1 MEMORANDUM

TO : Andrew Bouchard and Brenda Shine, EPA

FROM : Aylin Sertkaya, Jason Renzaglia, and Mae Thomas, ERG

DATE : July 15, 2013

SUBJECT : Flare Performance Data: Summary of Peer Review Comments and Additional Data Analysis for Steam-Assisted Flares

1.0 INTRODUCTION This memorandum describes the additional anal yses that were conducted after April 2012
in order to establish the values for the steam-a ssisted flare operating limits (i.e., the lower flammability limit of combustion zone gas [LFLcz], net heating value of combustion zone gas [NHVcz], and total volumetric fraction of comb ustible components present in the combustion zone gas [Ccz]), that are being proposed to be added to the refinery NESHAP (40 CFR 63, Subpart CC and 40 CFR 63, Subpart UUU). In Apr il 2012, the EPA released a technical report entitled “Parameters for Properly Designed and Oper ated Flares” to a peer review panel (U.S. EPA, 2012). This technical report documented all of the EPA’s analyses and the flare test data evaluated up to that point. This memorandum supplements the discussion about LFLcz, NHVcz, and Ccz that are provided in the April 2012 t echnical report for steam-assisted flares and explains additional analyses performed, including any peer review comments that have informed these analyses.

This memorandum is organized as follows:

1.0 Introduction 2.0 Background 3.0 Peer Review Comments Related To The EPA’s Data Quality Criteria 4.0 PFTIR Raw Data Review And Data Validation Procedures 5.0 Statistical Analysis 6.0 References
Appendix A: Microsoft Excel Workbook C ontaining All PFTIR Raw Data And Results Of Conducting PFTIR Raw Data Review And Data Validation Procedures Appendix B: Microsoft Excel Workbook Cont aining Results Of Statistical Analysis Including Plots and Outliers www.erg.com2 2.0 BACKGROUND In April 2012, the EPA released a technical re port for peer review that discussed flare operation and performance observations made by ev aluating the available fl are testing reports. In response to EPA Office of Enforcement requests pursuant to section 114 of the Clean Air Act, flare testing was conducted using the Passive Fourier Transform Infrared (PFTIR) technique on
steam-assisted flares located at Marathon Petroleu m Company, Texas City, Texas (MPC TX), Marathon Petroleum Company, Detroit, Michigan (MPC Detroit), Flint Hills Resources, Port Arthur, Texas (FHR), Shell, Deer Park, Texa s (Shell), and INEOS ABS (USA), Addyston, Ohio (INEOS). Each facility provided the EPA the PFTIR raw data, composition of the flare vent gas, flow rate of the flare vent gas and steam, and th e overall results from each of the flare tests. In addition to these tests, the PFTIR technique was also used in a comprehensive flare study conducted by Texas Commission on Environmental Qu ality (TCEQ) and the University of Texas at Austin. The results from the TCEQ study are available on TCEQ’s Stakeholder Group: Flare
Task Force website 1 ; and TCEQ provided the EPA the PFTIR raw data.

The April 2012 technical report was a result of the analysis of these flare efficiency studies and flare performance test reports. To provide an objectiv e evaluation of the analysis, the EPA asked a third party (an organization not i nvolved with the development of the April 2012 technical report) to facilitate an ad hoc peer review process of the technical report. This third party established a balanced peer review panel of reviewers from outside the EPA. The panel of
reviewers consisted of individua ls that could be considered “technical combus tion experts” within four interest groups: the refinery indus try, industrial flare cons ultants, academia, and
environmental stakeholders.

EPA developed a charge statement with ten ch arge questions for the review panel. The peer reviewers were asked to perform a thorough review of the technical report and answer the charge questions to the extent possible, based on their technical expertise. The technical report, data set used to support the techni cal report, details of the peer re view process, charge questions, as well as comments received from the peer review process, were posted online to the

1 http://www.tceq.texas.gov/airquality/stationary -rules/stakeholder/flare_stakeholder.html3 Consolidated Petroleum Refinery Rulemaking Repository at the EPA's Technology Transfer
Network Air Toxics Web Site 2 . These documents are also included in the docket.

3.0 PEER REVIEW COMMENTS RELATED TO THE EPA’S DATA QUALITY CRITERIA One of the charge questions peer reviewers were asked was whether the criteria used to
exclude flare test run data from final analyses were appropriate or if the criteria could lead to inappropriate exclusions of rele vant data points. All of the pe er reviewers commented to some extent on this question. For the most part, review ers agreed with the cr iteria used for excluding test runs, but there were severa l peer reviewers that were co ncerned about the discussion in section 3.1.3 in the 2012 report that focused on 11 test runs. The discussi on of the 11 test runs
was meant to be a discussion of whether this data that did not fit the identified trend was related to issues in data variability, measurement e rror, or an indication that there were other unidentified factors affecting th e results that were not accounted for when using the LFLcz. A few of the peer reviewers saw this discussi on as additional criteria for excluding data and believed the analysis to be biased to support a proposed hypothesis because the criteria was not applied to all data (data that fit the identified trend and the 11 points that did not fit the identified trend). One peer reviewer asserted that it was necessary to conduct “...a broader data validation/data usability exercise for each data poin t that would consider the data point in detail”.

There were a few specific comments on the da ta acceptance criteria. One peer reviewer objected to removing data points with a combusti on efficiency of 0% and for data points when smoking occurred, stating that these data “...do se rve a purpose in the analysis by defining the current limits of acceptable practice of operati on.” Another peer reviewer questioned removing the single test runs in favor of the average of these test runs for the TCEQ data because it was not convincing that the single test runs should be considered duplicates.

In response to these comments, the data point s with 0% combustion efficiency, those test runs where smoke was observed, and single TCEQ test runs were added back in to the data set. In addition, as a result of the comments on the data acceptance criteria, all of the raw PFTIR data
from all test runs were more closely reviewed and broader da ta validation procedures were

2 http://www.epa.gov/airtoxics/petref.html4 instituted. Section 4.0 discusses the findings from this review and provides a summary of the data validation procedures that were instituted.

4.0 PFTIR RAW DATA REVIEW AND DATA VALIDATION PROCEDURES PFTIR is a remote sensing technique that uses thermal imaging to detect infrared radiation. A spectrometer is positi oned on the ground to view hot ga ses from the flare and detect the radiated spectra that are unique to each compound. All of the PFTIR flare testing was
conducted with the assistance of both Clean Air E ngineering, Inc. and Industrial Monitoring and Controls Corporation (IMACC). For each PFTIR fl are test, IMACC’s proprietary software was used to estimate individual component concentr ations in the flare plume. This proprietary software includes an algorithm to match the measured spectra with reference spectra to determine the appropriate com pound and its concentration.

The PFTIR raw data for each test site was provided to the EPA in separate Microsoft Excel workbooks. In general, each Micros oft Excel file contains minute-by-minute measurements of individual component concentra tions in the flare plume that were recorded during the test runs. It took a little over a minute to capture the concentration measurement; therefore, a test run often had a minute of blank data within a bl ock of several minutes. With the exception of TCEQ, minute-by-minute information on flare vent gas flow rate, molecular weight, and composition, and steam flow rates were also recorded and provided. For TCEQ, vent gas composition and flow rate data are only available as run averages; these averages were used to populate each timestamp corresponding to the run. To examine the data from each test site more closely, the PFTIR raw data and flare vent ga s and steam input data are compiled into one
Microsoft Excel workbook and associated wi th this memorandum as Appendix A.

In order to address the peer reviewer comments that the EPA received regarding the data quality criteria used in the Apr il 2012 technical report, the PFTI R raw data were reviewed and data validation procedures were established and implemented. This section describes the observations made during the data review and the criteria de veloped to address different
inconsistencies found in the PFTIR raw data. Because the combusti on efficiency is the primary result of the PFTIR testing, s ection 4.1 describes the method th at was used to calculate the combustion efficiency for all the test data. It is informative to keep the parameters used in this
calculation in mind when consid ering the PFTIR data observations described throughout this5 section. Section 4.2 describes the PFTIR observations that were ma de; and section 4.3 discusses
the standard set of criteria th at was applied to all PFTIR raw data to address all of these
observations.

4.1 Combustion Efficiency Calculation The PFTIR testing technique measures car bon dioxide, carbon monoxide, and other speciated organic compounds (the speciated orga nic compounds are referred collectively in this memorandum as hydrocarbons) in the plume of th e flare in order to calculate combustion efficiency. Equation 1 shows how combustion effici ency is determined using PFTIR raw data.



### CE	ൌ CO 2 CO 2 ൅CO൅ THC w Equation 1

Where: CE = flare combustion efficiency (%) CO 2 = carbon dioxide from PFTIR (ppm

• m) CO = carbon monoxide from PFTIR (ppm

• m) THC w = total hydrocarbons from PFTIR (ppm
• m) weighted by number of carbons in each speciated organic compound. Equation 2 provides an example of how THC w is calculated.

THC w ൌ	C CH 4 ൅2 ൫ C C 2 H 2 ൅ C C 2 H 4 ൅C C 2 H 6 ൯ ൅3 ൫ C C 3 H 6 ൅C C 3 H 8 ൯ ൅4 ൫ C C 4 H 6 ൅C C 4 H 8 ൅C C 4 H 10 ൅C C 4 H 10 ൯ ൅5 ሺ C C5൅ ሻ Equation 2

4.2 Observations The following observations were made about th e PFTIR raw data; these observations are
discussed in further detail in the subsections that follow. Some additional observations, those not requiring detailed explanations, are referred to in the discussion of the data criteria in
Section 4.3.

 One to three CO 2 concentration measurements per minute, depending on the test report, with each test report using a differe nt method to consider the multiple CO 2 concentration measurements. (refer to section 4.2.1)

 The number of chemical compounds measured a nd reported varied by test report. (refer to section 4.2.2)

 Some CO 2 concentration measurements were reported as equal to zero, or in excess of 1,000,000 parts per million meters (ppm

• m). (refer to section 4.2.3)6  Values for “CO 2 Error” were reported as well as error values for CO and other hydrocarbons (e.g., “C 2 H 6 Error”). (refer to section 4.2.4)

4.2.1 Multiple CO 2 Measurements As one can see looking at Equation 1, the CO 2 measurement is critical for calculating the combustion efficiency. In PFTIR testing, CO 2 can be detected and measured at three different wavelengths, including 765, 1000, and 2080 cm -1 . Not all PFTIR equipment has the ability to scan at all three wavelengths, so the different combinations of CO 2 concentration measurements
are a result of different wavele ngth detectors that were used dur ing each of the flare testing campaigns. Table 1 shows a summary of CO 2 wavelength measurements that were reported by test site. Specific procedures were not provi ded in the test report to describe which CO 2 concentration measurements were selected for the combustion efficiency calculations performed by each test program. However, based on a review of the test reports and the spreadsheets for each test, it was determined that one wavele ngth was selected to perform the combustion efficiency calculation for each test report. The se lected wavelength is indi cated in Table 1 using green shading. For example, in the TCEQ testing, CO 2 measurements were available at all three wavelengths, but only the CO 2 measurements detected at a wavelength of 765 cm -1 were used to calculate the combustion efficiency.

TABLE 1. SUMMARY OF CO 2 WAVELENGTH MEASUREMENTS BY TEST SITE
Test Site Wavelengths At Which CO 2 Measurements Were Taken 765 cm -1 1000 cm -1 2080 cm -1 MPC TX Yes No No INEOS No Yes Yes Shell EPF No (a) No (a) Yes MPC Detroit Yes Yes Yes TCEQ Yes Yes Yes FHR AU Yes Yes Yes FHR LOU Yes Yes Yes Green Shaded Cells = CO 2 measurement used in test report combustion efficiency calculation. (a) Test Series A through C only have CO 2 data scans for the 2080 cm -1 wavelength. For Test Series D, CO 2 data was recorded at 765 cm -1 and 2080 cm -1 wavelengths. However, only the CO 2 value at a 2080 cm -1 wavelength was used to calculate the combustion effi ciency for Test Series D.

No rationale was provided on why one CO 2 measurement was selected over another measurement and once the wavelength was selected the entire set of CO 2 measurements for that wavelength was used for the combustion efficiency calculations for every minute of the testing campaign and all other CO 2 measurements were ignored.7 4.2.2 Variation in Number of Chemical Com pounds Measured For at Each Test Site Different chemical compounds were measured and recorded at each of the flare test sites. This was at least partially caused by the differe nt detectors used duri ng the testing. Specific chemical compounds show up at known wavelengths exhibiting characteristic behavior at that wavelength. The size of the response is indicat ive of the concentration. Some chemical compounds, like CO 2 , can be found at multiple wavelengths, but some chemical compounds only have one wavelength where it can be measure d. Because detectors only cover a set range of wavelengths, compounds that show up at a wave length outside the det ectors range, cannot be detected (unless an additional detector with a di fferent detection range is used). Table 2 provides a summary of the chemical compounds anal yzed and reported for each flare test.

TABLE 2. SUMMARY OF CHEMIC AL COMPOUNDS ANALYZED FOR IN THE FLARE PLUME BY TEST SITE
Chemical Compound Formula MPC TX (9/2009) INEOS (11/2009) Shell EPF (3/2010) MPC Detroit (7/2010) TCEQ (9/2010) FHR AU (10/2010) FHR LOU (11/2010) Carbon Monoxide CO x x x x x x x

Carbon Dioxide CO2 x x x x x x x Methane CH4 x x x x x x x Ethylene C2H4 x x x x x x x C5+ Hydrocarbons C5+ x x x x x x x Acetylene C2H2 x x x

Ethane C2H6 x x Propylene C3H6 x x x x x Propane C3H8 x x x 1,3 Butadiene C4H6 x x x x

2-Butene C4H8 x x n-Butane C4H10 x x x x x Isobutane C4H10 x x TOTAL (a) 7 8 5 8 7 13 13 (a) – Total number of chemical compo unds analyzed for; and th e maximum number of chemical compounds used to determine combustion efficiency.

Looking at Table 1, it can be deduced that the TCEQ, MPC Detroit, and both Flint Hills
Resources’ tests had detectors th at covered the full range of wa velengths available, because CO 2 at all 3 wavelengths were detected during t hose tests. However, the number of chemical compounds measured and reported for those test s were 7, 8, and 13, respectively. (Note that a given minute of data may have fewer chemical compounds measured than the values shown in Table 2, because not all chemical compounds we re necessarily found for each minute. The values shown in Table 2 are those that were analyzed for.)8 The test reports do not provide informati on on how the specific analyzed chemical compounds were selected. All of th e tests, except for INEOS and TC EQ used flare vent gas that contained ranges of many chemical compounds. Table 2-2 of the April 2012 technical report (U.S. EPA 2012) shows the potenti al chemical compounds in the flare vent gas for each test. The flare vent gas in these tests contained a maximum of between 14 to 17 different combustible compounds. (The Shell ground flare te st is shown in Table 2-2 of the April 2012 technical report; and was tested with 20 combustible compounds in the flare vent gas. This te st is not discussed in this memo.) The flare vent gas tested in the INEOS and TCEQ test s contained up to two combustible chemical compounds. INEOS flare vent gas contained 1,3 butadiene and/or methane and the flare vent gas tested by TCEQ either contained propane, propylene, methane, or a combination of two of these, but never both prop ane and propylene. It seems unlikely that the chemical compounds measured in each test were the only chemical compounds emitted; the flare vent gases, except those used in the INEOS and TC EQ tests, were similar. Out of a total of 21 different chemical compounds used in all the fl are tests, 15 chemical compounds were included in three of the test campaigns. With the FHR tests reporting up to 13 chemical compounds when testing a similar flare vent gas, it seems unusua l that some of these chemical compounds would not have been present during the other multi-chemi cal flare vent gas tests that only reported between 5 to 8 chemical compounds.

If significant amounts of non-CO 2 chemical compounds are emitted from the flare and are not measured (and therefore not included in the combustion efficiency calculation), an inappropriate (and high) combustion efficiency will be calculated. As more chemical compounds
are included in the “THC w ” term of Equation 1, the denominat or in Equation 1 becomes larger and the combustion efficiency decreases. There are numerous chemical compounds that could be emitted from flares burning the gases tested; for ex ample Davis et al. (19 99) used 71 species to model the combustion kinetics of propylene. Ho wever, there are far fewer compounds that are produced at a high enough level to impact the calc ulated combustion efficiency. These chemical compounds that are the most critical for an accurate combustion efficiency calculation are dependent on the composition of the flare vent gas, amount of assist steam, configuration of the flare, and many other factors. In evaluating how the variation in chemical compounds tested by the test campaigns affected the combustion e fficiencies calculated, three analyses were conducted, including:9 1. Reviewing the chemical compounds predic ted based on kinetic studies for the simplest flare vent gas tested (i.e., fo r the TCEQ and INEOS flare vent gases)

2. Determining the change in combustion e fficiency for TCEQ when additional chemical compounds tested using an extr action method were incorporated in the combustion efficiency calculation with the concentrations measured during the PFTIR testing; and

3. Determining the effect on the combustion efficiency for the FHR test data when fewer than 13 chemical compounds are considered.

Kinetic Studies. Two scientific journal articles were reviewed, including: one regarding the kinetics of combusting 1,3-but adiene (Laskin et al. 2000) and the other for combusting propylene (Davis et al. 1999). Thes e studies included lab testing of laminar flames in a flow reactor, as well as discussion of previous pape rs that evaluated other conditions. Although these studies do not match the configuration of the flare tests being discussed in this memo, the studies provide some insight on what should be expect ed when burning a specific chemical compound. Based on these studies, the chemical compounds listed in Table 3 would be expected to be emitted when combusting propylene and 1,3-butadiene.

TABLE 3. PRODUCTS OF COMBUSTION FOR PROPYLENE AND 1,3-BUTADIENE Chemical Compounds Expect ed When Combusting Propylene (a) Chemical Compounds Expect ed When Combusting 1,3-Butadiene Carbon Monoxide ( b ) Carbon Monoxide ( d ) Ethylene ( b ),(c) Ethylene ( d ) Acetylene (c) Acetylene ( d )

Ethane (c) 1,3-Butadiene ( d ) Propylene ( b ),(c) SMALL AMOUNTS OF: Methane ( b ),(c) Methane ( d ) Formaldehyde (c) Ethane Ethenone Allene SMALL AMOUNTS OF: Propyne 1,3-Butadiene Propylene

Benzene Vinyl Acetylene 1-Butene (c) Benzene ---- Other C4 and C5 Species ( d )
(a) The kinetics technical paper (Davis et al. 1999) indicated that formaldehyde and ethenone were not looked for during the experiment, but the kinetic model predicted they would be formed at relatively high concentrations. An oxygenated chemical was detect ed during the experiment, but it was not identified specifically. The researchers speculated that it was likel y formaldehyde, acetaldehyde or acrole in. The researchers also noted that acrolein would be more likely to be present when combusting ultra lean fuels. (b) These chemical compounds were meas ured during the TCEQ study using PFTIR.10 (c) These chemical compounds were measured during the TCEQ study using an extraction method, as well as Acetaldehyde, Propanal, Acrolein, Meth anol, Acetone, and Propylene Oxide. (d) These chemical compounds were measured during the INEOS test using PFTIR. Although the generic C5 species were measured in the INEOS test only two C4 compounds were tested, which were 1,3-Butadiene and n-butane.
The INEOS test measured for all the main ch emical compounds that were expected to be produced during combustion of 1,3-butadiene, as de termined from the technical paper (Laskin et al. 2000) reviewed and shown in Table 3. The pred iction from review of the technical paper was also supported from the flare test results. Tabl e 4 provides the average, maximum, and minimum concentration values for each chemical compound tested in the INEOS data when 1,3-butadiene was the only combustible component in the flare vent gas. Table 4 also shows the count of timestamps that the maximum, minimum, and av erage were based on. There were 102 minutes of test data (timestamps) when only 1,3-butadiene was tested (inerts, st eam and nitrogen, were also present in the combustion zone); therefor e, carbon monoxide, acetylene, ethylene, and 1,3- butadiene were found in the vast majority of minutes. Methane, butane, and C5+ compounds, if found, were present at relatively significant levels but they were found in less than 18 % of the timestamps.

TABLE 4. SUMMARY OF TIMESTAMP CONCENT RATION RESULTS FOR IN EOS PFTIR TESTING OF 1,3-BUTADIENE AS THE ONLY COMBUSTIBLE IN FLARE VENT GAS (a)

Value Carbon Monoxide

(ppm) Methane (ppm) Acetylene (ppm) Butane (ppm) Ethylene (ppm) 1, 3- Butadiene

(ppm) C5+ Hydrocarbons (ppm) Average 31 19 7.0 20 8.2 62 33 Maximum 280 47 34 27 30 410 35 Minimum 1.2 8.0 0.23 14 0.35 0.78 32

Count 100 18 95 4 74 95 2 (a) Propylene was also tested for during the INEOS flare testing, but no propyl ene was found that had an error value less than 50% of the measured concentration; see section 4.2.4 for discussion of the error value.
If data from both the PFTIR and extraction te sting are considered, the TCEQ study tested all the chemical compounds that are predictabl e based on the information provided in the Davis et al. (1999) article, ex cept for Ethenone. In addition, the TC EQ extraction test also measured acetaldehyde, propanal, acrolein, methanol, acetone, and propylene oxide. The scientific article
indicated that acrolein would be important to consider fo r lean-fuel conditi ons, which many of the flare test runs seem to qualify for that designation. Acetaldehyde was not mentioned as a main chemical compound that should be expe cted, but an oxidized compound was found that researchers assumed were either formaldehyde, acetaldehyde, or acrolein. Therefore, it seems11 prudent to measure for these compounds. A lthough ethenone was pred icted by the kinetics model, it is very reactive a nd would likely dissociate into CO and other compounds quickly. There were only four TCEQ test runs that us ed only propylene as the combustion gas and that
had both PFTIR and extraction test data available. Table 5 provides a summary of these results. Because ethylene, propylene, and methane were all tested with both the PFTIR and extraction techniques, their results were used to develop ratios of the extract ion results to the PFTIR results.
These ratios were used to c onvert the concentrations for compounds tested using only the extraction method into PFTIR results. In some cases, average ratios were used to fill in data for methane, ethylene, or propylene when there was no PFTIR value reported, but an extraction
method result was available. Based on the four te st runs, the chemical compounds that appear to be the most significant are those predicted by the kinetic study: carbon monoxide, methane, ethylene, propylene, acetylene, ethane, form aldehyde, acetaldehyde, and acrolein. Although it is unclear with only four data points how importa nt it is to measure so me of the chemical compounds that were not identif ied at large concentrations (such as ethane, acrolein, acetaldehyde, and acetylene), these concentratio ns may not be sufficient enough to effect the combustion efficiency.

TABLE 5. SUMMARY OF RUN CONCENTRATION RE SULTS FOR TCEQ PFTIR AND EXTRACTION TESTING WHEN PROPYLENE IS THE ON LY COMBUSTIBLE IN FLARE VENT GAS (a) Chemical compound Average (ppm) Maximum (ppm) Minimum (ppm) Count
Carbon Monoxide 8.3 13 5.4 4 Methane 13 29 1.8 4 Ethylene 2.2 3.9 0.31 4 Propylene 32 74 5.3 4 Acetylene 1.3 2.7 0.20 4

Ethane 0.47 0.94 0.055 4 Butene Isomers 0.0090 0.025 0.0049 3 Formaldehyde 2.8 6.1 0.44 4 Acetaldehyde 1.2 2.7 0.19 4

Propanol 0.024 0.049 0.0028 4 Acrolein 0.75 1.6 0.12 4 Methanol 0.061 0.13 0.010 4 Acetone 0.034 0.073 0.0056 4 Propylene Oxide 0.042 0.097 0.0056 4 (a) C5+ hydrocarbons were also tested for during the TCEQ flare testing, but no C5+ was found that had an error value less than 50% of the measured concentra tion; see section 4.2.4 for discussion of the error value.12 Effect on Combustion Efficiency using bo th Extraction and PFTIR data for TCEQ

runs. Using the combination of the extraction and PFTIR data from the TCEQ dataset, the combustion efficiency was calculated using al l the chemical compound data available and compared to the combustion efficiency calculate d based on just the PFTIR data. Incorporating the additional compounds found with the extraction method into the combustion efficiency calculation, decreased the combustion efficiency by an average of 0.4 percentage points (e.g., from 97.2 to 96.8%) and by a maximum of 0.9 percen tage points. This is a relatively small change and, although adding in the extraction testing results does incorporate chemical compounds that were not identifie d in the PFTIR testing, the con centration values are relatively small and does not affect the combustion efficiency very much.

Effect on Combustion Efficiency using fewer chemical compounds on FHR data. As evident from Table 2, FHR analyzed for more com pounds than for any other test site. Therefore, the FHR combustion efficiencies are likely more accurate and lower than those in other tests because the FHR combustion efficiency calculati on incorporates the most number of compounds. Although, depending on how much was actually found of the a dditional chemical compounds, considering them in the combustion efficiency ca lculation may not have a significant effect. In
order to determine how much the calculated combustion efficiencies are affected by the additional chemical compounds, the FHR combustion efficiencies were r ecalculated using only the chemical compounds that were available in the MPC TX test program. Table 7 summaries
the differences in combustion efficiencies f ound. The largest difference found was 38 percentage points; and assuming that the MPC TX and FHR flare vent gases are similar enough to assume similar constituents in the flare plume, Table 7 indicates that 84% (i.e., 20+13+18+13+8+5+7) of the FHR timestamps would be altered if only th e seven chemical compounds tested during the MPC TX were tested during the FHR tests. Over half (51%) of the FHR timestamp combustion efficiencies would be increased by greater than one percentage point.

TABLE 7. DIFFERENCES IN CO MBUSTION EFFICIENCY FOR FHR USING ONLY MPC TX TESTED CHEMICAL COMPOUNDS. Difference Found in Combustion Efficiency (Percentage Points) Number of Timestamps Percent of Timestamps No Difference 245 16 0.5% and less 315 20 ≤ 1.0% and > 0.5% 213 13 ≤ 2.0% and > 1.0% 286 1813 TABLE 7. DIFFERENCES IN CO MBUSTION EFFICIENCY FOR FHR USING ONLY MPC TX TESTED CHEMICAL COMPOUNDS (Cont.) Difference Found in Combustion Efficiency (Percentage Points) Number of Timestamps Percent of Timestamps ≤ 3.0% and > 2.0% 212 13 ≤ 4.0% and > 3.0% 125 8 ≤ 5.0% and > 4.0% 83 5 > 5.0% 104 7 To illustrate, Figure 1 shows the combustion e fficiencies that were computed from the PFTIR raw data for all 15 valid timestamps (more information about ‘valid’ timestamps is discussed in section 4.3) of the FHR LOU-A 2.0(1) test run (red lin e in Figure 1). All of the FHR test runs analyzed for thir teen chemical compounds using the PFTIR technique (i.e., carbon monoxide, carbon dioxide, methane, ethylene, C5+ hydrocarbons, acetylene, ethane, propylene, propane, 1,3 butadiene, 2-butene, n-butane, and isobutane). The blue line in Figure 1 also shows the combustion efficiencies computed using onl y the chemical compounds examined in the MPC TX test runs (i.e., carbon monoxide, carbon di oxide, methane, ethylene, C5+ hydrocarbons, propylene, and n-butane). As evident in Figure 1, most of the timestamps would have computed combustion efficiencies higher us ing the fewer compounds analyzed for in the MPC TX test (a few had the same efficiency no matter whic h set of compounds we re considered).

Figure 1: Combustion Efficiency Compar ison Using FHR LOU- A 2.0(1) Test Run (valid timestamps shown)14 4.2.3 CO 2 Concentrations Not Reported, or Reported as Equal to Zero or Greater Than One Million Parts Per Million Meters Occasionally, a CO 2 concentration measurement for a timestamp was not reported, or was reported as equal to zero, or greater than one million parts per million meters. It was not clear in
the test results whether a bla nk or zero indicated: no CO 2 , whether it was a missed measurement, or whether it was a poor data point that purposely got zeroed out. Therefore, the measurement was considered invalid. Concentrations greater than one million parts per million meters is not physically possible; therefore, these measurements were also considered invalid.

4.2.4 Chemical Compound Error As discussed previously, for each PFTIR flar e test, IMACC’s proprietary software was used to estimate individual component concentr ations in the flare plume. This proprietary software includes an algorithm to match the measured spectra with reference spectra to determine the appropriate compound and its concentration. For each chemical compound measured, an “error” was reported. This reported “error” value describe s the fit between the reference spectra and the measured spectra. IMACC indicates that an error should not be greater than or equal to 50% of the concentration va lue; otherwise, it may suggest that the PFTIR measurement is inaccurate. This criterion was applied to all non-CO 2 chemical compound
concentration measurements such that those with an error in the fit between the reference spectra and the measured spectra greater than or equa l to 50% of the measur ed concentration were considered invalid measurements. However, because the CO 2 measurement is critical for calculating the combustion efficiency a nd often there were two or three CO 2 measurements for each timestamp, a stricter vali dation rule was used for CO 2 . CO 2 measurements were considered
invalid if the error in the fit between the refere nce spectra and the measured spectra was greater than or equal to 30% of the measured concentration.

4.3 Standardized Data Validation Procedure and Data Quality Criteria In order to uniformly evaluate all of the PFTIR test runs (and address peer review comments that the EPA had received regarding da ta exclusion procedures used in the April 2012 technical report), the EPA establ ished a standardized data vali dation procedure that could be applied to each timestamp of PFTIR raw data from each test site. The valid timestamps were used to establish average combustion efficiencies for each run; some additional validation15 procedures were used when the average run values were determine d. Table 8 provides a summary of the standardized da ta validation procedure (includi ng a description of each data quality criterion). The procedures described in Table 8 were applie d to the PFTIR raw data after release of the April 2012 technical report.

TABLE 8: STANDARDIZED DATA VALIDATION PROCEDURE AND DATA QUALITY CRITERIA CRITERIA NOTE
STEP 1: Determine if CO 2 concentrations reported at each wavelength are invalid by applying the following criteria:

[CO 2 ] Not Available Blank or zero CO 2 concentration measurements do not c ontribute to the combustion efficiency calculation; therefore, the CO 2 concentration is considered invalid.

[CO 2 ] ≥ 1,000,000 CO 2 concentration measurements greater than or equal to 100% (1 million parts per million meters) are not possible; therefore, the CO 2 concentration is considered invalid.

CO 2 Error Values

≥ 30% Of Measured Concentration CO 2 error describes the fit between the refe rence spectra and the measured spectra.

CO 2 concentration measurements with an error in the fit between the reference spectra and the measured spectra that is greater than or equa l to 30% is considered not a close enough fit; therefore, the CO 2 concentration is considered invalid.

STEP 2: Determine if other non-CO 2 chemical compound concentrations are in valid by applying the following criteria:

Non-CO 2 Compound Error Values

≥ 50% Of Measured Concentration Non-CO 2 compound error describes the fit between the reference spectra and the measured spectra.
A non-CO2 chemical compound concentration m easurement with an error in the fit between the reference spectra and the measured spectra that is greater than or equal to 50% is considered not a close eno ugh fit; therefore, the non-CO 2 chemical compound concentration is considered invalid.

STEP 3: Average remaining valid CO 2 concentrations by timestamp and determine if the timestamp is invalid by applying the following criteria:

[CO 2 ] ≥ 90 th Percentile CO 2 concentration measurements that are in the 90 th percentile based on a statistical analysis of the population of CO 2 measurements for the timestamps for a full test run were removed as outliers. For timestamps with multiple CO 2 measurements (those at different wavelengths), the statistical analysis was done on the average of the different CO 2 measurements for each of the timestamps. As stated in Step 1, if CO 2 is blank or zero, the timestamp is considered invalid.16 TABLE 8: STANDARDIZED DATA VALIDATION PROCEDURE AND DATA QUALITY CRITERIA (Cont.) CRITERIA NOTE

[CO 2 ] ≤ 10 th Percentile CO 2 concentration measurements that are in the 10 th percentile based on a statistical analysis of the population of CO 2 measurements for the timestamps for the full test run were removed as outliers. For timestamps with multiple CO 2 measurements (those at different wavelengths), the statistical analysis wa s done on the average of the different CO 2 measurements for each of the timestam ps. As stated in Step 1, if CO 2 is blank or zero, the timestamp is considered invalid.

STEP 4: Determine if other timestamps are invalid by applying the following criteria:
More Than Two Non-CO 2 Chemical

Compounds With

Error Values ≥ 50% Of Measured Concentration If more than two non-CO2 chemical compound concentration measurements for an individual timestamp were considered invalid due to the criterion in Step 2, the combustion efficiency calculation may be biased high; ther efore, the timestamp is considered invalid.

MPC TX Video Score The “video score” is based on a video review panel and indicates the degree to which the PFTIR was aimed properly during each run. If the PFTIR measurements were recorded at a relatively low "video score" indicating th e PFTIR camera was lacking aiming accuracy during over half of the test run, all timestam ps associated with the test run are considered invalid.

MPC TX Steam Ring Issue Certain tests conducted at MPC TX were aff ected by a known steam leak located on the shaping steam ring of the flare tip. The report states that “it is suspected that the shaping steam leak may have caused premature flare ou tages” when wind blew towards the shaping steam leak. Timestamps where wind blew toward s the leak in the flare’s shaping steam ring (wind coming from 100 to 135 de grees) are considered invalid.

MPC TX Wind Issue Certain timestamps associated with MPC TX we re flagged by the facility because the wind was blowing away from the PFTIR and at a speed of 5 miles per hour or more. When these conditions were met, the plume was obscured by the flare and there were concerns that the PFTIR was not getting a good view of the plume. These timestamps are considered invalid.

MPC Detroit Wind Issue It was planned to use two PFTIR detectors durin g the MPC Detroit flare testing and then if the wind blew the plume away from one of the detectors, the other detector would get a good reading. However, one of the detectors was not functioning properly and only one detector was actually used. Therefore, ther e were times during testing that the flare obscured the PFTIR’s view of the plume, as described in the “MPC TX Wind Issue” note above. These timestamps are considered invalid.

Shell Vent Gas Flow Certain timestamps associated with Shell have zero or very low flare vent gas flow rates; and realistic values for the operating parameters being studied cannot be determined. These timestamps are considered invalid.17 TABLE 8: STANDARDIZED DATA VALIDATION PROCEDURE AND DATA QUALITY CRITERIA (Cont.) CRITERIA NOTE

TCEQ Negative Values Certain timestamps associated with TCEQ have negative non-CO 2 chemical compound concentrations which are not possible. These timestamps are considered invalid.

TCEQ No Input Data Certain timestamps associated with TCEQ have no flare input data (i.e., flare vent gas composition and flow rate; and steam flow rate) so operating parameters being studied cannot be determined. These timestam ps are considered invalid.

STEP 5: Determine if entire test run is invalid by applying the following criteria:

Number Of Valid Timestamps ≤ 5

For Test Run If five or less valid timestamps remain after applying the criteria in Steps 1, 2, 3 and 4, the specific test run has too much uncertainty and va riability in the reported values; therefore, the entire test run is considered invalid. [The valid timestamps were included in the final valid timestamp set of data.]

Number Of Valid Timestamps

< 49.5% Of Run's Total Timestamp Count If less than 49.5 percent of the timestamps in a test run remain after applying the criteria in Steps 1, 2, 3 and 4, the specific test run has to o much uncertainty and variability in the reported values; therefore, the entire te st run is considered invalid.

5.0 STATISTICAL ANALYSIS In accordance with EPA guidelines (EPA O ffice of Environmental Information, 2006), statistical analyses of the flare PFTIR data (i.e ., all flare PFTIR timestamp data remaining after having applied the standardized data validati on procedure discussed in section 4.3) were conducted to assist in establishing operati ng limits for NHVcz, LFLcz, and Ccz on steamassisted flares that represent good flare co mbustion (i.e., a combustion efficiency of 96.5 percent). For each of these operational variables of interest, ܺ , where ܺ = NHVcz, LFLcz, and Ccz, the statistical analysis c onsisted of the following steps:

 Step 1 – Preliminary data review with graphical representation of data

 Step 2 – Statistical outlier analysis

 Step 3 – Chow test to determine structur al change in relationship between ܧܥ and ܺ

 Step 4 – Univariate regression analysis to estimate relationship between ܧܥ and ܺ

 Step 5 – Prediction of threshold le vel that corresponds to 96.5% ܧܥ for ܺ18 These steps are described in further detail in the subsections that follow. All of the analyses outlined abov e were conducted in SAS ® , a statistical analysis software.

5.1 Step 1 – Preliminary Data Review with Graphical Representation of Data A two-dimensional scatter plot is a powerful graphical tool for examining the relationship
between two variables. It enables the analyst to assess: (1) the na ture of the relationship between the variables (i.e., linear versus nonlinear); (2) ex istence of discontinuities or “breaks” in the relationship; (3) potentially influential observa tions (i.e., outliers); and (4) existence of clustering. Thus, simple two-dimensional scatter plots were constructed and reviewed between
ܧܥ and ܺ , as well as ܮܰሺܧܥሻ and ܮܰሺܺሻ where ܮܰ denotes the natural logarithm of each
variable.

The preliminary data review also included c onducting univariate re gression analysis to determine whether the log-log transformation is preferable. This involved estimating the following two equations in SAS ® and comparing the resulting goodness-of-fit (e.g., adjusted R 2

, sum of square errors [SSE], and F-value) and parameter significance (e.g., t-value) statistics
between the two:

ߝ ൅ ܾܺ ൅ ܽ ൌ ܧܥ

ܮܰሺܧܥሻ ൌ ܽ ′ ൅ ܾ ′ ߝ ൅ ሻܺሺܰܮ ′

It was concluded that the natural logarith m transformed data is a better fit between ܧܥ
and ܺ for all except for LFLcz. Moreover, an examina tion of the scatter plot s revealed a need to test for structural change (see section 5.3) given the L-shaped di stribution of data.

5.2 Step 2 – Statistical Outlier Analysis Cooks’ D is a commonly used measure of the influence of a given observation in univariate regression analysis. In a nutshell, the statistic measures the effect of deleting an observation, ݅ , and is proportional to the sum of the squared differences between predictions made with all observations in the analysis, ܮܰሺܧܥሻ ෣ or ܧܥ ෢ depending on the operating variable of interest, and predictions made w ithout the observation in question, ܮܰሺܧܥሻ ෣ ሺି௜ሻ or ܧܥ ෢ ሺି௜ሻ . If the19 predictions are the same with or without the observation in questi on, then one can conclude that
the observation has no influence on the univariate re gression model. However, if the predictions differ greatly when the observation is not includ ed in the analysis, then the observation is
deemed influential. Cooks’ D for observation ݅ , i.e., ܦ ௜ , is given by:

ܦ ௜ ൌ ∑ ൫ܮܰሺܧܥሻ ෣ ௝ െ ܮܰሺܧܥሻ ෣ ௝ሺି௜ሻ ൯ ଶ ௡ ௝ୀଵ ܧܵܯ ൈ ݇

or

ܦ ௜ ൌ ∑ ൫ܧܥ ෢ ௝ െ ܧܥ ෢ ௝ሺି௜ሻ ൯ ଶ ௡ ௝ୀଵ ܧܵܯ ൈ ݇

depending on the operating vari able of interest, where:

݇ = Number of coefficients estimated wh ich is equal to 2 given the specification of the equation above

݊ = Number of non-zero observations ܯܵܧ = The mean square error of the regression model ܮܰሺܧܥሻ

෣ ௝ = Predicted value for observation ݆ from the full regression model that includes all non-zero observations ܧܥ ෢ ௝ = Predicted value for observation ݆ from the full regression model that includes all non-zero observations ܮܰሺܧܥሻ

෣ ௝ሺି௜ሻ = Predicted value for observation ݆ from the a regression model that includes all non-zero observations but excludes observation ݅ ܧܥ ෢ ௝ሺି௜ሻ = Predicted value for observation ݆ from the a regression model that includes all non-zero observations but excludes observation ݅

Although a statistical signifi cance test does not exist for ܦ ௜ , the rule-of-thumb cut off often used to identify influential observations is:

ܦ ௜ ൐ 4 ሺ ݊െ݇െ1 ሻ

where ݊ and ݇ are as defined above. This rule-of-thumb cut off value was used to identify the influential observations in the population of in terest. These observations were subsequently20 dropped from further analysis to primarily improve the fit, and hence the predictive power of the estimated equations. 3

5.3 Step 3 – Chow Test to Determine Struct ural Change in Relationship between CE and X The Chow test is a statistical test of whether the estimated coefficients in two different linear regressions are equal. The test is often utilized to determ ine whether there are structural breaks in the relationship between two variables (SAS Institute Inc., 2013). The test is conducted by first estimating one of the following equations us ing the full data set for the operating variable of interest, ܺ :

ܮܰሺܧܥሻ ൌ ܽ ൅ ܾܮܰሺܺሻ ൅ ߝ

or ߝ ൅ ܾܺ ൅ ܽ ൌ ܧܥ

and computing the sum of squared errors, ܵܵܧ , for the estimated equation. Next, the data set is split into two groups, 1 and 2, with a sepa rate equation estimated for each group:

ܮܰሺܧܥሻ ଵ ൌ ܽ ଵ ൅ ܾ ଵ ܮܰሺܺሻ ଵ ൅ ߝ ଵ ܮܰሺܧܥሻ ଶ ൌ ܽ ଶ ൅ ܾ ଶ ܮܰሺܺሻ ଶ ൅ ߝ ଶ

or ܧܥ ଵ ൌ ܽ ଵ ൅ ܾ ଵ ܺ ଵ ߝ ଵ ܧܥ ଶ ൌ ܽ ଶ ൅ ܾ ଶ ܺ ଶ ൅ ߝ ଶ

Similarly, the sum of squared erro rs for each of the two equations, ܵܵܧ ଵ and ܵܵܧ ଶ is computed. The Chow test statistic, ܨ , is given by:

ܨ ൌ ሾ ܵܵܧ െ ሺ ܵܵܧ ଵ ൅ ܵܵܧ ଶ ሻሿ ൊ 2 ሺ ܵܵܧ ଵ ൅ ܵܵܧ ଶ ሻ ൊ ሺ ݊ ଵ ൅ ݊ ଶ െ 4 ሻ

3 Further analysis of these influe ntial observations is warranted.21 which follows the F distribution with 2 numerator and ሺ ݊ ଵ ൅ ݊ ଶ െ 4 ሻ denominator degrees of freedom. Using this statis tic, the null hypothesis that ܽ ଵ ൌ ܽ ଶ and ܾ ଵ ൌ ܾ ଶ is tested. The null hypothesis is rejected if the test statistic, ܨ , is greater than the rejection region given by
ܨ

ሺ ௡ భ ା௡ మ ିସ ሻ ,ఈ/ଶ ଶ where ߙ is the level of significance.

For this analysis, structural change was te sted by employing an iterative process. The process involved: (1) partitioning the observatio ns into smaller clus ters; (2) picking an observation within a given cluster as a potential struct ural change point; and (3) computing the F statistic along with its p-value. Next, the F statistics (and their associated p-values) were compared across the different clusters. The proce ss was repeated for the cluster with the highest
F-value until the observation with th e highest F value was identified in the full data set. Then that
observation was established as the structural ch ange point for the remaining analysis steps.

5.4 Step 4 – Univariate Regression Analys is to Estimate Relationship between CE and X In this step, one of the fo llowing sets of equations were estimated for the operating
variable under consideration:

ܮܰሺܧܥሻ ൌ ܽ ൅ ܾܮܰሺܺሻ ൅ ߝ ܮܰሺܧܥሻ ଵ ൌ ܽ ଵ ൅ ܾ ଵ ܮܰሺܺሻ ଵ ൅ ߝ ଵ for ܮܰ ሺ ܺ ሻ ൑ ܮܰሺܺሻ ௌ் ܮܰሺܧܥሻ ଶ ൌ ܽ ଶ ൅ ܾ ଶ ܮܰሺܺሻ ଶ ൅ ߝ ଶ for ܮܰ ሺ ܺ ሻ ൐ ܮܰሺܺሻ ௌ்

or ߝ ൅ ܾܺ ൅ ܽ ൌ ܧܥ
ܧܥ ଵ ൌ ܽ ଵ ൅ ܾ ଵ ܺ ଵ ൅ ߝ ଵ for ܺ൑ܺ ௌ் ܧܥ ଶ ൌ ܽ ଶ ൅ ܾ ଶ ܺ ଶ ൅ ߝ ଶ for ܺ൐ܺ ௌ்

where ܮܰሺܺሻ ௌ் and ܺ ௌ் are the structural threshold values identified in Step 3 above.

5.5 Step 5 – Prediction of Threshold Level that Corresponds to 96.5% CE for X Using the estimated equations in Step 4.4, X wa s solved for the value that corresponds to the 96.5% CE, i.e., ܺ ் , as:22 ܺ ் ൌ ܧܺܲ ቆ ܮܰ ሺ 0.965 ሻ െ ܽ ො ܾ ෠ ቇ

or

ܺ ் ൌ 0.965 െ ܽ ො ܾ ෠

depending on the operating vari able of interest, where:

ܽ ො = Estimated intercept coefficient
ܾ ෠ = Estimated slope coefficient

5.6 Results of the Statistical Analysis Table 9 provides a summary of the results of the statistical analysis using timestamp data for each of the operational variable s of interest. The various plots and outliers that are associated with this statistical analysis are provided in Appendix B.

TABLE 9: STATISTIC RESULTS FOR NHVc z, LFLcz, and Ccz TIMESTAMP DATA

Model Parameter NHVcz LFLcz Ccz Dependent Variable LNNHVcz LFLcz LNCcz Total Number of Observations 3863 3863 3863 Number of Outliers 176 167 182 Single Equation Estimates Number of Observations 3687 3696 3681 Intercept

‐ 0.494 <.0001 [a] 1.03 <.0001 [a] 0.095 <.0001 [a] X or LNX 0.0789 <.0001 [a]

‐ 0.501 <.0001 [a] 0.084 <.0001 [a] Adj R

‐ Sq 0.331 0.421 0.371 Estimated X (and LNX where applicable) for 96.5% CE 336 (5.82) 0.135 0.209

‐ (1.57) Estimated Structural Change Threshold 202 (5.31) 0.289 0.102

‐ (2.29) Section 1 Estimates Number of Observations 1236 3422 729 Intercept

‐ 0.602 <.0001 [a] 1.02 <.0001 [a] 0.266 <.0001 [a] X or LNX 0.0967 <.0001 [a]

‐ 0.432 <.0001 [a] 0.159 <.0001 [a] Adj R

‐ Sq 0.0464 0.308 0.0737 Estimated X (and LNX where applicable) for 96.5% CE N/A N/A 0.134 N/A N/A Section 2 Estimates Number of Observations 2451 274 2952 Intercept

‐ 0.195 <.0001 [a] 1.13 <.0001 [a] 0.0561 <.0001 [a] X or LNX 0.0289 <.0001 [a]

‐ 0.847 <.0001 [a] 0.0573 <.0001 [a] Adj R

‐ Sq 0.123 0.137 0.236 Estimated X (and LNX where applicable) for 96.5% CE 250 (5.52) N/A 0.202

‐ (1.60) Estimated Overall Range for X for 96.5% CE 250 to 336 0.134 to 0.135 0.202 to 0.209 N/A = Not applicable [a] The estimated coefficient is statistically significant at a level greater than 99.99%.23 The predicted threshold NHVcz value that corr esponds to 96.5% CE has a relatively wide range of 250 to 336 BTU/SCF. The predicted th reshold LFLcz value that corresponds to 96.5% CE has a narrow range of 0.134 to 0.135 (volume fraction). The predicted threshold Ccz value that corresponds to 96.5% CE also has a na rrow range of 0.202 to 0.209 (volume fraction). 4 While the estimated equations have low adjusted R 2 values, all coefficient estimates are statistically significant at a conf idence level higher than 99%.

Given that the overall fit for the estimated equations is poor (i.e., low adjusted R2 values), the estimated confiden ce and prediction intervals aro und the mean operating parameter value of interest, X, are wide (refer to the depicted confid ence and prediction bounds for each operating parameter in the “fit plot s” tab of Appendix B). This i ndicates that there is a large degree of uncertainty associated with the estimated 96.5% CE va lues for each of the operating parameters. We expect that the uncertainty can be reduced by considering relevant omitted variables that may also be aff ecting combustion efficiency in a ddition to the operating parameter of interest, X, and employing multiple regression techniques.

6.0 REFERENCES

Allen, D. T., and V. M. Torres. 2011. 2010 Flar e study final report. Prepared for the Texas Commission on Environmental Quality, Tracking No. 2008-81.
< http://www.tceq.texas.gov/assets/public/imple mentation/air/rules/Flare/2010flarestudy/2010- flare-study-final-report.pdf > . September 20.
Clean Air Engineering, Inc. 2010a. Performance test of a steam-assisted elevated flare with passive FTIR. Prepared by Marathon Petrol eum Company, LLC, Findlay, OH and Clean Air Engineering, Inc., Palatine, IL.
Clean Air Engineering, Inc. 2010b. Performance test of a steam-assisted elevated flare with passive FTIR - Detroit. Prepared by Marat hon Petroleum Company, LLC Findlay, OH and Clean Air Engineering, Inc., Palatine, IL.
Clean Air Engineering, Inc. 2011. PFTIR test of st eam-assisted elevated fl ares – Port Arthur.

4 The statistic results for Ccz are based on values that have been converted to be on a propane basis so that a total hydrocarbon analyzer with a flame ionization detector could more easily be used as a monitoring device. This adjustment was made by dividing the carbon count of the various hydrocarbons by 3 (for propane) and then adding hydrogen. This adjustment was not done in the April 2012 technical report; therefore the threshold values shown in this memorandum differ from those Ccz threshold values discussed in April 2012 technical report.24 Prepared for Flint Hills Resources Port Arthur, LLC.
Davis, A, C. Law, and H. Wang. 1999. Propene py rolysis and oxidation kine tics in a flow reactor and laminar flames. Combustion and Flame. 119, 375-399.
INEOS ABS (USA) Corporation. 2 010. Passive Fourier transform infrared technology (FTIR) evaluation of P001 process control device at the INEOS ABS (USA) Corporation. Addyston, OH.
Laskin, A., H. Wang, and C.K. Law. 2000. Detailed kinetic mode ling of 1,3-butadiene oxidation at high temperatures. International Jour nal of Chemical Kinetics. 32:10, 589-614.
McDaniel, M. 1983. Flare efficien cy study. Prepared for U.S. EPA Office of Research and Development EPA-600/2-83-052 by Engin eering Science, Inc. (July).
Pohl, J., R. Payne, and J. Lee. 1984. Evaluation of the efficiency of industrial flares: Test Results. EPA-600/2-84-095. Prepared for U.S. EP A Office of Research and Development by Energy and Environmental Rese arch Corporation (May).
Pohl, J. and N. Soelberg. 1985. Evaluation of the efficiency of industria l flares: Flare head design and gas composition. EPA-600/2-85-106. Prepar ed for U.S. EPA Office of Air Quality Planning and Standards. (September).
Pohl, J. and N. Soelberg. 1986. Evaluation of the efficiency of industrial flares: H 2 S gas mixtures and pilot assisted flares. EP A-600/2-86-080. Prepared for U.S. EPA Office of Air Quality Planning and Standards. (September).
SAS Institute Inc. 2013. SAS/ETS(R) 9.2 User's Guide. Retrieved May 28, 2013, from SAS Product Documentation: http://support.sas.com/documentation/cdl/en/e tsug/60372/HTML/default/viewer.htm#etsug_mod el_sect051.htm
Shell Global Solutions (US) Inc. 2011a. Shell Deer Park Refining LP Deer Park Refinery East Property Flare Test Report. Prep ared by Shell Global Solutions (US) Inc., Houston, TX (April).
U.S. EPA. 2012. Parameters for Properly De signed and Operated Flares. Located at: http://www.epa.gov/ttn/atw/fl are/2012flaretechreport.pdf

U.S. EPA. 2006. Office of Environmental Info rmation. Data Quality Assessment: Statistical Methods for Practitioners, EPA QA/G-9S. Wash ington, DC: EPA Office of Environmental Information.

### APPENDIX A

Microsoft Excel Workbook Containing All PFTIR Raw Data And Results Of Conducting PFTIR Raw Data Review And Data Validation Procedures

### APPENDIX B

Microsoft Excel Workbook Containing Results Of

Statistical Analysis Including Plots and Outliers