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 analyses that were conducted after April 2012 

in order to establish the values for the steam-assisted 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 combustible 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 April 2012, the EPA released a technical report 

entitled “Parameters for Properly Designed and Operated 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 technical 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 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 

www.erg.com 
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2.0 BACKGROUND 

In April 2012, the EPA released a technical report for peer review that discussed flare 

operation and performance observations made by evaluating the available flare 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 Petroleum Company, Texas City, Texas (MPC TX), 

Marathon Petroleum Company, Detroit, Michigan (MPC Detroit), Flint Hills Resources, Port 

Arthur, Texas (FHR), Shell, Deer Park, Texas (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 the 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 Quality (TCEQ) and the University of Texas 

at Austin. The results from the TCEQ study are available on TCEQ’s Stakeholder Group: Flare 

Task Force website1

; 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 objective evaluation of the analysis, the 

EPA asked a third party (an organization not involved 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 individuals that could be considered “technical combustion experts” 

within four interest groups: the refinery industry, industrial flare consultants, academia, and 

environmental stakeholders. 

EPA developed a charge statement with ten charge 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 technical report, details of the peer review 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.html 
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Consolidated Petroleum Refinery Rulemaking Repository at the EPA's Technology Transfer 

Network Air Toxics Web Site2. 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 relevant data points. All of the peer reviewers commented to some 

extent on this question. For the most part, reviewers agreed with the criteria used for excluding 

test runs, but there were several peer reviewers that were concerned about the discussion in 

section 3.1.3 in the 2012 report that focused on 11 test runs. The discussion 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 error, or an indication that there were other 

unidentified factors affecting the results that were not accounted for when using the LFLcz. A 

few of the peer reviewers saw this discussion 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 point that would consider the data point in detail”. 

There were a few specific comments on the data acceptance criteria. One peer reviewer 

objected to removing data points with a combustion efficiency of 0% and for data points when 

smoking occurred, stating that these data “...do serve a purpose in the analysis by defining the 

current limits of acceptable practice of operation.” 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 points with 0% combustion efficiency, those test 

runs where smoke was observed, and single TCEQ test runs were added back into 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 data validation procedures were 

 

2

 http://www.epa.gov/airtoxics/petref.html 
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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 positioned on the ground to view hot gases 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 Engineering, Inc. and Industrial Monitoring and 

Controls Corporation (IMACC). For each PFTIR flare test, IMACC’s proprietary software was 

used to estimate individual component concentrations 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. 

The PFTIR raw data for each test site was provided to the EPA in separate Microsoft 

Excel workbooks. In general, each Microsoft Excel file contains minute-by-minute 

measurements of individual component concentrations 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 block 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 gas and steam input data are compiled into one 

Microsoft Excel workbook and associated with 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 April 2012 technical report, the PFTIR 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 developed to address different 

inconsistencies found in the PFTIR raw data. Because the combustion efficiency is the primary 

result of the PFTIR testing, section 4.1 describes the method that 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 considering the PFTIR data observations described throughout this 
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section. Section 4.2 describes the PFTIR observations that were made; and section 4.3 discusses 

the standard set of criteria that was applied to all PFTIR raw data to address all of these 

observations. 

4.1 Combustion Efficiency Calculation 

The PFTIR testing technique measures carbon dioxide, carbon monoxide, and other 

speciated organic compounds (the speciated organic compounds are referred collectively in this 

memorandum as hydrocarbons) in the plume of the flare in order to calculate combustion 

efficiency. Equation 1 shows how combustion efficiency is determined using PFTIR raw data. 

CE	ൌ	 CO2

CO2൅CO൅THCw

 Equation 1 

Where: 

CE = flare combustion efficiency (%) 

CO2 = carbon dioxide from PFTIR (ppm•m) 

CO = carbon monoxide from PFTIR (ppm•m) 

THCw = total hydrocarbons from PFTIR (ppm•m) weighted by number of carbons in each 

speciated organic compound. Equation 2 provides an example of how THCw is 

calculated. 

THCw	ൌ	CCH4൅2൫CC2H2 ൅ CC2H4൅CC2H6൯൅3൫CC3H6൅CC3H8൯൅4൫CC4H6൅CC4H8൅CC4H10൅CC4H10൯൅5ሺCC5൅ሻ Equation 2

4.2 Observations 

The following observations were made about the 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 CO2 concentration measurements per minute, depending on the test report, 

with each test report using a different method to consider the multiple CO2 concentration 

measurements. (refer to section 4.2.1) 

 The number of chemical compounds measured and reported varied by test report. (refer 

to section 4.2.2) 

 Some CO2 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) 
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 Values for “CO2 Error” were reported as well as error values for CO and other 

hydrocarbons (e.g., “C2H6 Error”). (refer to section 4.2.4) 

4.2.1 Multiple CO2 Measurements 

As one can see looking at Equation 1, the CO2 measurement is critical for calculating the 

combustion efficiency. In PFTIR testing, CO2 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 CO2 concentration measurements 

are a result of different wavelength detectors that were used during each of the flare testing 

campaigns. Table 1 shows a summary of CO2 wavelength measurements that were reported by 

test site. Specific procedures were not provided in the test report to describe which CO2

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 wavelength was selected to perform the combustion 

efficiency calculation for each test report. The selected wavelength is indicated in Table 1 using 

green shading. For example, in the TCEQ testing, CO2 measurements were available at all three 

wavelengths, but only the CO2 measurements detected at a wavelength of 765 cm-1 were used to 

calculate the combustion efficiency. 

TABLE 1. SUMMARY OF CO2 WAVELENGTH MEASUREMENTS BY TEST SITE 

Test Site Wavelengths At Which CO2 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 = CO2 measurement used in test report combustion efficiency calculation. 

 (a) Test Series A through C only have CO2 data scans for the 2080 cm-1 wavelength. For Test Series D, CO2 data was 

recorded at 765 cm-1 and 2080 cm-1 wavelengths. However, only the CO2 value at a 2080 cm-1 wavelength was used 

to calculate the combustion efficiency for Test Series D. 

No rationale was provided on why one CO2 measurement was selected over another 

measurement and once the wavelength was selected the entire set of CO2 measurements for that 

wavelength was used for the combustion efficiency calculations for every minute of the testing 

campaign and all other CO2 measurements were ignored. 
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4.2.2 Variation in Number of Chemical Compounds 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 different detectors used during the testing. Specific 

chemical compounds show up at known wavelengths exhibiting characteristic behavior at that 

wavelength. The size of the response is indicative of the concentration. Some chemical 

compounds, like CO2, can be found at multiple wavelengths, but some chemical compounds only 

have one wavelength where it can be measured. Because detectors only cover a set range of 

wavelengths, compounds that show up at a wavelength outside the detectors range, cannot be 

detected (unless an additional detector with a different detection range is used). Table 2 provides 

a summary of the chemical compounds analyzed and reported for each flare test. 

TABLE 2. SUMMARY OF CHEMICAL 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 compounds analyzed for; and the 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 that covered the full range of wavelengths available, because CO2

at all 3 wavelengths were detected during those tests. However, the number of chemical 

compounds measured and reported for those tests 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 were necessarily found for each minute. The 

values shown in Table 2 are those that were analyzed for.) 

 
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The test reports do not provide information on how the specific analyzed chemical 

compounds were selected. All of the tests, except for INEOS and TCEQ 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 potential 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 test 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 test is not discussed in 

this memo.) The flare vent gas tested in the INEOS and TCEQ tests 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 propane 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 TCEQ tests, were similar. Out of a total of 21 

different chemical compounds used in all the flare 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 unusual that some of these chemical compounds would 

not have been present during the other multi-chemical flare vent gas tests that only reported 

between 5 to 8 chemical compounds. 

If significant amounts of non-CO2 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 “THCw” term of Equation 1, the denominator 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 example Davis et al. (1999) used 71 species to 

model the combustion kinetics of propylene. However, there are far fewer compounds that are 

produced at a high enough level to impact the calculated 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 efficiencies calculated, three analyses were 

conducted, including: 
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1. Reviewing the chemical compounds predicted based on kinetic studies for the 

simplest flare vent gas tested (i.e., for the TCEQ and INEOS flare vent gases) 

2. Determining the change in combustion efficiency for TCEQ when additional 

chemical compounds tested using an extraction 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-butadiene (Laskin et al. 2000) and the other for combusting 

propylene (Davis et al. 1999). These studies included lab testing of laminar flames in a flow 

reactor, as well as discussion of previous papers 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 expected 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 Expected When Combusting 

Propylene(a)

Chemical Compounds Expected 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 detected during the experiment, but it was not identified specifically. The 

researchers speculated that it was likely formaldehyde, acetaldehyde or acrolein. The researchers also noted that 

acrolein would be more likely to be present when combusting ultra lean fuels. 

(b) These chemical compounds were measured during the TCEQ study using PFTIR. 
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(c) These chemical compounds were measured during the TCEQ study using an extraction method, as well as 

Acetaldehyde, Propanal, Acrolein, Methanol, 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 chemical compounds that were expected to be 

produced during combustion of 1,3-butadiene, as determined from the technical paper (Laskin et 

al. 2000) reviewed and shown in Table 3. The prediction from review of the technical paper was 

also supported from the flare test results. Table 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 average were based on. There were 102 minutes 

of test data (timestamps) when only 1,3-butadiene was tested (inerts, steam and nitrogen, were 

also present in the combustion zone); therefore, 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 CONCENTRATION RESULTS FOR INEOS 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 propylene 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 testing are considered, the TCEQ study tested 

all the chemical compounds that are predictable based on the information provided in the Davis 

et al. (1999) article, except for Ethenone. In addition, the TCEQ extraction test also measured 

acetaldehyde, propanal, acrolein, methanol, acetone, and propylene oxide. The scientific article 

indicated that acrolein would be important to consider for lean-fuel conditions, 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 expected, but an oxidized compound was found that 

researchers assumed were either formaldehyde, acetaldehyde, or acrolein. Therefore, it seems 
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prudent to measure for these compounds. Although ethenone was predicted by the kinetics 

model, it is very reactive and would likely dissociate into CO and other compounds quickly. 

There were only four TCEQ test runs that used 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 extraction results to the PFTIR results. 

These ratios were used to convert 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 test 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, formaldehyde, acetaldehyde, and acrolein. Although it is 

unclear with only four data points how important it is to measure some of the chemical 

compounds that were not identified at large concentrations (such as ethane, acrolein, 

acetaldehyde, and acetylene), these concentrations may not be sufficient enough to effect the 

combustion efficiency. 

TABLE 5. SUMMARY OF RUN CONCENTRATION RESULTS FOR TCEQ PFTIR AND EXTRACTION 

TESTING WHEN PROPYLENE IS THE ONLY 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 concentration; see section 4.2.4 for discussion of the error 

value. 
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Effect on Combustion Efficiency using both 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 all the chemical compound data available and 

compared to the combustion efficiency calculated 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 percentage points. This is a relatively small 

change and, although adding in the extraction testing results does incorporate chemical 

compounds that were not identified in the PFTIR testing, the concentration 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 compounds 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 calculation incorporates the most number of compounds. 

Although, depending on how much was actually found of the additional chemical compounds, 

considering them in the combustion efficiency calculation 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 recalculated using only 

the chemical compounds that were available in the MPC TX test program. Table 7 summaries 

the differences in combustion efficiencies found. 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 the 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 COMBUSTION 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 18 
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TABLE 7. DIFFERENCES IN COMBUSTION 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 efficiencies 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 line in Figure 1). All of the FHR 

test runs analyzed for thirteen 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 only the chemical compounds examined in the MPC 

TX test runs (i.e., carbon monoxide, carbon dioxide, methane, ethylene, C5+ hydrocarbons, 

propylene, and n-butane). As evident in Figure 1, most of the timestamps would have computed 

combustion efficiencies higher using the fewer compounds analyzed for in the MPC TX test (a 

few had the same efficiency no matter which set of compounds were considered). 

Figure 1: Combustion Efficiency Comparison Using FHR LOU-A 2.0(1) Test Run 

(valid timestamps shown) 
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4.2.3 CO2 Concentrations Not Reported, or Reported as Equal to Zero or Greater Than One 

Million Parts Per Million Meters 

Occasionally, a CO2 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 blank or zero indicated: no CO2, 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 flare test, IMACC’s proprietary software was 

used to estimate individual component concentrations 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 describes 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 value; otherwise, it may suggest that the PFTIR 

measurement is inaccurate. This criterion was applied to all non-CO2 chemical compound 

concentration measurements such that those with an error in the fit between the reference spectra 

and the measured spectra greater than or equal to 50% of the measured concentration were 

considered invalid measurements. However, because the CO2 measurement is critical for 

calculating the combustion efficiency and often there were two or three CO2 measurements for 

each timestamp, a stricter validation rule was used for CO2. CO2 measurements were considered 

invalid if the error in the fit between the reference 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 data exclusion procedures used in the April 2012 

technical report), the EPA established a standardized data validation 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 validation 
15 

procedures were used when the average run values were determined. Table 8 provides a 

summary of the standardized data validation procedure (including a description of each data 

quality criterion). The procedures described in Table 8 were applied 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 CO2 concentrations reported at each wavelength are invalid by applying the following criteria:

[CO2] Not 

Available 

Blank or zero CO2 concentration measurements do not contribute to the combustion efficiency 

calculation; therefore, the CO2 concentration is considered invalid. 

[CO2] 

≥ 1,000,000 

CO2 concentration measurements greater than or equal to 100% (1 million parts per million 

meters) are not possible; therefore, the CO2 concentration is considered invalid. 

CO2 

Error Values 

≥ 30% Of 

Measured 

Concentration 

CO2 error describes the fit between the reference spectra and the measured spectra. 

CO2 concentration measurements with an error in the fit between the reference spectra and the 

measured spectra that is greater than or equal to 30% is considered not a close enough fit; 

therefore, the CO2 concentration is considered invalid. 

STEP 2: 

Determine if other non-CO2 chemical compound concentrations are invalid by applying the following criteria:

Non-CO2

Compound 

Error Values 

≥ 50% Of 

Measured 

Concentration 

Non-CO2 compound error describes the fit between the reference spectra and the measured 

spectra. 

A non-CO2 chemical compound concentration measurement 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 enough fit; therefore, the non-CO2 chemical compound concentration is 

considered invalid. 

STEP 3: 

Average remaining valid CO2 concentrations by timestamp and determine if the timestamp is invalid by applying 

the following criteria:

[CO2] 

≥ 90th Percentile 

CO2 concentration measurements that are in the 90th percentile based on a statistical analysis of 

the population of CO2 measurements for the timestamps for a full test run were removed as 

outliers. For timestamps with multiple CO2 measurements (those at different wavelengths), the 

statistical analysis was done on the average of the different CO2 measurements for each of the 

timestamps. As stated in Step 1, if CO2 is blank or zero, the timestamp is considered invalid. 

 
16 

TABLE 8: STANDARDIZED DATA VALIDATION PROCEDURE AND DATA QUALITY CRITERIA 

(Cont.) 

CRITERIA NOTE 

[CO2] 

≤ 10th Percentile 

CO2 concentration measurements that are in the 10th percentile based on a statistical 

analysis of the population of CO2 measurements for the timestamps for the full test run were 

removed as outliers. For timestamps with multiple CO2 measurements (those at different 

wavelengths), the statistical analysis was done on the average of the different CO2

measurements for each of the timestamps. As stated in Step 1, if CO2 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-CO2 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; therefore, 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 the PFTIR camera was lacking aiming accuracy 

during over half of the test run, all timestamps associated with the test run are considered 

invalid. 

MPC TX 

Steam Ring Issue 

Certain tests conducted at MPC TX were affected 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 outages” when wind blew towards the shaping 

steam leak. Timestamps where wind blew towards the leak in the flare’s shaping steam ring 

(wind coming from 100 to 135 degrees) are considered invalid. 

MPC TX 

Wind Issue 

Certain timestamps associated with MPC TX were 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 during 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, there 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-CO2 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 timestamps 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 variability 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 too much uncertainty and variability in the reported 

values; therefore, the entire test run is considered invalid. 

5.0 STATISTICAL ANALYSIS 

In accordance with EPA guidelines (EPA Office 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 validation procedure discussed in section 4.3) were 

conducted to assist in establishing operating limits for NHVcz, LFLcz, and Ccz on steamassisted flares that represent good flare combustion (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 consisted 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 structural change in relationship between ܧܥ and ܺ

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

 Step 5 – Prediction of threshold level 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 above 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 nature of the relationship between 

the variables (i.e., linear versus nonlinear); (2) existence of discontinuities or “breaks” in the 

relationship; (3) potentially influential observations (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 conducting univariate regression 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 R2, 

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 logarithm transformed data is a better fit between ܧܥ

and ܺ for all except for LFLcz. Moreover, an examination of the scatter plots revealed a need to 

test for structural change (see section 5.3) given the L-shaped distribution 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 without the observation in question, ܮܰሺܧܥሻ ෣ ሺି௜ሻ or ܧܥ෢ሺି௜ሻ. If the 
19 

predictions are the same with or without the observation in question, then one can conclude that 

the observation has no influence on the univariate regression model. However, if the predictions 

differ greatly when the observation is not included in the analysis, then the observation is 

deemed influential. Cooks’ D for observation ݅, i.e., ܦ௜, is given by: 

ܦ ௜ൌ ∑ ൫ܮܰሺܧܥሻ ෣ ௝ െ ܮܰሺܧܥሻ ෣ ௝ሺି௜ሻ൯

ଶ ௡

௝ୀଵ

ܧܵܯ ൈ ݇

or 

ܦ ௜ൌ ∑ ൫ܧܥ ௝෢െ ܧܥ௝෢ሺି௜ሻ൯

ଶ ௡

௝ୀଵ

ܧܵܯ ൈ ݇

depending on the operating variable of interest, where: 

݇	 = Number of coefficients estimated which 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 significance test does not exist for ܦ௜, the rule-of-thumb cut off 

often used to identify influential observations is: 

൐ ௜ܦ

4

ሺ݊െ݇െ1ሻ

where ݊ and ݇ are as defined above. This rule-of-thumb cut off value was used to identify the 

influential observations in the population of interest. These observations were subsequently 
20 

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 Structural 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 determine 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 using 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 separate equation estimated for each group: 

ܮܰሺܧܥሻଵ ൌ ܽଵ ൅ ܾଵܮܰሺܺሻଵ ൅ ߝଵ

ܮܰሺܧܥሻଶ ൌ ܽଶ ൅ ܾଶܮܰሺܺሻଶ ൅ ߝଶ

or 

ܧܥଵ ൌ ܽଵ ൅ ܾଵܺଵߝଵ

ܧܥଶ ൌ ܽଶ ൅ ܾଶܺଶ ൅ ߝଶ

Similarly, the sum of squared errors for each of the two equations, ܵܵܧଵ and ܵܵܧଶ is 

computed. The Chow test statistic, ܨ, is given by: 

ܨ ൌ ሾܵܵܧ െ ሺܵܵܧଵ ൅ ܵܵܧଶሻሿ ൊ 2

ሺܵܵܧଵ ൅ ܵܵܧଶሻ ൊ ሺ݊ଵ ൅ ݊ଶ െ 4ሻ

 

3

 Further analysis of these influential observations is warranted. 
21 

which follows the F distribution with 2 numerator and ሺ݊ଵ ൅ ݊ଶ െ 4ሻ denominator degrees of 

freedom. Using this statistic, 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 

ܨሺ௡భା௡మିସሻ,ఈ/ଶ

ଶ where ߙ is the level of significance. 

For this analysis, structural change was tested by employing an iterative process. The 

process involved: (1) partitioning the observations into smaller clusters; (2) picking an 

observation within a given cluster as a potential structural 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 process was repeated for the cluster with the highest 

F-value until the observation with the highest F value was identified in the full data set. Then that 

observation was established as the structural change point for the remaining analysis steps. 

5.4 Step 4 – Univariate Regression Analysis to Estimate Relationship between CE and X

In this step, one of the following sets of equations were estimated for the operating 

variable under consideration: 

ܮܰሺܧܥሻ ൌ ܽ ൅ ܾܮܰሺܺሻ ൅ ߝ

ܮܰሺܧܥሻଵ ൌ ܽଵ ൅ ܾଵܮܰሺܺሻଵ ൅ ߝଵ for ܮܰሺܺሻ ൑ ܮܰሺܺሻௌ்

ܮܰሺܧܥሻଶ ൌ ܽଶ ൅ ܾଶܮܰሺܺሻଶ ൅ ߝଶ for ܮܰሺܺሻ ൐ ܮܰሺܺሻௌ்

or 

ߝ ൅ ܾܺ ൅ ܽ ൌ ܧܥ

ܧܥଵ ൌ ܽଵ ൅ ܾଵܺଵ ൅ ߝଵ for ܺ൑ܺௌ்

ܧܥଶ ൌ ܽଶ ൅ ܾଶܺଶ ൅ ߝଶ for ܺ൐ܺௌ்

where ܮܰሺܺሻௌ் 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 was solved for the value that corresponds to 

the 96.5% CE, i.e., ்ܺ, as: 
22 

்ܺ ൌ ܧܺܲ ቆܮܰሺ0.965ሻ െ ܽො

ܾ෠ ቇ

or 

்ܺ ൌ 0.965 െ ܽො

ܾ෠

depending on the operating variable 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 variables of interest. The various plots and outliers that are associated 

with this statistical analysis are provided in Appendix B. 

TABLE 9: STATISTIC RESULTS FOR NHVcz, 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 corresponds to 96.5% CE has a relatively wide 

range of 250 to 336 BTU/SCF. The predicted threshold 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 narrow range of 0.202 to 0.209 (volume fraction). 4

While the estimated equations have low adjusted R2 values, all coefficient estimates are 

statistically significant at a confidence level higher than 99%. 

Given that the overall fit for the estimated equations is poor (i.e., low adjusted R2 

values), the estimated confidence and prediction intervals around the mean operating parameter 

value of interest, X, are wide (refer to the depicted confidence and prediction bounds for each 

operating parameter in the “fit plots” tab of Appendix B). This indicates that there is a large 

degree of uncertainty associated with the estimated 96.5% CE values for each of the operating 

parameters. We expect that the uncertainty can be reduced by considering relevant omitted 

variables that may also be affecting combustion efficiency in addition to the operating parameter 

of interest, X, and employing multiple regression techniques. 

6.0 REFERENCES 

Allen, D. T., and V. M. Torres. 2011. 2010 Flare study final report. Prepared for the Texas 

Commission on Environmental Quality, Tracking No. 2008-81. 

<http://www.tceq.texas.gov/assets/public/implementation/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 Petroleum 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 Marathon Petroleum Company, LLC Findlay, OH and Clean 

Air Engineering, Inc., Palatine, IL. 

Clean Air Engineering, Inc. 2011. PFTIR test of steam-assisted elevated flares – 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 pyrolysis and oxidation kinetics in a flow reactor 

and laminar flames. Combustion and Flame. 119, 375-399. 

INEOS ABS (USA) Corporation. 2010. 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 modeling of 1,3-butadiene oxidation 

at high temperatures. International Journal of Chemical Kinetics. 32:10, 589-614. 

McDaniel, M. 1983. Flare efficiency study. Prepared for U.S. EPA Office of Research and 

Development EPA-600/2-83-052 by Engineering 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. EPA Office of Research and Development by 

Energy and Environmental Research Corporation (May). 

Pohl, J. and N. Soelberg. 1985. Evaluation of the efficiency of industrial flares: Flare head 

design and gas composition. EPA-600/2-85-106. Prepared 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: H2S gas mixtures 

and pilot assisted flares. EPA-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/etsug/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. Prepared by Shell Global Solutions (US) Inc., Houston, TX (April). 

U.S. EPA. 2012. Parameters for Properly Designed and Operated Flares. Located at: 

http://www.epa.gov/ttn/atw/flare/2012flaretechreport.pdf

U.S. EPA. 2006. Office of Environmental Information. Data Quality Assessment: Statistical 

Methods for Practitioners, EPA QA/G-9S. Washington, 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