Expanded Framework for the Prediction of Alternative Fuel Content

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An Expanded Framework for the Predictions of Alternative Fuel Content and Alternative Fuel Blend Performance Properties Using Near-Infrared Spectroscopic Data Jeffrey A. Cramer, Mark H. Hammond, Kristina M. Myers, Iwona A. Leska, and Robert Edmond Morris Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.5b01660 • Publication Date (Web): 12 Oct 2015 Downloaded from http://pubs.acs.org on October 12, 2015

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Figures 1a – 1d. The entire NFPM calibration data set (various wavelength ranges). 218x188mm (96 x 96 DPI)

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Figure 2. PCA score space representing the entire NFPM calibration data set. 196x146mm (96 x 96 DPI)


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Figure 3. PCA score space representing the NFPM jet fuel calibration data set. Rays have been included to approximately indicate the manner in which various alternative fuel types deviate from the petroleum fuel cloud. 197x147mm (96 x 96 DPI)


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Figure 4. PCA score space representing the NFPM diesel fuel calibration data set. Rays have been included to approximately indicate the manner in which various alternative fuel types deviate from the petroleum fuel cloud. 196x150mm (96 x 96 DPI)


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AN EXPANDED FRAMEWORK FOR THE PREDICTIONS OF ALTERNATIVE FUEL CONTENT AND ALTERNATIVE FUEL BLEND PERFORMANCE PROPERTIES USING NEAR-INFRARED SPECTROSCOPIC DATA Jeffrey A. Cramer*, Mark H. Hammond, Kristina M. Myers†, Iwona A. Leska†, and Robert E. Morris Naval Research Laboratory, 4555 Overlook Ave. SW, Washington, DC 20375 † Nova Research Inc., 1900 Elkin Street Suite 230, Alexandria, VA 22308 Author E-mail: [email protected] *Author to whom correspondence should be addressed. Phone: 202-404-3419, Fax: 202-7671716, E-mail: [email protected]

ABSTRACT Partial least squares (PLS) regression models can be constructed from near-infrared (NIR) spectroscopic data to identify and predict critical specification properties of jet and diesel fuels for quality surveillance pre-screening. This same approach has also been used previously to identify Fischer-Tropsch synthetic fuels and fatty acid methyl ester fuels, predict their quantities in blends with jet and diesel petrochemical fuels, and even correct fuel property predictions when alternative fuel contents in blends would affect the predictions of the properties in question. The present work expands upon these previous results by incorporating several additional alternative fuel types into a more generalized alternative fuel content and property modeling framework than was developed previously. The framework consists of a single generalized PLS modeling solution to simultaneously accommodate multiple alternative fuels considered isoparaffinic in nature, as well as smaller-scale modeling solutions to accommodate individual alternative fuels that are not similarly isoparaffinic in nature. This expanded framework provides the means to allow NIR PLS models to predict and quantify alternative fuel contents in blends, and accurately predict affected fuel properties, in a robust fashion that, due to the use of more generalized modeling than has been seen in previous work, better accommodates a future of unknown and unknowable alternative fuel types.

KEYWORDS: Alternative Fuels, Fuel Properties, Near-Infrared (NIR) Spectroscopy, Partial Least Squares (PLS) Regression, Principal Component Analysis (PCA)


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INTRODUCTION The Naval Research Laboratory (NRL) has been engaged in the development of sensor-based technologies to replace the Combined Contaminated Fuel Detector (CCFD), used to perform fuel quality surveillance onboard Navy ships. In the course of this research, a prototype instrument known as the Navy Fuel Property Monitor (NFPM)1 was developed that derives predictions of various critical specification fuel properties through the partial least squares (PLS) regression modeling of near infrared (NIR) spectra. This technology offers significant advantages by reducing the time and manpower currently required to measure specification properties2 and would also improve shipboard safety by minimizing the manual transport of fuel aboard Naval vessels, the use of consumables, and the generation of waste. It is because of these advantages that further work has recently been undertaken to develop a ruggedized, NIR-based Portable Fuel Quality Analyzer (PFQA, Real-Time Analyzers, Inc., Middletown, CT, rta.biz), an instrument prototype intended to identify and characterize a wider variety of fuel types than the NFPM.

The characterization of petroleum and petroleum products using NIR spectra has been an active field of research not only at NRL but elsewhere3,4,5,6,7,8. However, as synthetic fuels, fuels derived from biomass, and other alternative fuels are introduced into fuel supply systems, any given fuel analysis tool will be required to robustly perform its functions in their presence. Because standard fuel handling and storage practices lead to the comingling and blending of different fuels of a similar grade, regardless of initial alternative fuel content, one of the explicit design goals of the PFQA has been the capacity to successfully analyze fuels with alternative fuel contents ranging from 0% to 100%, or at least as close as possible to these limits. This laboratory has previously shown9 that PLS property predictions from models created from conventional petrochemical fuels can be used with blends containing an alternative fuel by applying corrections derived from the alternative fuel identity and its content in the blend. The accuracy and precision of these corrected property value predictions for alternative fuel blends were determined to be on par with the predictions derived from conventional fuels.

Because both the fuel properties themselves and the alternative fuel contents are calculated from NIR spectra using a PLS-based modeling procedure, there is no reason to suspect that this selfcontained procedure would not at least theoretically be applicable to alternative fuels other than


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those used to perform the original work. Furthermore, improvements to the fuel property modeling algorithms, and especially those portions of the modeling devoted to alternative and otherwise non-standard fuels, are continuously pursued to improve the performance of NIRbased instrumentation10,11,12. However, the improvements pursued to date have not fundamentally increased the number of alternative fuels that can be accommodated in the context of the PLS modeling of NIR data. It was initially presumed that NIR-based instrumentation would be required to model every potential alternative fuel individually, which would, in turn, require a devoted modeling effort for each alternative fuel by expert users, a situation seemingly corroborated by a lack of contraindicating evidence in the topical literature. This would limit the usefulness of the PFQA in assessing alternative fuel types not explicitly modeled prior to largescale instrument deployment, which is unacceptable given the active state of alternative fuels research13. The expanded alternative fuel modeling framework described herein was thus developed in an effort to improve the versatility of the alternative fuel content and fuel property modeling capabilities of the PFQA and similar instruments by both including a larger number of alternative fuels in the modeling and including as many alternative fuels as possible in a single primary model, with secondary models being used to accommodate special-case alternative fuels that are not well-predicted by the primary model.

The production of this framework using data collected from older hardware, and the validation of this framework using data collected from updated hardware, will clearly indicate that underlying alternative fuel chemistries, as opposed to instrument proclivities, are being realistically employed for the purposes of alternative fuel modeling. The data collected on the updated PFQA prototype instrumentation will then be used to validate the fuel property correction strategy, previously developed using Fischer-Tropsch (FT) alternative fuels, for other alternative fuel types, including the multiple fuel types gathered together into a primary isoparaffinic modeling solution. It should be noted that the term “isoparaffinic” is being used to describe this primary modeling solution because all of the alternative fuels that can be thus accommodated are significantly, though not necessarily entirely, isoparaffinic in composition.


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EXPERIMENTAL Fuels. A group of 1685 worldwide petrochemical fuel samples was used for the alternative fuel content and fuel property modeling in the present study. Although many of these fuel samples have uncertain or otherwise obscured points of origin, a significant number could be distinctly associated with sampling locations within a diverse array of countries, and the distribution of samples that could be thus associated is reported in Table I to more clearly indicate the diversity of the available fuel population. Since many of the fuel constituents that influence the critical fuel properties of interest are different for different fuel types, the precision of the predictive PLS models was improved by considering the fuel samples as two broad but distinct categories, jet and diesel. The 909-sample jet fuel calibration set consisted of a population of JP-5, JP-8, Jet A, and Jet A-1 fuels, and the 776-sample diesel fuel calibration set consisted of a population of F-76 Naval distillate samples, marine gas oil (MGO) samples, and ultra-low sulfur diesel (ULSD) samples. Most of the fuel samples were provided with property analyses, including measured ASTM fuel property results which were used as the calibration data for the fuel property model constructions. Due to differences in fuel property monitoring requirements when samples are collected from different sources, not every sample in the calibration data set has an associated ASTM value available for every desired fuel property, but every sample that has a usable ASTM value was included in every given model construction. The numbers of samples actually used to construct the individual ASTM fuel property models will be reported along with prediction results for appropriate alternative fuel blends.

Alternative Fuels. The jet fuel calibration data was additionally augmented with the following alternative fuel samples: 20 alcohol-to-jet (ATJ) jet fuels and blends of ATJ jet fuels with petrochemical jet fuels; 4 hydrotreated depolymerized cellulosic (HDC) jet fuel samples, all blends with petrochemical jet fuels; 10 Fischer-Tropsch (FT) coal-to-liquid (CTL) jet fuels and blends of FT CTL jet fuels with petrochemical jet fuels; 19 hydroprocessed esters and fatty acids (HEFA) jet fuels and blends of HEFA jet fuels with petrochemical jet fuels; 13 FT gas-to-liquid (GTL) jet fuels and blends of FT GTL jet fuels with petrochemical jet fuels; and 10 blends of petrochemical jet fuels with various types of alternative diesel fuels.


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The diesel fuel calibration data was augmented with the following alternative fuel samples: 11 direct sugar to hydrocarbon (DSH), also known as synthesized isoparaffin (SIP), diesel fuels and blends of DSH diesel fuels with petrochemical diesel fuels; 12 HDC diesel fuels and blends of HDC diesel fuels with petrochemical diesel fuels; 13 FT CTL diesel fuels and blends of FT CTL diesel fuels with petrochemical diesel fuels;; 13 synthetic FT diesel fuels and blends of synthetic FT diesel fuels with petrochemical diesel fuels; 44 HEFA diesel fuels and blends of HEFA diesel fuels with petrochemical diesel fuels; 12 catalytic hydrothermal conversion (CHC) diesel fuels and blends of CHC diesel fuels with petrochemical diesel fuels; 8 fatty acid (FAME) diesel fuels and blends of FAME diesel fuels with petrochemical diesel fuels, and 9 blends of petrochemical diesel fuels with various types of alternative jet fuels.

Although the available neat alternative fuels were produced as either jet or diesel fuels, there is some reason to suspect that there will be instances where alternative jet fuels could become comingled with conventional diesel fuels and vice versa. One situation where this could occur in the Navy is when JP-5 jet fuel is downgraded and blended with shipboard diesel fuel for ship propulsion. This occurs when an offloaded JP-5 fuel fails to meet thermal stability requirements due to acquisition of copper after contact with copper-bearing shipboard fuel system components.

Therefore,

alternative

jet/petrochemical

diesel

blends

and

alternative

diesel/petrochemical jet blends were included in the diesel and jet fuel modeling procedures. In the case of the initial modeling, any fuel blend containing either a petrochemical diesel fuel or an alternative diesel fuel blend was included in the diesel fuel modeling. Meanwhile, blends containing petrochemical jet fuels were included in the jet fuel modeling. This meant that there was a small number of alternative diesel/petrochemical jet samples included in both the jet and diesel fuel sample populations. While the effectiveness of this course of action will be made apparent in this work, it was decided, prior to model construction for the updated prototype hardware, that this overlap was best eliminated to simplify prediction results. Therefore, in the case of the PFQA, alternative diesel/petrochemical jet blends are no longer included in the jet fuel modeling, which might at least partially account for the minor differences seen in the alternative fuel modeling results. None of these alternative jet/petrochemical diesel blends and alternative diesel/petrochemical jet blends were included in the assessment of alternative fuels seen in the PCA results to be reported herein, as it was decided that these rare and unusual


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samples should not be used to make decisions regarding how alternative fuels should be modeled, even if they should be accommodated in the overall alternative fuel modeling framework. The correction strategy described previously9, once redeveloped for the expanded alternative fuel modeling framework, was applied to five alternative fuel samples, all of which already appear in the alternative fuel populations described previously. In the first set, a 100% alcohol-to-jet (ATJ) fuel and a 50% ATJ fuel blend were evaluated in the context of the jet fuel modeling, as ATJ fuels are considered a non-isoparaffinic special case. In the second set, two 100% hydroprocessed esters and fatty acids (HEFA) fuels and one 50% Fischer-Tropsch (FT) fuel blend were evaluated in the context of the diesel fuel modeling, as both alternative fuel types are modeled as isoparaffinic fuels. Although the present evaluation uses the known alternative fuel contents, these values will quite likely not be known a priori during anticipated PFQA operations. In these cases alternative fuel contents will most likely be derived from the complementary alternative fuel content modeling framework presented herein.

AVGAS, Gasoline, and E85/E75 Gasoline Blends. In addition to the Department of Defense (DOD)-grade jet and diesel fuels described previously, 2 samples of AVGAS, 3 samples of commercial gasoline, and blends of these commercial gasoline samples with ethanol were also included in the present study. This was done to obtain at least an approximate idea of how these fuels would be interpreted by a NIR fuel monitor as an evaluation of its potential versatility. It should be noted that, because the original commercial gasoline samples might have had as much as 10% blended ethanol content when they were initially acquired, two sets of blends were created, E85 (recognized fuel grade, 85% ethanol and 15% commercial gasoline) and “E75” (not a recognized fuel grade, 75% ethanol and 25% commercial gasoline), so that the results obtained from both types of blends could be compared with one another to ensure that E85, as a class of fuel, had been captured in the present modeling in one form or another.

Spectra. NIR absorbance spectra were collected from the fuel samples with two instruments. The first instrument, the NFPM, uses a self-contained light source/spectrometer (Bruker Optics FuelEx14, Bruker Optics, Billerica, MA), but employs a different computer and software. The


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FuelEx NIR spectrometer employs an F#/2 spectrograph and a thermoelectrically-cooled 512element GaAs detector array with a feedback-stabilized high-intensity tungsten halide lamp source and fiber-optic transflectance dip probe. This flexible dip probe introduces a small amount of variance in the raw data that the preprocessing strategies described below were developed to mitigate. Spectra were sampled at a rate of 500 ms, and data acquisition and spectral preprocessing were performed with a separate Avantech TPC-1070 touch screen computer with a 1 GHz Intel Celeron processor, running MS Windows XP Professional SP3. Data acquisitions were performed with software written in-house and compiled from LabVIEW 8.5 (National Instruments Corporation, Austin, TX). NFPM spectral data, covering a wavelength range from 874 to 1579 nm, were referenced to an air background.

The PFQA, much like the NFPM, is a dispersive NIR spectrometer, though it uses a 256 channel InGaAs photodiode array detector, with a spectral resolution reported to be between 2.3 and 3.5 nm. Eight individual data accumulations at 200 ms each were collected for each sample for the purposes of signal averaging. Each raw NIR spectrum consisted of 256 data points covering the range from 991 to 1607 nm, though the x-axis wavelength was calibrated over a wavelength range of 1000 to 1600 nm using a krypton lamp and the known absorptions from methylene chloride spectra. This instrument does not use a fiber optic dip probe, but rather incorporates an optical cell that accommodates standard 2 ml volume, 12 mm diameter vials, maintaining a constant optical path length. Spectral data were referenced to a background acquired from a sample vial containing carbon tetrachloride. NIR spectra were acquired from the fuel training set with two individual prototypes (PFQA2 and PFQA4) to establish the precision of this instrumental design.

Data Preprocessing. The spectra from all instruments were preprocessed by first applying a two-point baseline correction, which also required an interpolation of the data to 1 nm resolution. In the case of the NFPM, the data were interpolated to 701 data points covering the NIR wavelength range from 875 nm to 1575 nm. In the case of the PFQA, the data were interpolated to 601 data points covering the NIR wavelength range from 1000 nm to 1600 nm. The differences in these ranges are deemed acceptable in the present analysis because the overlapping range includes the NIR overtone absorbance bands of fuels most useful for PLS modeling. For


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all instruments, the two wavelengths chosen for the two-point baseline correction were the first available post-interpolation wavelength (875 nm for the NFPM and 1000 nm for the PFQA) and the post-interpolation wavelength amongst the last ten such wavelengths possessing the minimum absorbance value. These baseline-corrected data were then each normalized to unit area. A third preprocessing step was also applied to transform to first-derivative spectra through Savitsky-Golay (SG) filtering using a 3-point filter size and a 2nd order polynomial to minimize actual data smoothing.

The NIR data from the NFPM, thus preprocessed, can be found plotted across various wavelength ranges in Figures 1a through 1d. Although there are well over a hundred neat alternative fuel samples and blends included in this data set, a visual inspection of the data does not reveal similarly abundant patterns at any given wavelength, or utilizing any single data feature, that might reliably indicate the presence of alternative fuels or the quantities thereof. This attests to the need for a robust multivariate analysis strategy that is capable of finding multiple overlapping patterns within NIR data simultaneously.

Mean centering was applied to all spectra prior to chemometric analysis and modeling procedures.

Chemometric Analysis and Modeling. Principal Component Analysis (PCA) was performed, using a singular value decomposition (SVD) algorithm, to predict which alternative fuels could be modeled with one another and which alternative fuels required separate modeling solutions. Partial least squares (PLS) regression was also performed to correlate the NIR spectra of the fuel samples to their known fuel property values and their known alternative fuel contents. It should be noted here that additional supervised pattern recognition techniques, such as K-nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA), also exist that are explicitly used for predicting the classes of unknown samples15. Such techniques could very well be appropriate data analysis options when simply predicting the presence of alternative fuels. However, because the quantification of alternative fuel contents within fuel blends is a critical component of the fuel property correction strategy to be described herein, PLS is considered a more appropriate modeling technique in the present context.


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In the case of both PCA and PLS, the numerical spectral, fuel property, and alternative fuel content data, whenever necessary, were imported into MATLAB R2014a (MathWorks, Inc., Natick, MA), where the spectra were assembled into matrices in which each row represented the NIR spectrum of a different fuel sample. PCA and PLS algorithms were developed with functionality provided by the PLS_Toolbox for MATLAB ver. 7.9.4 (Eigenvector Research, Inc., Wenatchee, WA). In lieu of using separate validation data sets, results of the PLS modeling of fuel properties and alternative fuel contents were initially evaluated in terms of Root Mean Square Error of Cross-Validation (RMSECV) results obtained from leave-one-out crossvalidation16, in which each sample’s predicted value in a given model is based on a sub-model built from every other sample except the sample being given a prediction value, a technique which indirectly ascertains a given model’s performance with uncalibrated data. The number of constituent latent variables (LVs), the underlying linear factors to which the calibration data are converted for calibration purposes, for all quantitative prediction models were chosen automatically using a statistic called the F-test17,18,19. The F-test was applied to the crossvalidation results of the PLS fuel modeling with an 85% confidence interval, using a maximum of 10 LVs to maintain as much model versatility and utility as possible in the presence of uncalibrated samples, petrochemical or alternative. The F-test, by limiting the number of LVs, protects against models that are too biased towards a specific set of calibration data, which would make the models themselves improperly biased against and less effective when predicting the fuel properties and alternative fuel contents of uncalibrated data. A thorough analysis of this type of model bias, known as overfitting, can be found in previous work20. The present work does not directly perform any calibration transfer operations, as instrument-specific models are constructed for all instruments.

Once the RMSECV results were used to select an appropriate number of LVs, a non-crossvalidated PLS model was constructed from the same calibration data using this number of LVs. This operation produces root mean square error of prediction (RMSEP) values for the data that are more consistent and more realistic long-term measures of model quality than RMSECV values. The alternative fuel content of any given fuel sample, which was known at the time of sample collection, was expressed as a volume percent with a value ranging from 0 to 100.


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Model Pairs. In many cases, a pair of PLS models is used for a single individual alternative fuel content prediction, one “coarse” model that includes all available calibration data, and one “fine” model that only includes those fuel samples actually containing the alternative fuel type being modeled. Generally speaking, a coarse model is more capable of correctly discriminating lowconcentration blends of the target alternative fuel from the overall fuel sample population, while a fine model provides more accurate quantifications of alternative fuel contents themselves. Using both models in series, while setting a single cutoff value for both models, can provide the ability to detect and thus discriminate against false positive results. In the original, FT-based work9, these coarse and fine model types were referred to as “identification” and “quantification” models, respectively. As implied by this former naming convention, the fine model’s prediction results are used for all final quantitative predictions. In those cases in which either a model or a model pair could be the object being referred to in the following text, the generic phrase “modeling solution” will be used instead.

Compensating For Alternative Fuel Content When Predicting Fuel Properties. The method by which the alternative fuel content in a blend can be taken into consideration when modeling fuel properties has been previously9 described in greater detail. The error associated with the fuel property prediction of a neat alternative fuel, with a known fuel property value, is derived for a given fuel property modeling solution. This error thus defines an equal but opposite 100% correction factor. A given PLS property prediction can therefore be corrected for alternative fuel content by multiplying the appropriate 100% correction factor by the percentage of the alternative fuel present, as derived from either a priori information or the PLS modeling procedure described previously. Because the amount of alternative fuel in a fuel blend also tends to be reflected in PCA score space results, this chemometric technique could theoretically be used to predict these percent contents as well.

In the present work, rather than develop a specific set of alternative fuel content modeling solutions for each different alternative fuel, it was found that generalized modeling solutions could be derived for different classes of alternative fuels. Therefore, there are cases in which both fuel property prediction models and alternative fuel content prediction models incorporate


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multiple alternative fuels simultaneously and, hence, at least potentially possess multiple neat alternative fuel samples upon which to base correction factors. In these circumstances, the errors associated with all appropriate neat alternative fuel samples are simply averaged together to find the overall 100% correction factor.

In order to completely avoid false positive results during initial model development, cutoff values were initially defined that were above the largest falsely predicted alternative fuel contents in their respective modeling solutions. Below these cutoff values, alternative fuel content predictions are deemed unreliable and, thus, set to zero in the software. This is necessary to prevent the reporting of trivially low, or even negative, alternative fuel contents in petrochemical fuel samples. The lowest alternative fuel content that can be reliably detected at the given cutoff value is thus reported as the limit of detection (LOD). Though the complete elimination of false positive results was not similarly possible when the initial NFPM-developed modeling was applied to the PFQA data, this minor shortcoming is not seen as a reason to fundamentally rework the expanded framework as it was developed for the NFPM, for reasons that will be made apparent below.

RESULTS AND DISCUSSION PCA-Based Alternative Fuel Content Modeling. PCA was conducted with NIR spectra acquired from blends of alternative fuels with their petroleum counterparts. When projected into the principal component space, each chemically different alternative fuel falls along a ray emanating from the primary score cloud, where the direction of the ray is a function of the chemical class and the position along the ray is proportional to the concentration of that alternative fuel. Thus, PCA provides information about the identity and concentration of an alternative fuel when blended with petroleum fuel. Figure 2 shows the PCA score results for the entire preprocessed NFPM calibration data set, except for samples representing alternative jet fuels blended with petrochemical diesel fuels and alternative diesel fuels blended with petrochemical jet fuels, as indicated previously. It can be seen here that AVGAS, gasoline, and E85 can easily be distinguished from the petrochemical fuels and from the alternative fuels. The diesel fuels that appear in the jet score cloud are actually JP-5 fuels that have been downgraded


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for use in diesel engines after failing the requirements for thermal stability due to acquisition of copper from copper-nickel components of shipboard fuel systems.

Figure 3 reprints only the jet fuels and blends from Figure 2. Although the isoparaffinic alternative fuels depart from the petrochemical jet fuels in a ray heading up and to the left, the ATJ sub-population indicates a very different direction of down and to the right. The FT CTL jet fuels, meanwhile seem almost to split the difference between the majority of alternative jet fuels and the ATJ fuels, standing alone in a ray that points up and to the right. Finally, the HDC jet fuels exhibit a fourth distinct (aside from a single non-HDC blend) activity in the score space, maintaining a low-magnitude ray that points down and slightly to the left. It is thus predicted that these four activities can be consolidated into four distinct PLS alternative fuel content modeling solutions.

Figure 4 reprints only the diesel fuels and blends from Figure 2. As was the case with the jet fuels and blends, the primary sub-population of isoparaffinic alternative fuels and blends points up and to the left, although it should be noted that the higher level of compositional diversity in diesel fuels as compared to jet fuels obscures this trend somewhat. However, because the individual fuel types found among this figure’s isoparaffinic PCA results tended not to appear as coherent sub-groupings anyway, it was decided to maintain all of these isoparaffinic fuels within a single modeling solution for maximum predictive versatility. The HDC diesel blends respond similarly to the HDC jet blends, forming a ray pointing downward and slightly to the right. The DSH diesel fuels, deviate from these two trends in a manner similar to that seen in the case of the FT CTL jet fuels seen in Figure 3. The FAME fuels and blends appear to deviate substantially from the three other fuel types, with a ray pointing down and to the left. It is thus predicted that these four fuel types can be accommodated with four distinct PLS alternative fuel content modeling solutions.

PLS-Based Alternative Fuel Content Modeling. The results seen in Figures 2 through 4 indicate that alternative fuels would be best modeled as a single primary modeling solution, focusing upon fuel types known to be somewhat similar with respect to a primarily isoparaffinic composition, with a few additional modeling solutions serving to identify and quantify special


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cases prior to this primary modeling solution. Thus, the hypothesis to be tested is that all predictions of alternative fuel content can be produced using jet-specific and diesel-specific PLS models calibrated to the percent content of any given alternative fuel type, including the predominant isoparaffinic fuels. These PLS models are used to evaluate fuel types in a specific order, much as was the case in the original work9, and if a model or model pair detects the presence of an alternative fuel in a sample, then this sample is not subjected to subsequent alternative fuel prediction models, as these subsequent models are, for the sake of accuracy, not constructed to take previously evaluated alternative fuel types fully into account.

Alternative fuel content PLS prediction models are defined not only by model quality measures such as RMSEP, but also the more practical metrics of their corresponding cutoff values and limits of detection (LODs). The cutoff value is the value manually chosen to minimize, and in the case of initial instrument’s data completely eliminate, false positive alternative fuel blend identifications, as any predicted alternative fuel content below this value is simply considered to be zero. Such cutoff values are necessary to avoid the false detection of trivial alternative fuel contents, or even negative alternative fuel contents, in a diverse population of petrochemical samples. The LOD represents the lowest quantity of a given alternative fuel that can be reliably detected given a defined cutoff value.

Expanded Framework. An initial PCA model can be used to separate the jet and diesel fuel populations from each other, as has been done in previous work1. Such a model could also be used to identify AVGAS, gasoline, and E85 samples. There is also every reason to suspect that PLS modeling solutions could be constructed for these three fuel types, if as much were deemed necessary, given how distinct these fuel types are in Figure 2.

After this initial PCA discrimination, jet and diesel fuels can then be modeled in terms of their alternative fuel contents. In the case of jet fuels, the alternative fuels are modeled in the following order:

•

Alcohol-to-jet (ATJ) fuels and blends,

•

Hydrotreated depolymerized cellulosic (HDC) fuels and blends,


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•

Fischer-Tropsch coal-to-liquid (FT CTL) fuels and blends, and

•

Isoparaffinic fuels and blends including hydroprocessed esters and fatty acids (HEFA),

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non-CTL FT fuels and blends, and blends of previously-modeled alternative fuel types that would not be detected by the previous modeling solutions.

In the case of diesel fuels, the alternative fuels are modeled in the following order:

•

Direct sugar to hydrocarbon (DSH, or synthesized isoparaffin, SIP) fuels

•

HDC fuels and blends

•

Isoparaffinic fuels and blends, including a sample population similar to that of the jet isoparaffinic modeling solution but also including catalytic hydrothermal conversion (CHC) fuels and blends

•

FAME fuels and blends

Although FAME biofuels are not allowed for use by Navy tactical ships and vehicles, there are ample opportunities for the introduction of FAME in commercial diesel, thus this modeling solution was deemed necessary to develop.

The upper third of Table II shows the results of applying the proposed expanded framework to data collected on the NFPM instrumentation, complete with the cutoff values found to completely eliminate false positive results. Although it is presently unknown why DSH, a.k.a. SIP, an alternative fuel type that at least theoretically consists of nothing but synthesized isoparaffins, appears to be most effectively modeled separately from the primary isoparaffinic diesel modeling, this will continue to be done for the subsequent PFQA modeling based on its effectiveness.

In the case of diesel isoparaffinic fuels, an identification/quantification PLS model pair with a 19% cutoff for both models in the pair yields a 25% LOD, except for a blend of an FT CTL jet fuel with a petrochemical diesel fuel that was incorrectly identified. As these types of jet/diesel


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blends are considered non-critical in the context of model development, it was decided not to allow this single false negative to define the diesel isoparaffin modeling solution LOD. Similar considerations will be made for false negative results as they appeared when applying the expanded framework to PFQA spectra.

Expanded Framework as Applied to PFQA Spectra. Table II also shows the cutoff and LOD values derived during attempts to model alternative fuel contents, using the framework described above, with data collected from the two available PFQA prototype instruments. The occurrence of four or fewer false positive results in a small subset of modeling steps, out of a population of well over 1600 samples, indicates that the developed framework is adequate not only for the PFQA but also other similar NIR-based instrumentation.

The PFQA-based alternative fuel content modeling seemed to require fewer coarse/fine model pairs than was the case when the framework was first created using the NFPM, as can be seen when comparing the results in Table II for all three instruments. Regardless of this trend, the use of fine models can still provide much more accurate quantitative predictions, as indicated in Table IIIa, even if these same fine models do not provide any additional discrimination capabilities.

Table IIIa shows the RMSEP results of the alternative fuel content predictions derived from the NFPM instrument and two PFQA prototype instruments. These RMSEP results only represent the alternative fuel contents that are actually predicted at any given modeling step, and the table also includes RMSEP values as they would have been produced had the false positive predictions indicated in Table II not been made. This table indicates that the both PFQA prototype instruments perform equivalently when predicting alternative fuel content. Although spectra from PFQA4 often produced lower RMSEP values than spectra from PFQA2, the overall differences between the RMSEP values themselves are quite minor. For the sake of simplicity only PFQA4 data will be used when evaluating the alternative fuel property prediction correction strategy. The corresponding correlation coefficients, or R2 values, of the predicted alternative fuel contents can also be found in Table IIIb. These R2 values are another metric by which to indicate relative model performance as it applies to the prediction of alternative fuel content.


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Note, however, that even the models with the lowest predictive capabilities still tend to have rather high correlation coefficients, especially when disregarding false positive results.

Alternative Fuel Blend Property Modeling. Tables IV and V show the results of using the previously described alternative fuel property prediction correction strategy on data collected from PFQA4. As very few available alternative fuels and blends have reported fuel properties associated with them, fewer than the maximum number of available fuel properties can be evaluated in this context, though there is no reason to suspect that the overall trends seen herein will not hold across all fuel property modeling results. Because the prediction results from two neat HEFA fuels are being averaged to define the 100% isoparaffin correction factor, and because neither of these fuels are the same alternative fuel type as the FT GTL blend being used to test the correction strategy, the versatility of the proposed isoparaffinic modeling will also implicitly be evaluated.

What is immediately apparent in Tables IV and V is that the correction strategy remains effective in the expanded framework in the majority of cases, though it is unfortunately less effective than desired in the case of jet fuel density predictions, jet fuel FSII predictions, and two of the diesel fuel distillation value predictions. For those fuel properties for which the uncorrected fuel property prediction outperforms the corrected prediction, the correction strategy can simply be disregarded for said fuel property without hindering the strategy’s effectiveness with other fuel properties.

CONCLUSIONS The alternative fuel content and property modeling framework described here has been shown to be effective at accommodating a wide range of alternative fuel types. The alternative fuel content modeling effectively predicts the presence and quantities of alternative fuels in fuel blends, and takes more alternative fuels into account than previous efforts. The combination of several seemingly distinct alternative fuel types into a single isoparaffinic modeling solution will also allow for the overall alternative fuel modeling framework to more ably accommodate unknown and uncalibrated alternative fuel types that are compositionally similar to the diverse array of


16

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isoparaffinic fuels that were used during present model constructions. The previous correction strategy developed to allow for accurate fuel property predictions in the cases of FT fuels and blends has also been effectively extended to other types of alternative fuel modeling in the expanded framework. There are, of course, other existing multivariate modeling strategies9,21,22,23 focused upon discrete alternative fuel types that could very well offer more precise quantitative predictions than the framework developed herein under specific circumstances. However, there is, at present, no reason to suspect that these strategies would be the proper tools to employ when more thoroughly assessing fuel populations that might possess unknown and unknowable alternative fuel types. One must always consider one’s ultimate analytical goals when deciding upon which analysis strategies to employ.

ACKNOWLEDGMENTS The Authors wish to thank the Defense Logistics Agency Energy (DLA Energy) for supporting this work, and Joel Schmitigal at the Army Tank Automotive Research Development and Engineering Center (TARDEC) for providing additional analytical fuel data.


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Figures 1a – 1d. The entire NFPM calibration data set (various wavelength ranges).


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Figure 2. PCA score space representing the entire NFPM calibration data set.


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Figure 3. PCA score space representing the NFPM jet fuel calibration data set. Rays have been included to approximately indicate the manner in which various alternative fuel types deviate from the petroleum fuel cloud.


20

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Energy & Fuels

Figure 4. PCA score space representing the NFPM diesel fuel calibration data set. Rays have been included to approximately indicate the manner in which various alternative fuel types deviate from the petroleum fuel cloud.


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Table I. Distribution of fuels from the parent sample population whose sampling locations could be associated with distinct countries.

Associated Country United States Japan Cuba Panama Guatemala Australia Costa Rica Columbia Curaçao El Salvador Aruba Canada France Mexico United Arab Emirates Peru Singapore Bahamas Ecuador England Germany Netherlands

# Fuel Samples 659 39 31 16 14 13 13 11 11 11 8 8 7 6 6 5 5 4 4 4 4 4

Associated Country Philippines Spain Barbados Chile Dominican Republic Greenland Bahrain Jamaica Malaysia New Zealand Portugal South Korea Belize Brazil China Czech Republic Ireland Kuwait Nova Scotia Russia Saint Lucia South Africa


22

# Fuel Samples 4 4 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1

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Table II. Summary of the PLS steps in the expanded alternative fuel content modeling using both NFPM and PFQA data sets. *Indicates a blend of an FT CTL jet fuel with a petrochemical diesel fuel. **Indicates a blend of a CHC diesel fuel with a petrochemical diesel. Setting the cutoff values low enough to detect either of these blends would have added false positive results.

Jet (NFPM)

Diesel (NFPM)

Alt. Fuel Model DSH ATJ HDC FT CTL Isoparaff. FAME

Model / Pair N/A Model Model Pair Pair N/A

False Cutoff LOD Pos. N/A N/A 7.5% 10% 5% 10% 15% 20% 13.5% 15% N/A N/A Jet Fuels (PFAQ2)

False Neg.


Model / Pair N/A Model Model Model Model N/A

False Cutoff LOD Pos. N/A N/A 7.5% 10% 1.55% 20% 8% 10% 13% 15% 2 N/A N/A Jet Fuels (PFAQ4)

False Neg.


Model / Pair N/A Model Model Model Model N/A

False Pos.

False Neg.

Cutoff LOD N/A N/A 7.5% 10% 1.45% 20% 8% 10% 13% 15% N/A N/A

4 2

Model / Pair Pair N/A Pair N/A Pair Pair

False Cutoff LOD Pos. 9% 15% N/A N/A 14% 20% N/A N/A 19% 25% 4% 5% Diesel Fuels (PFQA2)

False Neg.

Model / Pair Model N/A Model N/A Model Pair

False Cutoff LOD Pos. 8% 10% N/A N/A 12.5% 15% N/A N/A 17% 25% 4.5% 5% 3 Diesel Fuels (PFQA4)

False Neg.

Model / Pair Model N/A Model N/A Pair Pair

False Pos.

False Neg.


23

Cutoff LOD 11% 15% N/A N/A 14% 15% N/A N/A 17.5% 25% 4.5% 5%

1*

1**

1**

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Table IIIa. Alternative fuel RMSEP results found for the overall alternative fuel modeling framework for the data collected on the NFPM and PFQA prototypes. Values in parentheses are the RMSEP values as they would have been produced without the presence of false positive results.

Alt. Fuel Model DSH ATJ HDC FT CTL Isoparaffinic FAME

Jet Fuels (Detected Samples) NFPM PFQA2 PFQA4 RMSEP RMSEP RMSEP N/A N/A N/A 2.06 0.3 0.2 21.28 7.8 55.9 (7.6) 1.30 2.4 2.1 3.24 8.8 (3.9) 7.3 (3.3) N/A N/A N/A

Diesel Fuels (Detected Samples) NFPM PFQA2 PFQA4 RMSEP RMSEP RMSEP 1.11 1.0 0.8 N/A N/A N/A 4.33 1.2 1.9 N/A N/A N/A 4.84 7.1 6.8 2.64 4.0 (0.3) 0.2


24

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Table IIIb. Alternative fuel correlation coeffecient (R2) results found for the overall alternative fuel modeling framework for the data collected on the NFPM and PFQA prototypes. Values in parentheses are the R2 values as they would have been produced without the presence of false positive results.

Alt. Fuel Model DSH ATJ HDC FT CTL Isoparaffinic FAME

Jet Fuels (Detected Samples) NFPM PFQA2 PFQA4 2 2 R R R2 N/A N/A N/A 1.00 1.00 1.00 0.95 0.99 0.28 (0.99) 1.00 1.00 1.00 0.99 0.97 (0.97) 0.98 (1.00) N/A N/A N/A

Diesel Fuels (Detected Samples) NFPM PFQA2 PFQA4 2 2 R R R2 1.00 1.00 1.00 N/A N/A N/A 0.99 1.00 1.00 N/A N/A N/A 0.98 0.97 0.96 1.00 1.00 (1.00) 1.00


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Table IV. Results obtained when correcting the fuel property predictions associated with a 50% ATJ jet fuel blend with the correction factor derived from a 100% ATJ jet fuel sample. The correction strategy improved predictions in fourteen out of sixteen cases. The cases in which the corrections were not effective have their corrected predictions in the last column highlighted with bold italics.

Fuel Property viscos., -20C aromatics hydrogen ext. gum freeze pt. sulfur density flash point particulates FSII dist. IBP dist. 10 dist. 20 dist. 50 dist. 90 dist. FBP

ASTM Results Modeled (total number of samples) D445 (57) D6379, D1319 (50) D3701, D3343, D7171 (55) D381 (227) D5927, D7153 (471) D4294, D5453, D3227 (47) D4052 (244) D93, P.-M. (355) D5452 (297) D5006 (409) D86 (401) D86 (402) D86 (396) D86 (402) D86 (402) D86 (400)

measured 6.3 6 14.2 7 -57 0.001 0.792 62 0.1 0.02 180.0 191.0 195.0 209.0 243.0 266.0


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50% ATJ predicted 2.1 18 14.5 3 -88 -0.069 0.796 8 -0.2 0.02 68.4 131.8 147.0 154.0 215.3 233.9

corrected 6.3 12 14.4 5 -62 0.055 0.781 54 0.3 0.07 172.7 179.3 183.9 191.7 239.5 266.3

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Table V. Results obtained when correcting the fuel property predictions associated with one 50% diesel FT fuel blend with the correction factor derived from two 100% diesel HEFA fuel samples. The correction strategy improved predictions in seven out of nine cases. The cases in which the corrections were not effective have their corrected predictions in the last column highlighted with bold italics.

Fuel Property pour point cet. index viscos., 40C density dist. IBP dist. 10 dist. 50 dist. 90 dist. FBP

ASTM Results Modeled (total number of samples) D5949 (160) D976, D4737 (499) D445 (522) D4052 (578) D86 (278) D86 (288) D86 (299) D86 (485) D86 (472)

50% Diesel FT measured predicted corrected -33 -11 -22 57.1 53.2 59.4 1.970 1.673 1.905 0.804 0.799 0.802 182.0 186.1 182.6 199.0 180.0 191.4 241.0 235.5 242.1 306.0 305.5 286.5 343.0 334.2 303.3


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Expanded Framework for the Prediction of Alternative Fuel Content

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