3194
Energy & Fuels 2007, 21, 3194–3201
Linear Regression Analysis of Emissions Factors When Firing Fossil Fuels and Biofuels in a Commercial Water-Tube Boiler Sharon Falcone Miller* and Bruce G. Miller Energy Institute, The PennsylVania State UniVersity, UniVersity Park, PennsylVania 16802 ReceiVed July 25, 2007
This paper compares the emissions factors for a suite of liquid biofuels (three animal fats, waste restaurant grease, pressed soybean oil, and a biodiesel produced from soybean oil) and four fossil fuels (i.e., natural gas, No. 2 fuel oil, No. 6 fuel oil, and pulverized coal) in Penn State’s commercial water-tube boiler to assess their viability as fuels for green heat applications. A linear regression analysis was performed on emissions factors calculated by accepted EPA methods with those determined by performing a mass balance around the boiler. In addition, AP-42 emissions factors for fossil fuels were compared to an EPA method (CFR Title 40) and a mass balance method. The EPA method emissions were identified as the “response” and the mass balance method identified as the “predictor”. In general, the regression models, when considering all fuels, could predict greater than 90% (R2) of the emissions (except for CO2, R2 ) 42.5%), suggesting there is a good relationship between the EPA method and the mass balance emissions factors. Coefficients ranged from 0.964 to 1.08. The data were broken into two subsets, i.e., fossil fuels and biofuels. The regression model for the liquid biofuels (as a subset) did not perform well for all of the gases (R2 ranged from 0.1 to 73.3%). In addition, the coefficient in the models showed the EPA method underestimating CO and NOx emissions. The fits for CO2 and NOx for the liquid biofuels were poor (R2 ) 0.1 and 73.3%, respectively). No relation could be studied for SO2 for the liquid biofuels as they contain no sulfur; however, the model showed a good relationship between the two methods for SO2 in the fossil fuels (R2 ) 99.9%). AP-42 emissions factors for the fossil fuels were also compared to the mass balance emissions factors and EPA CFR Title 40 emissions factors. (No AP-42 emissions factors exist for the biofuels tested.) Overall, the AP-42 emissions factors for the fossil fuels did not compare well with the mass balance emissions factors or the EPA CFR Title 40 emissions factors. Regression analysis of the AP-42, EPA, and mass balance emissions factors for the fossil fuels showed a significant relationship only for CO2 and SO2. However, the regression models underestimate the SO2 emissions by 33%. The regression model comparing the EPA emissions factors and the mass balance emissions factors was better at predicting the data variation (>99% at coefficients approximately equal to 1) for CO, SO2, and NOx and 93% of the CO2 data for the fossil fuel tests (at a coefficient of 1.35). These tests illustrate the importance in performing material balances around boilers to obtain the most accurate emissions levels, especially when dealing with biofuels. The EPA emissions factors were very good at predicting the mass balance emissions factors for the fossil fuels and to a lesser degree the biofuels. While the AP-42 emissions factors and EPA CFR Title 40 emissions factors are easier to perform, especially in large, full-scale systems, this study illustrated the shortcomings of estimation techniques especially when applied to biofuels.
1. Introduction While many industrial boilers are designed to burn multiple fuels in order to take advantage of the fuel most economically available,1–4 there are ever increasing opportunities to expand this concept even into the cogeneration and utility sectors, especially in light of changing emissions regulations, legislative action enacting renewable portfolio standards requiring electricity generation from renewable sources, volatility in natural gas and fuel oil prices, and the desire to further expand the range of fuel options. Fuel flexibility allows the industrial boiler operator to reduce energy (fuel) costs by taking advantage of * To whom correspondence should be addressed: telephone 814 863 8893; Fax 814 863 8892; e-mail
[email protected]. (1) Woodruff, E. B.; Lammers, H. B.; Lammers, T. F. Steam Plant Operation; McGraw-Hill Book Co.: New York, 1984. (2) Babcock, G. H.; Wilcox, S. Steam, Its Generation and Use; The Babcock & Wilcox Co.: Barberton, OH, 1978. (3) Singer, J. G., Ed. Combustion, Fossil Power Systems; Combustion Engineering, Inc.: 1981. (4) CIBO (Council of Industrial Boiler Owners), Industrial Combustion Boiler & Process Heater MACT Summary Sheet, July 3, 2002.
opportunity fuels and avoiding price spikes, accommodate changes in fuel availability, utilize a waste product, and use indigenous resources. Historically, Penn State has been known for its coal-based fuel fundamental and applied research, but for the reasons stated above, Penn State is actively studying sewage sludge,5,6 refuse-derived fuels (RDF), animal fats/ vegetable oils,7–9 animal proteins,7,8 animal-tissue biomass,10–14 manure/litter,15–17 grasses/crop residues,15 waste wood products,8,15 and waste seeds. (5) Miller, B. G.; Falcone Miller, S.; Wincek, R. T. Combustion of Sewage Sludge and Waste Coal in a Pilot-Scale Circulating Fluidized-Bed Combustor. Prepared for Illinois State Geological Survey, March 9, 2004, 40 pages. (6) Rostam-Abadi, M.; Harvey, R.; Lu, Y.; Miller, B. G.; Falcone Miller, S.; Wincek, R. T. “Combustion of Sewage Sludge and Coal Fines in a PilotScale Circulating Fluidized-Bed Combustor, Electric Power 2005”, 7th Annual Conference and Exhibit, Chicago, IL, April 5–7, 2005. (7) Miller, B. G.; Falcone Miller, S.; Scaroni, A. W. “Utilizing Agricultural By-Products in Industrial Boilers: Penn State’s Experience and Coal’s Role in Providing Security for our Nation’s Food Supply”, Nineteenth Annual International Pittsburgh Coal Conference, University of Pittsburgh, September 23–27, 2002.
10.1021/ef700441d CCC: $37.00 2007 American Chemical Society Published on Web 10/17/2007
Analysis of Emissions Factors
Penn State performed a test program for the Commonwealth of Pennsylvania to obtain performance data from a suite of biofuels with the overall goal to garner widespread acceptance of various biofuels in industrial, cogeneration, and utility boilers. A range of Pennsylvania indigenous resources, including animal proteins and fats, a meatpacking/rendering byproduct, a crop oil, a waste restaurant grease, a herbaceous plant, and animaltissue biomass, along with biodiesel, were identified and tested in a commercial boiler and/or a pilot-scale combustor to assess their viability as fuels for green heat applications.18 Testing was performed to investigate fuel characterization, fuel handling, combustion performance, and emissions characterization. Since early fall 2000, there has been considerable interest shown in using animal fats and recycled cooking oils/restaurant grease as energy sources in industrial boilers as many industries have been seeking alternatives to trim their energy expenses. In the rendering/meatpacking industries, the utilization of a product they process or produce has been shown to be an excellent alternative to fossil fuels. This fact, coupled with the price differential between the products and fossil fuels, makes them excellent candidates for alternative fuels. Unfortunately for the interested users, in some cases, state and federal regulatory agencies are unsure of how to handle the requests to modify operating permits to fire the animal fats/greases. In some cases, the agencies have considered reclassifying the boilers to incinerators because of the lack of emissions information when (8) Miller, B. G.; Falcone Miller, S. “Utilizing Biomass in Industrial Boilers: The Role of Biomass and Industrial Boilers in Providing Energy/ National Security”, The First CIBO Industrial Renewable Energy & Biomass Conference, Minneapolis, MN, April 7–9, 2003. (9) Miller, B. G.; Clark, D. A.; Hill, M. A.; Larsen, J.; Clemens, T.; Wehr, T. “A Demonstration of Pig Lard as an Industrial Boiler Fuel”, 24th International Conference on Coal Utilization & Fuel Systems, Coal & Slurry Technology Association, Clearwater, FL, March 8–12, 1999, pp 743– 754. (10) Miller, B. G.; Falcone Miller, S.; Wincek, R. T.; Wasco, R. S.; Harlan, D. W.; Detwiler, L. A.; Rossman, M. L. “Cofiring Animal-Tissue Biomass in Coal-Fired Boilers to Dispose of Specified Risk Materials and Carcasses”, The 31st International Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL, May 21–25, 2006. (11) Miller, B. G.; Falcone Miller, S.; Fedorowicz, E. M.; Harlan, D. W.; Detwiler, L. A.; Rossman, M. L. Pilot-Scale Fluidized-Bed Combustor Testing Cofiring Animal-Tissue Biomass with Coal as a Carcass Disposal Option. Energy Fuels 2007, 20, 1828–1835. (12) Miller, B. G.; Falcone Miller, S.; Wasco, R. S.; Wincek, R. S.; Clifford, D. J. Demonstrate the Technical Feasibility of Cofiring AnimalTissue Biomass (SRMs and Carcasses) with Coal in a Pilot-Scale Bubbling Fluidized-Bed Combustor. Prepared for the National Cattlemen’s Beef Association, May 31, 2005, 216 pages. (13) Miller, B. G.; Harlan, D. W.; Detwiler, L. A.; Falcone Miller, S. “Utilizing Animal Tissue Biomass in Coal-Fired Boilers: A Step Closer Towards Implementation?”, 30th International Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL, April 17–21, 2005. (14) Miller, B. G.; Falcone Miller, S.; Harlan, D. W. “Utilizing AnimalTissue Biomass in Coal-Fired Boilers: An Option for Providing Biosecurity,” 29th International Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL, April 18–22, 2004. (15) Miller, B. G.; Falcone Miller, S.; Cooper, R.; Gaudlip, J.; Lapinsky, M.; Raskin, N.; Steitz, T.; Battista, J. J. Feasibility Analysis for Installing a Circulating Fludized Bed Boiler for Cofiring Multiple Biofuels and Other Wastes with Coal at Penn State University Final Report. Prepared for U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA, March 24, 2003, DE-FG26-00NT40809, 181 pages. (16) Falcone Miller, S.; Miller, B. G. “The Effect of Cofiring Coal and Biomass on Utilization of Coal Combustion Products: The U.S. Perspective”, 11th International Conference on Ashes from Power Generation,Zakopane, Poland, October 13–16, 2004. (17) Miller, B. G.; Falcone Miller, S.“Agricultural By-Products as Alternative Boiler Fuels”, 2005 Pennsylvania Poultry Sales and Service Conference,September 22 and 23, 2005. (18) Miller, B. G.; Falcone Miller, S.; Wincek, R. T.; Wasco, R. S. Fuel Flexibility in Boilers for Fuel Cost Reduction and Enhanced Food Supply Security. Prepared for the Pennsylvania Energy Development Authority, June 30, 2006, 509 pages.
Energy & Fuels, Vol. 21, No. 6, 2007 3195 Table 1. Fossil Fuels Analyses No. 2 fuel oil proximate analysis, wt % (as received) moisture volatile matter fixed carbon ash ultimate analysis, wt % (as received) carbon hydrogen nitrogen sulfur oxygen ash chlorine (ppm) heating value, Btu/lb (as received) density, g/cm3 particle size distributiona, µm D(v, 0.9) D(v, 0.5) D(v, 0.1)
No. 6 fuel oil
pulverized coal
0 99.6 0.4 0
0 98.3 1.5 0.2
2.2 30.1 63.0 4.7
89.3 10.4 0.1 0.1 0.1 0 450 19444
86.4 11.3 0.3 1.8 0 0.2 375 18462
80.1 4.7 1.3 0.7 6.3 4.7 2900 13917
0.81 N/Ab
0.97 N/Ab
0.90 140.7 45.6 10.4
a The D(v, 0.1), D(v, 0.5), and D(v, d0.9) values are the particle sizes where respectively 10, 50, and 90 vol % of the particles are less than the indicated size. b Not applicable.
firing these alternative fuels.19 Although facilities in some states have received permission to fire the alternative fuels, there are others still awaiting permission or have been instructed that more data are necessary. One objective of the testing was to generate a database of emissions factors when firing a variety of animal fats/greases in a commercial water-tube boiler to facilitate the use of these alternative fuels. This paper discusses the results from liquid biofuels testing that was performed in Penn State’s commercial-scale water-tube test boiler, specifically the emissions factors, and compares their emissions factors to fossil fuels (i.e., natural gas, No. 2 fuel oil, No. 6 fuel oil, and pulverized coal). A linear regression analysis was performed on emissions factors calculated by two accepted EPA methods with those determined by mass balance around the boiler. The liquid biofuels tested included three animal fats, waste restaurant grease (i.e., yellow grease), pressed soybean oil, and a biodiesel produced from soybean oil were tested. The animal fats were produced as part of rendering operations where a fat/grease and protein (i.e., meal) are produced. The animal fats selected for testing represent three major animal production industries: choice white grease, which is an inedible (for human consumption) fat from pork production, tallow, which can be either inedible or edible from beef production, and poultry fat, which is inedible from poultry production. These materials are used in the animal feed industry. Similarly, the pressed soybean oil tested is a byproduct from pressing soybeans to produce a meal for chicken feed. The yellow grease tested is a waste byproduct from the restaurant industry. The biodiesel tested is a commercial product produced using soybean oil. 2. Experimental Section 2.1. Biofuel and Fossil Fuel Analysis. Proximate, ultimate, and heating value (HV) analyses were performed according to ASTM methods D 5142, D 5373, D 4239, and D 5865 on samples taken periodically during testing. These samples were used to form a composite sample that was analyzed and used for calculating the emissions factors. Chlorine analyses were determined using ASTM (19) Thornock, D. Personal Communiqué, Johnston Boilers, March, 2001.
3196 Energy & Fuels, Vol. 21, No. 6, 2007
Falcone Miller and Miller Table 2. Liquid Biofuel Analyses
soybean oil proximate analysis, wt % (as received) moisture volatile matter fixed carbon ash ultimate analysis, wt % (as received) carbon hydrogen nitrogen sulfur oxygen ash chlorine (ppm) heating value, Btu/lb (as received) density, g/mL
yellow grease
choice white grease
tallow
poultry fat
biodiesel
0 99.7 0.2 0.1
0.2 99.6 0.2 0
0.1 99.5 0.2 0.2
0.1 99.6 0.2 0.1
0.1 99.7 0.1 0.1
0 99.6 0.3 0.1
79.2 10.1 0 0 10.6 0.1 800 17043
73.5 10.7 0 0 15.6 0 450 16979
69.6 10.1 0 0 20.0 0.2 475 17040
74.3 10.7 0 0 14.8 0.1 375 17054
61.5 8.7 0 0.3 29.3 0.1 525 17013
76.7 12.3 0.1 0 10.8 0.1 1,100 17189
0.90
0.90
0.88
0.88
0.90
0.88
method D 4208-02e1. Apparent bulk density of the pulverized coal was determined according to ASTM Historical Standard D285496(2000). The relative densities of the liquid fuels were determined per ASTM D 1298-99(2005), and their viscosities were determined per ASTM D 445-03. The particle size distribution of the pulverized coal was determined using a Malvern 2600C droplet and particle sizer. The analyses of the fossil fuels and biofuels are provided in Tables 1 and 2, respectively. 2.2. Description of the Commercial Water-Tube Boiler. Penn State’s research boiler and ancillary equipment are shown in Figure 1. The 1000 lb saturated steam (at 150 psig)/h boiler is an A-frame water-tube boiler, designed and built by Cleaver Brooks. The combustion chamber is 3 × 3 × 7 ft (63ft3) with a maximum heat release rate of 42000 Btu/(ft3 h). It contains 288 ft2 of heating surface, and the maximum firing rate is 2 million (MM) Btu/h (60 hp). A detailed discussion of the boiler is found elsewhere.8,9 Barrel heaters were used to preheat some of the liquid fuels prior to atomization/combustion. Viscosities, as a function of temperature, were determined for all liquid fuels. No. 6 fuel oil was heated to ≈200 °F, which is a typical heavy fuel oil preheat temperature. The maximum preheat temperature for the liquid
Figure 1. Schematic diagram of the water-tube research boiler system.
biofuels was 140 °F, which is a maximum temperature recommended by Hatfield Quality Meats so as not to degrade the fat. When preheating the biofuels, the temperature was varied between 120 and 140 °F, with the final temperature determined by the viscosity. The tests were performed such that the viscosities would be similar to ensure that atomization characteristics were uniform among all the tests and that variability in viscosity would not negatively influence combustion performance. A firing rate of 1.5 million Btu/h was used for all tests. Emissions data were collected at 30-s intervals during steadystate operation, which averaged 4–5 h in duration for the biofuels. The result is that an average of 464 data points was collected during the biofuel tests. The fossil fuel tests were longer in duration (7–8 h) due to greater supply and have a correspondingly greater number of data points. Difficulties in accessing the computer-stored data during the biodiesel test were experienced; hence, only nine data points (from the operator’s data sheet) was generated. A large number of data points were taken during steady-state conditions to ensure that temporal variations during testing would be minimized. From a statistical perspec-
Analysis of Emissions Factors
Energy & Fuels, Vol. 21, No. 6, 2007 3197
Table 3. Summary of Fuels, Fuel ID, and Total Data Points Used in Data Analysis fuel ID
fuel
number of data points (n)
1 2 3 4 5 6 7 8 9 10 11 12
natural gas coal No. 2 fuel oil No. 6 fuel oil soybean oil (no preheat) soybean oil (120 °F preheat) yellow grease (140 °F preheat) yellow grease (120 °F preheat) choice white grease tallow poultry fat biodiesel
850 938 476 427 368 480 489 480 482 485 485 9
Elb⁄106Btu )
Table 4. Comparison of the Emissions Factors from the Three Methods for the Fossil Fuels Fired in the Commercial Water-Tube Boiler fuel
No. 6 fuel oil
No. 2 fuel oil
natural gas
EPA CFR Title 40 pollutant (lb/MM Btu) CO 0.117 0.009 0.028 163.6 155.6 116.5 CO2 SO2 2.505 0.115 0.000 NOx 0.496 0.103 0.159 mass balance around the boiler pollutant (lb/MM Btu) CO 0.120 0.010 0.031 CO2 168.3 171.6 129.5 SO2 2.542 0.127 0.000 NOx 0.509 0.113 0.176 AP-42 emissions factors (lb/MM Btu) CO 0.033 0.036 0.083 CO2 162.7 158.2 118.3 SO2 1.704 0.111 0.000 NOx 0.367 0.142 0.099
balance method, EPA method (CFR Title 40),20 and AP-42.21 In the mass balance method, the emissions factors were determined using a mass balance around the combustion unit based on the average emissions and boiler characteristics at steady-state conditions. The volumetric concentration of the flue gas was converted to a mass basis and reported on a lb/million (MM) Btu basis. In the EPA method, emissions factors were calculated using eqs 1 and 2.20
pulverized coal
0.113 206.5 0.932 1.260 0.106 193.2 0.873 1.179 0.018 211.5 0.956 0.431
tive, each data point is treated as a test. Table 3 summarizes the number of data points obtained during each test.
3. Results and Discussion A comparison was made between the emissions factors determined from the measured emissions, i.e., by performing a mass balance around the water-tube boiler and emissions factors calculated using EPA CFR Title 40 procedures.20 These emissions factors were also compared to AP-42 emissions factors for those fuels where there are AP-42 emissions factors available, specifically the fossil fuels.21 A linear regression analysis was also performed, using MINITAB Release 14 software22, for the measured and calculated emissions factors on the water-tube boiler to determine the relationship between the two methods. In addition to CO, SO2, and NOx, CO2 was included, primarily because of the interest in avoided CO2 emissions when firing the biofuels since they are considered CO2 neutral. A key to identify the fuels is given in Table 3. 3.1. Methodology for Determining Emissions Factors. The emissions factors were determined by three methods: mass (20) EPA, Code of Federal Register, Title 40, Part 75, Chapter 1, Section 3, July 1, 2002, pp 321–323. (21) AP-42, External Combustion Sources. In Emission Factors, 5th ed.; Office of Air Quality, Planning, and Standards and Office of Air and Radiation, U.S. Environmental Protection Agency: Washington, DC,1993. (revisions in 1998); also at www.epa.gov/ttn/chief/ap42. (22) MINITAB Statistical Software, Release 14 Windows, 2003. (23) Miller, B. G. Coal Energy Systems; Elsevier: Oxford, U.K., 2005;526 pages.
(Cppm)(Mgas)(Fd)(20.9) (385.3)(106)(20.9 - O2)
(1)
where Cppm ) measured concentration in the gas stream (ppm), Elb/106Btu ) emission rate (lb/106 Btu), Fd ) volume of combustion component per unit of heat content (dscf/106 Btu); O2 ) proportion of oxygen in the gas stream by volume (%), Mgas ) molecular weight of pollutant (lb/lb mol), 20.9 ) proportion of oxygen in ambient air, 385.3 ) conversion factor (ft3/lb mol), and 106 ) conversion factor (ppm). The fuel factor, Fd, is calculated from the fuel composition (composite sample) by the equation Fd ) K
Khd(%H) + Kc(%C) + Ks(%S) + Kn(%N) - Ko(%O) GCV (2)
where %H ) concentration of hydrogen from an ultimate analysis of fuel (wt %), %C ) concentration of carbon from an ultimate analysis of fuel (wt %), %S ) concentration of sulfur from an ultimate analysis of fuel (wt %), %N ) concentration of nitrogen from an ultimate analysis of fuel (wt %), %O ) concentration of oxygen from an ultimate analysis of fuel (wt %), GCV ) gross calorific heating value of fuel (Btu/lb) (note that the heating value must be on the same basis as the ultimate analysis), Fd ) volume of combustion component per unit of heat content (scf/106 Btu), K ) conversion factor (Btu/106 Btu) ) (106), Khd ) conversion factor (scf/lb %) ) 3.64, Kc ) conversion factor (scf/lb %) ) 1.53, Ks ) conversion factor (scf/lb %) ) 0.57, Kn ) conversion factor (scf/lb %) ) 0.14, and Ko ) conversion factor (scf/lb %) ) 0.46. The AP-42 emissions factors are discussed in section 3.4. 3.2. Emissions Factors Results. The emissions factors for the fossil fuels and liquid biofuels are summarized in Tables 4 and 5, respectively. Note AP-42 emissions factors are reported only for fossil fuels in Table 4 since AP-42 emissions factors are not available for biofuels. For the fossil fuel testing, the SO2 emissions increased with increasing sulfur content in the fuel, i.e., No. 6 fuel oil > pulverized coal > No. 2 fuel oil > natural gas, as expected. Similarly, the CO2 emissions increased as the C/H ratio increased, i.e., coal > No. 6 fuel oil > No. 2 fuel oil > natural gas, as expected. It is not evident why the CO emissions are higher for the natural gas test as compared to the fuel oil. The tests firing natural gas and No. 2 fuel were performed with 1.5% and 2.5% excess O2 in the flue gas, respectively, which is typical industry operation. The tests were performed using typical industry operating conditions instead of making burner modifications to optimize each fuel (i.e., increasing excess air to nontypical conditions) in order to accurately compare the performance of the various fuels using the same burner/boiler. It is possible that the test with natural gas may not have been as well mixed as the test firing No. 2 fuel oil; however, the burner contains a standard natural gas ring burner, and proper mixing is expected. The CO concentrations (corrected to 3% O2) for the natural gas and No. 2 fuel oil tests were 38 and 12
3198 Energy & Fuels, Vol. 21, No. 6, 2007
Falcone Miller and Miller
Table 5. Summary of Emissions Factors for the Liquid Biofuels Fired in the Commercial Water-Tube Boiler
fuel EPA CFR Title 40 pollutant (lb/MM Btu) CO CO2 SO2 NOx mass balance around the boiler pollutant (lb/MM Btu) CO CO2 SO2 NOx
soybean oil (no preheat)
soybean oil yellow grease yellow grease choice white grease tallow poultry fat (preheated (preheated (preheated (preheated (preheated (preheated biodiesel to 120 °F) to 140 °F) to 120 °F) to 140 °F) to 140 °F) to 140 °F) (no preheat)
0.032 167.6 0.000 0.134
0.040 166.5 0.000 0.148
0.018 155.2 0.000 0.092
0.043 154.1 0.000 0.102
0.010 138.5 0.000 0.083
0.000 153.9 0.000 0.092
0.013 123.4 0.000 0.097
0.064 160.9 0.000 0.105
0.034 181.5 0.000 0.145
0.043 179.0 0.000 0.159
0.020 176.9 0.000 0.104
0.050 176.5 0.000 0.117
0.012 179.5 0.000 0.103
0.000 173.5 0.000 0.104
0.019 180.6 0.000 0.143
0.072 184.1 0.000 0.119
ppm, respectively. However, the NOx concentrations (corrected to 3% O2) were 133 and 82 ppm for the natural gas and No. 2 fuel tests, respectively, and one would expect the NOx concentration to be lower in the case of poorer mixing. 3.3. Linear Regression Analysis of the Emissions Factors from the Commercial Water-Tube Boiler. Linear regression analysis of the emissions factors was conducted on the data obtained from the commercial water-tube boiler. Linear regression examines the relationship between a response and predictor(s) and determines whether or not the observed relationship between the response and predictors is statistically significant. The model generates an R2, which describes the amount of variation in the observed response values that is explained by the predictor(s). In this case, the predictor is the mass balance emissions factor, and the response is the EPA emissions factor. The output also generates a regression equation, which is an algebraic representation of the regression line and is used to describe the relationship between the response and predictor variables. The regression equation takes the form of response ) constant + coefficient × predictor (3) where the response is the calculated emissions factor via the EPA method and the predictor is the mass balance method emissions factor. The P-value generated for the predictor indicates whether or not the association between the response and predictor(s) is statistically significant. A P-value is also generated for the constant to determine whether it is significant in determining the response. As an example, a partial output for the CO emissions is given below:
In this example, the constant in the regression equation (-0.001210) is not significant as the P-value is >0.05. The predictor “CO mass”, that is, the mass balance emission factor, is significant as its P-value is 0.05). The regression model did not predict a significant relationship between the two emissions factors methods in the case of CO2 when evaluating all the fuels. Only 42.5% of the data could be described by the regression model. It is apparent in Figure 2 that there is considerable spread of the CO2 data as compared to the other emissions. This spread in data results in the low R2 value obtained for CO2. For this reason, a regression analysis was conducted on two subsets: fossil fuels (fuels 1–4) and liquid biofuels (fuels 5–12). The R2 values obtained for the subsets are given in Table 6. The regression model for fossil fuel CO2 is able to predict 93.4% of the CO2 data, indicating that there is a good relationship between the EPA and the mass balance methods (i.e., CO2 EPA method ) 1.35 × CO2 mass balance method). The regression equation for the liquid biofuels is not able to predict the distribution of the emissions factors data points (R2 ) 0.1%), meaning that there is not a strong relationship between the two methods for the liquid biofuels. Consequently, the EPA method is not as “good” at predicting CO2 emissions for the liquid biofuels as compared to fossil fuels. This discrepancy is apparent for CO and NOx as well. The regression model for CO emissions factors for all the fuels was able to predict 90.1% of the variation in the data. However, the model was only able to explain 60.5% of the variation in the biofuel CO emissions data as compared to 99.4% of the fossil
Analysis of Emissions Factors
Energy & Fuels, Vol. 21, No. 6, 2007 3199
Figure 2. CO and CO2 emissions factors obtained by epa and mass balance methods as a function of fuel type. Liquid biofuels are indicated by red circles, and fossil fuels are indicated by blue squares.
Figure 3. SO2 and NOx emissions factors obtained by EPA and mass balance methods as a function of fuel type. Liquid biofuels are indicated by red circles, and fossil fuels are indicated by blue squares.
fuel data. In the case of NOx, the regression model was able to explain 99.9% of the variation in the data for all of the fuels, whereas the model for liquid biofuel NOx is capable of explaining only 73.3% of the distribution of the data points (Table 6). It is not clear what is causing the difference in the fit of the regression analysis between the fossil fuels and the biofuels. Although the EPA emissions factors were originally developed for fossil fuels, which have compositional differences (specifically in C, N, and O), the fuel factor, Fd, should address this.
The regression equations given in Figures 2 and 3 are based on analysis of all the fuels. In every case, except for NOx emissions, the constant in the regression model was not significant. The regression equations (coefficients) for the subset data (fossil fuel and liquid biofuel) are given in Table 6. No regression equation is given for CO2 for the liquid biofuels as the “mass balance method” was not statistically significant (all P-values > 0.05) in predicting the EPA emissions factor method values. In addition, no regression equation is given for SO2 as none of the liquid biofuels had measurable SO2 emissions.
3200 Energy & Fuels, Vol. 21, No. 6, 2007
Falcone Miller and Miller
Table 7. Factors for the Fossil Fuels Fired in the Water-Tube Boilera natural gasb (lb/106 scf) No. 2 fuel oilc (lb/103 gal) No. 6 fuel oile (lb/103 gal) pulverized coalf (lb/ton)
CO
NOx
SO2
CO2
84 5 5 0.5
100 20 55 12
0 157Sd 142Sd 38Sd
120000 22300 24400 72.6Cg
a Boiler size is 0.05). By comparison, the regression model comparing the EPA emissions factors to the mass balance emissions factors was able to predict 99.4% of the CO data points at a coefficient of 1.03, which shows good agreement. In the case of NOx, the regression models were only able to fit 80.1 and 78.1% of the NOx data points for the AP-42/mass balance method and AP-42/EPA method, respectively. In both cases, the model coefficients were not statistically significant (P > 0.05). The EPA method/mass balance method model was able to predict 99.9% of the NOx data points at a coefficient of 1.09, which shows good agreement. The regression models comparing the factors to the mass balance method and EPA emissions factors were able to predict g 92% of the data variation for CO2 and SO2. However, based on the regression models, the factors significantly underestimated SO2 emissions by approximately 33% (1–0.66) as compared to the EPA method/mass balance model, which underestimated SO2 by only 1.3% (1–0.987). In general, the regression model comparing the EPA emissions factors to the mass balance emissions factors was capable of predicting g99% of the data variation (at coefficients approximately equal to 1) for CO, SO2, and NOx and 93% of the CO2 data for
Analysis of Emissions Factors
the fossil fuel tests (at a coefficient of 1.35). It is acknowledged that there are a limited number of fuels in the regression analysis, but it raises the issue of the use of factors with regard to fossil fuels as well as its use in developing emissions factors for biofuels. 4. Conclusions A suite of biofuels, including three animal fats—a waste restaurant grease (i.e., yellow grease), a pressed soybean oil, and a biodiesel produced from soybean oil—were fired in a commercial water-tube boiler to generate a database of emissions factors. A linear regression analysis was performed on emissions factors calculated by accepted EPA methods with those determined by performing a mass balance around the boiler to determine the relationship between the two methods. In general, the regression model shows that the mass balance emissions factors are only slightly higher than the EPA emissions factors (i.e., all model coefficients ranged from 0.964 to 1.08). A coefficient less than 1.0 means that the EPA method underestimates the emission levels for those gases and fuels. The model was not able to predict very well the variation in the CO2 data (R2 ) 42.5%) for the total sample set. The data were divided into subsets, fossil fuel and biofuels, and regression analysis was performed on each subset. The regression analysis shows a strong relationship between the two methods for the fossil fuels for all the gases (R2 g 93.4%); however, the coefficients vary between 0.987 and 1.35. The regression model comparing the EPA and mass balance emissions factors for the biofuels performed poorly showing little relationship between the two emissions factor methods. The “poorest” fit of the model was for CO2 emissions (R2 ) 0.1%), and the “best” fit was for NOx (R2 ) 73.3). The only coefficients that were shown to be statistically significant were CO (0.706) and NOx (0.877), indicating that the EPA method underestimated the level of these pollutants. There were no data to model for SO2 as the biofuels contained no significant or detectable SO2. AP-42 emissions factors for the fossil fuels were compared to the mass balance emissions factors and EPA CFR Title 40 emissions factors. (No AP-42 emissions factors exist for the biofuels tested.) Overall, the AP-42 emissions factors for the fossil fuels did not compare well with the mass balance emissions factors and the EPA CFR Title 40 emissions factors
Energy & Fuels, Vol. 21, No. 6, 2007 3201
except for CO2 and, to a lesser extent SO2 (except for No. 6 fuel determined by the mass balance), which had reasonably good agreement. The regression models were only able to fit 30.8 and 34.9% of the CO data points relating the AP-42 emissions factors to the mass balance and EPA emissions factors, respectively. By comparison, the regression model relating the EPA and mass balance method emissions factors was able to predict 99.4% of the CO data points at a coefficient of 1.03, which shows good agreement. In the case of NOx, the regression models were only able to fit 80.1 and 78.1% of the NOx data points for the AP-42 emissions factors and the mass balance and the EPA emissions factors, respectively. The regression model for the EPA and mass balance emissions factors was able to predict 99.9% of the NOx data points at a coefficient of 1.09, which shows good agreement. The AP-42 regression models were able to predict ∼80% of the data variation; however, the AP-42 emissions were ∼33% lower than the mass balance and EPA method emissions factors. No general trends were observed except that in several instances the AP42 emissions factors were significantly different (i.e., either significantly lower or higher) than the other two methods. While the EPA CFR Title 40 and the AP-42 emissions factors are easier to perform, the test results illustrate the shortcomings of these estimation techniques and the importance of performing material balances around boilers to obtain the most accurate emissions levels when dealing with biofuels. Even in the case of fossil fuels, the EPA emissions factor and the AP-42 emissions factors were only related or could be modeled (at a statistically significant level) to the mass balance emissions factors for CO2 and SO2. Acknowledgment. Funding was provided by the Pennsylvania Energy Development Authority under agreement number PG050020. Oilmatic International, LLC, provided funding for the yellow grease testing. The staff of the Energy Institute are acknowledged for assisting in operating the boiler, providing analytical support, and reducing data. The Clemmens Family Corporation, Cargill Taylor Beef, Keystone Protein Company, Oilmatic International, LLC, and Soy Energy, LLC, are acknowledged for providing the biofuels. EF700441D
