Article pubs.acs.org/EF
Determination of Simulated Crude Oil Mixtures from the North Sea Using Atmospheric Pressure Photoionization Coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Matthias Witt* and Wiebke Timm Bruker Daltonik GmbH, 28359 Bremen, Germany ABSTRACT: Defined artificial mixtures of crude oils from the North Sea were analyzed using atmospheric pressure photoionization coupled to Fourier transform ion cyclotron resonance mass spectrometry (APPI FT-ICR MS). The main objective was the evaluation of the accurate determination of calculated mixing ratios of similar crude oils. The mixing ratios of two oils were determined on the basis of the relative abundances of the main compound classes detected as protonated species and radical cations. The calculation of the ratios of ternary mixtures was based on two different approaches. The first approach used the relative abundances of the main compound classes in combination with the non-negative least squares (NNLS) method. The second approach used principal component analysis (PCA) using the first and second principal components (PC1 and PC2) of the pure oils and defined ternary mixtures to generate a ternary PCA scoring plot. The exact compositions of three ternary mixtures were calculated on the basis of the measured PC1 and PC2 values. Relative compound class abundance plots of the binary mixtures showed remarkably good linear regression factors, indicating linear mixing behavior and high reproducibility of mass spectrometric measurements of petroleum samples using atmospheric pressure photoionization (APPI). As expected, ternary mixtures displayed a higher relative error for the determined ratios than binary mixtures. Nevertheless, the composition of ternary mixtures could be determined with reasonably high accuracy. The errors in the method based on multivariate statistical analysis were smaller than the NNLS method based on the relative abundance of compound classes. This study indicates that APPI FTICR MS can be used to identify ratios of crude oils in mixtures, making this approach useful for reservoir connectivity studies.
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INTRODUCTION Crude oils are some of the most complex mixtures in nature, containing tens of thousands of different organic compounds. They consist of saturated and aromatic hydrocarbons, heteroatomic compounds containing mainly nitrogen, oxygen, and sulfur, and organometallic compounds, such as vanadyl and nickel porphyrins. Their composition varies from oil to oil and depends, for example, upon the origin and maturity of the crude oil. Alongside bulk properties and gradients, this chemical composition can be used to assess reservoir connectivity.1 As a result of the fact that many recently found deposits contain heavy crude oils, new and improved analytical methods are required to better characterize heavy crude oils. Most of the heavier oil pools are biodegraded, and traditional techniques for assessing reservoir continuity, such as gas chromatography (GC) and gas chromatography−mass spectrometry (GC−MS), fail. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is a very powerful technique for studying oil at the molecular level. FT-ICR MS has become a wellestablished method in this analytical field, which has become known as petroleomics.2−6 A very high resolving power can be routinely achieved by FT-ICR MS.7 Masses can be detected with extremely high accuracy (in the parts per billion range), resulting in unambiguous determination of molecular formulas.8 Therefore, using this technique enables thousands of components to be identified in ultrahigh-resolution mass spectra of crude oils.9 As a result of the high technical performance of FT-ICR MS, small mass differences in the spectra of very complex samples, such as crude oils and heavy oils, can be resolved. Because polar compounds containing different heteroatoms and © XXXX American Chemical Society
aromatic hydrocarbons can be detected as protonated species as well as radical cations, very small mass differences (as small as 1.1 mDa) are common, especially in atmospheric pressure photoionization (APPI).10 Nevertheless, as a result of its ability to detect a wide variety of compound classes, APPI has become established as a common ionization method in petroleomics.11 Even nickel and vanadyl porphyrins can be directly detected by APPI in crude oil without any purification.12−14 Polycyclic aromatic sulfur-containing compounds can also be efficiently detected by APPI in positive-ion mode.15 As a result of ionization effects, some compounds, such as alkanes and hydrocarbons without a benzene ring, cannot be detected in oils using APPI. However, the chemical composition of polar compounds in crude oil can be extracted from ultrahigh-resolution mass spectra. Therefore, a high-resolution mass spectrum is highly specific and can be regarded as a “fingerprint” of the analyzed oil.16 The amounts and ratios of chemical biomarkers in crude oil can be correlated to the type of reservoir and maturity of the oil.17 The influence of maturity on the chemical composition of crude oils and shale samples has been studied using electrospray ionization (ESI) and APPI coupled to FT-ICR MS.18,19 As a result of the very high chemical complexity of oils, ultrahigh-resolution mass spectra Special Issue: 16th International Conference on Petroleum Phase Behavior and Fouling Received: October 7, 2015 Revised: December 10, 2015
A
DOI: 10.1021/acs.energyfuels.5b02353 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels must be used to distinguish different oils by statistical methods.20,21 It has been shown that principal component analysis (PCA) is a valuable method for grouping oils according to their chemical composition. APPI coupled to FT-ICR MS provided important information that reflected the geographical location and from which well the oils were extracted. Crude oil samples could be grouped by PCA as well as hierarchical clustering according to the difference in chemical composition.22,23 These results were confirmed by a study using gas chromatography tandem mass spectrometry.24 In this work, we evaluated the accuracy and reproducibility of APPI FT-ICR mass spectrometric determination of compound classes by measuring their relative abundances in mixtures of two crude oils. On the basis of the relative abundances of compound classes, ternary mixtures of crude oils were analyzed using the non-negative least squares (NNLS) method. In a second approach, PCA was used to characterize ternary mixtures. The mass spectra of the pure oils and ternary mixtures with known ratios were used to calculate the principal components (PC1 and PC2) of the pure oils very accurately in PCA scoring plots. Subsequently, PC1 and PC2 were used to calculate the composition of three ternary mixtures.
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high-performance liquid chromatography (HPLC) system. The ion accumulation time was set to 30 ms, and 128 single scans were added for the final mass spectrum. Internal mass calibration and generation of mass lists were performed using DataAnalysis 4.2 (Bruker Daltonik, Bremen, Germany). Data analysis including calculation of molecular formulas and relative abundances of compound classes was performed using Composer 1.0.6 (Sierra Analytics, Modesto, CA). Elemental composition assignment was based on the Kendrick mass defect sorting in the Composer software. A maximum mass error of 0.5 ppm was allowed for molecular formula calculation with a maximum number of heteroatoms set to N = 3, O = 3, and S = 3. The following equation was used to calculate the double bond equivalence (DBE) values for the elemental formula CcHhNnOoSs.26 (1)
DBE = c − h/2 + n/2 + 1
This value represents the sum of rings and double bonds in a molecule. NNLS Calculation and PCA. The NNLS solution was calculated with the statistical software R, version 3.1.2, using the Lawson− Hanson implementation.27,28 Details of the NNLS method can be found in the literature.29,30 This method used the relative abundances of compound classes of the pure oils and the mixtures. The PCA for the calculation of the ternary mixtures was performed with ProfileAnalysis 2.1 (Bruker Daltonik, Bremen, Germany). Details of the PCA statistical methods can be found in the literature.31 The calculation was performed with an average peak list using only bins with masses present in the mass spectra. For the calculation of the bucket table for the multivariate statistical analysis, an average spectrum of all mass spectra was created to calculate all possible bins. The width of the buckets was calculated on the basis of the mass resolution of the mass spectrum. Roughly 20 000 buckets were created for the bucket table. The complete bucket table was used for the PCA calculation. The bucket transformation (intensity scaling) was performed with logarithmic scaling without data reduction and without baseline subtraction. The calculated principal components PC1 and PC2 of all measurements from all samples (pure oils and binary and ternary mixtures) were imported into Excel (Microsoft). The average values of PC1 and PC2 were used to determine the values xoil A, xoil B, xoil C, yoil A, yoil B, and yoil C in the following equation:
EXPERIMENTAL SECTION
Samples. The crude oil samples in this study were collected from North Sea off-shore reservoirs and were kindly provided by SINTEF Materials and Chemistry, Trondheim, Norway. The oils were used without any purification. FT-ICR MS Analysis. Samples were analyzed using a solariX XR FT-ICR mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany) equipped with a 12 T refrigerated actively shielded superconducting magnet (Bruker Biospin, Wissembourg, France) and the Paracell analyzer cell. An Apollo II Dual ESI/matrix-assisted laser desorption/ionization (MALDI) ion source was used, and the samples were analyzed using APPI in positive-ion mode. The transient length of the mass spectrometric measurements was 3.3 s. Sine apodization was applied before Fourier transformation, and spectra were processed in magnitude mode, resulting in a resolving power of 900 000 at m/z 400. The spectra were externally calibrated with arginine clusters in ESI in positive-ion mode. During acquisition, the mass spectrum was single-point-calibrated with a known mass at m/z 400 (lock mass calibration). The final spectrum was internally calibrated in DataAnalysis 4.2 (Bruker Daltonics) with a known homologous series using quadratic calibration. All root-mean-square (RMS) mass errors of the internal calibration were below 200 ppb. Samples were prepared by diluting them 1:100 (v/v) in toluene as stock solution. These stock solutions were used to generate defined oil mixtures. Samples for the characterization of ternary mixtures contained different ratios of each oil in 20% increments, resulting in 18 mixtures. Binary mixtures contained different ratios of each oil in 10% increments. Pure and mixed stock solutions were diluted 1:100 in 50:50 CH3OH/toluene to give a final sample solution of 100 ppm. Toluene has been shown to be effective as a solvent of deposits and asphaltenes in crude oils and as a dopant in the APPI ionization process.25 Six replicate measurements of each binary sample and five replicate measurements of each ternary sample were performed using flow injection analysis (FIA). An Agilent G1367A autosampler with a 100 μL loop was used for measurements. The sample loop was completely filled with the sample solution. Measurements were performed at a flow rate of 10 μL/min. During the first 12 s of the measurement, the solvent flow was set to 100 μL/min to quickly transport the sample to the mass spectrometer. Then, the flow was reduced to 10 μL/min to obtain ion signals from the sample for at least 8 min. At the end of the measurement, the flow was increased to 100 μL/min. The sample loop was washed at 100 μL/min between sample measurements to reduce carry-over effects. CH3OH/toluene (50:50) was used as the transport solvent and washing solvent in the
⎛ PC1 ⎞ ⎛ xoil A ⎞ ⎛ xoil B ⎞ ⎛ xoil C ⎞ ⎟⎟ = aoil A ⎜ v ⃗ = ⎜⎜ ⎟ + aoil B⎜ y ⎟ + aoil C⎜ y ⎟ y ⎝ oil A ⎠ ⎝ oil B ⎠ ⎝ oil C ⎠ ⎝ PC2 ⎠
(2)
The xoil and yoil values are coordinates in the ternary diagram of the pure oils A, B, and C. On the basis of the PC1 and PC2 values of three known ternary mixtures, the values aoil A, aoil B, and aoil C (which are the relative portions of each oil A, oil B, and oil C in the mixture) were calculated by vector analysis.
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RESULTS AND DISCUSSION Binary Crude Oil Mixtures. The mass spectra of pure crude oils A and B and two different mixing ratios are shown in Figure 1a. As seen, the spectra are very similar, and only zooming in on a mass region can show the differences between the spectra. The spectra around m/z 400 of the pure crude oils A and B and two different mixing ratios are shown in Figure 1b. Even in the magnified area, the spectra look very similar, indicating the similarity of both crude oils. However, the relative intensities of mass peaks were seen to change slightly from oil A to oil B, which correlates directly with the change of the relative abundances of compound classes. The typical mass difference of 3.4 mDa from sulfur-containing compounds, which results from the exact mass difference between C3 and SH4, can be seen in the spectrum peak pairs at m/z 400.1821, 400.2219, and 400.3124. Therefore, the differences in the sulfur content in each sample can be seen directly in these pairs of mass peaks. B
DOI: 10.1021/acs.energyfuels.5b02353 Energy Fuels XXXX, XXX, XXX−XXX
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Table 1. Calculation of DBEaverage for Compound Classes N1 and O1 Detected as Radical Cations and Protonated Species ratio oil A/oil B 100% A/0% 70% A/30% 40% A/60% 0% A/100%
B B B B
class N1 radical cations
class N1 protonated species
class O1 radical cations
class O1 protonated species
14.10 14.47 14.73 14.93
13.57 13.84 14.07 14.22
14.32 14.06 13.75 13.21
11.67 11.30 11.03 10.48
(DBEaverage) of the compound classes. DBEaverage values for compound class N1 were higher in oil B than in oil A (+0.83 and +0.65 for radical cations and protonated species, respectively). The difference in the DBEaverage of radical cations and protonated species could be a result of the different response factors of compounds ionized preferentially as radical cations or protonated species. DBEaverage values for compound class O1 were lower in oil B than in oil A (−1.11 and −1.19 for radical cations and protonated species, respectively). However, differences in the DBEaverage value or DBE distribution are independent of the relative abundance of a compound class. Oils could have identical relative abundances of specific compound classes but different DBE distributions. The DBE distribution could still be used to distinguish the oils, but this information is not used in the described method to calculate the mixing ratio of binary mixtures. Figure 3 shows the relative abundances of the most abundant compound classes O1, S1, and N1 in mixtures of oils A and B
Figure 1. (a) APPI mass spectra of the pure oils A and B and two mixtures of both oils and (b) magnified mass spectra at m/z 400.12− 400.43 of the pure oils A and B and two mixtures of both oils.
The DBE distribution of the compound classes N1 and O1 detected as radical cations and protonated species in mixtures of oils A and B is shown in Figure 2. The DBE distribution of both compound classes increases slightly as the ratio of oil A/oil B is reduced. Table 1 shows the average DBE value
Figure 2. DBE distribution plot from samples with different ratios of crude oils A and B. C
DOI: 10.1021/acs.energyfuels.5b02353 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 3. Plot of relative abundances of compound classes N1, O1, and S1 detected as radical cations, protonated species, and sum of both species versus the percentage of oil B in the mixture.
detected as radical cations and protonated species. Class N1 is more abundant in oil B than in oil A, whereas compound class O1 is more abundant in oil A than in oil B. Applying a linear fit provided linear regression factors (R2) all above 0.985, indicating a linear mixing behavior and a high reproducibility of the APPI relative signal intensity measurements in the mass spectra. Six replicates of each mixture were measured to improve reliability of the results. The composition of three additional binary mixtures of oils A and B was calculated on the basis of the equation of the calculated linear fit of the three compound classes O1, S1, and N1 using radical cations, protonated species, and sum of both detected species. The results are shown in parts a−c of Table 2. The composition of binary mixtures could be determined with an average absolute error below 1.5%. Even the relative error of the percentage of oil B in the mixture was below 2.5%. The lowest error was measured for the mixture containing 45% oil B. Ternary Crude Oil Mixtures. Ternary crude oil mixtures were analyzed using two different methods: NNLS and PCA. The NNLS method is based on the change in the relative abundance of compound classes. For the NNLS method, the ratios of three crude oil samples (A, B, and C) were measured in mixtures. The relative abundances of several different compound classes in pure oils are shown in Figure 4 and listed in Table 3. The relative abundances of compound class CH of all three crude oils A, B, and C are very similar (45−48%). Oil C had the lowest relative abundance of class CH detected as radical cations but the highest relative abundance of class CH
detected as protonated species, reflecting the different composition of aromatic hydrocarbons in the crude oils. The principal differences were detected in the compound classes N1, S1, and O1, with absolute differences of up to 5%. The compounds were mainly detected as radical cations. However, the ratio of radical cations to protonated species for a detected compound class is also characteristic for a crude oil. Five replicate samples of the pure oils and mixtures were measured to increase the accuracy of the mass spectrometric measurement. The average values of all replicates were used. Table 3 lists the relative abundances of several different compound classes for the ternary mixtures. Interestingly, all mixtures had a very similar relative abundance of compound class N1, despite containing different ratios of the three oils. The composition of the ternary mixtures was calculated using the NNLS method and compared to the actual ratio (see Table 4). The average absolute and relative errors of the 1:1:1 mixture (mixture 1) were quite small (1.8 and 5.4%, respectively). The errors of the 10:10:80 mixture (mixture 2) were higher, with absolute errors of up to 5.6% of oil A and an average relative error of more than 20%. These results show that the determination of ternary mixing ratios using NNLS and the change of relative abundance of compound classes are not very accurate. The change of the relative abundance of compound classes [especially in mixture 2 (10:10:80), where the proportion of oils A and B to oil C was low] is quite low, resulting in rather inaccurate results for the calculation of mixing ratios using the NNLS method. Even if the composition of binary oil mixtures D
DOI: 10.1021/acs.energyfuels.5b02353 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 2. Calculation of Percentage of Oil B Based on the Relative Abundance of Compound Classes [Actual Composition of (a) 35%, (b) 45%, and (c) 66% Oil B] measured percentage of B class N (radicals) class N (protonated) class N (radicals + protonated) class O (radicals) class O (protonated) class O (radicals + protonated) class S (radicals) class S (protonated) class S (radicals + protonated) average class N (radicals) class N (protonated) class N (radicals + protonated) class O (radicals) class O (protonated) class O (radicals + protonated) class S (radicals) class S (protonated) class S (radicals + protonated) average class N (radicals) class N (protonated) class N (radicals + protonated) class O (radicals) class O (protonated) class O (radicals + protonated) class S (radicals) class S (protonated) class S (radicals + protonated) average
absolute error percentage of B
Table 3. Measured Relative Abundance of Compound Classes of Oils A, B, and C and Three Ternary Oil Mixtures
relative error percentage of B (%)
(a) Oil Mixture (65% A/35% B) 35.3 0.3 35.8 0.8 35.4 0.4
0.8 2.4 1.2
34.5 36.7 35.1
−0.5 1.7 0.1
1.3 4.9 0.3
36.2 36.2 36.5
1.2 1.2 1.5
3.6 3.6 4.3
35.8 0.8 (b) Oil Mixture (45% B) 46.6 1.6 47.3 2.3 46.8 1.8
2.2
1.8 0.3 1.5
4.0 0.7 3.4
43.1 43.1 44.3
−1.9 −1.9 −0.7
4.3 4.3 1.5
45.5 0.5 (c) Oil Mixture (66% B) 67.6 1.0 69.7 3.1 68.2 1.6
1.2
1.1 0.6 1.1
1.7 0.9 1.7
67.4 67.4 67.5
0.8 0.8 0.9
1.2 1.2 1.3
67.8
1.2
1.8
oil B
oil C
mixture 33:33:33
mixture 10:10:80
mixture 45:35:20
HC HC (H) N N (H) NO NO (H) O O (H) O2 OS OS (H) S S [H] S2
47.37 11.65 4.15 0.44 0.75 0.53 10.44 1.23 1.50 1.64 1.32 11.60 2.24 0.29
48.41 10.05 9.33 1.44 1.16 0.97 5.93 0.70 0.17 0.51 0.38 13.22 2.33 0.44
44.57 12.10 6.60 0.88 0.53 0.85 5.15 0.86 0.15 0.63 0.65 17.03 3.80 1.25
47.51 11.79 6.74 0.92 0.77 0.78 6.88 0.94 0.46 0.87 0.73 13.75 2.79 0.60
46.17 12.70 6.44 0.85 0.60 0.80 5.64 0.89 0.23 0.68 0.67 15.55 3.42 0.95
47.73 11.12 6.56 0.86 0.79 0.73 7.47 0.98 0.57 0.96 0.75 13.61 2.63 0.49
mixture mixture mixture mixture mixture mixture mixture mixture mixture
1.5 4.7 2.3
67.7 67.2 67.7
oil A
Table 4. Calculation of Relative Percentages of Three Ternary Oil Mixtures Using the NNLS Method Based on the Relative Abundance of Detected Compound Classes
3.6 5.1 4.0
46.8 45.3 46.5
compound class
1: 1: 1: 2: 2: 2: 3: 3: 3:
oil oil oil oil oil oil oil oil oil
A B C A B C A B C
measured (%)
actual (%)
absolute error (%)
relative error (%)
32.4 36.0 31.6 15.6 10.0 74.4 39.0 38.1 22.9
33.3 33.3 33.3 10.0 10.0 80.0 45.0 35.0 20.0
0.9 2.7 1.7 5.6 0.0 5.6 6.0 3.1 2.9
2.8 8.1 5.2 56.2 0.3 7.1 13.4 8.9 14.5
In an alternative approach, PCA was used to determine the composition of ternary mixtures. In addition to the pure oils, binary and ternary mixtures of all three oils were measured in 20% increments of each oil. All data from the pure oils and mixtures was used to generate the PCA scoring plot shown in Figure 5. The result of the scoring plot was a ternary diagram with specific principal components PC1 and PC2 for the mixtures and pure oils. On the basis of the calculated PC1 and PC2 values of the pure oils A, B, and C, the measured PC1 and PC2 values of three additional ternary mixtures were used to calculate their composition (see Figure 6 and Table 5). The measured PC1 and PC2 values for the five samples were very close to the calculated values. The variation of the measured ratios of the replicates was very small. The absolute errors of all mixtures were below 2% and on average less than 1%. Even the values of relative errors were low, with an average value below 4%. This indicates that multivariate statistical analysis of crude oils using all mass peaks is a very accurate method for determining ternary mixtures of these complex samples.
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CONCLUSION These results show that APPI FT-ICR MS of crude oil is a very reproducible technique. Flow injection analysis of the oil samples resulted in very accurate determination of the chemical composition and the relative abundance of compound classes of crude oils. This technique can be used to determine the composition ratios of binary and even ternary crude oil mixtures. The composition of ternary mixtures was determined more
Figure 4. Compound class distribution plot of the three crude oils A, B, and C.
can be determined very accurately using the relative abundances of compound classes, the characterization of ternary mixtures using the same method appears to be problematic. E
DOI: 10.1021/acs.energyfuels.5b02353 Energy Fuels XXXX, XXX, XXX−XXX
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accurately by multivariate statistical analysis using PCA than with a NNLS method based on the relative abundance of detected compound classes. The difference in accuracy between the NNLS and PCA methods can be explained by the fact that, in contrast to the NNLS method, which uses only the bulk property of relative compound class abundances, PCA uses the relative abundances of all peaks in the mass spectrum. Analyzing oil mixtures on the basis of their chemical composition could be very helpful in identifying their origin. Useful criteria could include relative abundances of compound classes, DBE distribution, and carbon distribution of each compound class. Reservoir connectivity studies could be based on not only bulk properties and proxies of oil but also their chemical composition using mass spectrometric analysis as a fingerprint. Therefore, this approach could be used in a way similar to current methods for crude oil differentiation and identification based on analysis of hydrocarbon chemical markers by GC−MS.32−34
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
Figure 5. PCA scoring plot of oils A, B, and C and defined mixtures.
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Kolbjørn Zahlsen and Anders Brunsvik from SINTEF, Trondheim, Norway, for providing the crude oil samples. The authors also thank Jochen Friedrich from Bruker for his help in the vector calculation and Jason Smith from Bruker for helpful discussions and suggestions.
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Figure 6. Ternary diagram based on detected PC1 and PC2 values of pure oils and ternary mixtures. Measured (rectangle) and theoretical (triangle) values are shown for the three ternary mixtures.
Table 5. Calculation of Relative Percentages of Three Ternary Oil Mixtures Using Vector Analysis of the Ternary Diagram Calculated with the PC1 versus PC2 of the PCA Scoring Plot
mixture mixture mixture mixture mixture mixture mixture mixture mixture
1: 1: 1: 2: 2: 2: 3: 3: 3:
oil oil oil oil oil oil oil oil oil
A B C A B C A B C
measured (%)
actual (%)
absolute error (%)
relative error (%)
32.1 32.9 34.9 11.1 9.8 79.1 43.2 35.5 21.2
33.3 33.3 33.3 10.0 10.0 80.0 45.0 35.0 20.0
1.2 0.4 1.6 1.1 0.2 0.9 1.8 0.5 1.2
3.5 1.3 4.7 10.7 1.6 1.1 3.9 1.5 6.2
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DOI: 10.1021/acs.energyfuels.5b02353 Energy Fuels XXXX, XXX, XXX−XXX
