Fast Procedure for the Analysis of Hydrothermal Liquefaction Biocrude

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Fast Procedure for the Analysis of Hydrothermal Liquefaction Biocrude with Stepwise Py-GC-MS and Data Interpretation Assisted by Means of Non-negative Matrix Factorization Cristian Torri,*,†,‡ Diego López Barreiro,§ Roberto Conti,‡ Daniele Fabbri,†,‡ and Wim Brilman∥ †

Dipartimento di Chimica “G. Ciamician”, Università di Bologna. Via Selmi 2, 40126 Bologna, Italy Centro Interdipartimentale di Ricerca Industriale Energia Ambiente, Università di Bologna. Via Sant’Alberto 163, 48123 Ravenna, Italy § Department of Biosystems Engineering, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B9000 Ghent, Belgium ∥ Thermo-Chemical Conversion of Biomass Group, Faculty of Science and Technology, University of Twente, P. O. Box 217, 7500AE Enschede, The Netherlands ‡

S Supporting Information *

ABSTRACT: The phenomena occurring during hydrothermal liquefaction (HTL) of algal biomass to form biocrude are not fully understood. It is still not clear which species are optimal for a microalgae biorefinery, as well as the influence of their composition on the biocrude oil molecular composition. Moreover, the molecular characterization itself of the HTL biocrude oils is troublesome because of the presence of a huge number of different molecular constituents. In this work, a stepwise Py-GC-MS procedure, originally developed to solve certain issues of conventional GC-MS analysis, was further improved using a nonnegative matrix factorization (NNMF) assisted peak resolution. This procedure not only increased the speed of GC-MS interpretation and made it more objective, it also disclosed additional information on certain HTL biocrude constituents which are usually neglected by the manual data handling and it minimized the fraction of unidentified compounds. With this novel analysis strategy, the biocrude oils produced during a screening with 8 different microalgae strains at two different process conditions (250 and 375 °C, both for 5 min) were analyzed. Main chemical constituents produced by HTL were quantified, obtaining a satisfactory chemical description of this matrix. The results revealed that the influence of process conditions was more important than the difference in the strains applied. Moreover, even if different biocrude yields were observed, the chemical composition results were similar for the different algal strains when processed at the same temperature.

1. INTRODUCTION

The phenomena occurring during HTL are not fully understood. It is still uncertain which algal strains are optimal for a microalgae biorefinery, as well as their influence in the biocrude molecular composition. Moreover, the molecular characterization of the HTL biocrude is troublesome because of the presence of a huge number of different molecular constituents. Traditional GC-MS analyses allow one to obtain a limited view of the molecular composition. This is due to the presence of a significant non-GC-amenable portion (e.g., oligomers, peptide derivatives, and asphaltene-like compounds), as well as a large number of nonresolved compounds that exceed the theoretical plates of a common GC system.6−8 This calls for studies such as the one presented in this work, which is aimed at developing methods and techniques to gather insights on the molecules of the biocrude oil that cannot be unraveled by traditional analytical techniques. In a previous work we demonstrated the potential of a simple hyphenated technique, which used stepwise analytical pyrolysis (Py) and GC-MS in order to separate the compounds by volatility/reactivity prior to GC-MS analysis.7 This procedure

Among the different types of biomass, microalgae have the potential of becoming an important energy source in the future due to their faster growth rate, higher photosynthetic efficiency, and higher area specific yields, compared to lignocellulosic biomass.1 Hydrothermal liquefaction (HTL) appears as a promising technology for producing biofuels from microalgal biomass.2 The main advantage of this technology is that it can be applied to wet microalgae slurries (hence circumventing the need for drying it) to obtain a carbon-rich liquid energy carrier, usually called biocrude oil. This biocrude oil can be then upgraded to fossil fuel analogues, after removing the undesired heteroatoms present in it (e.g., nitrogen and sulfur), by means of consolidated upgrading approaches.3,4 The main microalgae biochemical constituents are proteins, lipids, carbohydrates, algaenan, and ash. All of these compounds are degraded during the HTL process throughout multiple reaction pathways. Although the pathways are multifold and difficult to track, the basic reaction mechanisms of HTL can be described as follows:5 (1) depolymerization of the biomass; (2) decomposition of biomass monomers by cleavage, dehydration, decarboxylation, deamination, and retroaldol reactions; (3) recombination of reactive fragments. © XXXX American Chemical Society

Received: November 13, 2015 Revised: January 4, 2016

A

DOI: 10.1021/acs.energyfuels.5b02688 Energy Fuels XXXX, XXX, XXX−XXX

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Table 1. Scheme of the Conceptual Assumptions Used for Interpretation of Stepwise Pyrolysis and Quantification of Various Classes of Compounds 200 and 280 °C phytols carotenoid fragments free fatty acids fatty amides alkenes alkanes cyclic dipeptides indole and other C8H7N methyl-indoles and other C9H9N pyrrolidones amines phenols a

thermal thermal thermal thermal thermal thermal thermal thermal thermal thermal thermal thermal

desorption desorption desorption desorption desorption desorption desorption desorption desorption desorption desorption desorption

350, 420, and 500 °C a a bound fatty acids bound fatty acids/amides algaenan/large hydrocarbons algaenan/large hydrocarbons peptides peptides peptides a peptides peptides

Not detected with sample set.

2. MATERIALS AND METHODS

requires a decoupling method, such as solid-phase microextraction (SPME) trapping or cryogenic traps. A series of chromatograms with acceptable peak resolution are produced due to thermal fractionation, while the non-GC-amenable portion of the biocrude oil is analyzed in the high temperature steps. Subsequently, the data obtained by Py-GC-MS are coupled with the quantitative information on the weight loss with temperature obtained by thermogravimetric analysis (TGA). This provided a reliable quantitative analysis of the biocrude from HTL of Desmodesmus sp, which was in turn used for understanding the molecular mechanism of HTL performed under different conditions and reaction times.7 Although stepwise pyrolysis allows one to solve several analytical issues of HTL biocrude, the process of peak identification is, to a certain extent, subjective and cumbersome because the data analysis is performed manually. First of all, the peak resolution will be not acceptable if the peaks are tailing (e.g. strongly polar substances) or overlapping strongly or completely (group of peaks). In these situations, human intervention can overestimate the larger and sharper peaks and actually underestimate the compounds characterized by poor tailored peaks (fatty acids) or peak groups (e.g., branched hydrocarbons). In order to fix this problem, several algorithms were proposed. In particular, some efforts have been made to improve the resolution of analytical signals by using non-negative matrix factorization (NNMF), as proposed in the pioneering work of Paatero and Tapper.9 NNMF is a mathematical algorithm that factorizes a non-negative matrix M as the product of two matrices W × H, to identify the activity factors (here, concentration) of different contributors (here, compound classes) to the total response signal. The technique is often applied for image analysis and text processing. Gao et al. successfully showed that this NNMF methodology can be used for resolving different types of overlapping chemical signals in GC-MS spectra.10 According to these findings the NNMF could be a good candidate for solving some of the issues related to the interpretation of stepwise pyrolysis data. In this work the NNMF algorithm was adapted and tested in order to interpret a large data set from stepwise pyrolysis of HTL biocrude oils produced from eight different microalgae species. The aim was to investigate the effect of the species on the type and composition of the biocrude oil produced by HTL and to provide a general description of possible composition arising from the process.

2.1. Biocrude Oil Samples. Biocrude oil was produced by subjecting slurries of eight different microalgae species to HTL. Three freshwater species were used: Scenedesmus obliquus (S. obliquus; UTEX 2630), Scenedesmus almeriensis (S. almeriensis; CCAP 276/24), and Chlorella vulgaris (C. vulgaris; SAG 211-11b). Also five marine species were converted: Phaeodactylum tricornutum (Ph. tricornutum; CCAP1055/1), Nannochloropsis gaditana (N. gaditana; Lubián CCMP 527), Tetraselmis suecica (T. suecica; CCAP 66/4), Porphyridium purpureum (P. purpureum; SAG 113.79), and Dunaliella tertiolecta (D. tertiolecta; SAG 13.86). The biocrude oils were produced at two different temperatures: 250 and 375 °C. The experiments were performed in a stainless steel cylindrical microautoclave with an inner volume of 45 mL, by submerging it in a hot bed of sand fluidized with preheated air. Once the reaction temperature was attained, it was held for 5 min. After that, the microautoclave was quenched in a water bath to stop the reaction. A detailed information about the reaction and the subsequent product separation and analyses can be found in López-Barreiro et al.11 2.2. Stepwise Py-GC-MS and Thermogravimetric Analyses. Five subsequent steps at different temperatures (200, 280, 350, 425, and 500 °C) were applied to the biocrude oil samples for 10 min, with a temperature ramp of 600 °C·min−1 with a CDS 1000 pyroprobe. Samples were placed in a quartz tube and kept under 100 mL·min−1 of N2 during the experiment. The sampling method was described in a previous work.7 SPME (Carboxen/PDMS from Supelco) was used in order to decouple stepwise pyrolysis (which takes 10 min/step) and GC-MS (which requires fast analyte injection). The GC-MS system (Agilent 6851 equipped with a 5668 quadrupole detector) was lined with an SPME low expansion volume liner; desorption was performed in splitless mode at 300 °C, and analytes were separated by a HP ultra 1 fused-silica capillary column (stationary phase, polydimethylsiloxane; 30 m; 0.25 mm i.d.; 0.25 μm film thickness) with the following thermal program: 35 °C for 5 min, then 10 °C·min−1 until 350 °C, and held for 10 min. Biocrude oil samples were subjected to TGA analyses on a NETSZCH STA 449 F1 Jupiter thermal gravimetric analyzer, under N2 flow (100 mL·min−1) from 25 to 700 °C with a ramp of 10 °C· min−1, in order to obtain information on the weight loss with increasing temperatures of the biocrude oil. The mass loss caused by the thermal desorption/pyrolysis at each temperature step (measured by TGA) was correlated to the area detected by GC-MS in each temperature step. This allows calculating the concentration of each compound detected by GC-MS, thus resulting in a pseudoquantitative characterization of the molecular composition of the biocrude oils. All GC-MS signals except baseline (column bleed and SPME fiber bleed) were considered. As in previous work, the molecules detected during the first two steps (200 and 280 °C) were attributed to the thermal desorption of GC detectable compounds (compounds actually B


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Figure 1. Graphical representation of the factorization procedure used for interpretation of stepwise Py-SPME-GC-MS analysis of HTL biocrude analysis.

Figure 2. Example of factorization of GC-MS chromatogram obtained from analysis of four compounds, showing the original chromatogram, the resolution of compounds by means of increasing factors (shown in different colors), and the resulting TIC for comparison with the experimental TIC plot. Residuals in the graph consist of a root-mean-squared residual between raw data (M) and the approximation W × H. present in the biocrude oil), whereas for higher temperatures (350, 420, and 500 °C), the compounds detected were assumed to evolve from the pyrolytic degradation of precursor compounds (e.g., macromolecules). Marker interpretation rationale is reported in detail in Table 1. 2.3. NNMF Assisted GC-MS Interpretation and Integration. The procedure for NNMF of GC-MS data followed the method developed by Gao et al.10 A slight modification was introduced in order to improve the detection of overlapped peaks in GC-MS humps. Briefly, a GC-MS chromatogram can be expressed by a matrix M(m × n) formed by the signal at a certain scan (m, corresponding to a retention time) for an n m/z ion. The data often contain abundant qualitative and quantitative information, and non-negativity is the basic property of the chemical data. It can be demonstrated that the application of NNMF to a GC-MS with completely overlapped peaks would fail in the “isolation” of different pure MS spectra forming the peak, thus avoiding the identification of those peaks. Applying the NNMF to several pyrolysis steps and to several HTL biocrude oil samples minimizes the statistical probability of finding overlapping compounds with the same relative

abundance in all of the GC-MS spectra. A higher peak power of resolution can be obtained if a large number of samples with overlapping peaks are concatenated and processed together. According to this, the two first pyrolysis steps (200−280 °C, corresponding to volatilization of GC-amenable substances) and the high temperature steps (350−420−500 °C, corresponding to pyrolytic degradation of macromolecules) were grouped into two data sets, using the rule shown by Figure 1. These data sets were subjected to NNMF separately. Each data set produced a matrix (M) with sm rows and n columns, where sm is given by the number of scans per GC run (Nscans = 3946 scans, corresponding to 45 min runs) multiplied by the number of samples (snum) and n equal to the m/z range (10−650 Da). The Matlab nnmf function (included in the Matlab statistical toolbox) was used to factorize the non-negative N-by-M matrix M into nonnegative factors W (sm-by-k) and H (k-by-n). In order to obtain solutions that satisfy the following condition:

M≈W×H H contains the factors that, thanks to the non-negativity of the solution, can be directly interpreted as MS spectra and compared with the MS library for known substances. C


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Figure 3. Example of factorization of the chromatogram from commercial diesel sample showing the original chromatogram, the resolution of compounds by means of 10 factors (shown in different colors), and the resulting TIC for comparison with the experimental TIC plot. mmax

k can be chosen deliberately, therefore the algorithm was run for several k values, and the optimal k was chosen in order to minimize the root-mean-squared residual between M and the approximation W × H. By this way k should approach the number of independent spectra (number of molecules and/or class of molecules) in all of the concatenated chromatograms. This transformation was done by means of the equations that follows. First, the W obtained by the algorithm can be cut into snum submatrices corresponding to each sample.

H(TIC)i =

∑ Hij j=1

N

Q ik =

∑i =scans Wijk · H(TIC)j 1 N

nf

∑i =scans ∑ j = 1 Wijk · H(TIC)j 1

These data (Qik) were corrected considering the contribution of the baseline due to GC column bleeding (identified as a clear and distinct factor) dividing by (1 − Qcolumn bleed,k), in order to exclude this contribution, which is not sample-related. As a quality check, for each sample k, if the algorithm approximates properly the data set, the following condition should be respected:

Wijk = Wij kNscans > i > (k + 1)Nscans Each column for each sample Wijk (vector associated with each nf factor in a certain factor) was multiplied by the corresponding H(TIC)i, obtained by summing along rows of Hij. In this way it was possible to obtain which corresponds to the resolved TIC chromatogram of the “isolated” molecule (or of the group of molecules with similar MS spectra) for each sample k. Subsequently, with the following equation, it is then possible to obtain in the overall chromatogram the relative amount of GC-MS signal that is caused by each particular component (molecules, groups of molecules, or baseline).

nf

mmax

∑ Wijk ·H(TIC)j ≅ ∑ GCMSijk j=1

j=1

The final M-file with the calculations beyond commercial NNMF algorithm are provided in the Supporting Information. D


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Figure 4. Comparison of semiquantitative analysis of commercial diesel performed with manual integration and identification and NNMF assisted quantification.

3. RESULTS AND DISCUSSION 3.1. Preliminary NNMF Assisted Interpretation of Raw GC-MS Data. During the initial setup of the algorithm, the method was applied onto several samples with known composition (biodiesel, pyrolysis oils, mixture of commercial chemicals, diesel, and gasoline). A simple description on the effect of the NNMF pretreatment of data on the interpretation of a chromatogram can be obtained by factorization of a very simple GC-MS obtained from the analysis of a mixture of four compounds (dodecane, biphenyl, p-chlorobiphenyl, and methyl hexadecanoate). This file is the “evaldemo.d” file usually provided with Agilent MSD ChemStation software and therefore can be used for initial testing and standardization of the algorithm. Figure 2 shows a graphical summary on how NNMF can factorize the raw GCMS spectra. When the number of factors approaches 4 (which is the actual number of compounds in the chromatogram), additional factors do not decrease further the residuals and the simulated chromatogram approaches the actual chromatogram. In this situation we can see that the NNMF correctly identifies 4 independent MS spectra along the chromatogram. Moreover, the relative area of each compound recalculated (see section 2.3) on factored GC-MS is similar to those of the original GCMS chromatogram. Although quite simplified, this example shows how NNMF can be used on largely more complex GC-MS data in order to find the number of compounds in the GC-MS and resolve the corresponding signal. In order to test the features of NNMF pretreatment of complex chromatogram data, NNMF was applied to GC-MS obtained from the analysis of a commercial diesel sample. As shown before, using different numbers of variables, the rootmean-square residuals of NNMF should show a minimum when the number of variables approaches the number of independent MS spectra in all of the GC-MS chromatograms.9 This, in principle, equals the number of different mass spectra but not necessarily the number of chemically different substances (since several substances have similar mass spectra). The factorization of the GC-MS spectra from a commercial diesel sample shows clearly how the NNMF factorizes the data. In this case, a local minimum can be obtained at k = 10 with a satisfactory correspondence between the raw data (M) and factorized data (W × H).

As in the previous simple example, with this optimal number of variables (10 in this case; see Figure 3) the correspondence between factorized and raw data set (testified to by the similarity of the first and third chromatograms) can actually be considered quite good. Obviously, in this case the number of variables is less than the number of substances but it should be equal to the number of different mass spectra. Indeed, NNMF acts mainly by identifying factors corresponding to (a) the most abundant single molecules (distinctive MS spectra of highly abundant molecules, in this instance the internal standard shown in Figure 3) and (b) groups of molecules with the most abundant MS spectra. If plotted, the first type of factors resulted in single peaks which exactly corresponded to the resolved peak,10 whereas the second type of factors were multiple and corresponded to all of the peaks with similar MS spectra. The typical example of a single peak was the internal standard (tri-tert-butylbenzene, ions at m/z 231 and 246), whereas the second type of factor, for instance, groups together all of the saturated hydrocarbons (having quite similar MS spectra with the ions at m/z 57, 71, and 41) in the chromatogram into a single factor. This feature of the algorithm, which would be a clear disadvantage in the traditional GC-MS analysis, is advantageous for fast analysis of the complex oils produced by HTL. In this case, the need for summarizing is more critical than the identification of every single peak, and grouping the data into chemical classes (e.g., hydrocarbons) would be anyway performed in order to understand the data. Using the aforementioned rationale, it was possible to perform the quantitation and to compare it to the quantitation performed by manual integration and identification (peak by peak). As shown in Figure 4, the two analyses are consistent in describing the chemical composition of the sample, although the comparison reveals some interesting differences. First, the contribution of the clearest and unusual peaks (namely, fatty acid methyl esters) are larger in the manual integration than in the NNMF assisted method. Second, the NNMF application seems to easily “detect” a large number of peaks attributed to compounds with similar elemental composition but with different random arrangement (e.g., alkyltetrahydronaphthalenes) which are known to be important constituents of commercial diesel. E


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Table 2. Yield (wt %), Elemental Composition (wt %), and HHV (MJ·kg−1) of the Biocrude Oils Produced by HTL of Eight Different Species (Taken from López-Barreiro et al.11) species Chlorella vulgaris Dunaliella tertiolecta Nannocholoropsis gaditana Porphyridium purpureum Phaeodactylum tricornutum Scenedesmus obliquus Scenedesmus almeriensis Tetraselmis suecica

T (°C)

sample code

yield

N

C

H

O

S

others

HHV

250 375 250 375 250 375 250 375 250 375 250 375 250 375 250 375

CV250 CV375 DT250 DT375 NG250 NG375 PP250 PP375 PT250 PT375 SO250 SO375 SA250 SA375 TS250 TS375

33 55.3 44.8 55.3 34.4 54.3 24.7 47.1 40.8 54.3 17.6 50.6 35.7 58.1 29.4 45.6

5.5 7.1 5.3 6.2 3.7 5.2 5.0 6.8 4.7 5.8 5.1 6.3 4.1 6.1 4.8 6.1

70.6 72.5 71.3 72 71.5 74.7 69.1 73.9 62.9 73.4 69.3 73.2 72.6 74.3 62.6 74.0

9.2 8.7 9.1 8.8 9.7 9.9 8.4 8.2 8.0 9.1 9.1 8.9 9.4 9.1 7.4 9.0

12.3 8.6 12.2 9.9 11.5 8.5 15.2 8.7 12.0 7.8 12.9 8.1 12.5 8.4 14.0 7.7

0.4 0.5 0.4 0.3 0.2 0.4 0.5 0.7 0.3 1.0 0.2 0.3 0.3 0.4 0.4 0.9

2.2 2.6 1.7 2.7 3.4 1.3 1.7 1.7 12.2 2.9 3.4 3.1 1.2 1.7 10.9 2.4

34.4 35.0 34.6 34.9 35.4 37.2 32.7 35.0 30.3 35.9 33.8 35.6 35.3 36.2 29.3 36.0

Figure 5. Mass percentage of the biocrude oil fractions obtained by thermal desorption (0−280 °C), by pyrolysis (280−500 °C) and nonvolatile matter (>500 °C) for the biocrude oils analyzed in this study (sample codes as in Table 2).

3.2. HTL Biocrude Oil Production. Table 2 shows the biocrude oil yields obtained for each species at 250 and 375 °C, as well as their elemental compositions and the high heating value (HHV). A detailed discussion on these results can be found in López-Barreiro et al.11 As a general summary, it can be said that the use of 375 °C enhances the formation of biocrude oil. The yields obtained at that temperature vary between 45.6 wt % for T. suecica to 58.1 wt % for S. almeriensis. Larger differences can be found at 250 °C, with yields varying from 17.6 wt % for S. obliquus to 44.8 wt % for D. tertiolecta. A similar behavior can be observed for all of the species tested: a higher temperature results in a biocrude oil richer in carbon and poorer in oxygen, thus increasing its HHV. The increase of nitrogen at higher temperatures is also noticeable. This effect can be related to different phenomena, namely, “pyrolysis-like” reactions (e.g., formation of cyclic dipeptides) and increased production of Maillard precursors (aldehydes through retroaldol reactions and ammonia through β-elimination), which in turn produced more nitrogen-bearing compounds that are not water-soluble.7 In general, no dramatic influence of the type of species applied can be detected in the data from Table 2 at 375 °C in terms of yield and elemental composition. This indicates that the HTL process is less sensitive to the type of species applied when near-critical conditions are applied. The following sections will elucidate how the species used affect the type of molecules that can be identified in the biocrude oils. 3.3. Thermogravimetric Analysis and Analysis of Evolved Compounds. The biocrude oils obtained were

analyzed by means of TGA to analyze their volatility, which is needed for the subsequent peak identification by coupling the TGA information with the data from stepwise Py-GC-MS. As previously indicated, the mass volatilized at the first two temperature steps (200 and 280 °C) was considered as originated by thermal desorption of molecules present in the biocrude oil. The mass volatilized during the temperature steps of 350, 420, and 500 °C was considered to be mainly produced by the pyrolysis of larger molecules present in the biocrude oil. Furthermore, the mass of biocrude oil that could not be volatilized at 500 °C was considered as asphaltene-like matter. Figure 5 shows the results obtained for each biocrude oil. The volatile fraction accounted for 30−55% of the biocrude oil mass for the vast majority of the samples analyzed. The remaining mass was formed by more complex or larger molecules that need high temperature to be pyrolyzed to more volatile fragments, plus some nonvolatile matter. In general, the portion of biocrude oil that could be volatilized by thermal desorption increased when the biocrude oil was produced at 375 °C, while the amount of matter that can be volatilized by pyrolysis was reduced. Contradictory results were obtained for the amount of nonvolatile matter, which increased at high HTL temperatures for some species but decreased for others. Following the stepwise Py-GC-MS rationale,7 it was then possible to identify the compounds evolved during various desorption/pyrolysis steps. The procedure and results of stepwise Py-GC-MS for a single sample can be schematically represented in a picture such as those in Figure 6. F


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Figure 6. Chromatograms obtained from Py-GC-MS analysis of biocrude oil from HTL of Chlorella vulgaris at 250 (a) and 375 °C (b).

3.4. Quantitative Analysis through NNMF Assisted Quantitation. Preliminary evaluation of the NNMF algorithm on the single GC-MS from each step and on all concatenated steps (see section 2.3) provided preliminary data on the typical constituents of GC-MS chromatograms and allowed studies of the repeatability of the method. This test shows a good reproducibility of both analyses (relative standard deviation of quantified compounds of about 10−25%, slightly lower than in previous work7), and NNMF assisted data handling produces similar results for replicated samples (several analysis and NNMF runs produce the same results in terms of the MS spectra identified). Therefore, after the initial setup, the algorithm was applied to the entire data set, as described in section 2.3. In order to obtain the best trade-off between speed of the algorithm, peak resolution, and consistency with stepwise Py rationale, the data set made by 16 samples (for the 5 Py-GC-MS steps) was divided into two subgroups: one including the two “thermal desorption” steps (200 and 280 °C) and another including the remaining “pyrolysis” steps (350−420−500 °C). Even with this quite large amount of data, it was possible to perform NNMF by overnight data processing (6−8 h total time) with an average laptop. Looking at the root-mean-square residuals of NNMF, the optimal solution was found for k = 50 for low temperature steps, and k = 25 variables for high temperature steps. This number of variables corresponded to a local minimum in the root-mean-squared residual. The number of variables was significantly higher for HTL oils than for diesel (see section 3.1), and several variables were not easily attributed to a molecule or group of molecules (thus referred to as “not identified”) as expected from the higher complexity of the former chemical mixtures. Concerning the identified portion, NNMF factorization assisted in the quantification of a large number of molecules and groups of homologue series (e.g., alkanes, alkenes, cyclic dipeptides, and fatty acids) already detected by manual interpretation, but the NNMF assisted peak resolution revealed an important contribution of a previously neglected compound. In particular, NNMF highlights a large number of peaks attributed to compounds with similar elemental composition but with different random arrangements (e.g., branched alkanes, alkenes, C9H9N, and C8H7N structural isomers) which usually produces a large number of small peaks with important overall contribution along the chromatogram. By means of this approach, the identified mass fraction result was similar to that identified in previous work, but with a minimal use of subjective human intervention. Following the

The chromatograms obtained for each step were characterized by the presence of a complex mixture of organics, which were qualitatively similar within the various algae processed at the same temperature (see the Supporting Information for details). Despite the complexity of the chromatograms, it could be recognized that the HTL temperature had a significant impact on the molecular composition of the biocrude oils. Clear differences were found between the most volatile fraction (200−280 °C steps) from biocrude oils produced at 250 and 375 °C. The volatile fraction of the biocrude oil obtained at 250 °C was constituted by typical markers of “native” extracted oil (fatty acids, phytol, and isophytols), together with the degradation products of the most labile part of microalgae lipids (e.g., carotenoid fragments and fatty acids). The complexity of the volatile part of the biocrude oils produced at 375 °C increased clearly with the appearance of large amounts of nitrogen-containing compounds, namely, pyrroles, indoles, and cyclic dipeptides. These molecules come mainly from the degradation of proteins, which is promoted at high temperatures.12 The high temperature steps of the Py-GC-MS analysis (350, 425, and 500 °C) revealed that biocrude oils produced at 250 °C appeared to have sterols, long chain hydrocarbons (e.g., from algaenans), and peptides as main constituents. Conversely, the biocrude oils obtained at 375 °C tended to produce a more complex hump of peaks upon pyrolysis, which can be ascribed to poorly identified substances originated from the increasingly severe treatment. The results from the Py-GC-MS were also consistent with those obtained by FTIR, which are included in the Supporting Information (Figure S1). As a general overview, the vibrations at 2957, 2926, 2854, and 1457 cm−1 in the biocrude oils obtained at 250 °C indicated the presence of hydrocarbons. The vibrations at 1706 and 1659 cm−1 could be attributed to the presence of fatty acids. The bond -OH (related to sterols or phytols) presented a vibration at 3010 cm−1. The presence of aliphatic chains could also be linked to the vibrations at 1559 and 1540 cm−1. In the spectra of biocrude oils obtained at 375 °C, no vibration was observed at 3010 cm−1, which could indicate the conversion of sterols by increasing the HTL temperature. Vibrations at 2957, 2926, 2854, and 1457 cm−1 confirm the presence of alkanes in the biocrude. The vibrations at 1700 and 1653 cm−1 are also present, consistently with the content of fatty acids detected by Py-GC-MS. The vibration detected at 1170 cm−1 is consistent with the higher content of molecules containing nitrogen. The vibrations at low wavenumbers (742 and 700 cm−1) are consistent with the higher presence of aromatic rings. G


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Figure 7. Concentration (% (w/w)) of various compound classes in HTL at 250 and 375 °C of various microalgae (sample codes as in Table 2). *, biocrude oil matter not volatile at 500 °C; **, mainly dimethylbenzonitrile and benzenepropanenitrile; ***, mainly benzylnitrile and methylbenzonitrile.

identical assumptions made in previous work, the newly developed procedure allowed one to cover about 50% of the mass fraction. This can be applied to obtain insights into the molecular composition of the HTL biocrude oil and its main constituents. Figure 7 shows the yield of various classes of compounds detected in the HTL biocrude oil obtained from different microalgae, in relation to the total mass of biocrude. Table 3 shows the “average” composition of the HTL oils obtained at 250 and 375 °C from the tested species. From a molecular perspective, the composition of the biocrudes produced from all of the species tested does not differ significantly from one species to the other, with a relatively narrow distribution around an average composition (Table 2). This could be hinting that the biochemical constituents are very quickly depolymerized upon HTL to their monomeric units;13 hence the monomers taking part in the HTL reaction are essentially the same, independently of the species used, thus producing the same types of molecules. This would justify why the molecular composition of the biocrude oils obtained via HTL is rather insensitive to the species applied. The HTL process comprises several cross-linked reactions between degradation products from each biochemical fraction (e.g., sugars and amino acids can condense through Maillard reactions; fatty acids and ammonia can produce fatty amides). The species selected and the relative concentrations of its biochemical constituents might influence the amount of biocrude oil formed through these cross-linked reactions, but to a lesser extent the type of molecules present in it. At 250 °C, the major compounds present in the biocrude oil are peptides and free fatty acids. In previous work,7 it was observed that these compounds are important products from the hydrolysis of lipids and proteins. Fatty acids are almost insoluble in water and, upon formation, easily end up in the

Table 3. Average Chemical Composition of the Oils Obtained at 250 and 375 °C from Different Strains Tested HTL oils mean ± std dev (% (w/ w)oil) 250 °C phytols carotenoid fragments free fatty acids fatty amides alkenes alkanes cyclic dipeptides indole and other C8H7Na methylindoles and other C9H9Nb pyrrolidones amines phenols large hydrocarbons bounded fatty acids peptides not identified asphaltene-like matter c ash

5.2 0.6 8.9 0.4 3.3 2.1 0.8 0.2 0.1 3.4 0.2 1.3 7.2 3.3 13 33 12 4.5

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.7 0.2 2.2 0.2 0.9 0.6 0.5 0.1 0.1 1.3 0.1 0.4 1.5 2.6 2.6 4.4 4.4 4.4

375 °C 3.7 1.2 8.4 0.8 3.2 1.6 1.1 0.4 0.4 7.2 0.6 2.0 6.0 4.1 8.7 38 10 2.3

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.8 0.4 1.7 0.4 0.8 0.5 0.2 0.1 0.1 1.2 0.1 0.5 0.7 2.2 1.3 1.8 3.0 0.7

a Mainly dimethylbenzonitrile and benzenepropanenitrile. bMainly benzylnitrile and methylbenzonitrile. cPortion of biocrude not volatile at 500 °C.

biocrude fraction. Concerning peptides, their occurrence in biocrude oil, which is extracted with dichloromethane from water, is quite surprising considering the polarity of N/O functionalities. Nevertheless, it can be expected that solvent can recover peptides rich in hydrophobic amino acid residues. These proteins or protein fragments are usually inserted into H


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A peculiarity of the NNMF-based method is to highlight the contribution of a large number of peaks attributed to compounds with similar elemental composition but with different random arrangements (e.g., C9H9N and C8H7N structural isomers) which are usually neglected by manual data handling. The method developed was then applied to compare the composition of HTL biocrude oils from a large set of HTL samples generated from different algal species at two different temperatures. From a molecular perspective, the differences in the type of biocrude oil produced do not appear to be enough to recommend any species from the ones tested in this study. Regarding the process temperature, it seems that the higher temperature applied (375 °C) produced a more volatile oil and in a higher amount, which might be preferred for upgrading purposes. According to these results, the research on microalgae biorefineries that use HTL as core conversion technique should focus on process optimization, exploiting the advantages from the use of strains with a high biomass productivity, rather than on the accumulation of certain biochemical constituents. The analytical methodology developed is believed to be instrumental in further developments in the area of microalgae HTL.

the phospholipid bilayer or buried inside the proteins core and can be released by the HTL treatment, which couples high temperature with the action of high temperature (and lower dielectric constant) “apolar” water. This work shows that the phenomena can be accomplished to a large extent already at temperatures of 250 °C.14,7 The increase in the HTL temperature has clear effects on the biocrude oil, increasing the complexity of its molecular composition. Indeed, at 375 °C the peptide content of biocrude is lower and the content of smaller derivatives, namely, N-heterocycles or cyclic dipeptides, is higher. Some cracking reactions seem to take place as well, as the amount of large hydrocarbons is reduced when the HTL temperature increases. The amount of phytol decreases also in most of the cases when 375 °C is used. Phytol is a substance generated from the degradation of chlorophyll at hydrothermal conditions, and its decomposition is accelerated by increasing the HTL temperature, yielding mainly alkanes and alkenes.15 In general, the molecules detected in the biocrude oils produced at 375 °C have a shorter length than at 250 °C. This is in accordance with the GPC results of these oils shown elsewhere. This was also consistent with the visual appreciation that biocrude oils produced at 250 °C were more viscous that at 375 °C. The data herewith presented are useful in view of biocrude oil upgrading applications. As indicated before, HTL biocrude oil obtained from microalgae presents several heteroatoms, among which the most troublesome is nitrogen. Nitrogen in the biocrude oils at 250 °C mainly occurred in hydrophobic peptides, while, for those at 375 °C, the nitrogen-containing compounds are simple molecules such as pyrrolidones or aromatic heterocycles. The latter nitrogen-containing compounds are structurally more similar to nitrogen constituents of fossil crude oil that are typically processed in conventional refineries. Hence, it is expected that biocrude produced at high HTL temperatures is better suited for denitrogenation upgrading utilizing the existing petrochemical facilities.16 These findings are also relevant in view of developing a microalgae biorefinery that uses HTL as the core conversion technique. It has been shown that HTL produces almost the same type of oils, regardless of the species used. Therefore, the biochemical composition does not seem to be the most critical parameter in order to select the optimal species for HTL. Another parameter seems to be more critical: the biomass productivity. The species herewith tested produce similar types of biocrude oil with high yields at near-critical conditions (375 °C). Therefore, the use of robust species capable of maximizing the biomass production seems more promising in order to maximize the biocrude oil productivity and improve the economics of a microalgae biorefinery.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.5b02688. Figures showing HTL and FTIR absorption spectra and the MATLAB script used for data analysis (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Dr. Marco Paglione from ISAC-CNR for helpful discussions about NNMF applications in the GC-MS analysis. REFERENCES

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4. CONCLUSIONS In this work a robust and fast methodology for analyzing the complex biocrude mixtures, produced by hydrothermal liquefaction of microalgae, is reported. The method developed combines stepwise pyrolysis with GC-MS analysis and resolves the complex humps of peaks with an established deconvolution methodology (non-negative matrix factorization, NNMF) to determine contributions of the most abundant single molecules and groups of molecules with the most abundant MS spectra. It discloses a faster and objective approach for data handling. I


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Fast Procedure for the Analysis of Hydrothermal Liquefaction Biocrude

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