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Energy & Fuels 2009, 23, 894–902
Partial Least-Squares Predictions of Nonpetroleum-Derived Fuel Content and Resultant Properties When Blended with Petroleum-Derived Fuels Jeffrey A. Cramer,*,†,‡ Robert E. Morris,† Braden Giordano, and Susan L. Rose-Pehrsson† U.S. NaVal Research Laboratory, Chemical Sensing and Chemometrics Section Code 6181, 4555 OVerlook AVenue Southwest, Washington, District of Columbia 20375 ReceiVed October 29, 2008. ReVised Manuscript ReceiVed December 16, 2008
The U.S. Naval Research Laboratory has been engaged in a research program to develop sensor-based technologies to perform rapid automated fuel-quality surveillance. This approach is based on the development of quantitative models from the partial least-squares (PLS) regression of near-infrared (NIR) spectroscopic measurements of a representative calibration set of petroleum-derived fuels. As fuels from nonpetroleum sources become available it will be necessary to extend these chemometric models to accommodate Fischer-Tropsch (FT) synthetic fuels and biofuels. This extension is complicated by the fact that these new fuels will be initially introduced as blending components with petroleum-derived fuels. Chemometric modeling methodologies have been developed to identify and estimate the content of FT and biofuel present; then this information is used to estimate the bulk properties of the blends. With this approach, biodiesel content can be predicted, with respect to absolute error, to within 1.7% of its true value 95% of the time with a lower limit of detection of 1.5% using a single PLS model. The diesel fuel PLS property prediction models are applicable to diesel fuels blended with biodiesel fuel once that particular biodiesel fuel is incorporated in said models. The FT content in blends with petroleum fuels can be predicted, with respect to absolute error, to within 6.9% of its true value 95% of the time with a lower limit of detection of 15% using a series of paired PLS models for identification and quantification. In the presence of FT fuel, the PLS property models can be used after applying a correction factor that is derived from the identity and concentration of the FT fuel present.
Introduction The U.S. Navy will adopt the use of mobility fuels derived from alternate (i.e., nonpetroleum) sources, as they become available, for a variety of reasons, including a reduction in the carbon footprint and greater strategic flexibility. Alternatives to petrochemical fuels are already in use nationally and internationally and will continue to be introduced into fuel supplies as they become available. For example, the Commercial Aviation Alternative Fuels Initiative (CAAFI), sponsored by the Airports Council International-North America (ACI-NA), Aerospace Industries Association (AIA), Air Transportation Association (ATA), and Federal Aviation Administration (FAA), promotes the development of alternative fuel options that can compete with petroleum-based fuels in terms of cost, availability, utility, and environmental impact. CAAFI has set for itself the goal of a 50% Fischer-Tropsch (FT)/50% petrochemical fuel blend being certified for commercial aviation use by the end of 2008, 100% FT fuel certification by 2010, and 100% biodiesel certification by 2013.1 Utilization of fuels produced from alternate sources will not, in general, require extensive changes to engine designs. However, they will exert an impact on traditional handling practices and methods to ensure acceptable * To whom correspondence should be addressed. Telephone: 202-4043419. Fax: 202-767-1716. E-mail:
[email protected]. † U.S. Naval Research Laboratory. ‡ NRC Postdoctoral fellow. (1) Price, H. J. Fact Sheet: Commercial Aviation Alternative Fuels Initiative, United States Department of Transportation: Federal Aviation Administration (Jan 3, 2008), http://www.faa.gov/news/fact_sheets/news_story.cfm?newsId)10112, accessed June 19, 2008.
quality. Fuel-quality surveillance has been traditionally performed through a series of chemical and physical property measurements, which require nontrivial amounts of time, manpower, and workspace to execute. Partial least squares (PLS)2 is a mathematical technique which finds wide use in the field of chemometrics for its ability to correlate complex multivariate data (such as that found in spectra or chromatograms) to univariate calibration data for the purposes of future predictions by means of regression models. PLS models have been created to predict fuel properties such as density, viscosity, and flashpoint as derived from such diverse techniques as capillary gas chromatography (GC),3 gas chromatography-mass spectrometry (GC-MS),4,5 two-dimensional gas chromatography (GC × GC),6 mid-infrared (mid-IR) spectroscopy,7,8 near-infrared (NIR) spectroscopy,9-11 and Ra(2) Brereton, R. G. Chemometrics: Data Analysis for the Laboratory and Chemical Plant; John Wiley & Sons, Ltd.: Chichester, 2003; pp 297313. (3) Morris, R. E.; Hammond, M. H.; Shaffer, R. E.; Gardner, W. P.; Rose-Pehrsson, S. L. Energy Fuels 2004, 18, 485–489. (4) Bernabei, M.; Reda, R.; Galiero, R.; Bocchinfuso, G. J. Chromatogr. A 2003, 985, 197–203. (5) Johnson, K. J.; Rose-Pehrsson, S. L.; Morris, R. E. Energy Fuels 2004, 18, 844–850. (6) Johnson, K. J.; Synovec, R. E. Chemom. Int. Lab. Sys. 2002, 60, 225–237. (7) Go´mez-Carracedo, M. P.; Andrade, J. M.; Calvin˜o, M. A.; Prada, D.; Ferna´ndez, E.; Muniategui, S. Talanta 2003, 60, 1051–1062. (8) Go´mez-Carracedo, M. P.; Andrade, J. M.; Calvin˜o, M.; Ferna´ndez, E.; Prada, D.; Muniategui, S. Fuel 2003, 82, 1211–1218. (9) Breitkreitz, M. C.; Raimundo, I. M.; Rohwedder, J. J. R.; Pasquini, C.; Dantas Filho, H. A.; Jose´, G. E.; Arau´jo, M. C. U. Analyst 2003, 128, 1204–1207.
10.1021/ef800945c CCC: $40.75 2009 American Chemical Society Published on Web 01/23/2009
Predictions of Nonpetroleum-DeriVed Fuel Content
man spectroscopy.10,11 The advantage of such predictive modeling lies in its potential to evaluate incoming fuels to determine if a more rigorous characterization by the applicable ASTM12 test methods is necessary. The U.S. Naval Research Laboratory has been engaged in a research program to explore and develop rapid automated fuelquality surveillance technologies.10,11,13 Chemometric modeling methodologies have been employed to derive mathematical relationships between spectroscopic measurements and measured fuel specification properties of current petroleum-derived Navy diesel and jet fuels. Thus, deployment of this technology will require that this methodology be extended to accommodate alternate fuels as they become available and are introduced into the fuel supply system. This accommodation is complicated by the fact that the neat alternative fuels themselves are not likely to be the fuels to be monitored in end-use Navy applications. Although biodiesel and FT fuels will be blended with petrochemical fuels at 50%, comingling in the fuel supply system with other fuels will, at least initially, result in the likelihood that incoming fuels can contain unknown and widely variable amounts of alternative fuels. It thus becomes important to have the capability to rapidly ascertain the identities and amounts of alternate fuels that may be present. This is to facilitate quality assessment, but it is also important in order to assess the impact of the compositional differences induced by the presence of FT14 and biofuels15,16 on critical fuel performance parameters such as lubricity17,18 and elastomer seal swelling. Furthermore, biodiesel fuels are more prone to water absorbance than petrochemical fuels, and this promotes metal oxidation and biological growth in the Navy’s fuels infrastructure. Until a thorough additive strategy can be implemented for these alternative fuels, blending them with standard petrochemical fuels is the most straightforward manner to reap their benefits without compromising equipment currently in use. The present work is focused on the development of a series of novel PLS-based NIR data modeling strategies designed not only to quantify the amount of biodiesel or FT fuel present in an unknown fuel sample as a percentage but also to use this information, if necessary, to adjust alternative fuel-influenced property predictions based on pre-existing (and primarily petrofuel-based) NIR fuel property models currently in use. Experimental Section Petroleum Fuels. A fuel data set of 821 unadulterated, internationally collected petroleum fuel samples was used for the present study. Many of the fuel constituents that are correlated to the properties of interest are different for different fuel types, so the precision of the predictive PLS models was improved by segregating the fuel samples into two broad categories (jet and diesel) with no cross-analyses between the two populations. The jet fuel training set consisted of 64 JP-5, 214 JP-8, 107 Jet A, and 28 Jet A-1 fuels. The diesel fuel training set consisted of 261 F-76 Naval distillate (10) Johnson, K. J.; Morris, R. E.; Rose-Pehrsson, S. L. Energy Fuels 2006, 20, 727–733. (11) Cramer, J. A.; Kramer, K. E.; Johnson, K. J.; Morris, R. E.; RosePehrsson, S. L. Chemom. Int. Lab. Sys. 2008, 92, 13–21. (12) ASTM, Annual Book of ASTM Standards; ASTM: Philadelphia,1997. (13) Kramer, K. E.; Johnson, K. J.; Cramer, J. A.; Morris, R. E.; RosePehrsson, S. L. Energy Fuels 2008, 22, 523–534. (14) Knottenbelt, C. Catal. Today 2002, 71, 437–445. (15) Dwivedi, D.; Agarwal, A. K.; Sharma, M. Atmos. EnViron. 2006, 40, 5586–5595. (16) Agarwal, A. K. Prog. Energy Combust. Sci. 2007, 33, 233–271. (17) Appeldoom, J. K.; Tao, F. F. Wear 1968, 12, 117–130. (18) Krylov, I. F.; Emel’yanov, V. E.; Nikitina, E. A.; Vizhgorodskii, B. N.; Rudyak, K. B. Chem. Technol. Fuels Oils 2005, 41, 423–428.
Energy & Fuels, Vol. 23, 2009 895 Table 1. Specification Properties of the Fischer-Tropsch Synthetic Jet Fuelsa property density @ 15 °C (kg/m3) flash point (°C) total acid number (mg KOH/g) viscosity @ -20 °C (cSt) freeze point (°C) hydrogen content (mass %) FSII content (vol %) aromatics (vol %) olefins (vol %) saturates (vol %) sulfur (mass %) existent gum (mg/100 mL) distillation IBP (°C) 10% point (°C) 50% point (°C) 90% point (°C) end point (°C) residue (vol %) loss (vol %) saybolt color cetane index smoke point (mm) MSEP Cu strip corrosion doctor test heating value (mJ/kg) a Asterisk (*) MIL-DTL-5624T.
denotes
ASTM method
CTL FTJP-5
GTL FTJP-5
D 4052 D 93 D 3242 D 445 D5972 D 3701 D 5006 D 1319 D1319 D1319 D 4294 D 381
764* 65.5 0.000 6.060 -51.5 15.73 0.00 0.0 N/A N/A 0.000 0.4
796 57.5 0.001 4.741 -67.6 14.36 0.00 13.5 4.4 82.1 0.000 0.6
D 86 D 86 D 86 D 86 D 86 D 86 D 86 D156 D976 D1322 D3948 D130 D4952 D4809
180 194* 219 254 267 1.5 0.4 30 67.1 31.5 98 1a Neg 44.5
173 182 194 232 264 1.4 0 30 43.4 26 95 1a Neg 43.6
noncompliance
with
requirements
of
samples, 134 marine gas oil (MGO) samples, and 13 ultralow sulfur diesel (ULSD) samples. When references in the following text are made to the alternative fuel populations without specifying neat or blended data (e.g., “biodiesel samples” or “FT data”), it should be assumed that these populations include both all of the neat alternative fuel samples as well as all of the blends of these samples with the petrochemical fuels. Fischer-Tropsch Fuels. The FT training set was comprised of two synthetic JP-5 fuels and two synthetic diesel fuels. One FT JP-5 fuel was produced by a coal to liquid (CTL) process and one from a natural gas to liquid (GTL) process. All of these fuels were intended to be drop-in replacements for MIL-specification JP-5 jet and F-76 diesel fuels. The specification properties of the two FT JP-5 fuels are shown in Table 1. Both FT jet fuels met the requirements of MIL-DTL-5624T for JP-5 fuel with the exception of the density and 10% distillation point for the CTL FT jet. The FT diesel fuel sample set consisted of one CTL product and a GTL FT diesel fuel derived from ethylene (simply referred to as “synthetic” in the following text for the purposes of differentiation). Both of these FT diesel fuels met the requirements of MIL-PRF16884K, for F-76 naval distillate, with the exception of the demulsification time for the ethylene-derived CTL FT diesel fuel. The specification properties of these two FT diesel fuels are given in Table 2. A total of 62 blends were prepared by combining various amounts of these FT fuels with 31 randomly selected petroleumderived jet fuels and 31 with randomly selected petroleum-derived diesel fuels. Although the neat FT fuels available are each classified as either jet or diesel fuels themselves, there is some reason to suspect that FT fuels classified as jet could be found blended with petrofuels classified as diesel and vice versa due to jet fuels (either FT or petrofuel) being downgraded to diesel fuels; therefore, all of the neat FT fuels are evaluated in both the jet-specific and diesel-specific FT content modeling, and FT blends are classified as jet or diesel fuels based on the petrochemical component of the blend. Biodiesel Fuels. Biodiesel fuels derived from animal fat, rapeseed, and canola seed were used at various quantities to prepare a training set of 37 biodiesel samples blended with various
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Table 2. Specification Properties of the Fischer-Tropsch Synthetic Diesel Fuelsa
a
property
ASTM method
CTL FTDiesel
FT Syn. diesel
density @ 15 °C (kg/m3) flash point (°C) cetane index total acid number (mg KOH/g) viscosity @ 40 °C (cSt) cloud point (°C) pour point (°C) aromatics (vol %) sulfur content (wt %) heating value (mJ/kg) heating value by volume (mJ/L) ASTM color Cu corrosion @ 100 °C demulsification @ 25 °C (min) ash (wt %) carbon residue (10% bottoms) (wt %) particulates (mg/L) hydrogen content (wt %) sulfur (wt %) distillation initial boiling point (°C) 10% point (°C) 50% point (°C) 90% point (°C) end point (°C) residue + loss (vol %) trace metals, calcium (ppm) trace metals, lead (ppm) trace metals, Na + K (ppm) trace metals, vanadium (ppm)
D 4052 D 93 D 976 D 974 D 445 D 5773 D 5949 GC/MS D 4294 D 4809 D 4809 D 1500 D 130 D 1401 D 482 D 524 D 6217 D 3701 D 4294
774 63.0 76.1 0.003 2.5 -2.7 -12 not detectable 0.0043 43.2 33.5 0 1b 1
