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Prediction of Properties and Elemental Composition of Biomass Pyrolysis Oils by NMR and Partial Least Squares Analysis Gary D. Strahan,# Charles A. Mullen,*,# and Akwasi A. Boateng Eastern Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture, 600 E. Mermaid Lane, Wyndmoor, Pennsylvania 19038, United States S Supporting Information *

ABSTRACT: As with many substances derived from natural products, pyrolysis bio-oils are complex chemical mixtures and are extremely challenging to chemically characterize, requiring multiple separation and pretreatment steps followed by several different analytical techniques that need tedious adjustments and modifications when sample properties change. In this study, we present a way to simplify this analysis by using 13C NMR to characterize such substances as a whole without modification. Using partial least-squares (PLS) regression, we report what we believe to be the first reported use of 13C NMR to derive elemental composition information (mass fractions of C, H, N, and O) as well as the enthalpy of combustion (higher heating value), phenol and cresols concentrations, and the total acid number. Several PLS models were created correlating these various properties with the binned intensities of the 1H and 13C NMR spectra of 73 different samples consisting of pyrolysis bio-oils from various biomass sources and treatment protocols as well as finished fuels (gasoline, diesel, and biodiesel) and small molecule standards. Two models based exclusively on 13C NMR data demonstrated the best overall ability to predict these same properties for unknown samples. The R2 and RMSE of the predicted values are discussed in detail and are acceptable for many biofuel-related applications. That such properties and compositional measurements may be extracted from 13C NMR spectra is a direct result of the detailed chemical structural information influencing the chemical shifts and resonance patterns. Because these models were built using a wide range of samples and conditions, they are expected to also be useful for a wider range of applications.

1. INTRODUCTION Currently, the large majority of the world’s liquid fuels and many of its chemical and synthetic materials supplies are sourced from petroleum. With the demand for these products increasing, their production and use will continue to emit carbon pollution at high rates, contributing to global climate change. Mitigation of this circumstance requires the development of methods for the production of these products from renewable carbon resources. Lignocellulosic biomass is the most abundant such renewable resource available for conversion to fuels and chemicals.1 Thermochemical conversion methods, particularly pyrolysis processes, offer the most direct route to liquefy biomass for use in refining applications.2,3 Pyrolysis of biomass produces pyrolysis-oil (bio-oil), which is a complex mixture of organic compounds that are often highly oxygenated, making them thermally unstable and therefore difficult to use for refining into fuels and chemicals.2,3 Therefore, researchers have explored a variety of different feedstocks4−13 and various process changes, such as the USDA-ARS’s tail gas reactive pyrolysis (TGRP)15 and catalytic fast pyrolysis methods16−21 in an effort to produce a more fungible intermediate for fuels and chemicals production. However, even most of the more advanced pyrolysis methods produce a liquid product that will need further upgrading before it is a suitable fuel or petroleum blendstock. Upgrading steps can include separations22,23 and commonly hydrotreatment24−28 or other chemical transformations, where the usual focus is on defunctionalization (mostly deoxygenation) of the bio-oil to produce a mixture comparable in composition to petroleum or petrochemicals. These processes This article not subject to U.S. Copyright. Published XXXX by the American Chemical Society

can take on many forms, producing new, complex mixtures of intermediates to final fuel or chemical products. The products from each of these feedstock-process combinations pose analytical challenges for complete characterization of relevant chemical and physical properties. Each of these products can contain a mixture of highly volatile and highly nonvolatile species along with both low and high polarity compounds. Using only one chromatographic technique will provide information on the composition of just a fraction of the sample. Therefore, for typical fast pyrolysis bio-oils, a combination of several different analytical techniques, including gas and liquid chromatography, elemental analysis, infrared (IR) spectroscopy, gel permeation chromatography (GPC), and wet chemistry methods (e.g., titrations for water and/or acid content) have been developed to characterize the complex mixture.29,30 Even with a combination of all of these analyses, a complete picture of the chemical properties of the mixture is still not achieved. Furthermore, the chemical nature of the mixture changes when there is a change in feedstock, process, or refining treatment. This often requires the tedious development of new or altered methods (e.g., GC or HPLC columns or conditions) compared to those already developed to obtain the same information. In this work, we use nuclear magnetic resonance (NMR) spectroscopy, which has the important advantage of being able to characterize nearly the whole sample in one analytical step Received: October 6, 2015 Revised: December 3, 2015

A

DOI: 10.1021/acs.energyfuels.5b02345 Energy Fuels XXXX, XXX, XXX−XXX

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Table 1. Summary of Samples Used in the Models no. 1

feedstock

2

2,2,6,6-tetramethyl-4piperidone 2-ethylphenol

3

2-methyl-3-pentanone

4 5

4-hydroxy-4-methyl-2pentanone alanine

6 7

alfalfa stems arginine

8 9 10 11

bamboo barley-derived DDGS barley straw bis-phenol A

12 13 14 15 16 17 18 19

Camelina presscake chicken litter corn cob corn stover corn stover cow manure diesel dodecane

20 21

dodecane (neat; external d4-methanol) D-xylose

22 23 24 25

eel grass elephant grass gasoline (E10) glycerol

26 27 28 29 30 31 32 33 34 35 36 37

guayule guayule bagasse guayule bagasse guayule bagasse guayule bagasse guayule bagasse hardwood pellets hardwood pellets hardwood pellets horse litter horse litter horse manure

pyrolysis processa

post pyrolysis treatment

no.

small molecule standard small molecule standard small molecule standard small molecule standard small molecule standard fast pyrolysis small molecule standard fast pyrolysis fast pyrolysis fast pyrolysis small molecule standard fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis commercial fuel small molecule standard small molecule standard small molecule standard fast pyrolysis fast pyrolysis commercial fuel small molecule standard fast pyrolysis fast pyrolysis TGRP extraction TGRP extraction TGRP TGRP torrefaction - fast pyrolysis fast pyrolysis torrefaction - fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis

38 39 40 41

mixed hardwoods mixed hardwoods mixed hardwoods N-acetylglucosamine

42

nicotinic acid

43 44 45 46 47 48 49 50 51 52 53 54

58

oak oak oak oak pennycress presscake pennycress presscake pennycress presscake pennycress presscake pennycress presscake pennycress presscake pennycress presscake pennycress presscake (defatted) rye grass soy biodiesel sucrose (in 1:1 D2O: MeOH-d4) sucrose (in D2O)

59 60 61 62 63 64 65 66 67 68

swine manure switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass switchgrass

69

switchgrass

70

switchgrass

71

switchgrass

72 73

switchgrass tryptophan

55 56 57

a

feedstock

pyrolysis processa fast pyrolysis fast pyrolysis TGRP small molecule standard small molecule standard fast pyrolysis fast pyrolysis TGRP TGRP fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis

post pyrolysis treatment

Hydrotreatment

hydrotreatment hydrotreatment hydrotreatment hydrotreatment hydrotreatment hydrotreatment

fast pyrolysis commercial fuel small molecule standard small molecule standard fast pyrolysis fast pyrolysis fast pyrolysis fast pyrolysis TGRP torrefaction - fast pyrolysis torrefaction - fast pyrolysis TGRP HZSM-5 catalytic pyrolysis TGRP extraction - column chromatography TGRP extraction - column chromatography TGRP extraction - column chromatography TGRP extraction small molecule standard

TGRP = tail gas reactive pyrolysis.

expanded on our initial NMR work and reported a chemometric model using principle component analysis (PCA) of the 13 C NMR data of 15 pyrolysis oils and three finished fuels, which allowed for obtaining information from the fine structure of the NMR spectra without nearly impossible manual analysis.34 With that model, we were able to classify the pyrolysis oils based on their biological origin, dominant functional groups, and range of heats of combustion.34 Others have also combined NMR with chemometrics in a number of petroleum applications, including proprietary industrial ones.35−37 Herein, we report a further expansion of our prior work by using partial least-squares (PLS) to generate models capable of predicting various chemical properties and the

without prior treatment or destruction of the sample. Equally important is that the spectra have very high informational content, particularly of chemical properties. Some of this information is “hidden” but becomes accessible with proper data analysis. NMR is also advantageous because it does not require frequent instrument recalibration between analyses and provides relative standardization across a variety of samples. Consequently, NMR techniques have been used by us and others for various bio-oil characterizations. We have previously reported on the use of NMR for the quantification of functional groups in biomass pyrolysis oils from a variety of biomasses.31 Others have used NMR to assess aging reactions in bio-oil32 and to determine water content and relative viscosity.33 We B


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final volume of 600 μL; however, significantly more solvent was used for very viscous samples or when sample-limited. Selected samples were tested for the effect of concentration variation on chemical shifts. The changes were minimal,with the greatest observed variation being less than 2 ppm. For the majority of the samples, d4-methanol was used as a solvent, whereas d6-acetone was used for selected samples, and some were analyzed in both solvents. Spectra were reacquired in methanol on all samples that were used in our previous study where acetone was used as the solvent.31,34 All samples were studied at 40 °C except for gasoline and diesel, which were acquired at 20 °C due to their volatility at the higher temperature. Additionally, a series of small molecule standard compounds were measured under identical 40 °C conditions, including: n-dodecane, sucrose, xylose, glycerol, 2,2,6,6-tetramethyl4-piperidone, 2-ethylphenol, bis-phenol A, alanine, arginine, Nacetylglucosamine, nicotinic acid, tryptophan, 2-methyl-3-pentanone, and 4-hydroxy-4-methyl-2-pentanone. Several of these were analyzed twice. The two pentanone samples were analyzed using d6-acetone and also in d4-methanol; n-dodecane was analyzed once with 100 μL of added d4-methanol and again without solvent (neat) using an external CD3OD reference. Sucrose, xylose, glycerol, and the amino acids were analyzed in a 50% mixture of CD3OD and D2O to improve solubility. None of the samples were analyzed as replicates under identical conditions; certain types of feedstock were analyzed multiple times, but they originated from different batches and were processed and analyzed separately (e.g., switchgrass). The 1H spectra were acquired with a 30° or 45° pulse angle, a 4−8 s relaxation delay, and a spectral width of 12 ppm (centered at 5.5 ppm) using 32 or 64 k data points. The sodium salt of 3-(trimethylsilyl)propionic acid-d4 (TSP) was added as an internal reference. Suppression of residual water signal was not used. All 13C spectra had a spectral width of 250 ppm (centered at 116 ppm) and were acquired using either 32 or 64 k data points and inverse-gated proton-decoupling to avoid NOE enhancement of the 13 C signal from attached protons. The number of transients averaged for each spectrum varied from 1242 to 32528 with most at the lower end of the range. The Ernst pulse angle (θ) was used to maximize the signal in the least amount of time while optimizing the longitudinal relaxation. It was calculated from the equation: cos(θ) = e−(d1+at)/T1, where “d1” is the experimental interscan relaxation delay, “at” is the acquisition time of the fid, and T1 is the relaxation time of the resonance. The experimental delay time (d1) for all experiments was chosen to be 6 s, and the acquisition time (at) is determined by the number of data points (0.43 or 0.87 s, respectively). We used a T1 value of 20 s, which resulted in an Ernst angle of 44.8°.40 Inversionrecovery experiments were used to determine the relaxation times of the resonances in selected samples and were found to range from 0.3 to 12 s. Hence, all signals are expected to be fully relaxed under these experimental conditions. It should be noted that bio-oils contain many peaks, including some that are relatively weak. The spectral noise associated with those weak resonances prohibited the determination of their T1 values but also resulted in greater intensity error than would be likely to arise if their relaxation was incomplete. For the purpose of comparison, the spectra of 39 samples were additionally acquired using the standard 13C pulse sequence without gating of the proton decoupling. This improves the signal-to-noise of the experiment but makes integrations less meaningful. The effect of using these spectra in the models is discussed. 2.5. Construction of Partial Least Squares (PLS) Models. All 1D 1H and 13C spectra were processed using Chenomx software (Alberta, Canada). The chemical shifts of the 1H spectra were referenced to the TSP standard, and the 13C spectra were referenced to the methyl peak of either the methanol or acetone solvent. Line broadening of 2.5 Hz was used in the 13C spectra. Baseline corrections for all spectra were minimal and were performed with care to ensure retention of correct peak intensities. Because baseline correction of the 1 H spectra near the HOD peak was not possible, the region was excluded during later analysis. The 13C spectra of the small molecules were often harder to baseline correct than those of the bio-oils due to their extended empty regions. For some small molecule spectra, we

elemental compositions. These models were built using the 1H and 13C NMR spectra of 73 different samples consisting of 56 pyrolysis oils as well as finished oils and small molecule standards. The pyrolysis oils were derived from various biomass sources, some of which were produced via catalytic pyrolysis or tail gas reactive pyrolysis (TGRP) and some were also partially upgraded by hydrotreatment. The properties predicted include the mass fractions of carbon, hydrogen, oxygen and nitrogen, the higher heating value (HHV, the enthalpy of combustion), the concentration of phenol and cresols, and the total acid number (TAN).

2. METHODS 2.1. Feedstocks. The pyrolysis oils in this study were produced from different biomass feedstocks and using various processing methods (see Table 1). Details on most of the feedstocks from which pyrolysis oils in this study were derived have been previously published.4−7,38,39 Gasoline and diesel samples were standard road vehicle fuels with the former being a typical US E-10 gasoline, a blend of petroleum distillate (∼90%) and ethanol (∼10%). Soy biodiesel was purchased from AGP, Incorporated (SoyGold) and used as received. 2.2. Pyrolysis-Oil Production. For all case samples, pyrolysis oil was produced by a fluidized-bed pyrolysis reactor over inert silica sand medium at temperatures between 450−550 °C. Detailed descriptions of the pyrolysis system have been previously published.4,5 For each raw pyrolysis oil, the sample used for the NMR characterization was that fraction collected in the electrostatic precipitator (ESP), which is the last point in the condensation train where most of the pyrolysis oil is collected, and with the least moisture, typically 5−8 wt %. Catalytic pyrolysis,15,16 tail gas reactive pyrolysis (TGRP),14 and hydrotreatment procedures24,25 have also been previously published. 2.3. Bio-Oil Characterization. The elemental analysis of feedstock and product streams (C, H, N, S) was carried out using a Thermo EA1112 CHNS analyzer. Higher heating values (HHV) of pyrolysis oils were determined using a bomb calorimeter (Leco AC3000). Water content was measured using Karl Fischer titration in methanol with Hydranal Karl Fischer Composite 5 (Fluka) used as titrant. Oxygen was then determined by difference after accounting for CHNS and water. Total acid number (TAN) was measured using a Mettler T70 automatic titrator using 0.1 M KOH in isopropanol as titrant and wet ethanol as the titration solvent. Phenol/cresol concentration was determined by GC with mass spectroscopy (MS) detection analysis of pyrolysis oil performed on a Shimadzu GCMS QC-2010. The column used was a DB-1701, 60 m × 0.25 mm, 0.25 μm film thickness. The oven temperature was programmed to hold at 45 °C for 4 min, ramped at 3 °C/min to 280 °C, and held at 280 °C for 20 min. The injector temperature was 250 °C, and the injector split ratio was set to 30:1. The flow rate of the He carrier gas was 1 mL/min. The pyrolysis oil samples for GC analysis were prepared as 3 ± 1 wt % solutions in acetone, which were filtered through 0.45 μm polytetrafluoroethylene (PTFE) filters prior to injection. For quantification of phenol and cresol, response factors relative to the internal fluoroanthene were determined using the authentic compounds (purchased from Sigma-Aldrich and used as received). It should be noted that only phenol and its ortho-, meta-, and para-methyl-substituted variants, the cresols, are included in this quantitation. Phenols with other substituents, or compounds containing phenol as a substituent, are explicitly excluded. 2.4. Nuclear Magnetic Resonance (NMR) Spectroscopy. Solution-state NMR spectra were recorded at 9.4 T on a Varian Inova NMR Spectrometer or at 14.1 T on an Agilent VNMRS DD2 NMR Spectrometer using either a 5 mm dual broad-band probe or a OneNMR probe, respectively, equipped with z-axis pulsed field gradients. A minimal volume of solvent was added to each sample sufficient for allowing ease of handling and to provide a lock signal. For “nearly neat” samples, it was often possible to acquire 13C NMR spectra with good signal-to-noise within a few hours. Typically, approximately 100 μL of solvent was added to the bio-oil to produce a C


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Table 2. Statistical Analysis of the Overall Fits of PLS Models 1

H-only C-Model-1

13

13

C-Model-2

13

C-Model-2A

no. of samples in calibration/testing data sets

no. of spectral data blocks

no. of PLS latent variables (A)

R2Y

67/0 91/0 73/18 73/0 58/15 73/0

1 1 1 2 2 3

4 5 5 5 5 3

0.668 0.813 0.841 0.871 0.871 0.624

complete model complete model validation model complete model validation model complete model

Table 3. Statistical Analysis of the Fit for Each Y Variable in PLS 13C-Model-1a complete model

range of values H (% mass) C (% mass) O (% mass) N (% mass) HHV (MJ/ kg) TAN (mgKOH/ g) phenol and cresols (% mass) a

4.09−13.06 39.12−86.94 0−53.29 0−32.16 22.5−45.3

no. of calibration set observations

R2v Y (cumulative)

83 83 83 78 68

0.84 0.85 0.85 0.90 0.83

0−135

46

0.65

0.11−5.19

65

0.67

RMSEE

validation model

degrees of freedom

0.96 4.48 5.05 1.63 2.72 30.1 0.90

P-factor × × × × ×

−13

10 10 10 10 10

1.3 1.9 2.9 9,8 1.1

10 10−8 10−12 10−5 10−11

10

1.8 × 10−3

10

1.5 × 10−3

bias

no. of observations calibration/ testing

R2v Y (cumulative)

RMSEP

bias

0.05 −0.42 0.03 0.25 0.15

65/18 65/18 65/18 61/17 50/18

0.86 0.88 0.9 0.88 0.83

1.08 5.42 6.22 2.26 2.41

−0.7 0.12 1.69 −1.28 −0.17

−2

33/13

0.75

47/18

0.7

0.11

44.2 0.81

12.1 −0.31

RMSEE values were calculated using only samples in the calibration set; RMSEP values were calculated using only samples in the testing set.

Table 4. Statistical Analysis of the Fit for Each Y Variable in PLS 13C-Model-2a complete model

H (% mass) C (% mass) O (% mass) N (% mass) HHV (MJ/ kg) TAN (mgKOH/g) phenol and cresols (% mass) a

validation model

range of values

no. of calibration set observations

R2v Y (cumulative)

RMSEE

degrees of freedom

4.09−13.06 39.12−86.94 0−53.29 0−32.16 22.5−45.3

67 67 67 64 52

0.92 0.92 0.93 0.92 0.89

0.68 3.36 3.43 1.58 2.21

10 10 10 10 10

1.0 3.0 7.9 5.9 7.1

36

0.67

10

5.6 × 10−3

0−135 0.11−5.19

51

0.72

30 0.94

P-factor × × × × ×

10−10 10−9 10−13 10−4 10−10

1.2 × 10

10

−3

bias

no. of observations calibration/ testing

R2v Y (cumulative)

RMSEP

bias

−2 × 10−4 −0.66 0.07 0.47 −0.14

52/15 52/15 52/15 49/15 37/15

0.93 0.92 0.91 0.93 0.88

0.35 4.06 5.07 1.57 1.58

0.15 −1.7 −0.21 1.84 −0.54

−3.73

24/12

0.67

36/15

0.70

3 × 10

−3

35 1.38

7.92 0.25

RMSEE values were calculated using only samples in the calibration set; RMSEP values were calculated using only samples in the testing set.

achieved better baseline flattening results by using NMRPipe to auto fit the baseline to a polynomial function.41 Using Chenomx 7, the 1H spectra were binned from 10.0−0.04 ppm in 0.04 ppm increments, excluding the methanol and HOD regions of 3.28−3.36 and 4.0−5.5 ppm, respectively (only 1H spectra acquired in d6-methanol was used in this model). The 1H data was normalized to the total spectral intensity imported into Simca-13 (Umetrics, Umeå, Sweden) statistical analysis software and processed as discussed below. This resulted in the 1H-only model (Table 2). Likewise, the 13C spectra were binned from 2 to 220 ppm in 2 ppm increments, excluding the appropriate solvent regions, normalized to the total spectral intensity and were imported into Simca. Two different 13C-models were created with their difference being in how solvent regions were handled, as detailed below. In 13C-Model-1, the solvent regions for both methanol and acetone were excluded from all spectra regardless of the solvent that was actually used in the collection of the data. Thus, the following regions were excluded from all 13C spectra: 29−31.25, 47−51, and 204−211

ppm. Because this model excludes significant peak intensities in both the methyl and carbonyl regions associated with the acetone solvent, it must rely on more subtle redundant information in the NMR spectra. Nevertheless, this model is simple to construct and use and is also effectively solvent-independent. In 13C-Model-2, the spectra were explicitly distinguished on the basis of their solvent by excluding only their explicit solvent region and by using separate data blocks for each within the Simca software. Each solvent-specific spectral block was scaled such that the sum of their variances was unity [the scaling was 1/(k)1/2, where k is the number of bins in each block]. In this model, the spectral regions excluded from binning were only those that corresponded to the specific solvent used. The 47−51 ppm region was excluded from samples using methanol, and the 29−31.25 and 204−211 ppm regions were excluded from those using acetone. The excluded methanol region has very little overlap with sample resonances and is a better solvent to use when possible. By combining the spectral data from the two solvents, the model is made more general and retains more information than does D


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Table 5. Comparisons of Measured and Predicted Elemental Compositions (% Mass) of Samples in the 20% Test Validation Model for 13C-Model-1a sample no. 6 8 9 12 13 16 17 22 31 32 37 38 43 47 49 54 59 61

feedstock alfalfa stems bamboo barley DDGS Camelina presscake chicken litter corn stover cow manure eel grass guayule bagasse hardwood pellets horse manure mixed hardwoods oak pennycress presscake pennycess presscake pennycress presscake (defatted) swine manure switchgrass

measured H (% mass)

predicted H (% mass)

measured C (% mass)

predicted C (% mass)

measured N (% mass)

predicted N (% mass)

measured O (% mass)

predicted O (% mass)

6.6 6.07 6.83 8.91 8.36 5.3 5.74 7.02 8.5 6.33 6.78 6.38 5.82 8.38 9.24 6.96

8.03 6.18 7.68 9.16 9.12 6.74 8.16 7.69 8.46 5.67 6.80 6.00 6.29 8.98 9.63 7.45

58.21 61.15 60.53 71.19 70.05 60.92 56.54 62.62 77.91 70.03 64.53 61.34 54.54 69.57 70.64 59.21

67.62 59.91 53.61 70.36 68.46 57.9 65.31 60.33 76.11 60.72 67.18 57.42 59.32 63.38 70.95 53.99

4.96 0.36 8.05 7.47 7.6 1.2 3.3 6.43 1.99 0.35 1.96 0.24 0.19 7.22 7.93 10.63

2.94 0.69 2.59 4.43 3.97 1.14 3.92 3.95 2.751 1.13 2.79 −0.035 0.84 5.069 6.6 5.58

30.24 32.42 24.59 12.42 14.0 32.58 34.42 23.94 11.59 23.3 26.73 32.04 39.45 14.83 12.19 23.19

21.69 32.91 35.85 16.58 18.91 33.95 22.85 27.99 13.34 32.15 23.33 36.18 33.23 22.85 13.5 32.67

7.73 6.05

9.51 6.33

67.82 63.81

72.05 62.23

9.1 1.03

4.41 1.82

15.36 29.11

14.7 29.48

a

Predicted values are reported to the same number of significant digits as for the measured values to ease comparison. They are not intended as an indicator of the precision of the predicted values. 13

C-Model-1. We also tested a variant of this model (13C-Model-2A) in which an extra spectral data block was added containing 13C spectra acquired using the standard non-gated pulse sequence. After importing the binned intensities into Simca, all intensities were mean-centered and scaled using unit variance. The nonspectral properties whose values we desired to predict were treated as Y variables in the PLS models and were also unit variance scaled. For each initial model, the entire data set was used for calibration and cross-validation. The performances of the models were assessed by Pearson’s correlation coefficient, R2Y (eq 1), and the root mean square error of estimation (RMSEE, eq 2). These models are referred to as “complete set” models because all spectral data were included in the model. The probability that the PLS regression fit did not result from chance was determined by the P-factor (P), which is based on an F-test of the residuals of the model. Typically, a statistically significant model has P values lower than 0.0542,43 (see Tables 3 and 4.) Because the complete set 13C-Models fit the data well (R2Y > 0.8), these two models were subjected to additional evaluation. From each of the previous models, a new “validation model” was created by splitting their respective data into separate calibration and testing sets. The calibration set consisted of 80% of the original samples and was used to calibrate the new validation model. The separate testing set consisted of 20% of the original samples and was used to evaluate the model’s ability to predict the desired Y properties from the NMR data alone. The 20% of samples used for testing were randomly chosen, and using only their spectral data as input, the error of prediction (RMSEP, eq 3) for each Y was evaluated. The fit of all models were assessed using Pearson’s correlation coefficient (R2Y), the root mean square error of estimation (RMSEE), and the root mean square error of prediction (RMSEP) as given by eqs 1−3. The RMSEE was used to appraise the performance of the complete set models, and the RMSEP was used to evaluate the validation models. These RMSE values are a useful estimate of the theoretical accuracy obtainable for the specified variables. In addition, the bias was calculated to determine the influence of systematic errors.

n

RMSEE =

bias =

n

(2)

∑i = 1 (yi ̂ − yi )2 n

n ∑i = 1 (yi ̂

n

(3)

− yi ) (4)

where yi is the measured reference value for sample i, ŷi is the estimated or predicted value, y ̅ is the mean of the reference measurements, n is the number of samples, and A is the number of latent variables in the PLS model. R2Y is the cumulative correlation coefficient for the model as a whole, and R2v Y is the correlation coefficient for a given Y variable.

3. RESULTS AND DISCUSSION In this study, PLS models were created to correlate NMR spectral data with the fractional masses of H, C, O, and N, the molecular higher heating values (HHV), total acid number (TAN), and the concentration of phenol and cresols. Direct quantitation of these property data requires the combined use of three different laboratory analyses using specialized instruments (a combustion elemental analyzer, a bomb calorimeter, and a calibrated gas chromatograph) and two titrations (one to determine TAN and one to determine moisture content required to complete the elemental analysis). The ability to estimate these properties (and more as the technique continues to improve) from one NMR analysis method would provide this information in a more rapid fashion for researchers, producers, and users of these materials. Furthermore, information on elemental composition, HHV, and TAN can be used as a test for the suitability of the materials for downstream use and in combination with the molecular composition data (e.g., phenol/cresols content) could be used to evaluate the performance of a pyrolysis system (e.g., catalyst activity). For example, information about HHV would

∑i = 1 (yi ̂ − yi )2 ∑i = 1 (yi − yi ̅ )2

n−1−A n

RMSEP =

n

R2Y = 1 −

∑i = 1 (yi ̂ − yi )2

(1) E


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Table 6. Comparisons of Measured and Predicted Values of HHV, TAN, and Phenol/Cresols Content for Samples in the 20% Test Validation Model for 13C- Model-1a sample no. 4 6 8 9 12 13 16 17 22 31 32 37 38 43 47 49 59 61

feedstock alfalfa stems bamboo barley DDGS camalina presscake chicken litter corn stover cow manure eel grass guayule bagasse hardwood pellets horse manure mixed hardwoods oak pennycress presscake pennycess presscake pennycress presscake (defatted) swine manure switchgrass

measured HHV (MJ/kg)

predicted HHV (MJ/kg)

measured TAN (mgKOH/g)

predicted TAN (mgKOH/g)

measured phenol and cresols (% mass)

predicted phenol and cresols (% mass)

29.1 30.6 25.0 27.7 32.0 31.2 24.9 28.8 26.1 36.3 27.7 28.8 25.4 22.5 32.0 37.3

23.2 31.5 25.5 23.8 33.5 33.0 25.8 31.3 27.6 35.6 25.6 30.3 23.4 24.9 30.9 35

63 70 152 59 67

110 29 56 97 173 130 84 11

110 66 95 84 60 61 86 70 88 39 94 71 96 98 73 53

0.86 1.86 0.91 0.33 0.88 1.04 0.95 1.25 0.7 4.08 1.06 0.51 0.59 0.6 0.84 1.00

0.25 1.82 1.58 0.5 1.68 1.26 1.04 1.16 0.82 3.33 2.08 2.52 1.69 1.52 0.45 1.51

30.1 26.1

35.3 27.7

133 96

48 78

1.4 1.01

1.68 2.02

57

a

Predicted values are reported to the same number of significant digits as for the measured values to ease comparison. They are not intended as an indicator of the precision of the predicted values.

Table 7. Comparison of Measured and Predicted Values of Sample Elemental Composition for the 20% Test Validation Model for 13C-Model-2a sample no. 8 12 14 54 22 29 32 36 37 43 52 49 53 60 34

feedstock bamboo Camelina presscake corn cob pennycress presscake (defatted) elephant grass guayule bagasse hardwood pellets horse litter horse manure oak pennycress presscake pennycress presscake pennycress presscake switchgrass hardwood pellets

measured H (% mass)

predicted H (% mass)

measured C (% mass)

predicted C (% mass)

measured N (% mass)

predicted N (% mass)

measured O (% mass)

predicted O (% mass)

6.07 8.91 6.41 6.96

6.45 8.60 6.14 7.34

61.15 71.19 55.75 59.21

60.11 69.33 59.4 53.83

0.36 7.47 0.57 10.63

0.96 6.66 1.19 6.08

32.42 12.42 37.27 23.19

32.29 15.99 33.05 32.77

6.07 8.68 6.38 6.43 6.78 5.82 8.38 9.24 9.68 6.31 6.33

6.60 8.91 6.89 6.61 6.98 6.04 7.97 9.32 10.3 6.43 6.1

53.83 81.44 61.34 64.42 64.53 54.54 69.57 70.64 74.39 58.4 70.03

58.85 75.13 59.47 60.14 67.33 57.18 63.98 67.65 70.22 58.90 63.61

0.4 2.67 0.24 1.43 1.96 0.19 7.22 7.93 6.33 2.47 0.35

2.52 3.44 −0.75 2.521 2.82 −0.004 6.79 5.52 6.49 1.69 1.05

39.7 7.21 32.04 27.72 26.73 39.45 14.83 12.19 9.59 32.83 23.30

31.85 13.09 34.22 30.6 22.96 36.48 21.54 18.02 13.76 32.78 29.1

a


bio-oils.31 Therefore, two 13C-based PLS models were created, differing only in how the 13C NMR data is organized in the model and in the solvent-excluded regions. In 13C-Model-1, all spectra were treated identically, whereas in 13C-Model-2, the data was separated on the basis of which solvent, d6-acetone or d4-methanol, was used (see Methods). Both models were best fit with five latent variables that were qualitatively similar to each other (data not shown). The first two of these were also qualitatively similar to the equivalent principle components published previously.34 The probability factors (P) for both PLS models are small, indicating that the models are fitting real data and do not merely arise from chance. The two models do differ in their values of R2Y, R2v Y, and RMSEE and clearly

allow one to determine fuel requirements for a boiler or furnace application, whereas elemental composition would allow one to estimate potential emissions from such a system (for example, fuel-produced NOx). Acidity as measured by TAN is directly related to the corrosiveness of a substance and is strictly monitored in petroleum refineries. Finally, the concentration of phenol/cresols can be used as a gauge for the concentration of the larger class of phenolic compounds, which may provide specific challenges in downstream refining applications (e.g., as coke precursors). The PLS model developed in this study based on the 1H NMR data did not perform well as its R2Y was comparatively low (Table 2). This is likely a result of the very broad and diffuse nature of the 1H spectra for most of these F


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Table 8. Comparisons of measured and Predicated Values of sample HHV, TAN and Phenol/Cresols content for 20% test Validation Model for 13C- Model-2a sample no. 8 12 14 54 22 29 32 36 37 43 52 49 53 60 34

feedstock bamboo Camelina presscake corn cob pennycress presscake (defatted) elephant grass guayule bagasse hardwood pellets HorsLit-2 horse manure oak pennycress presscake pennycress presscake pennycress presscake switchgrass hardwood pellets

measured HHV (MJ/kg)

predicted HHV (MJ/kg)

measured TAN (mgKOH/g)

predicted TAN (mgKOH/g)

measured phenol and cresols (% mass)

predicted phenol and cresols (% mass)

25 32 26.2 29.1

25 34 24.5 25.6

152 67

91 49 98 87

0.91 0.88 1.4 0.86

1.83 1.54 1.65 0.66

26.00 38.04 25.4 28.3 28.8 22.5 32 37.3 36.2 26.2 27.7

26.3 36.6 24.5 26.7 30.5 22.6 32.2 35.0 37.4 25.2 26.4

83 31 99 81 61 108 52 39 29 93 86

2.25 6.13 0.59 0.96 0.51 0.6 0.84 1 1.44 0.66 1.06

1.55 2.47 1.66 1.75 2.68 1.83 0.98 0.66 0.23 1.53 2.77

63

27 173 97 130 84 11 24 85 56

a


indicate that 13C-Model-2 is a better fit of the data, although 13 C-Model-1 fit surprisingly better than expected (see Tables 2−4.) These 13C NMR models used the complete set of spectra data for calibration purposes. To test the models more stringently, validation models were created from each by randomly removing 20% of the data and using them as test cases for the prediction of their properties from their spectral data. The remaining 80% of the data was used as a calibration set for the corresponding validation model (see Methods). As seen in Table 2, the R2Y for the validation models are similar to those of the complete models even though the number of calibrant spectra is fewer. Likewise, the ability of the validation models to predict the Y variables is only slightly diminished relative to the complete models, as seen in their slightly smaller R2v Y values and slightly larger RMSEP values. Again, this is expected because fewer spectra were used to calibrate the validation models. These RMSEP values (Tables 3 and 4) provide an estimation of the theoretical accuracy for each variable in the models. A more qualitative evaluation of the accuracy of the predictions can be found in Tables 5−8, in which are listed the measured and predicted values for those bio-oils that were used as test samples of the validation models for 13C-Model-1 (Tables 5 and 6) and 13C-Model-2 (Tables 7 and 8). Even though the validation models are based on a smaller calibration data set, the values predicted for these unknown samples are still useful estimates. In general, the fit for each of the Y variables improves with the number of measured observations in the data set used for calibration of the model. This can be seen for both 13C models in Tables 3 and 4, where TAN has the fewest number of measured values (observations) and also has the lowest R2v Y value. This is followed by the percentage of phenol and cresols, which has the second fewest number of measured values and a slightly higher R2v Y value. On the other hand, the calibration data set for atomic composition is comparatively large, resulting in higher R2v Y values, which is indicative of a better fit. Because of the way in which the data in 13C-Model-1 is organized, it has a larger number of spectral entries, and therefore, its R2v Y values

are higher than might be anticipated given the larger spectral regions excluded from consideration. All of the 13C models have reasonably low systematic bias for all Y, as the bias values are smaller than the respective RMSEE or RMSEP values. As expected, the validation sets tend to have slightly higher bias due to smaller sampling. It should be noted that others have had success using PLS to fit the TAN values of the 1H spectra of crude oil samples from different extraction fields.36 In their work, they had 64 samples with corresponding TAN values, whereas we had 46 samples with TAN values, which is substantially fewer for these statistical approaches. In addition, the TAN values in this study cover a larger range because pyrolysis oils contain more acids than does petroleum, and they are therefore more challenging to fit. Reviewing the plots of the observed versus predicted values (Figures 1 and 2) for each of the Y values, we observe additional indications that those Y variables with the greater number of calibration values have the tightest spread, whereas those with fewer calibration values have more outliers. Careful examination of the original processed spectra indicates that the greatest source of experimental error is in improper baseline correction, which usually results in the sample being poorly predicted in the models. Although these baseline errors are sometimes obvious to detect and correct, they are also a potential sources of bias. Therefore, only modest baseline corrections were performed. Both of the 13C models have some difficulty with the proper prediction of N content and phenol/ cresols concentration when the sample had a value of zero for that parameter. Most of the samples that were incorrectly predicted in this manner were small standard molecules, not bio-oils. However, these errors are generally not significantly different than the greatest spread in the rest of the data. We assume that this will also improve as more data is added to the model. An interesting observation is that the predicted values for the percentage of H by the models is about the same as or slightly better than the prediction of the percentage of C. This is surprising given that the 13C NMR data were acquired using inverse-gating, which negates the NOE transfer of magnetG


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Figure 1. Using 13C-Model-1, plots of measured versus predicted values for (A) percentage of H by mass, (B) percentage of C by mass, (C) percentage of O by mass, (D) percentage of N by mass, (E) HHV (MJ/kg), (F) TAN (mg KOH/g), and (G) percentage of phenol and cresols by mass. The dotted diagonal line indicates the R2 line calculated by simple least-squares regression of the plotted data.

ization to a carbon from its attached hydrogen. Likewise, the percentages of O and N are well-predicted. Clearly, this information is coming exclusively from the patterns within the chemical shifts of the carbon. We attempted to improve 13CModel-2 by adding in a third data block containing standard 13 C spectra that allowed NOE transfer, but the resulting fit was significantly worse than the other two models (Table 2). Although there is no reason why this methodology would not work with such data, we postulate that greater care is required

to ensure uniform signal intensity when NOE enhancement of the carbon resonance is allowed. We noted earlier that one of the advantages of NMR is that the sample can be analyzed in its entirety rather than pretreated and possibly analyzed in parts. Thus, it is possible that some of the deviations between calculated and measured results are actually real, arising from differences in sample handling, such as solubility and vaporization. H


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Figure 2. Using 13C-Model-2, plots of measured versus predicted values for (A) percentage of H by mass, (B) percentage of C by mass, (C) percentage of O by mass, (D) percentage of N by mass, (E) HHV (MJ/kg), (F) TAN (mg KOH/g), and (G) percentage of phenol and cresols by mass. The dotted diagonal line indicates the R2 line calculated by simple least-squares regression of the plotted data.

4. CONCLUSIONS In this work, we tested several PLS models constructed of either 1H or 13C NMR data and have shown that, for these samples, the 13C spectra produced models that fit and predicted the desired physical properties better than those of the corresponding 1H spectra. This is likely due to the generally broader and diffuse nature of the resonances in the 1H spectra for the complex mixtures in biofuels. That elemental analyses, the enthalpy of combustion, and other useful chemical properties can be determined by 13C NMR may be surprising, but the ability to do so arises from the depth of chemical information embedded in the patterns of the 13C resonances and in their chemical shifts, which owe their sensitivity to local environments to the extent of electronic (de)shielding by

neighboring atoms. These PLS models were built with calibrant NMR spectra of both crude and modified biofuels as well as finished fuels and small molecule standards. Two different solvents were used, and the concentrations of these samples varied greatly with many acquired under nearly neat conditions, whereas others were much more dilute. Consequently, these models should be rather general in their applicability to many different sample types and conditions. Acquiring the spectra at high concentrations greatly decreased the acquisition time, which is often the primary hurdle with 13C NMR. As expected, 13 C-Model-2 produced a better overall fit of the data as it separates the samples according to their respective solvents. However, 13C-Model-1 performed nearly as well even though several important spectral regions were excluded due to solvent I


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(3) Huber, G. W.; Iborra, S.; Corma, A. Synthesis of Transportation Fuels from Biomass: Chemistry, Catalysts and Engineering. Chem. Rev. 2006, 106, 4044−4098. (4) Boateng, A. A.; Daugaard, D. E.; Goldberg, N. M.; Hicks, K. B. Bench-scale fluidized-bed pyrolysis of switchgrass for bio-oil production. Ind. Eng. Chem. Res. 2007, 46, 1891−1897. (5) Boateng, A. A.; Mullen, C. A.; Goldberg, N.; Hicks, K. B.; Jung, H. G.; Lamb, J. F. S. Production of bio-oil from alfalfa stems by fluidized-bed fast pyrolysis. Ind. Eng. Chem. Res. 2008, 47, 4115−4122. (6) Mullen, C. A.; Boateng, A. A.; Goldberg, N. M.; Hicks, K. B.; Moreau, R. A. Analysis and Comparison of bio-oil produced by fast pyrolysis from three barley biomass/byproduct streams. Energy Fuels 2010, 24, 699−706. (7) Boateng, A. A.; Mullen, C. A.; Goldberg, N. M. Producing Stable Pyrolysis Liquids from the Oil-Seed Presscakes of Mustard Family Plants: Pennycress (Thlaspi arvense L.) and Camelina (Camelina sativa). Energy Fuels 2010, 24, 6624−6632. (8) Schnitzer, M. I.; Monreal, C. M.; Facey, G. A.; Fransham, P. B. The conversion of chicken manure to biooil by fast pyrolysis I. Analyses of chicken manure, biooils and char by 13C and 1H NMR and FTIR spectrophotometry. J. Environ. Sci. Health, Part B 2007, 42, 71− 77. (9) Boateng, A. A.; Mullen, C. A.; Goldberg, N.; Hicks, K. B.; McMahan, C. M.; Whalen, M. C.; Cornish, K. Energy-dense liquid fuel intermediates by pyrolysis of guayule [Parthenium argentatum) shrub and bagasse. Fuel 2009, 88, 2207−2215. (10) Wang, K.; Brown, R. C.; Homsy, S.; Martinez, L.; Sidhu, S. S. Fast pyrolysis of microalgae remnants in a fluidized bed reactor for bio-oil and biochar production. Bioresour. Technol. 2013, 127, 494− 499. (11) Mante, O. D.; Babu, S. P.; Amidon, T. E. A comprehensive study on relating cell-wall components of lignocellulosic biomass to oxygenated species formed during pyrolysis. J. Anal. Appl. Pyrolysis 2014, 108, 56−67. (12) Hodgson, E. M.; Fahmi, R.; Yates, N.; Barraclough, T.; Shield, I.; Allison, G.; Bridgwater, A. V.; Donnison, I. S. Miscanthus as a feedstock for fast pyrolysis: Does agronomic treatment affect quality? Bioresour. Technol. 2010, 101, 6185−6191. (13) Garcia-Perez, M.; Wang, X. S.; Shen, J.; Rhodes, M. J.; Tian, F.; Lee, W.; Wu, H.; Li, C. Fast Pyrolysis of Oil Malle Woody Biomass: Effect of Temperature on the Yield and Quality of Pyrolysis Products. Ind. Eng. Chem. Res. 2008, 47, 1846−1854. (14) Mullen, C. A.; Boateng, A. A.; Goldberg, N. M. Production of Deoxygenated Biomass Fast Pyrolysis Oils via Product Gas Recycling. Energy Fuels 2013, 27, 3867−3874. (15) Mullen, C. A.; Boateng, A. A.; Mihalcik, D. L.; Goldberg, N. M. Catalytic Fast Pyrolysis of White Oak Wood in a Bubbling Fluidized Bed. Energy Fuels 2011, 25, 5444−5451. (16) Mullen, C. A.; Boateng, A. A. Accumulation of Inorganic Impurities on HZSM-5 Zeolites during Catalytic Fast Pyrolysis of Switchgrass. Ind. Eng. Chem. Res. 2013, 52, 17156−17161. (17) Williams, C. L.; Chang, C.; Do, P.; Nikbin, N.; Caratzoulas, S.; Vlachos, D. G.; Lobo, R. F.; Fan, W.; Dauenhauer, P. J. Cycloaddition of Biomass-Derived Furans for Catalytic Production of Renewable pxylene. ACS Catal. 2012, 2, 935−939. (18) Jae, J.; Coolman, R.; Mountziaris, T. J.; Huber, G. W. Catalytic fast pyrolysis of lignocellulosic biomass in a process development unit with continual catalyst addition and removal. Chem. Eng. Sci. 2014, 108, 33−46. (19) Zhang, H.; Xiao, R.; Jin, B.; Shen, D.; Chen, R.; Xiao, G. Catalytic Fast Pyrolysis of straw biomass in an internally interconnected fludized bed to produce aromatics and olefins: effect of different catalysts. Bioresour. Technol. 2013, 137, 82−87. (20) Zhang, H.; Xiao, R.; Huang, H.; Xiao, G. Comparison of NonCatalytic and Catalytic Fast Pyrolysis of Corncob in a Fluidized Bed Reactor. Bioresour. Technol. 2009, 100, 1428−1434. (21) Cheng, Y.; Jae, J.; Fan, W.; Huber, G. W. Production of Renewable Aromatic Compounds by Catalytic Fast Pyrolysis of

interference. This demonstrates that for NMR data for complex samples there is enough intrinsic redundancy of structural information to compensate for the excluded regions and therefore provide similar accuracy. It should be noted, however, that small molecules with fewer resonances might be predicted as well. Regardless of the model, the overall accuracy of the predictions clearly improves as the number of samples in the calibration set is increased, and hence future improvements are expected. It is also expected that, with further expansion of the data set predictions, other chemical compounds or classes will become possible. To use these models, one generally only needs to acquire one 13C spectrum in an appropriate solvent and submit it to the model for predictions, rather than several different analyses. Thus, these models can provide rapid, singlemethod estimations of physical properties of bio-oils produced from different sources or by different procedures and may prove useful for many other complex substances as well, especially those not easily studied by standard methods due to properties such as low volatility. It may also be useful to verify ambiguous results obtained by other methods.

■

ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.5b02345. Instructions on calculating the predicted property values from binned 13C NMR data (PDF) Complete PLS model coefficients as tab-delimited data file (1 of 2) (TXT) Complete PLS model coefficients as tab-delimited data file (2 of 2) (TXT)

■

AUTHOR INFORMATION

Corresponding Author

*Phone: 215-836-6916. Fax: 215-233-6559. E-mail: charles. [email protected]. Author Contributions #

G.D.S. and C.A.M. contributed equally to this work.

Notes

Disclosure: The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply a recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank David Chang (Metabolomic Technologies Inc.) and Chris McCready (Umetrics) for helpful discussions.

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REFERENCES

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K


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