## Memorandum

To: Jaime Baggenstoss
From: Eric Edwalds Subject: SO 2 Statistics
Date: November 20, 2009 Project: 23/69

- 0B79.07

c: Erik Boleman

Hongming Jiang of the MPCA conducted a statistical analysis of the SO 2 CEMS data collected in Marc h 2008 to develop potential SO 2 emission limits for Arcelor Mittal Minorca‟s induration furnace stacks. The
statistical analysis is presented in the spreadsheet “SO 2 Data to Hongming”. The SO 2 emission limit developed from the CEMS data was 0.165 lb SO 2 / LT pellet. A potential 30

- day rolling average limit of
0.120 lb SO 2 / LT pellet was also presented.

Background

I spoke with Hongming to verify my understanding of his calculations. Hongming was asked by other
MPCA staff to provide technical assistanc e in the development of SO 2 emission limits based on the CEMS data. His analysis considered the form of a compliance test (i.e., the average of three 1

- hour runs) and a
30

- day rolling average (a common compliance limit for the utility industry).

Methodol ogy

The CEMS SO 2 data were collected from March 12

– March 22, 2008. Production data of long tons pellets produced per hour were provided for each hour of CEMS data used in the analysis . The CEMS data included both the hourly average emission rate (calc ulated as the average of 4 15

- minute readings)
and the „ on the hour ‟ single 15

- minute SO 2 emission rate. The statistical analysis was conducted only for
lb SO 2 / LT pellet for the emissions from all four stacks combined.

The statistical analysis con sisted of the following steps:

Barr Engineering Company 4700 West 77th Street  Minneapolis, MN 55435

- 4803 Phone: 952

- 832

- 2600  Fax: 952

- 832

- 2601  www.barr.com An EEO Employer

Minneapolis, MN  Hibbing , MN  Duluth, MN  Ann Arbor, MI  Jefferson City, MO  Bismarck, ND



### Request # 7

Document 7

- 3To: Jaime Baggenstoss

From: Eric Edwalds Subject: SO 2 Statistics Date: November 20, 2009 Project: 23/69

- 0B79.07 c: Erik Boleman

P: \ Duluth \ 23 MN \ 69 \ 23691284 114(a) Response Assistance \ WorkFiles \ 01 = 2012

- 04 BART 114 Request \ Response CD \ Folder 07 \ Document 7

- 3.doc  Compile summary statistic s and check data distribution

 Calculate statistical intervals

 Conduct resampling statistics

Summary Statistics and Data Distribution

Summary statistics (average, standard deviation, minimum, maxim um, count) are in cells K150:L154 in tab „Table 1‟ of the calculation spreadsheet . The summary statistics were very similar for the average hourly emissions and the „on the hour‟ emissions which show s that the emissions are generally stable for any given hour (although emissions will vary from hour to hour).

The lb SO 2 / LT data generally followed a normal distribution as shown on the probability plot and histogram in tab „Table 1‟. Therefore, additional statistical calculations were conducted without

tr ansformation and assuming a normal distribution (i.e., traditional statistics).

Statistical Intervals

Statistical intervals (e.g., confidence intervals, prediction limits) are commonly used statistics for representing the expected range of a population based on a s ample of the population. In tab „Table 1‟ (cells K150:L176) , 95% and 99% confidence intervals were calculated for the sample mean, for a future
stack test , and for a 30

- day rolling average. Confidence intervals for a future test are generally referred to as prediction limits, as they represent the ranges that a future sample should be within to be considered statistically similar to the original population. Based on the CEMS data, the 99% upper prediction limit for
a future stack test is 0.16 5 lb SO 2 / LT (cell L169) . Th i s upper prediction limit account s for the fact that a
stack test is an average of three 1

- hour runs

. Similarly, the 99% upper prediction limit for a 30

- day rolling

average of 0.125 lb SO 2 / LT (cell L170) represents the exp ected upper limit for the average of 9 0 future samples (i.e., 30 future stack tests of 3 runs per test)

.

Resampling StatisticsTo: Jaime Baggenstoss

From: Eric Edwalds Subject: SO 2 Statistics Date: November 20, 2009 Project: 23/69

- 0B79.07 c: Erik Boleman

P: \ Duluth \ 23 MN \ 69 \ 23691284 114(a) Response Assistance \ WorkFiles \ 01 = 2012

- 04 BART 114 Request \ Response CD \ Folder 07 \ Document 7

- 3.doc R esampling statistics (also known as Jackknife, Bootstrapping, or Monte Carlo simulation) use the
number

- crunching power of co mputers to create multiple data sets from the original sample data set and then compute statistics based on the simulated data sets. The resampling statistics shown in „ Simulated
Stack Test Results ‟ used the bootstrap method and were conducted as a check on the results of the confidence interval statistics, not as a substitute for them. In this case, Hongming used resampling
statistics to generate a one

- year data set of one stack test per day to evaluate the likelihood of a stack test exceeding 0.165 lb S O 2 / LT and to estimate the expected variability of a 30

- day rolling average. The resampling statistics showed 0.8% of the simulated daily stack tests exceeded the prediction limit of 0.165
lb SO 2 / LT

– which is essentially the same likelihood of a futur e „in

- control‟ stack test exceeding a 99% prediction limit

. The 30

- day rolling average from the resampling statistics showed a slightly lower limit of
0.120 lb SO 2 / LT as compared to 0.125 lb SO 2 / LT using the confidence interval methodology. The 30
da y rolling average result highlights the fact that 146 data points (from the CEMS data) is a relatively small sample size for longer term predictions.

Discussion

Given the objective of developing an SO 2 limit based on the SO 2 CEMS data, Hongming‟s statist ical approach of using a 99% upper prediction limit to set a n emission limit i s str aightforward and reasonable. However, the underlying assumption of setting the limit is that the CEMS data set represents the expected
„in

- control‟ variability of SO 2 emiss ions during normal process operations

.

The primary concern for the facility regarding this emission limit is understanding the representative ness of
the SO 2 CEMS test as a predictor of future process variability. Because the ore is the primary source of
sulfur and therefore SO 2 emissions (natural gas combustion contributes negligible amounts of SO 2 emissions) , the question s to answer are 1) what was the sul fur content of the ore during the CEMS test ? , and 2) what is the e xpected range of sulfur content in the ore ?

The fact that the MPCA is willing to set a limit based on the CEMS data means that the MPCA agrees that
the process was „in

- control‟ during the test and that emissions were acceptable

. Apart from determining
the variability in sulfur content of the ore, another consideration for the facility is to assess the representative ness of other test conditions as compared to overall „in

- control‟ processes and their potentialTo: Jaime Baggenstoss

From: Eric Edwalds Subject: SO 2 Statistics Date: November 20, 2009 Project: 23/69

- 0B79.07 c: Erik Boleman

P: \ Duluth \ 23 MN \ 69 \ 23691284 114(a) Response Assistance \ WorkFiles \ 01 = 2012

- 04 BART 114 Request \ Response CD \ Folder 07 \ Document 7

- 3.doc effects on SO 2 emissions

. For example, would the SO 2 emissions be expected t o vary based on other factors such as kiln temperatures, process consumables, fuel , ambient conditions , etc., such that the CEMS test did not adequately capture the variability of SO 2 emissions across the range of „in

- control‟ operations.

A dditional C o mments:

The analysis was conducted for lb SO 2 / LT pellet. By developing a limit based on an emission factor instead of emission rate (lb SO 2 / hr), there is an underlying assumption that emissions are directly correlated to production. This assumption should be valid providing the ore is the source of sulfur emissions. However, th e statistical analysis draws data from two separate sources (testi ng and production data), and the analysis did not consider the uncertainties around these variables independ ently. For example, is the measurement of LT pellets / hour con stant or does it vary over time? In the case of this CEMS test, what were the measurement uncertainties i n the reported LT pellet / hour?

Another way to analyze SO 2 emissions would be to conduct a sulfur mass balance S ore = S pellet + S tailings + S emitted , although in general the mass balance approach has too much uncertainty in each number to draw solid conclusions. However, the mass balance approach could be useful if the amount of sulf ur in each stream is a function of process conditions. For instance, it could be true that SO 2 emissions may not be too sensitive to the sulfur content in the ore, but instead the sulfur content of the tailings or pellets would change.

The BART visibil ity modeling analysis is evaluated on a 24

- hour basis , i.e., the model determines the number of days over visibility thresholds. The production based limit of lb SO 2 / LT does not have a daily component, although daily production may have been factored in to the limit if the production rate during the CEMS test was at the nominal capacity level of the facility.