Article pubs.acs.org/IECR
Application of the Carbon Balance Method to Flare Emissions Characteristics Scott C. Herndon,*,† David D. Nelson, Jr.,† Ezra C. Wood,†,‡ W. Berk Knighton,§ Charles E. Kolb,† Zach Kodesh,∥ Vincent M. Torres,⊥ and David T. Allen⊥ †
Aerodyne Research, Inc., Billerica, Massachusetts, United States Department of Public Health, University of Massachusetts, Amherst, Massachusetts, United States § Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, United States ∥ John Zink Company, LLC, Tulsa, Oklahoma, United States ⊥ Center for Energy and Environment Resources, University of Texas, Austin, Texas, United States ‡
S Supporting Information *
ABSTRACT: The destruction and removal efficiency (DRE) computation of target hydrocarbon species in the flaring process is derived using carbon balance methodologies. This analysis approach is applied to data acquired during the Texas Commission on Environmental Quality 2010 Flare Study. Example DRE calculations are described and discussed. Carbon balance is achieved to within 2% for the analysis of flare vent gases. Overall method uncertainty is evaluated and examined together with apparent variability in flare combustion performance. Using fast response direct sampling measurements to characterize flare combustion parameters is sufficiently accurate to produce performance curves on a large-scale industrial flare operating at low vent gas flow rates.
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sponsored a study to systematically evaluate the flare performance at low flow rates and relatively low heating content of the process gas. The final report for the TCEQ 2010 Flare Study6 describes the flare measurements and reports the combustion characteristics observed in flares operating at high turndown ratios. This paper describes an atomic carbon conservation methodology that enables the quantification of flare combustion characteristics using time dependent concentration measurements of chemical species in the flare exhaust plume. The approach uses an array of in situ sampling technologies operating with fast time response to determine the representative flare plume properties. Two of the flare characteristics, combustion eff iciency (CE) and destruction and removal ef f iciency (DRE), are the performance metrics computed with this approach. This paper discusses the results from the TCEQ 2010 Flare Study in order to separate apparent variability (from the flare) from sources of systematic error in the performance metric computation. Finally, an example flare performance curve as a function of “assist ratio” is discussed in the context of uncertainty analysis as it pertains to this methodology and the apparent variability in the flare itself.
INTRODUCTION The primary purpose of a flare is to safely dispose of discharged gases in an environmentally compliant manner through the use of combustion.1 Flares are used in refineries, petro-chemical plants, oil and gas production facilities, landfills, and most other production facilities that require safe disposal of large quantities of flammable gas mixtures. Flare capacity is typically based on the maximum anticipated flow that can be discharged to the flare by the production process to which it is connected. These maximum anticipated cases are typically emergency cases such as fire or power loss, and being such their frequency is low. Flares most frequently operate in turndown mode in which the flow rate of discharged fluid (the material to be combusted) is significantly lower than the design capacity of the flare. When flares are operated at such high turndown ratios there are concerns that the desired combustion efficiency is not attained. The U.S. Environmental Protection Agency Basis and Purpose Document on Specification For Hydrogen-Fueled Flares states that operating in accordance with 40 CFR § 60.182 (b)−(d) and 40 CFR 63.11 (b) will result in destruction of volatile organic compounds (VOC) or volatile hazardous air pollutants (HAP) with a destruction efficiency of 98% or greater. A number of studies report the presence of unexplained, elevated VOC concentrations in the Houston area in ambient measurement data.3,4 A review of the available literature and summary of research needs related to flare performance has identified that one of the needs in flare research is to evaluate performance characteristics of industrial scale flares under low flow rate, when the effect of cross-wind may be important.5 Recently, the Texas Commission on Environmental Quality, through the Center for Energy and Environmental Resources at the University of Texas, © 2012 American Chemical Society
Special Issue: Industrial Flares Received: Revised: Accepted: Published: 12577
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Table 1. Selected Species Measured with the Extractive Sample Suite species propene methane ethane propane butane CO CO2 ethene ethyne formaldehyde acetaldehyde 1,3 butadiene butane + acrolein C3H4O isomers benzene methanol molecular oxygen NO NO2 particle number particle mass particle composition
direct methoda PTRMS QCL
QCL Licor QCL QCL QCL PTRMS PTRMS PTRMS PTRMS PTRMS PTRMS paramagnetic chemiluminescence QCL CPC MAAP HRTOF-AMS
sample line
time response (s)
GC-FID
continuous FID
gas gas gas gas gas gas gas and particle gas gas gas gas gas gas gas gas gas gas gas gas particle particle particle
1.2 0.7 600b 600b 600b 0.7 0.9 0.7 0.7 0.7 1.2 1.2 1.2 1.2 1.2 1.2 1 1 0.7 2.3 3 1−30
Y Y Y Y Y Y Y Y Y Y Y -
Y Y Y Y Y no no Y Y ∼ ∼Y Y ∼Y Y Y Y -
a PTRMS: proton transfer reaction mass spectrometer. QCL: quantum cascade laser. Licor: refers to Li-Cor Biosciences’ non-dispersive infrared measurement approach, various models were used in this study and are described elsewhere.8 CPC: condensation particle counter. MAAP: multiangle absorption photometer. HRTOF-AMS: high-resolution time-of-flight aerosol mass spectrometer. bThe number of seconds in the time response column for these species is the sample interval by gas chromatographic methods.
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EXPERIMENTAL DESCRIPTION The details of the overall experiment design and results are thoroughly described in Torres et al.6 Briefly, the data set was acquired during September 2010 at the John Zink Company industrial scale research facility in Tulsa, Oklahoma. The flare characterization experiments were conducted with detailed knowledge of process gas composition and flow rate as well as flare operational parameters. Various infrared and visible cameras were used to observe the flare and interaction of the flare plume with the wind during the test.7 This work focuses on the results from extractive sampling data. Two additional papers in this Special Issue discuss the experimental measurement equipment details8 and particulate sample characterization.9 A sampling device constructed for these tests consisted of an entry cone (50.8 cm internal diameter) tapering to 30.5 cm internal diameter tube. On the inlet entry cone, three thermocouples were mounted to measure the temperature of the gas entering the sampling device. Mixing tabs were employed to homogenize the gas mixture prior to sampling. The probes used to conduct gas to the instrumentation extracted 0.8−1.2 standard liters per minute (slpm) of sample from the main large diameter sampler section. Downstream from sample extraction, 18.7−19.2 slpm of inert molecular nitrogen was added to immediately dilute the sample prior to transfer to the various instruments. Details of this aspect of the sample system are described in the Supporting Information. The dilution rapidly cooled the sample while preventing vapor condensation (notably water) and suppressed further oxidation chemistry in the exhaust flow. Detailed descriptions of the analytical methods used in this study are provided in a companion article.8 The instrumentation was deployed to and operated at the site in the Aerodyne Mobile Laboratory.10−12 The essential characteristics of the
instrument suite were chosen to provide fast response (∼1 s), continuous, direct detection of unburned process gas hydrocarbons (such as propene and methane), combustion products (such as CO2 and CO), and particulate characteristics (number, size, mass, and composition), as well as the combustion intermediates that could be detected (e.g., formaldehyde, acetaldehyde, propene oxide). Fast response measurements were supplemented by gas chromatographic analyses of flare plume samples collected during selected periods of each test point. This work also employed nonspecific measurement data from a flame ionization detector (FID) as a proxy measurement of total hydrocarbons (as ppmC) and an oxidation catalyst system to verify total carbon detected as CO2. The detection methods for the direct measurement of selected compounds and particle parameters are provided in Table 1.
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THEORETICAL BASIS The key goal of the project was to determine the DRE of the flares from direct measurements of the concentrations of the gaseous and particulate species in the exhaust plume. The DRE is defined as ⎡ F (TS) ⎤ DRE = ⎢1 − out ⎥ × 100 Fin(TS) ⎦ ⎣
(1)
where Fin(TS) and Fout(TS) are the flow rates of the target species, noted by (TS), into the flare and out of the flare, respectively. Clearly, if the outflow is zero, then the DRE is 100%. If the outflow is equal to the inflow, then the DRE is 0%. The difficulty that confronts us is to relate DRE to concentration measurements made in the exhaust plume rather than the flow rates that appear in the definition. This is further complicated by the fact that the entire plume is not captured 12578
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If we express the input and output gas contents as carbon fractions, however, then the DREs follow easily. The input carbon fraction for methane is 25% and the output carbon fraction is 12.5% so its DRE is 50%. Similarly, for propene the input CF is 75% and the output is 37.5% so its DRE is also 50%. Note that this analysis will work even with incomplete combustion that produces CO and other combustion products as long as all partial-combustion (non-CO2) products are measured. The sample collected at the flare output plume contains a variable quantity of entrained atmospheric background gas, complicating a direct evaluation of carbon fraction. This is unavoidable since the DRE must be measured after combustion in the turbulent diffusion flame is complete. If the atmospheric background contains a significant concentration of carboncontaining species then it becomes important to differentiate the background species from the plume species in order to apply this analysis method. Distinguishing the flare plume constituents from the ambient background species requires a time series analysis of plume samples with varying degrees of dilution. The covariance of the measured mole fraction allows us to calculate the carbon fraction of each species without precise knowledge of the extent of dilution. More precisely we are able to calculate ratios of the concentrations of the plume species before dilution and these are sufficient for the calculation of the desired carbon fractions as explained below. We first discuss the time- and dilutiondependent concentration data. The concentration measured by each of the continuous monitors consists of a weighted average of flare combustion and ambient mole fractions.
and that the portion of the plume that is captured is diluted with background gas to a variable degree. So a direct measurement of the flow rate of the target species in the flare exhaust is quite challenging. Instead we relate the relative flow rates of the target species to relative mole fraction by volume, which we can directly measure. The flow rate of any carbon-containing species (noted below by M) can be expressed as the product of the total flow rate of carbon atoms with the fraction of carbon atoms that are present in the form of the target species. We call this latter quantity the carbon fraction (CF). Hence, F(M) = CF(M)*F(C)
(2)
where F(C) is the flow rate of carbon atoms (atoms of C/sec), F(M) is the flow rate of species M (expressed as atoms of C/s), and CF(M) is the carbon fraction for species M: CF(M) =
Nc(M) × [M] ∑i Nc(Mi) × [Mi]
(3)
Nc(Mi) is the number of carbon atoms in each molecule of species i, [Mi] is the concentration of species i (in molar units), and the sum is taken over all species in the sample, including the term where M is TS. Typically the sum in the denominator will be dominated by the contributions from CO2 and CO (and possibly the feedstock if combustion efficiency is low). Substitution of eq 2 into eq 1 gives ⎡ CFout (TS) × Fout(C) ⎤ DRE = ⎢1 − ⎥ × 100 CFin(TS) × Fin(C) ⎦ ⎣ ⎡ CFout (TS) ⎤ = ⎢1 − ⎥ × 100 CFin(TS) ⎦ ⎣
[X ]measured = f × [X ]post‐combustion‐plume + (1 − f )
(4)
× [X ]ambient
where the second equivalence in eq 4 depends on the conservation of carbon atoms which implies that Fin(C) = Fout(C). The carbon fraction of the target species entering the flare is easy to calculate from flow rate measurements of the vent gas (the flare feedstock) that are generally available. In the case of a pure vent gas composition (only one carbon-containing species) the carbon fraction is unity for that species. For an equimolar mixture of methane and propene for example, the methane carbon fraction is 0.25 and the propene carbon fraction is 0.75. The carbon fraction of the target species exiting the flare can be calculated directly from eq 3 assuming that one measures the mole fraction of the target species and of all other carboncontaining species with significant abundance. This is the primary approach taken in this project and the reason for the suite of chemical monitors listed in Table 1. Expressing DRE as a function of carbon fraction (rather than molar fraction) simplifies the equation since the carbon fraction approach is not affected by the change in the number of moles of carbon-containing species that accompanies combustion. A brief example will clarify this comment. If the input feedstock is 50% propene and 50% methane (both by mole fraction) and if half of each combusts to CO2, then the flare output of carbon containing species will be 16.7% methane, 16.7% propene, and 66.7% carbon dioxide (by mole fraction). This is because each mole of propene consumed produces 3 mols of carbon dioxide. Based on the output percentages, it may not be immediately evident that the DREs for methane and propene are each 50%.
(5)
where f represents the time-dependent volume fraction of exhaust that is sampled. The quantity f(t) is modulated by the sampling scheme or by the combustion phenomenon itself under most circumstances. The turbulent mixing in the sampling process does not differentially mix the various molecular species between the point of combustion and the sampling point. Thus, each compound will follow eq 5 and share a common value of f. An example of this time- and dilution-dependent covariation is given in Figure 1 for two species measured during the flare
Figure 1. Time series of sampled concentrations for two different species.
testing13 and labeled “vector a” and “vector b” in the figure. The vector “a” in this illustration is the actual carbon monoxide mole fraction in ppbv and vector “b” is the mole fraction of CO2 in ppmv. Similar covariation is observed for any pair of species present in the plume. The background concentrations are measured when the flare is off and no process gas is being 12579
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directed to the flare or the sampler is not positioned in the downwind vicinity of the operating flare. The increases in vectors a and b occur at times that coincide with the arrival of heat to the sample inlet as imaged by the infrared camera array.7 Furthermore, during the event depicted in Figure 1, the thermocouples mounted on the inlet of the sample collector also registered elevated temperatures (rising from ∼80 to 110 °F) suggesting the sample collector was entraining a mixture of ambient and combustion exhaust. The data depicted in Figure 1 are characterized by plume encounters that last ∼5 to 12 s. The clear covariance between vectors a and b, which is displayed in Figure 1, can be quantified by plotting the value of vector a at time t versus that of vector b at the same time for all times in Figure 1. The result is shown in Figure 2. There is a
good choices since each is present in significant concentration for all or nearly all plume conditions. Note that at the limit of trying to characterize very low DRE conditions, the measurement of a vent gas hydrocarbon species could be used in the normalization in eq 8. Because the focus of the test13 is on higher DRE conditions, combustion species such as CO2 and CO are preferable over the vent gas hydrocarbon. If we choose CO, we can express the carbon fraction of species M as Nc(M) × CF(M) = ∑i Nc(Mi)
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strong linear relationship between vector a and b. We will show that the slope of this line is approximately equal to the ratios of the mole fraction of species a and b in the undiluted flare exhaust. The conclusion that the covariance slope (m) is approximately equal to the ratio of the plume mole fractions can be derived from eq 5 to show
RESULTS The method of carbon-balance and time series analyses has been used to compute DRE and CE for all of the official test points in the TCEQ 2010 Flare Study.6,13 In this section we examine the time series data collected for a specific test sequence (multiple test points) where the operation of the flare produced different combustion characteristics for a fixed vent gas composition. The flare used in this example is a steam assisted flare, which employs two sources of “steam assist”. We will evaluate how quantitatively the measurements meet the analysis requirement (e.g., the denominator in eq 3) for total carbon-balance. Finally, the computed DRE for the selected test series as a function of the assist ratio is discussed. In the Discussion section, various methods to quantify method error, instrumentation error, and intratest variability, as well as the reproducibility of the test point data are considered. The “S7” series of tests employed a vent gas composition of 80% propene and 20% Tulsa natural gas and sufficient dilution nitrogen to reduce the vent gas lower heating value to 350 BTU/scf . This series of tests examined the influence of varying the vent gas flow rate from 2300 to 937 lbs/h, while holding the steam assist components constant. The target “center steam” and “upper steam” parameters were held at ∼500 lbs/h while the total vent gas flow rate was varied. The test series resulted in a variation of the vent gas to total steam ratio from 0.35 to 1.13 (vent gas lbs/h per total steam lbs/h). All of the measurements tabulated in Table 1 were available for the majority of the total testing period, including the “S7” test sequence. Because the tests which substituted propane for the propene in the vent gas composition were inserted into the test matrix during the campaign, the flame ionization detector and the total ppmC via oxidation catalyst was integrated for tests
[a]measured − [a]ambient [b]measured − [b]ambient [a]post‐combustion‐plane − [a]ambient [b]post‐combustion‐plane − [b]ambient
=m
(8)
where ([Mi]/[CO]) is the slope derived from a plot of species i versus CO as discussed above. It is interesting to note that the Σ Nc(Mi) [Mi] which appears in the denominators of eqs 3 and 8 can be interpreted as the mole fraction of CO2 that would result if combustion were complete. This is more than just an interpretation. This term can, in fact, be measured by converting all of the carbon containing species in the exhaust plume to CO2, eliminating the need for an array of expensive molecule specific chemical sensors. This approach can be implemented with a catalytic converter followed by a fast CO2 sensor. These measurements were performed during the test campaign13 and are described in the Results section. An alternative evaluation of the quantitative validity of the term Σ Nc(Mi) is pursued using the flame ionization detector (FID) measurements.8 The drawback of the FID is its reduced sensitivity to oxygenated VOCs (e.g., HCHO).
Figure 2. Time series in the example data for this derivation are plotted against one another.
=
[M] [CO] [M i] × [CO]
(6)
or equivalently, [a]measured = m*[b]measured + ([a]ambient − m*[b]ambient ) (7)
Because the mole fractions for all species of interest are much greater in the undiluted exhaust plume than in the ambient background, the derived slope is equal to the ratio of the mole fractions of the two species in the undiluted flare exhaust. For the carbon-containing compounds quantified in this study, the approximation [b]measured/[a]measured = [b]post‑combustion‑plume/ [a]post‑combustion‑plume introduces less than 2% error based on typical mixing ratios observed in the ambient and on the mixing ratios present in the combustion zones.14 Finally, we must show that these ratios can be used to calculate carbon fraction for each species. The carbon fraction eq 4 can be expressed in terms of ratios of plume concentrations by dividing the numerator and denominator by the same species. Both CO and CO2 are 12580
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Figure 3. Selected time series data for S7 test depecting a subset of the compound measured during the test. The three panels noted as (a), (b), and (c) show ∼2 min of data from the test condition noted in the lower axis label. The lowest y-axis trace shows the CO2 time series and the uppermost trace shows the propene concentration. The middle y-axis cluster of traces shows the CH4, CO, ethene, ethyne, and formaldehyde measurements.
the sum of butene isomers and acrolein are similar in magnitude to the ethene and formaldehyde concentration in the flare plume. Actual carbon-balance is explored by comparing the sum of the individual measured compounds, expressed as ppmC to the total carbon quantified by the measurement of CO2 after the sample has passed through an oxidation catalyst. The “S7” series of test data points are depicted in Figure 4. The test points S7.5 and S7.4 have significant non-CO2 compounds in the total carbon budget because the oxidation catalyst CO2 (xaxis) is systematically greater than the CO2 measured in the sample stream (upper panel, y-axis). This is consistent with the data depicted in Figure 3, panel (a) where significant concentrations of propene are observed. The relationship between CO2 in the sample stream and post-oxidation catalyst CO2 is closer to 1 for the conditions S7.6 and S7.1, which is also consistent with the data in Figure 3, panel (c) where relatively little propene is observed. The lower panel of Figure 4 compares the sum of all measured compounds to the total ppmC measured by the oxidation catalyst. The color scheme is consistent with the legend from the upper panel. Each instrument was operated with a time reporting interval of ∼1 s, and each data point in this figure is a 1-s data point that has been interpolated onto a common time base. The periodic zero air overblow of the sample inlet has been used to time align the various data vectors that comprise the summed ppmC. This accounts for the small lag time differences (0−4 s) that depend on where the sample was drawn from the main sample flow. Based on calibration testing, the oxidation catalyst degrades the temporal response slightly (from 0.9 to 1.2 s), but this has not been accounted for in this analysis. The contribution from the particulate mass has been ignored in the summation. The particulate data, described elsewhere,9 suggest the particulate carbon can approach 0.2% of the total carbon by mole at the conditions most favorable for soot production . For the S7 test series, the greatest molar ratio is 2 × 10−4 moles of carbon in soot per measured mole of CO2. The sample probe used for the gas phase species is made from Teflon, which transmits
after 9/26/2010 (approximately the second half of the campaign). A subset of the available measurements for three of the nine “S7” tests is depicted in Figure 3. Note that each of the panels include a subset of approximately 2 min of data, but the total duration of each test was typically 7−10 min. All of the y-axes depicted in the time series of Figure 3 are on equivalent scales from left to right. The uncombusted vent gas species propene and methane (blue and orange in Figure 3) are present in the plume in the largest amounts in test S7.5. They are reduced in S7.2 and barely present in S7.6. A key difference between these conditions is how the flare is being operated. The total vent gas flow rates in each of the tests S7.5, S7.2, and S7.6 were 1180, 1874, and 3028 lbs/h. Thus, the relative amount of steam assist (which was held constant at 500 center and 500 upper) to the total vent gas flow rate (with fixed composition) across the tests depicted in Figure 3 varies from ∼0.9 in panel (a), 0.57 in panel (b), and 0.33 in panel (c). The time series analysis, using eq 6, has been performed on the entire test sequence of each of the measured carbon-containing species (including other compounds not depicted in Figure 3). The slope of correlation between each carbon containing compound and carbon monoxide has been tabulated and used in eq 4 to compute the DRE flare combustion parameter. In Figure 3, the analysis results in propene DRE for these tests of 73 ± 5, 88 ± 2, and 97 ± 1 for S7.5, S7.2, and S7.6, respectively. A critical assumption in eq 3 is that all forms of combustion carbon are accounted for in the summation term. Additional details about the relative contribution to the product carbon fraction by trace combustion intermediates is described elsewhere in this issue.8 It is worthwhile to note in the inspection of the time series data in Figure 3 that the dominant source of carbon in the process gas (in this example, propene) is the dominant hydrocarbon, by an order of magnitude. Here though, the context for analysis involves the sum of all combustion carbon species in order to achieve the carbon balance asserted in eq 3. In the middle time series of Figure 3, ethyne and formaldehyde are less than ethene. Acetaldehye and 12581
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Another possible source of unmeasured carbon involves compounds that are not transmitted through the sample line. The dilution gas applied at the tip should inhibit condensation of most semivolatile gas species. If, however semivolatile VOC were present at concentrations sufficient to influence the carbon fraction calculation in and along the sample line, they should produce a slow signal offset in the oxidation catalyst with some scale of hysteresis. During the experiment, no appreciable drift in the oxidation catalyst signal was observed during the 1−2 h breaks between testing. Though the overnight standby condition maintained ambient flow through all instruments with a particulate filter, no significant drift was observed in the oxidation catalyst signal. Another route to bounding the amount of condensable carbon is to consider the organic to elemental carbon ratio detected in the particle phase. The quantification of organic matter to black carbon mass is less than 6%.9 If we assume the ratio of oxygen to carbon is 1, in order to represent species with reduced volatility15 the amount of detected organic carbon in the particle phase is ∼3% of the black carbon mass which is less than 0.2% of total carbon in the exhaust. If the flame were producing significant quantities of carbon species with reduced volatility, it might show up in the particulate emissions. Several types of combustion soot often have organic particulate matter present.16−18 The soot quantified on the other sample line described elsewhere in this issue9 did not have a significant organic carbon fraction. For these reasons, we conclude there are no significant semi- or nonvolatile carbon species that contribute appreciably to the carbon fraction calculations. The carbon balance demonstrated in Figure 4 means that carbon fraction calculations can be used to compute flare combustion characteristics. We now examine the effect of assist ratio on the computed DRE. The depiction of the DRE as a function of assist ratio is one method of assessing the flare performance characteristic. The resulting performance curve is presented as an example for the discussion of the uncertainty in the carbon balance method. As described above, the “S7” test sequence involved fixing the vent gas composition and the flare assist ratio while varying the total vent gas flow rate. Thus the ratio of assist flow rate to vent gas flow rate was varying during the test. As is discussed in Torres et al.,13 many of the other test points involved fixing the vent gas composition and flow rate while varying the flare assist parameter. The “S7” test series was added to the measurement campaign because it was easier to set the flare assist parameters and vary the vent gas flow rate. The computed DREs as a function of assist ratio for the test sequence “S7” are depicted in Figure 5. Each of the data points is the DRE for a corresponding test point or repeated test point. The implication is that as the assist ratio is increased the flare combustion characteristics result in less propene combustion. The implications for flare operation as well as the results of the complete study are discussed elsewhere in this issue.13 The point of depicting the computed DRE via the method described here relative to the assist ratio is to demonstrate that the approach used to compute DRE can be employed to map characteristic flare performance curves. In the case of the TCEQ 2010 Flare Study, the objective was to investigate largescale industrial flares operating at low flow conditions. The performance trend (depicted by the solid black line in Figure 5) is an arbitrary third order polynomial fit. It is not based on a fundamental theory of flare combustion, it is only a curve used to project various sources of method uncertainty
Figure 4. Carbon balance. The upper panel depicts the measurement of CO2 in the diluted sample stream plotted vs measured CO2 from an oxidation catalyst system. The lower panel depicts the sum of all measured species (propene, CO, CH4, etc.) also plotted vs the measured CO2 coming from the oxidation catalyst. The legend shows the color scheme used to denote different flare operation characteristics within the “S7” test set. The series of linear fits for each test are 1.0 ± 0.02.
particles poorly. Thus, the oxidation catalyst determination of ppmC is unlikely to include the minor contribution of carbon in the particulate form, but it is also neglected in the summation depicted in the lower panel of Figure 4. The correspondence depicted in Figure 4 between the summation of the individually measured species and the oxidation catalyst determination of total ppmC suggests there are minimal unmeasured compounds that contribute significantly to the total carbon balance. An examination of the y-axis scales in Figure 3 shows that propene and CO2 are responsible for the majority of the carbon-containing gas phase compounds. Figure 4 shows that carbon balance is achieved, suggesting that all gas phase species transmitted down the sample line are properly closing the carbon balance. This observation is an indirect verification of the propene calibration used by the PTR-MS.8 The calibration procedures for the two Licor CO2 measurements (before and after the catalyst) were identical and referenced to the same standards, documented in Appendix I of Allen and Torres.6 The calibration of the propene involved using several standard mixtures and verification using gas chromatographic methods.8 The correlation between the sum of all measured carbon species and oxidation catalyst output ranges from 0.98 to 1.02. 12582
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performance for the test condition. Sample bias, as it applies to the flare study results described elsewhere6,13 is a separate issue from uncertainty in the analysis method (carbon balance). The other subsections in this section evaluate the intratest and test point reproducibility as approaches to attribute an uncertainty to this work based on the specific application in the TCEQ 2010 Flare Study. The results of these uncertainty estimates based on the entire test data set are depicted in Figure 5 by the solid blue curves. Method Accuracy. The approach of using carbon balance has been employed in numerous circumstances to quantify emissions from combustion sources that produce mechanical work and been shown to be capable of achieving agreement with other emissions performance methods.16,20−22 The application of this method to flare emissions is novel as far as we can determine. A step in the time series analysis requires that a measurement of a specific compound be used as the flare impact dilution tracer (eqs 5 and 6). The measurement data for both CO and CO2 have been used as the abscissa in all correlation figures and the slopes of all species relative to each compound determined. Respectively, they have been used in expanded form of eq 8 to compute DRE according to eq 4. Based on analysis of the results, the average difference between the DRE calculated using CO2 vs CO for all test points spanning all DREs in the measurement is less than 1% with a standard deviation of 3%. We arbitrarily use this evaluation of method uncertainty to assign an overall accuracy of the CFout/ CFin to be 6% (2σ). This is a fixed method error and will be used below in conjunction with the combined analytical error estimates. Error Analysis in the Analytical Instrumentation. The analytical uncertainty in the target species (in this study, propene) and the principal combustion product, CO2 will dominate the uncertainty in the computed DRE. In the case of the TCEQ 2010 flare study the focus was on propene DRE. The Supporting Information presents an expanded discussion and tabulation of all analytical uncertainties. Expanded instrumentation performance is discussed elsewhere in this issue.8 The combined error due the analytical uncertainty analysis and the fixed method error described above is depicted in Figure 5. The dashed lines show the projection of combined analytical uncertainty and method error around the solid black trend line. The largest source of analytical uncertainty is the measurement of propene using the PTR-MS. Because the CF(propene) in the flare plume increases with decreasing DRE, the analytical uncertainty envelope increases. Additional details on the quantification of propene using PTR-MS and the agreement with the GC-FID quantification of propene are also presented elsewhere in this issue.8 Intra-Test Variability Part I: Propene−CO Tendency. A simple method has been used in this work to quantify the apparent variability in flare combustion performance within each test. This simple method is contrasted with a more elaborate computationally intense approach described later. Rather than use the standard error of the slope parameter in the fit as a basis for error bars, which are relatively small, the tabulated “error” attempts to quantify the actual test performance variability for all data points reported. All of the measurement data for the entire test were fit with a linear regression. Using the measured CO and the linear propene/CO relationship, propene residuals were computed. The residuals were used to produce two data set populations, the points that had a positive residual and those that had a negative residual,
Figure 5. DRE vs assist ratio. The computed DRE is plotted as a function of assist ratio. The gray squares are the DRE for each test point. The “error” bars on the data points are generated using the propene tendency described in the Discussion section and represent an extent of the intratest variability. The solid black line is a simple polynomial fit to the data and is only present to visually depict the apparent trend in the results. The dashed gray line is a projection of the analytical uncertainty analysis. The solid blue lines are quantification of the test-to-test variability projected onto the solid black trend line. See the Discussion section for additional comments on these quantities.
bands to help evaluate how stringently the data derived from this study can be interpreted. It is tempting to clump all sources of apparent noise and uncertainty together as “error” bars. The next section contains a discussion of the factors contributing to uncertainty in the method described in this work for flare performance characterization.
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DISCUSSION To demonstrate the analysis method described in this work, it has been applied to the data collected during the TCEQ 2010 Flare Study. The Results section has shown the character of the measurement data via the time series depiction. It established that carbon balance has been achieved and verified analytically. A sub set of the campaign test points, corresponding to variation in the assist ratio, have been grouped and the results of the DRE computation plotted. Here, we discuss how well the method actually works. What meaning can be imparted to the observed trends in DRE computed via this method? There are really two fundamental questions that emerge in the context of uncertainty analysis. The first topic in uncertainty analysis involves the analytical approach of computing DRE based on the array of measurements. In the subsections below entitled, “Method Accuracy” and “Error Analysis in the Analytical Instrumentation” two methods for attributing uncertainty are discussed. The results of these uncertainty analyses are combined and depicted in Figure 5 by the dashed curves. The second topic in uncertainty analysis involves issues associated with the specific application of the method to the data collected in the TCEQ 2010 Flare Study. The form of combustion that occurs in the flaring process is best represented as an open-air diffusion flame.19 Considerable effort was made in designing a sampling system to collect and mix a volume of flare plume prior to compositional analysis. If the spatial scale of the sampled flare plume was large relative to small-scale fluctuations in the combustion and it was observed that the measured data trended to a most common value, the measured value can be deemed representative of the overall flare 12583
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manifold also included an immediate second stage of dilution that further cooled the sample and lowered the relative humidity. The extracted DRE quickly converges as additional data are incorporated into the time series analysis. If the sampling had a strong sensitivity to location within the flare plume it would manifest itself in the data as a much wider distribution of DRE. The large volume of sample flow, mixed prior to extraction for analysis, appears to be a successful strategy for using extractive sampling to produce a representative DRE for the bulk flare plume emissions to the atmosphere. To determine the intratest point variability that this analysis produces, test condition S4.6R1 has been parsed into different intervals. The entire test point (∼ 10.5 min) has been parsed into 10-s, 20-s, 40-s, and 1-min bins and processed using the same methodology as the total ensemble analysis. The results of this analysis are depicted in Figure 6. The red diamond is the
corresponding to a data set that had relatively more propene to CO than the total data set trend line and a second data set that was relatively deficient in propene to CO. These two populations were then fit independently through all flare plume ratios and used to compute an upper and lower limit for the observations. This method quantifies the intratest variability using a common metric and is referred to as the propene−CO tendency. It is the basis for the error bar associated with each data point in Figure 5 and in the tabulated values reported elsewhere.6,13 Test Point Reproducibility. The flare study involved varying the vent gas flow rate with a fixed flare assist condition, or varying the flare assist for a fixed vent gas flow rate. The test conditions (e.g., vent gas flow, assist flow rates, vent gas composition groupings) are reported elsewhere.6,13 For the majority of the tests, the net combustion condition was repeated twice, often several tens of minutes later. The potential shift in wind direction, sample collector height, and distance from the flare center were inevitably varied, sometimes drastically between the replicate test points. Thus, examining the reproducibility can yield insight into overall test scheme uncertainty. The standard deviations of the DRE computed for the nominal test repeats have been tabulated in the Supporting Information part C. The distribution of standard deviations in this table for the noted test conditions shows that 66% of the deviations in the test repeats are less than or equal to 1.8%. Note that the repeated test points do not completely reproduce the combustion conditions perfectly since there are unavoidable changes in the ambient state. This point is discussed more thoroughly in the paper discussing the measurement results.13 Despite this caveat, we assign the absolute reproducibility for any given test point to be within 3.6%, which encompasses 95% of the test-to-test reproducibility quantified by this metric. Intra-Test Variability Part II: Representative Sampling. The purpose of the TCEQ 2010 Flare Study was to determine the representative DRE as a function of flare operational parameters. It was not part of the project to quantify inhomogeneity in the combustion at varying flare radii. The study design deliberately attempted to sample a large volume of air and force mixing before characterizing the sampled constituents. Furthermore, during the project design phase, the sample collector was positioned so the plume temperature measured by the thermocouples on the collector inlet were between 50 and 140 °F. The sampling device was positioned outside the visible flame and on the downwind side of the visible flame, approximately one additional flame length away from the flare. This was performed in order to sample completely combusted or quenched combustion gases. The sample collector drew up to ∼15−20% of the total flare plume volume based on estimates of stoichiometric combustion and the measured volumetric draw of the sample collector. At the distances the sample collector was deployed offset from the flare center, the flare combustion products were unavoidably diluted by an unknown, uncontrolled amount of noncombustion ambient air prior to entering the sample collector. It is clear though that the sampler did rapidly pull in air that was highly perturbed by the flare plume. Based on inlet probe temperature the mixture of flare plume gases and ambient resulted in elevated temperature (between 50° and 140 °F above ambient). The airmass sampled during plume encounters saw increases in CO2 above ambient by a factor of 2 to 50 (e.g., diluted flare plume CO2 mixing ratios of 800 to 20 000 ppmv). As noted in the Experimental Description , the sampling
Figure 6. DRE variability analysis for S4.6R1. The graph depicts the tabulated “ensemble” DRE and error bars (red diamond) along with the results of finer scale analysis described in text (histogram results and fit).
result from the whole event taken as an ensemble and reduced to a single DRE. The dark blue histogram in Figure 6 reflects the frequency that the shorter duration analysis resulted in a DRE within that bin width. The central value of this histogram (fit to a Gaussian function) agrees well with the tabulated value 98.1 vs 98.3%.
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CONCLUSIONS The conservation of carbon atom method to quantify flare combustion characteristics has been described. Using the TCEQ 2010 Flare Study data set, the carbon balance method has been applied to time series data. Alternative measurements suggest the approach is achieving the needed quantification of all significant carbon-containing compounds. Uncertainty analyses of the method suggest this approach is sufficiently accurate to quantify flare performance parameters. This powerful approach can be used to quantify the emission ratio of any compound that can be sampled rapidly with in situ instrumentation. It has been applied to both the controlled test data forming the basis of the TCEQ 2010 Flare Study, but also to analysis of sampled industrial flare plumes in the Houston 12584
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area.12 Additional detailed analyses (here and in Torres et al.13) of selected test points suggest the choice of time window for analysis does not affect the calculated DRE. We interpret the apparent insensitivity of DRE to analysis approach as a strong argument for representative sampling during the TCEQ 2010 Flare Study.
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and Tunable Infrared Laser Differential Absorption Spectroscopy. Ind. Eng. Chem. Res. 2012, DOI: 10.1021/ie202695v. (9) Fortner, E.; Brooks, W.; Onasch, T. O.; Jayne, J. T.; Herndon, S. C. Particulate Emissions Measured during the TCEQ Flare Emissions Study. Ind. Eng. Chem. Res. 2012, submitted. (10) Kolb, C. E.; Herndon, S. C.; McManus, J. B.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D.; Jayne, J. T.; Canagaratna, M. R.; Worsnop, D. R. Mobile Laboratory with Rapid Response Instruments for Real-time Measurements of Urban and Regional Trace Gas and Particulate Distributions and Emission Source Characteristics. Environ. Sci. Technol. 2004, 38, 5694−5703. (11) Herndon, S. C.; Jayne, J. T.; Zahniser, M. S.; Worsnop, D. R.; Knighton, B.; Alwine, E.; Lamb, B. K.; Zavala, M.; Nelson, D. D.; McManus, J. B.; Shorter, J. H.; Canagaratna, M. R.; Onasch, T. B.; Kolb, C. E. Characterization of urban pollutant emission fluxes and ambient concentation distributions using a mobile laboratory with rapid response instrumentation. Faraday Discuss. 2005, 130, 327−339. (12) Wood, E. C.; Herndon, S. C.; Fortner, E. C.; Onasch, T. O.; Wormhoudt, J.; Kolb, C. E.; Knighton, W. B.; Lee, B. H.; Zavala, M.; Molina, L.; Jones, M. Combustion and destruction/removal efficiencies of in-use chemical flares in the greater Houston area. Ind. Eng. Chem. Res. 2012, submitted. (13) Torres, V. M.; Herndon, S.; Kodesh, Z.; Allen, D. T. Industrial flare performance at low flow conditions: Part 1. Study Overview. Ind. Eng. Chem. Res. 2012, DOI: 10.1021/ie202674t. (14) Baukal, C. E., Jr.; Schwartz, R. E. The John Zink Combustion Handbook; CRC Press: Boca Raton, FL, 2001; p 750. (15) Kroll, J. H.; Seinfeld, J. H. Chemistry of secondary organic aerosol: Formation and evolution of low-volatility organics in the atmosphere. Atmos. Environ. 2008, 42, 3593−3624. (16) Kirchstetter, T. W.; Harley, R. A.; Kreisberg, N. M.; Stolzenburg, M. R.; Hering, S. On-Road Measurement of Fine Particle and Nitrogen Oxide Emissions from Light and Heavy-Duty Motor Vehicles. Atmos. Environ. 1999, 33, 2955−2968. (17) Canagaratna, M. R.; Jayne, J. T.; Ghertner, D. A.; Herndon, S.; Shi, Q.; Jiménez, J. L.; Silva, P.; Williams, P.; Lanni, T.; Drewnick, F.; Demerjian, K. L.; Kolb, C. E.; Worsnop, D. R. Chase Studies of Particulate Emissions from in-use New York City Vehicles. Aerosol Sci. Technol. 2004, 38, 555−573. (18) Timko, M. T.; Onasch, T. B.; Northway, M. J.; Canagaratna, M. R.; Herndon, S. C.; Wood, E. C.; Miake-Lye, R. C.; Knighton, W. B. Gas Turbine Engine Emissions - Part II: Chemical Properties of Particulate Matter. J. Eng. Gas Turbines Power 2010, 132 (6), 061505. (19) Noble, R. K.; Keller, M. R.; Schwartz, R. E. An Experimental Analysis of Flame Stability of Open Air Diffusion Flames. American Flame Research Committee International Symposium on Alternate Fuels and Hazardous Waste, Tulsa, OK, 1984. (20) Bishop, G. A.; Stedman, D. H.; Ashbaugh, L. L. Motor Vehicle Emissions Variability. J. Air Waste Manage. Assoc. 1996, 46, 667−675. (21) Herndon, S. C.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D. J.; Jayne, J. T.; Brown, R. C.; Miake-Lye, R. C.; Waitz, I. A.; Silva, P.; Lanni, T.; Demerjian, K. L.; Kolb, C. E. NO and NO2 Emissions Ratios Measured from in use Commercial Aircraft during Taxi and Take-Off. Environ. Sci. Technol. 2004, 38, 6078−6084. (22) Shorter, J. H.; Herndon, S. C.; Zahniser, M. S.; Nelson, D. D., Jr.; Wormhoudt, J.; Demerjian, K. L.; Kolb, C. E. Real-time Measurements of Nitrogen Oxide Emissions from In-use New York City Transit Buses using a Chase Vehicle. Environ. Sci. Technol. 2005, 39, 7991−8000.
ASSOCIATED CONTENT
S Supporting Information *
Data and expanded discussion of the following topics: analytical instrumentation uncertainty tabulation, the test data for the particular test called S7, and repeatability of DRE and CE results. This information is available free of charge via the Internet at http://pubs.acs.org/.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by the State of Texas through the Air Quality Research Program administered by The University of Texas at Austin by means of a Grant from the Texas Commission on Environmental Quality. We thank Dr. Jim Barufaldi and TRC for assistance with the FID measurements. We thank Dr. Peter Gogolek for several insightful comments on sampler design. We thank Joda Wormhoudt, Jon Franklin, Ed Fortner, William Brooks, Tim Onasch for assistance with the measurements and analysis.
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REFERENCES
(1) American Petroleum Industry. Process Safety Performance Indicators for the Refining and Petrochemical Industries; ANSI/API Recommended Practice 754,K75401; 2010. http://www.api.org. (2) United States Government. Code of Federal Regulations Standards of Performance for New Stationary Sources, General Control Device and Work Practice Requirements, 40 CFR § 60.18. http://edocket.access.gpo.gov/cfr_2009/julqtr/40cfr60.18.htm. (3) Gilman, J. B.; Kuster, W. C.; Goldan, P. D.; Herndon, S. C.; Zahniser, M. S.; Tucker, S. C.; Brewer, W. A.; Lerner, B. M.; Williams, E. J.; Harley, R. A.; Fehsenfeld, F. C.; Warneke, C.; de Gouw, J. A. Measurements of volatile organic compounds during the 2006 TexAQS/GoMACCS campaign: Industrial influences, regional characteristics, and diurnal dependencies of the OH reactivity. J. Geophys. Res. 2009, 114, D00F06 DOI: 10.1029/2008JD011525. (4) Parrish, D. D.; Allen, D. T.; Bates, T. S.; Estes, M.; Fehsenfeld, F. C.; Feingold, G.; Ferrare, R.; Hardesty, R. M.; Meagher, J. F.; NielsenGammon, J. W.; Pierce, R. B.; Ryerson, T. B.; Seinfeld, J. H.; Williams, E. J. Overview of the Second Texas Air Quality Study (TexAQS II) and the Gulf of Mexico Atmospheric Composition Climate Study (GoMACCS). J. Geophys Res. 2009, 114, D00F13 DOI: 10.1029/ 2009JD011842. (5) Gogolek, P.; Caverly, A.; Schwartz, R.; Seebold, J.; Pohl, J. Emissions from Elevated Flares - A survey of the literature. Prepared for the International Flaming Consortium. April 2010. (6) Allen, D. T.; Torres, V. M. TCEQ 2010 Flare Study Final Report; The University of Texas at Austin, 2011. (7) FLIR thermography. Home page. 2011. http://www.flir.com/ thermography/americas/us/content/?id=18358. (8) Knighton, W. B.; Herndon, S. C.; Franklin, J. F.; Wood, E. C.; Wormhoudt, J.; Brooks, W.; Fortner, E. C. Direct measurement of volatile organic compound emissions from industrial flares using realtime on-line techniques: Proton Transfer Reaction Mass Spectrometry 12585
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