1 

### MEMORANDUM
Date: November 2011

To: Brian Shrager, US EPA

From: Eastern Research Group, Inc.

Subject: CO CEMS MACT Floor Analysis (November 2011) for the Industrial, Commercial, and Institutional Boilers and Pr ocess Heaters National Emission Standards for Hazardous Air Pollutants

– Major Source
This memorandum describes the rationale, available data, and methodology for the proposed alternative compliance option of a CO continuous emissions monitor (CEMS) MACT
f loor emission limit . This alternative is provided to increase compliance flexibility for boilers which already employ CEMS or may need to install CEMS for other regulations and it also provides flexibility for load

- swinging units.
The memorandum is organi zed as follows:

1.0 Background
2.0 CEMS MACT Floor Calculation Methodology

– Existing Source
2.1 Determining Best Performers
2.2 U pper P rediction L imit Calculation
3.0 CEMS MACT Floor Calculation Methodology

- New Source
4.0 Available CEMS Data
5.0 CO CEMS MACT Floor Results
5.1 Existing Source MACT Floors
5.2 New Source MACT Floors
6 .0 Comparison of CEMS Data to Stack Test Data
7.0 Alternative MACT Floor Calculation Methodology
8.0 References

Appendix A: CO CEMS Subcategory Rankings and Raw Data2 

### Appendix B: CO CEMS Existing Source UPL Calculations Appendix C: CO CEMS New Source UPL Calculations

1.0 BACKGROUND
For the Boiler MACT rule reconsideration proposal, a MACT floor analysis using the available CO CEMS data was performed in response to requests from petitioners. While an inordinate amount of data is not available, there is enough data available to consider and analyze the options for most of the subcategories outlined in the proposal. It is noted, however, that additional data would provide a more reliable and accurate picture of long

- term CO emissions from best performing units. Given the limited dataset available in this proposal, the MACT floor was computed using two methods, as discussed in Sections 2 and 7 of this memorandum.

2.0 CEMS MACT FL OOR CALCULATION METHODOLOGY

– EXISTING SOURCE

2

. 1 DETERMINING BEST PERFORMERS
For existing sources, MACT cannot be less stringent than the average emission limitation achieved by the best performing 12 percent of existing sources (for which emission information is available) for categories and subcategories with 30 or more sources o r the best performing five sources for subcategories with less than 30 sources. The size of the subcategory was determined according to the number of units in the boiler and process heater inventory, not the number of units within each subcategory that ha d emission data available.

To identify the universe of best performing units in each subcategory, we first looked at all boilers which were best performers based on their CO stack test results. These were compared to the available units which had correspo nding CO CEMS data; however, there was very little overlap in the number of best performing units that had both CO stack test and CO CEMS data available. After comparing the data, only five subcategories had units with both types of data. Given such limited overlap, we opted to select CO CEMS best performers by ranking each unit according to its minimum 10

- day rolling average CO CEMS value

. F or the
five subcategories where we have CEMS data for units that are part of the stack test

- based3 

### MACT floors, we also included the CEMS data from those units in the CEMS

- based floors because those units are demonstrated best performers for CO . There are two exceptions to ranking by the 10

- day minimum rolling av erage, which are discussed below. Within each subcategory, the minimum 10

- day rolling averages were ranked from lowest to highest to determine the best performing 12 percent. If the number of units in the subcategory was greater than 30, the best performing 12 percent wa s determined by multiplying the number of units in each subcategory with CEMS data by 12 percent and rounding up to the nearest whole integer. Given the limited dataset, the top 12 percent represented a single combustion unit for all of the subcategories with greater than 30 units, except for wet biomass stoker, where the top 12 percent represented two units. If the number of units in the subcategory was less than 30, the units with
the five lowest minimum 10

- day rolling averages were identified as best p erformers; however, in these instances most subcategories only had a single unit with CO CEMS data, with the exception of the biomass fluidized bed subcategory which had two units with CO CEMS data.

For the non

- continental liquid and light liquid subcate gories there was insufficient data to
calculate 10

- day rolling averages. Based on the available non

- continental liquid data the averaging basis selected was 3

- hours; only one non

- continental liquid boiler submitted CO CEMS data, with a total of 24 hourly averages. For light liquid boilers, a one

- day average was selected based on the available data. Six light liquid boilers had CEMS data but they each have only four or eight daily average values. Further
, two of these units had CEMS data based on co

firi ng natural gas and liquid fuel
, and those data were not used in the analysis since the contribution of the gaseous fuel is unknown and the MACT floor data eligibility criteria for cases where a single unit serves as the basis of the floor calculation r equire that the data must be based on firing 100 percent of a given fuel category. The ranking of the dataset can be seen in Appendix A.

2

. 2 UPL CALCULATION
With the best performing 12 percent for each subcategory determined, a 99 percent upper predictive limit (UPL) was calculated for each subcategory

. This is the same level of confidence
selected for the stack test

- based emission limits. First, it was determined if each CO CEMS dataset was best characterized by a normal or lognormal dis tribution. To evaluate the distribution4 

### of the best performing dataset, we computed the skewness and kurtosis statistics and then
conducted the appropriate small

- sample hypothesis tests.

The skewness statistic (S) characterizes the degree of asymmetry o f a given data distribution. Normally distributed data have a skewness of 0. A skewness statistic that is greater (less) than 0 indicates that the data are asymmetrically distributed with a right (left) tail extending toward positive (negative) values. Fur ther, the standard error of the skewness statistic (SES) is given by SES = SQRT(6/N) where N is the sample size. According to the small sample skewness hypothesis test, if the skewness statistic (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 normal distribution. Normally distributed data have a kurtosis of 0. A kurtosis statistic that is g reater (less) than 0 indicates a relatively peaked (flat) distribution. Further, the standard error of the kurtosis statistic (SEK) is given by SEK = SQRT(24/N) where N is the sample size. According to the small sample kurtosis hypothesis test, if K is gre ater than two times the SEK, the data distribution is typically considered to be non

normal.

We applied the skewness and kurtosis hypothesis tests to both the reported daily average
CO CEMS values and the natural log of the reported daily average CO CEMS values. If the S and K statistics of the reported dataset were both less than twice the SES and SEK, respectively, the dataset 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, then the skewness and kurtosis hypothesis tests were conducted for the natural log

- transformed data. Then the distribution most similar to a normal distribution was selected as the basis for calculating the
UPL. If both the r eported values and the natural log

- transformed reported values had S and K statistics that were greater than twice the SES or SEK, respectively, the lognormally distributed dataset was selected as the basis of the floor, consistent with guidelines from EPA Guidance document, Data Quality Assessment: Statistical Methods for Practitioners EPA QA/G

- 9S which notes that a lognormal distribution is a commonly used distribution for most environmental
contaminant data. 1 

### This guidance document also notes that the l ognormal is an important probability distribution when analyzing environmental data where normality cannot be assumed.5 

### If the results of the skewness and kurtosis hypothesis tests were mixed for the reported values and the natural log

- transformed reported values, or if the dataset was too small to apply skewness or kurtosis tests, we also chose the lognormal distribution, consistent with the guidance noted above. We believe this approach is more accurate and obtained more representative results than a

more simplistic normal distribution assumption.

For datasets that were determined to be normally distributed, the UPL is computed using
a student’s t

- test statistic. The t

- statistic is calculated using the following Excel equation:

t

- statistic = TINV(2*(1

- 0. 99),n

- 1)

Where: n = the number of daily averages in the best performing 12 percent (or hourly averages
for non

- continental liquid units)

Next, the mean and standard deviation of the CO CEMS data were calculated and the UPL calculated via the following equa tion:

Where: the mean of the daily averages for the best performing 12 percent (or hourly averages for non

- continental liquid units)

s 2 

### = the standard deviation of the daily averages in the best performing 12 percent (or hourly averages fo r non

- continental liquid units)

n = the number of daily averages in the best performing 12 percent (or hourly averages
for non

- continental liquid units)

m = the number of daily averages (or hourly averages for non

- continental units) in the compliance average; m = 10 for all subcategories based on 10

- day rolling averages;
m = 3 for non

- continental units which are based on a 3

- hour rolling average; and m = 1 for light liquid units which are based on a one

- day block average.

For datasets that were determi ned to be lognormally distributed, the same t

- statistic was applied, but a slightly different UPL equation was used.6 

### Where:

n = the number of daily averages in the best performing 12 percent (or hourly averages
for non

- continental liquid units)

m = the number of daily averages (or hourly averages for non

- continental units) in the compliance average; m = 10 for all subcategories based on 10

- day rolling averages;
m = 3 for non

- continental units which are based on a 3

- hour rolling average; and m
= 1 for li ght liquid units which are based on a one

- day average..

For each subcategory the analysis showed that the datasets were lognormally distributed. Given the rolling

- average compliance metric, many of the datasets also exhibit varying degrees
of autocorrelati on. Autocorrelation describes the correlation between values of the process at different points in time. 7,8,9 

### Although the UPL calculation is affected by autocorrelation, no adjustments were made to incorporate autocorrelation in this dataset since EPA i s aware of ongoing CEMS testing for many of the affected boilers and the dataset at this time is preliminary. Depending on the final compliance metric selected, EPA may adjust the dataset for the promulgated rule to better address autocorrelation.

3.0 CEMS MACT FLOOR CALCULATION METHODOLOGY

- NEW SOURCE
A similar methodology used to calculate the existing source UPL w as used to calculate the new source UPL. For the new source calculations, however, only data from the top performing unit in each subcategory were used in the analyses. Since most of the subcategories only have one unit in the top 12 percent this makes lit tle difference in the computed MACT floors. However, the top performing unit must be firing a fuel which is representative of and reasonable to obtain for the entire subcategory and not process specific nor based on co

- fired configurations . If a boiler w ere not firing a similar fuel available to others in the same subcategory, then the new source floor could be biased. This same exception is also used in the new source floors for the CO stack test MACT floors. In the case of fluidized bed units firing
bi omass, the third ranked unit, GATempleInlandRome (WF), is the unit with the lowest rolling average that is not firing process

- specific fuels. For the light liquid subcategory, the top two7 

### ranked facilities are co

- firing natural gas and are thus no t representative of light liquid fuel boilers; the third ranked facility is used for the UPL.

There was also one additional step for new sources to ensure that the emission limit for new sources is not less stringent than the emission limit for existing so urces in the same subcategory. If the UPL selected to represent the MACT floor value for new sources is less stringent than the emission limit selected to represent the MACT floor for existing sources, then the floor corresponding to the same subcategory for existing sources was used as the basis for the floor for new sources. Although the minimum average test run for the best performing source
resulted in the lowest 10

- day CEMS average, the 99 percent UPL limit incorporates variability. As the sample si ze

- in this case the number of hourly CEMS values

- gets smaller, the t

- statistic increases. When the sample size is small, and there is large variability in the CEMS values, the calculated limit using the UPL approach can be larger than the variability among a larger set of data in the best performing 12 percent.

4 .0 AVAILABLE CEMS DATA
The CEMS data was obtained from two Information Collection Requests (ICRs) approved under OMB Control No. 2060

- 0616: ICR numbers 2286.01 and 2286.03, as well as
additi onal voluntary data submittals. 2,3,4 ,5



### The ICR Phase I survey requested that owners submit CO CEM S data for the most recent 30 days of operation (i.e., thirty 24

- hour averages). Owners were asked to exclude data if they were from periods of start

- up, shutdown, or malfunctions (SSM) and submittal was not required
if the CEMs monitor was not installed f or regulatory purposes. Owners were also requested to submit a “typical” operating rate, which often represented the average rate for the 30

- day period, but it did not request daily load averages to correspond to the daily average CO CEM S emission

concent rations. Phase II of the ICR requested 30 days of CEMS data, including hourly data and the corresponding hourly heat input and steam output of the boiler

. The additional voluntary data submittals were provided as minute, hourly or daily averages.

Consistent with the data eligibility criteria in the stack

- test MACT floor analysis, when
de termining the applicable data for the CEMS analysis, the fuel fired during the CEMS analysis8 

### must meet a 90% heat input threshold to be considered for a subcategory. 6 

### While the fuel was indicated for the majority of the data, assumptions for three boilers were necessary as described
here:

1. GATempleInlandRome, WF: Used oil along with wood bark are listed for certain data, but the percentage of used oil contributing to the heat input is not provided in all instances. For those instances when the percentage o f used oil was provided it was always substantially less than 10 percent of total heat input, which would still allow the data to be considered in the biomass fluidized bed CEMS analysis.
It is a ssumed all CEMS data points for this unit meet the 90% thres hold for biomass since used oil is typically burned in such small quantities

. 2. USVIHovensa, H

- 4101A: #6 oil and refinery gas are listed as the fuels burned in the unit. However, there is no fuel data associated with the dates corresponding to the CO CEMS data. As this unit has a limited amount of data and is the only non

continental unit with data, it was assumed the boiler met the 90% fuel input
threshold criteria for #6 oil.
3. MITBSimonPowerPlant, Unit 4: Bituminous coal and natural gas are f ired and the percentage each contributed to the heat input are not provided for the dates associated with the CO CEMS monitoring data. However, other fuel data for this boiler in other portions of the Phase I ICR survey shows that natural gas is burned
in miniscule amounts. Therefore, it is assumed all CEMS data meets the 90%
threshold for bituminous coal.
Generally most sources that reported CEMS data had 30 days of data e ither reported as hourly or daily averages. Given this limited length of time, we selected a 10

- day rolling averaging period in order to allow us to compute multiple rolling averages from each source’s
dataset. Although a longer averaging time would have been preferable in order to account for dramatic load swings, there was simply not enough data available to analyze a longer averaging period. Where sources reported CEMS data on both an hourly and daily average basis we first
reduced the hourly data into daily averages

. The hourly data points for each boiler were sorted sequentially, grouped together by day, and then averaged to compute daily averages. Only the hours meeting the 90 percent fuel criteria were included in the daily averages. Next, we combined the two datasets, sorted the data in sequential calendar dat e order and compu ted a9 

### series of 10

- day rolling averages from each unit. The 10

- day rolling averages represent sequential, but not necessarily continuous, calendar dates. In some cases the data reported by the facility skipped certain days due to unit downtime or data asso ciated with SSM conditions. Table 1 shows the subcategories which have daily CO CEMS data available to calculate 10

- day rolling

averages

. A very limited amount of CEMS data was available from non

- continental liquid units, see
Table 2 , and a 10

- day rollin g average could not be computed. This data was provided at minute intervals and was converted to hourly averages. The minute average data points for each boiler were sorted sequentially, grouped together by hour, and then averaged to compute hourly

avera ges. Next, the data was sorted in sequential calendar dat e order and a series of 3

- hour rolling averages from each unit were calculated

. The data reported to EPA was not provided in
continuous hours so the 3

- hour rolling averages represent consecutive, bu t not continuous, rolling averages. Based on the data available it was determined that 3

- hour averages were an appropriate option for this subcategory. As mentioned above, all data from this unit were used, since the exact fuel percentages were unknown.
The data available for the light liquid subcategory is presented in Table 3. All data was provided as daily averages. Based on the limited data a daily average was selected as the compliance metric and used to calculate a UPL and identify the maximum ave rage; a multiple day rolling average would have too few data points to evaluate variability in the performance of
the unit. In all instances, the averaging times were selected as the best alternatives in light of the available dataset; longer averaging ti mes would not be practical to calculate an appropriate
MACT floor unless additional data is provided.

Table 1 . Available daily average CO CEMS data for each subcategory, to determine a 10

- day rolling average.

Fuel Category Combustor Design Number of Units in Subcategory Number of Units with Daily

Avg CO CEMS Data Number of Units In Top 12%

Coal FB 35 6 1 PC 190 3 1 Stoker/SlopedGrate/Other 391 6 1

Biomass Dutch Oven/Pile Burner 24 1 1 FB 24 3 3 Fuel Cell 14 a a10 Fuel Category Combustor Design Number of Units in Subcategory Number of Units with Daily

Avg CO CEMS Data Number of Units In Top 12%

Suspension Burner 47 1 1 Suspension/Grate 18 1 1 Wet Biomass Stoker/SlopedGrate/Other 305 14 4 (b) Dry Biomass Stoker/SlopedGrate/Other 74 a a Heavy Liquid N/A 320 2 1 Light Liquid N/A 581 a a Non

- Continental Liquid N/A 44 a a Process Gas N/A 129 a a

Note:

a. Data was not available to calculate a rolling average either on a daily or hourly basis

. b. Includes 2 units which were stack test best performers.

Table 2 . Available hourly average CO CEMS data, to determine a 3

- hr rolling average.

Fuel Category Combustor Design Number of Units in Subcategory Number of Units with Hourly Avg CO CEMS Data Number of Units In Top 12%

Non

- Continental Liquid N/A 44 1 1

Table 3 . Available hourly average CO CEMS data, to determine UPL on a one

- hour average basis.

Fuel Category Combustor Design Number of Units in Subcategory Number of Units with Hourly Avg CO CEMS Data Number of Units In Top 12% Light Liquid N/A 581 6 1



### 5 .0 CO CEMS MACT FLOOR RESULTS

5 .1 EXISTING SOURCE MACT FLOOR S

After the CO CEMS MACT floor calculations were performed for each subcategory the

same rounding technique used in the stack

- test based MACT floors emission limits was applied. Each final MACT floor was determined by rounding the limit up to two significant figures. Rounding up is appropriate for establishing emission standards that must be complied with at all11 

### times. The CO CEMS MACT floors for existing sources are summarized in Tables 4 t hrough 6. The rankings for each subcategory are shown in Appendix A and the UPL floor calculations for each subcategory are shown in Appendix B.

In reviewing Tables 1 through 3 above, note there are three subcategories which do not
have CO CEMS data avail able. While substituting a MACT floor from a different subcategory for the subcategories with no data was considered, ultimately it was determined that an alternative CO CEMS floor would not be provided for subcategories with no data. If other subcategor ies’ data were used to fill in incomplete subcategories, this data would not be representative of the applicable boilers operations and it would not be appropriate to subject boilers to a limit which is not based off representative data.

Table 4 . Summary of CO CEMS MACT floor limits for existing boilers and process heaters, 10

- day rolling average.

Fuel Category Combustor Design Category 99% UPL for 10

- Day Rolling Avg Rounded CO CEMS MACT Floor, 10

- Day Rolling Avg (ppm @ 3% O2) Coal FB 58.2 59 PC 27.6 28 Stoker/SlopedGrate/Other 33.2 34 Biomass Dutch Oven/Pile Burner 434 440 FB 176 180 Fuel Cell a a Suspension Burner 1333 1400 Suspension/Grate 723 730 Wet Biomass Stoker/SlopedGrate/Other 405 410 Dry Biomass Stoker/ SlopedGrate/Other a a Heavy Liquid N/A 18.0 18 Light Liquid N/A a a Non

- Continental Liquid N/A a a Process Gas N/A a a

Note:

a. No applicable CO CEMS data available for this subcategory12 Table 5

. Summary of CO CEMS MACT floor limits for non

- continental existing boilers and process heaters, 3

- hr rolling average.

Fuel Category Combustor Design Category 99% UPL for 3

- Hour Rolling Avg Rounded CO CEMS MACT Floor, 3

- Hour Rolling Avg (ppm @ 3% O2)

Non

- Continental Liquid N/A 90.5 91

Table 6 . Summary of CO CEMS MACT floor limits for light liquid existing boilers and process heater, 1

- hr average.

Fuel Category Combustor Design Category 99% UPL for 1

- Day Avg Rounded CO CEMS MACT Floor, 1

- Hour Avg (ppm @ 3% O2) Light Liquid N/A 59.9 60



### 5

. 2 NEW SOURCE MACT FLOOR S
The new source CO CEMS MACT floors are summarized in Tables 7 through 9 and the UPL calculations are provided in Appendix C.

Table 7 . Summary of CO CEMS MACT floor limits for new boilers and process heaters, 10

- day rolling average.

Fuel Category Combustor Design Category 99% UPL for 10

- day Rolling Avg Rounded CO CEMS MACT Floor, 10

- Day Rolling Avg (ppm @ 3% O2)

Coal FB 58.2 59 PC 27.6 28 Stoker/SlopedGrate/Other 33.2 34

Biomass Dutch Oven/Pile Burner 434 440 FB 174 180 Fuel Cell a a Suspension Burner 1333 1400 Suspension/Grate 723 730 Wet Biomass Stoker/SlopedGrate/Other 451 410 (b) Dry Biomass Stoker/SlopedGrate /Other a a Heavy Liquid N/A 18.0 18 Light Liquid N/A a a13 Non

- Continental Liquid N/A a a Process Gas N/A a a

Note s :

a. No applicable CO CEMS data available for this subcategory
b New source UPL is less stringent than existing source UPL so this value has defaulted to the corresponding existing source value.

Table 8 . Summary of CO CEMS MACT floor limits for new non - continental boilers and process heaters, 3

- hr rolling average.

Fuel Category Combustor Design Category 99% UPL for 3

- Hour Rolling Avg Rounded CO CEMS MACT Floor, 3

- Hour Rolling Avg (ppm @ 3% O2) Non

- Continental Liquid N/A 90.5 91

Table 9

. Summary of CO CEMS MACT floor limits for new light liquid boilers and process heaters, 1

- hr average.

Fuel Category Combustor Design Category 99% UPL for 1

- Day Avg Rounded CO CEMS MACT Floor, 3

- Hour Rolling Avg (ppm @ 3% O2) Light Liquid N/A 59.9 60



### 6 .0 COMPARISON OF CEMS DATA TO STACK TEST DATA
With separate sets of boilers in the CO stack test and CO CEMS datasets there is the chance of incongruent results between the two compliance options. This is due to the fact that ranking the CO CEMS dataset according to the minimum 10

- day rolling average does not correlate, due to the limited data, with the ranking s used to identify the best performers from
stack test data. To evaluate whether the CO CEMS MACT floor could relate with the CO stack
test MACT f loor , a comparison was made between the CEMS data and the computed stack test CO MACT floor for each subcategory. For each best performing unit in the CEMS analysis, a
minimum 3

- hour rolling average was calculated from the hourly CEMS data. A 3

- hour peri od14 

### was selected because stack tests are based on three 1

- hour averages. If hourly data were not available for a best performer, the minimum daily average was determined instead. See Table 10 for the results of this analysis, along with both the CO CEMS a nd CO stack test new and existing source floors for comparison. Each unit identified as a best performing unit in the CO CEMS analysis had at least one 3

- hour or daily CEMS average at or below the corresponding stack test CO MACT floor for the subcategory suggesting that the CEMS floors are reasonable. The fact that CO CEMS floors are lower or higher than stack test data shows the variability that does exist for CO over the range of a boiler’s operation.15 Table 10 . Comparison of CO CEMS floors versus CO stack test floors.

Fuel Category Combustor Design Boiler Subcategory Rank Minimum CEMS 3

- Hr Avg Minimum CEMS Daily Avg, If No Hourly Data is Available CO CEMS Existing Source

Floor CO CEMS New Source

Floor CO Stack Test Existing Source

Floor CO Stack Test New Source

Floor

Coal FB 1 No Data 1.2 59 59 56 17
PC 1 No Data 15.4 28 28 41 9 Stoker/SlopedGrate/Other 1 12.0

- 34 34 220 19

Biomass Dutch Oven/Pile Burner 1 No Data 11 2 440 440 810 810
FB 1 No Data 7.7 180 180 370 230
FB 2 0.02

- 180 180 370 230
FB 3 35.3

- 180 180 370 230 Fuel Cell N/A No Data No Data

- - 1500 210 Suspension Burner 1 9.5

- 14 00 14 00 58 58 Suspension/Grate 1 4.3

- 730 730 3900 1500

Wet Biomass Stoker/SlopedGrate/Other 1 0.1

- 410 410 790 590 Stoker/SlopedGrate/Other 2 No Data 131.3 410 410 790 590 Dry Biomass Stoker/SlopedGrate/Other N/A No Data No Data

- - 250 250 Heavy Liquid N/A 1 No Data 0.1 18 1 8 10 10 Light Liquid N/A N/A No Data No Data 60 60 7 3
Non

- Continental Liquid N/A 1 0.2

- 91 91 18 18
Process Gas N/A N/A No Data No Data

- - 4 4

Notes:

**•** Blue text indicates the CEMS data is lower than the corresponding CO stack test floor.

**•** Red text indicates the CEMS data is higher than the corresponding CO stack test floor.16 

### 7.0 ALTERNATIVE MACT FLOOR CACLCULATION METHODOLOGY
Given the limited datasets, a second method of calculating a MACT floor value was
evaluated. After the rankings were determined, the emissions of the top half (6%, or often the top single unit) of the best performing units was further analyzed. For each of these units, the rolling averages were calculated, as shown in section 2.1, and reviewed to identify the maximum rolling average. After the maximum rolling average was determined for each unit, the highest maximum rolling average from the top half of the best performers for each subcategory was selected as a MACT floor option. In the CEMS

- based compliance alternative, it is necessary for
MACT floors to be achieved by the best performers at all times, even during large CO swings ; the maximum rolling average value from the top half of the best performers is chosen to represent the performance of the average emission limitation achieved by the best performing 12 percent of existing sources (for which emission information is available)

. Also, since only one unit’s maximum rolling average is used for this option, the unit’s operation must be representative of the entire subcategory. Therefore, the unit selected must be firing a fuel which is reasonable to obtain for the entire subcategory and not process specific nor
based on co

- fired configurations; this exception is the same as for new sources. In the case of fluidized bed units firing biomass, the third ranked unit, GATempleInland Rome (WF), is the unit
with the lowest rolling average that is not firing process

- specific fuels. For the light liquid subcategory, the top two ranked facilities are co

- firing natural gas and are thus not representative of light liquid fuel boilers ; the third ranked facility is used for the maximum rolling average

. Appendix A

- 3 shows the maximum rolling average for the top 6 percent of units in each subcategory.

Tables 11 to 13 of this Section compare the highest rolling average from the top 6 perc ent of units with the calculated UPL. Tables 14 to 16 of this Section compare the highest rolling average from the top performing unit with the calculated UPL for new sources. Since the value must be achieved at all times, the calculated UPL emission limi ts were evaluated to determine whether or not the units in the top 6 percent of each subcategory were consistently meeting the limits. If not, the highest rolling average from the top 6 percent of units in each subcategory was
selected as an alternative M ACT floor. These tables also show how often the top 6 percent of each subcategory are meeting the calculated UPL.17 Table 11 . Summary of Alternative CO CEMS MACT floor limits for existing boilers and process heaters, 10

- day rolling average.

Fuel Category Combustor Design Category 99% UPL for 10

-

Day Rolling

Avg Percent of Rolling Avgs from Top 6% of Units Meeting UPL Maximum 10

- Day Rolling

Avg Rounded CO CEMS MACT Floor, 10

- Day

Rolling Avg (ppm @ 3% O2)

Coal FB 58.2 79.1% 77.5 78 PC 27.6 82.4% 34.3 35 Stoker/SlopedGrate/Other 33.2 100% 31.03 34

Biomass Dutch Oven/Pile Burner 434 100% 406.2 440 FB 176 89.8% 478 480 Fuel Cell a a a Suspension Burner 1333 71.4% 2249 2300 Suspension/Grate 723 76.7% 1356 1400 Wet Biomass Stoker/SlopedGrate/Other 405 75.2% 919 920 Dry Biomass Stoker/SlopedGrate/Other a a a a Heavy Liquid N/A 18.0 100% 10.1 18 Light Liquid N/A a a a a Non

- Continental Liquid N/A a a a a Process Gas N/A a a a a
Note s :

a. No applicable CO CEMS data available for this subcategory

- Cells highlighted blue designate the value chosen for the alternative MACT Floor.

Table 12 . Summary of Alternative CO CEMS MACT floor limits for non

- continental existing boilers and process heater s, 3

- hr rolling average.

Fuel Category Combustor Design Category 99% UPL for 3

- Hour Rolling Avg Percent of Rolling Avgs from Top 6% of Units Meeting UPL Maximum 3

- Hour Rolling

Avg Rounded CO CEMS MACT Floor, 3

- Hour Rolling Avg (ppm @ 3% O2)
Non

- Continental Liquid N/A 90.5 90.9% 117 120

Note:

- Cells highlighted blue designate the value chosen for the alternative MACT Floor.18 Table 13

. Summary of CO CEMS MACT floor limits for light liquid existing boilers and process heater, 1

- hr average.

Fuel Category Combustor Design Category 99% UPL for 1

- Day Avg Percent of Rolling Avgs from Top 6% of Units Meeting UPL Maximum 1

- Day Avg Rounded CO C EMS MACT Floor, 1

- Hour Avg (ppm @ 3% O2)
Light Liquid N/A 59.9 100% 50.5 60

Note:

- Cells highlighted blue designate the value chosen for the alternative MACT Floor.

Table 14 . Summary of Alternative CO CEMS MACT floor limits for new boilers and process heaters, 10

- day rolling average.

Fuel Category Combustor Design Category 99% UPL for 10

- day Rolling Avg Maximum 10

- D ay Rolling

Avg Rounded CO CEMS MACT Floor, 10

- Day Rolling Avg (ppm @ 3% O2)

Coal FB 58.2 77.5 78 PC 27.6 34.3 35 Stoker/ SlopedGrate/Other 33.2 31.0 34

Biomass Dutch Oven/Pile Burner 434 406 440 FB 174 478 480 Fuel Cell a a a Suspension Burner 1333 2249 2300 Suspension/Grate 723 1356 1400 Wet Biomass Stoker/SlopedGrate/Other 451 919 920 Dry Biomass Stoker/ SlopedGrate/Other a a a Heavy Liquid N/A 18.0 10.1 18 Light Liquid N/A a a a Non

- Continental Liquid N/A a a a Process Gas N/A a a a

Note s :

a. No applicable CO CEMS data available for this subcategory

- Cells highlighted blue designate the value chosen for the alternative MACT Floor.19 Table 15 . Summary of CO CEMS MACT floor limits for new non - continental boilers and process heaters, 3

- hr rolling average.

Fuel Category Combustor Design Category 99% UPL for 3

- Hour Rolling Avg Maximum 3

- Hour Rolling Avg Rounded CO CEMS MACT Floor, 3

- Hour

Rolling Avg (ppm @ 3% O2) Non

- Continental Liquid N/A 90.5 117 120

Note:

- Cells highlighted blue designate the value chosen for the alternative MACT Floor.

Table 16 Summary of Alternative CO CEMS MACT floor limits for new light liquid boilers and process heaters, 1

- hr average.

Fuel Category Combustor Design Category 99% UPL for 1

- Day Avg Maximum 1

- Day Avg Rounded CO CEMS MACT Floor, 3

- Hour Rolling Avg (ppm @ 3% O2) Light Liquid N/A 59.9 50.5 60

Note:

- Cells highlighted blue designate the value chosen for the alternative MACT Floor.20 

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2. Phase I ICR 2008 Combustion Survey. [ICR No. 2286.01]. OMB Control No. 2060

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3. Phase II ICR Test Plan Requesting Emission Testing from Boilers, Process Heaters, and CISWI. [ICR No. 2286.03]. OMB Control No. 2060

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5. Eastern Research Group, Inc

. Handling and Processing of Corrections and New Data in the
EPA ICR Databases

– Revised November 2011.
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Standards for Hazardous Air Pollutants

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