) K/SEK

- 1.60

normal K/SEK<2

99% UPL (normal distribution) 79.9 2 ( μ g/dscm @ 2% CO2)



### Table 3

- 5

. Existing Source MACT Floor and Variability Analysis Results for Furnace PAH

PAH (Furnace) Total Facility Count 2 # Facilities

Top 1 1 # Facilities

Ave Top 1

- All Runs 402.94 ( μ g/dscm @ 2% CO2)

Min Run in Top 1 305.00 ( μ g/dscm @ 2% CO2)

Max Run in Top 1 458.82 ( μ g/dscm @ 2% CO2)

Top 2 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S)

- 1.68
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES

- 1.19

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 886.87 ( μ g/dscm @ 2% CO2)10/ 2 4 /2011 14 DRAFT

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### Table 3

- 6

. Existing Source MACT Floor and Variability Analysis Results

for Furnace Formaldehyde Formaldehyde (Furnace) Total Facility Count 2 # Facilities

Ave Top 2

- All Runs 67.02 (μg/dscm @ 2% CO2)

Min Run in Top 2 41.87 (μg/dscm @ 2% CO2)

Max Run in Top 2 91.01 (μg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs
Sample Size (n) 6 # of runs
Skewness (S)

- 0.17 Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES

- 0.17

normal S/SES<2
Kurtosis (K) 0.81
Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values) K/SEK 0.40

normal K/SEK<2

99% UPL (normal distribution) 106.21 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 67

3x RDL 201



### Table 3

- 7

. Existing Source MACT Floor and Variability Analysis Results

for Product Sizing PM

Total PM ( Product Sizing ) Total Facility Count 2 # Facilities

Ave Top 2

- All Runs 4.87 (mg/dscm)

Min Run in Top 2 2.21 (mg/dscm)

Max Run in Top 2 10.03 (mg/dscm)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.99
Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES 0.99

normal S/SES<2

Kurtosis (K)

- 0.85

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values) K/SEK

- 0.43

normal K/SEK<2

99% UPL (normal distribution) 12.68 (mg/dscm)10/ 2 4 /2011 15 DRAFT

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### Table 3

- 8

. Existing Source MACT Floor and Variability Analysis Results

for MOR PM

Total PM ( MOR ) Total Facility Count 1 # Facilities

Top 1 1 (mg/dscm @ 2% CO2)

Ave All Runs 1.10 (mg/dscm @ 2% CO2)

Min Run 0.61 (mg/dscm @ 2% CO2)

Max Run 1.60 # of runs

Run Count 3 # of runs Skewness (S) 0.13
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES 0.10

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 3.9 (mg/dscm @ 2% CO2)



### 3.4.2 New Sources

New source limits were determined by selecting the top performing sourc e . A variability analysis similar

to that used for existing sources was then performed to calculate a 99% UPL using the three run test data

from the top source. As described in section 3.4.1, b ecause only one facility provided useful test data for

furnace PAH (the other provided PAH data based on analysis of baghouse dust), the calculated new

source UPL for PAH is the same as f or existing sources. Because only a single facility performs MOR

operations, the new source UPL is calculated based on the three test runs from that facility, and is the

same as for existing sources. As described in Section 1, as MACT has already been est ablished for PM

from crushing and screening operations , we conducted a technology review for those emissions and

adopted the same limit for new and existing crushing and screening sources

.

The 99 percent UPL calculated for HCl , and formaldehyde based on the best

- performing source is less

stringent than the corresponding MACT floor for existing furnaces based on the 99 percent UPL

calculated for both sources . We determined that the use of this method is not appropriate in this situation

because the high variability and small data pool would result in a new source MACT floor limit that is

less stringent than the limit based on the UPL calculated from the larger data pool for existing sources.

Accordingly, in these cases, the 99 percent UPL based on bo th sources was used for our analyses.10/ 2 4 /2011 16 DRAFT

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### As described in section 3.3 , because of data reported below the detection limit, three times the RDL for

HC l , and formaldehyde was compared to the calculated UPL. For HCl, the calculated UPL was higher

than three time s the RDL, and thus was selected as the new source MACT floor. For formaldehyde, the

calculated UPL was less than three times the RDL, and thus three times the RDL was selected as the

new source MACT floor.

Results of the new source variability analyses are presented in Tables 3

- 9 through 3

- 1 5

.

Table 3

- 9

. New Source MACT Floor and Variability Analysis Results for Furnace PM Total PM (Furnace) Total Facility Count 2 # Facilities

Ave Top 1

- All Runs 2.38 (mg/dscm @ 2% CO2)

Min Run in Top 1 1.20 (mg/dscm @ 2% CO2)

Max Run in Top 1 3.62 (mg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) 0.23
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES 0.16

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 23.44 (mg/dscm @ 2% CO2)10/ 2 4 /2011 17 DRAFT

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### Table 3

- 1 0

. New Source MACT Floor and Variability Analysis Results for Furnace HCl

HCl (Furnace) Total Facility Count 2 # Facilities

Ave Top 1

- All Runs 469.41 (μg/dscm @ 2% CO2)

Min Run in Top 1 341.34 (μg/dscm @ 2% CO2)

Max Run in Top 1 659.26 (μg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs
Sample Size (n) 3 # of runs

Skewness (S) 1.43 Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES 1.01

normal S/SES<2
Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 1423.18 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 60 μg/dscm

3x RDL 180 μg/dscm



### Table 3

- 1 1

. New Source MACT Floor and Variability Analysis Results for Furnace Hg

Hg (Furnace) Total Facility Count 2 # Facilities

Ave Top 1

- All Runs 3.12 ( μ g/dscm @ 2% CO2)

Min Run in Top 1 0.52 ( μ g/dscm @ 2% CO2)

Max Run in Top 1 4.77 ( μ g/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S)

- 1.55
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES

- 1.09

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 16.06 ( μ g/dscm @ 2% CO2)10/ 2 4 /2011 18 DRAFT

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### Table 3

- 1 2

. New Source MACT Floor and Variability Analysis Results for Furnace PAH

PAH (Furnace) Total Facility Count 2 # Facilities

Top 1 1 # Facilities

Ave Top 1

- All Runs 402.94 ( μ g/dscm @ 2% CO2)

Min Run in Top 1 305.00 ( μ g/dscm @ 2% CO2)

Max Run in Top 1 458.82 ( μ g/dscm @ 2% CO2)

Top 2 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S)

- 1.68
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES

- 1.19

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 886.87 ( μ g/dscm @ 2% CO2)

Note: Because only one useful data set was available, the calculated new source UPL for PAH is the same

as for existing sources.



### T able 3

- 1 3

. New Source MACT Floor and Variability Analysis Results

for Furnace Formaldehyde Formaldehyde (Furnace) Total Facility Count 2 # Facilities

Ave Top 1

- All Runs 56.09 (μg/dscm @ 2% CO2)

Min Run in Top 1 41.87 (μg/dscm @ 2% CO2)

Max Run in Top 1 68.80 (μg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs
Sample Size (n) 3 # of runs
Skewness (S)

- 0.49 Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES

- 0.35

normal S/SES<2
Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 133.00 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 67

3x RDL 20110/ 2 4 /2011 19 DRAFT

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### Table 3

- 1 4

. New Source MACT Floor and Variability Analysis Results

for Product Sizing PM

Total PM ( Product Sizing ) Total Facility Count 2 # Facilities

Ave Top 1

- All Runs 2.41 (mg/dscm)

Min Run in Top 1 2.21 (mg/dscm)

Max Run in Top 1 2.69 (mg/dscm)

Top 2 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) 1.26
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES 0.89

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 3.83 (mg/dscm)



### Table 3

- 15

. New Source MACT Floor and Variability Analysis Results

for MOR PM

Total PM ( MOR ) Total Facility Count 1 # Facilities

Top 1 1 (mg/dscm @ 2% CO2)

Ave All Runs 1.10 (mg/dscm @ 2% CO2)

Min Run 0.61 (mg/dscm @ 2% CO2)

Max Run 1.60 # of runs

Run Count 3 # of runs

Skewness (S) 0.13
Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES 0.10

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values) K/SEK n/a

99% UPL (normal distribution) 3.9 (mg/dscm @ 2% CO2)

Note: Because only one facility performs MOR operations , the calculated new source UPL for PM from MOR is the

same as for existing sources.10/ 2 4 /2011 20 DRAFT

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### 4.0 BEYOND THE FLOOR

To evaluate opportunities for emissions reductions beyond those that would result from requiring

facilities to meet the MACT floor emissions limits, we typically identify control techniques that have the

ability to achieve an emissions limit more stringent than the MACT floor and evaluate whether the

standard should be more stringent based on consideration of the factors enumerated in section 112 (d)(2)

of the Act. The following paragraphs summarize the results of this “beyond

- the

- floor” analysis for the

af fected sources in the Ferroalloys production source category.

Through our technology review and risk analyses , we identified beyond

- the

- floor option s to achieve

further reduction in emissions of m ercury , PAH, acid gases , and formaldehyde

. Additional detail on

these options is presented below and in Cost Impacts for the Ferroalloys Source Category

.

Mercury

For mercury, t he beyond

- the

- floor option we considered is based on setting a MACT limit for existing

sources based on the performance of the best performing source ( i.e. , based upon the test data used to

calculate the MACT floor for new sources) such that the MACT limit for existing sources would be the

same as the MACT limit for new sources

. Compliance would be demonstrated by periodic performance

testing and continuous parameter monitoring. We estimate that under this beyond the floor option, one

facility would need to install additional controls such as activated carbon injection to meet th e limit of

16 ug/dscm, and that this would achieve about 420 pounds reduction in mercury emissions. The capitol

costs are estimated to be $1.7 million, annual costs to be $1.4 million, with an overall cost

- effectiveness

of $3,300 per pound. Overall, we bel ieve these controls are feasible and cost

- effective.

PAH

For PAH emissions, we also evaluated contro l performance that could be achieved via co

- control of

mercury emissions with activated carbon injection as a beyond

- the

- floor option. Based on information

from carbon vendors, an activated carbon system that is designed to achieve a 90 percent reduction i n

mercury emissions (which we believe would be applied to meet the proposed mercury standard

discussed above) should also achieve a high degree of reduction in PAH with no additional costs.

HCl

No facilities in the source category use add

- on control devices or work practices to limit emissions of10/ 2 4 /2011 21 DRAFT

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### HCl beyond what is normally achieved as co

- control of the emissions with particulate matter control

device. There are available add

- on control devices, such as dry sorbent injection (DSI) or lime injection

that is believed to be a viable control technology to reduce HCl emissions from furnaces. The DSI

control system works by injecting a sodium

- based powder such as, Trona (Na 2 

### CO 3 

### ) or sodium

bicarbonate (NaHCO 3 

### ) into the flue gas duct upstream of a baghouse used for particulate control. For

the acid gases, th e NaHCO 3 

### or Na 2 

### CO 3 

### reacts with HCl to form sodium chloride (NaCl), carbon dioxide

(CO 2 

### ) and water (H 2 

### O). The NaCl is a solid which can be removed from the exhaust stream using a

particulate control device. This technology is currently used to reduce acid gas emissions from coal

fired boilers and achieves up 90 percent reduction of HCl.

The capital cost for DSI is estimated to be $10 per kilowatt for coal

- fired boilers 2 

### . Converting this cost

to an EAF produces an estimated capital cost of $625,000 for a 2 5 megawatt EAF 3 

### . However, the major

cost of DSI systems is the sorbent 4 

### . The DSI control technology has a relatively high operating cost,

which ranges from $3.4 to $5 per kilowatt

- year ($/kW

- yr) 5 

### , which averages to a cost of $263,000 a year

for a 25 mega watt EAF. In addition, there are currently only a few suppliers of Trona, which is

primarily mined in the western United States. Therefore, transporting the supply of Trona to facilities in

the eastern United States could increase the delivered cost of T rona from $150 per ton to approximately

$200 per ton.

The use of sodium

- based DSI also affects the marketability and beneficial use of baghouse fly ash,

which is currently used in the concrete, wallboard and asphalt products industries as well as road an d

bridge construction. This is because sodium

- based dry sorbents are soluble and contribute greatly to the

available alkali content. As a result, fly ash produced with emissions controls systems employing

sodium

- based DSI technologies exceed the availabl e alkali criteria established by the American

Association of State Highway and Transportation Officials for concrete. Therefore, the collected fly ash

from DSI technologies would need to be disposed of in a landfill, and would prevent the current practice

of reusing or selling baghouse dust and the resulting waste reduction benefits.

2 URS, Assessment of Technology Options Available to Achieve Reductions of Hazardous Air Pollutants, April 5, 2011.

3 Assumes 40 percent efficiency f or the coal

- fired boiler.

4 Sargent & Lundy, IPM Model

– Revisions to Cost and Performance for APC Technologies, Dry Sorbent Injection Cost Development Methodology Final, August 2010.

5 URS, Assessment of Technology Options Available to Achieve Reductions of Hazardous Air Pollutants, April 5, 2011.10/ 2 4 /2011 22 DRAFT

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### Formaldehyde

We have not identified any appropriate beyond

- the

- floor control technology options specifically for

formaldehyde. Control technologies used to reduce formaldehyde emissions include thermal incineration

and catalytic oxidation. However, these control technolo gies are not technologically or economically

feasible for EAF emissions because of the high volume of exhaust gas that would need to be treated.

We recognize the potential for some co

- control of formaldehyde emissions that would be achieved by

using activ ated carbon injection to control mercury emissions, but we were unable to quantify those

reductions.

5.0 SUMMARY OF MACT FLOOR ANALYSIS

We determined emissions limits for each MACT floor option and pollutant by rounding the UPL values

up to t wo significant figures . It should be noted that if the UPL values were rounded down, then the

possibility exists that the best

- performing units that comprise the MACT floor may not be able to

achieve the emissions limit on an ongoing basis. In all cases, the rounding does not meaningfully change

the emissions limits. Tables 5

- 1 and 5

- 2 summarize the MACT floor limits calculated in this document.10/ 2 4 /2011 23 DRAFT

– Do Not Quote or Cite10/ 2 4 /2011 24 DRAFT

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