Pilot-Scale Gasification of Corn Stover, Switchgrass, Wheat Straw, and

Ind. Eng. Chem. Res. 2009, 48, 10691–10701

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Pilot-Scale Gasification of Corn Stover, Switchgrass, Wheat Straw, and Wood: 2. Identification of Global Chemistry Using Multivariate Curve Resolution Techniques Whitney Jablonski,* Katherine R. Gaston, Mark R. Nimlos, Daniel L. Carpenter, Calvin J. Feik, and Steven D. Phillips National Bioenergy Center, National Renewable Energy Laboratory, Golden, Colorado 80401

A pilot-scale study was conducted to examine the effect of the steam-to-biomass ratio, the gasification temperature, and the thermal cracker temperature for Vermont wood, wheat straw, switchgrass, and corn stover on the formation and speciation of tars. This study is divided into two parts; the first paper detailed the processing conditions and gives quantitative information on low-molecular-weight species. This paper, which is the second part of this study, uses multivariate curve resolution techniques to correlate process variables with the mass spectra gathered during the study to (1) identify the global chemistry of the system and (2) to identify differences or similarities of the product gas streams for each feedstock. Three main groups of products were identified statistically: (1) primary and secondary pyrolysis products (e.g., guaiacol, furfural), (2) cracking products (e.g., phenol, cresol), and (3) polynuclear aromatic hydrocarbons (PAHs). Our findings support known global reaction mechanisms that delineate the formation of the more-refractory PAHs, whereby oxygenated pyrolysis products are cracked into smaller fragments that contain less oxygen. These crack further into small hydrocarbons and radicals that undergo molecular weight growth to produce PAHs. The results from this statistical analysis indicate that, at high temperatures, where PAHs dominate, there is little variation observed between the feedstocks. 1. Introduction Biomass gasification can be used to create renewable, domestic, and more carbon-neutral transportation fuels that can replace dwindling oil-derived fuels.1,2 Synthesis gas (“syngas” CO and H2) that has been produced using the biomass gasification process can be catalytically converted to mixed alcohols, Fischer-Tropsch liquids, and a variety of other intermediates and direct transportation fuels. Because biomass is available in virtually every part of the world, biomass gasification processes can be located close to major distribution points. This minimizes infrastructure size, which could, in turn, make biomass-derived fuels more economical than petroleumbased fuels.3,4 In addition, biomass gasification feedstocks can potentially be any variety of herbaceous and woody plants; waste products from industry, humans, and animals; and other miscellaneous agricultural residues.5 This feedstock flexibility gives gasification processes a 2-fold advantage over oil refining processes: (1) processes can be located wherever there is biomass and (2) any nation with biomass can produce its own transportation fuels using biomass gasification.6-9 Gasification is a relatively well-established technology that can be traced back as far as the 18th century, where a lowtemperature gasification process was used to produce “town gas”.10 The history of gasification progresses from there in a way that largely tracks with countries unable to access fossilderived fuels. For example, during World War II, Germany used coal gasification to produce synthesis gas, which was subsequently turned into Fischer-Tropsch diesel.10 There are several types of gasifiers, each of which must be optimized to maximize syngas output and minimize the output of impurities and unwanted byproducts such as tar. For our purposes, we define tar as any condensable organic molecule * To whom correspondence should be addressed. E-mail: [email protected].

(e.g., aldehydes, ketones, ethers) present in the nonconditioned syngas stream. The gasifier used during this study consists of two consecutive stages and is indirectly heated. For liquid fuel production, it is important to minimize the total tar in the product stream, because it can deactivate downstream reforming or fuel synthesis catalysts. This increases the overall cost of operation.11 It is the goal of this work to identify a mechanism for tar formation within this specific gasification system. The present study focuses on determining the feedstock dependence of the syngas composition in a two-stage gasifier by statistically determining the global chemistry of biomass gasification across 22 different gasification conditions and 4 feedstocks. Tars in the gasifier were analyzed in real time using a molecular beam mass spectrometer (MBMS). Mass spectra taken during this study are examined using multivariate curve resolution (MCR) and interactive self-modeling multivariate analysis (ISMA). ISMA analysis of MBMS results has been used in the past to characterize the classes of products formed from the pyrolysis of biomass and biopolymers.12,13 The goals for data mining are very similar to those outlined by Bjo¨rkman14 for the analysis of coal: • Validate MCR as a replacement for ISMA for curve-fitting analysis of mass spectra; • Determine the most important factors in the dataset and bypass noisy or less-relevant information; • Correlate important factors with process parameters such as temperature; • Compare important factors to the literature;12,15-33 and • Expose product variation for different process conditions between the four feedstocks that were used. Because of the intermittent research for liquid fuels derived from biomass resources, the field of literature for research on biomass gasification is similarly intermittent. However, the field of literature for biomass pyrolysis is rich.12,13,23,27,34-50 This study seeks to use proposed kinetics for pyrolysis products in

10.1021/ie900596v CCC: $40.75  2009 American Chemical Society Published on Web 09/25/2009

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Ind. Eng. Chem. Res., Vol. 48, No. 23, 2009

Figure 1. Process flow sheet for the pilot-scale gasification system. For this study, the product stream was sampled before point G at the “MBMS Sample Port”. Syngas (CO, H2) was measured using a Varian micro gas chromatograph, and those data are discussed in detail elsewhere.51 Table 1. Partial Factorial Design of Experiments Used To Explore the Effect of Gasification Process Conditions on the Quality of the Syngas Produced by Four Different Feedstocks Steam-to-Biomass Ratio

8FBR temperature (°C) 600 650 710

1:3 600

650 650

1:2 750 750

875 875

600

temperature and pressure ranges of interest (>650 °C and atmospheric) as a starting point for understanding the complex chemistry of the gasification process studied here. The first part of this study examines the operation and lowmolecular-weight products from a biomass gasification pilot plant. Results and analysis for this portion of the study are detailed in an accompanying paper.51 The second part of this study, which is the subject of this paper, seeks to use multivariate curve techniques to deduce qualitative information about the global chemistry of this gasification system from a set of spectral data. These techniques are applicable in circumstances where a carefully controlled kinetic study is difficult or impossible. The purpose of this is to determine what operating conditions minimize tar formation and if feedstock plays a role in product variation. 2. Experimental and Data Analysis Methods 2.1. Pilot-Scale Gasifier and Molecular Beam Mass Spectrometer (MBMS). All experiments were completed in a half-ton-per-day pilot plant where synthesis gas was produced from pelletized biomass feedstocks by steam gasification. Gasification occurred in two consecutive stages: an 8-in.diameter fluidized-bed reactor (8FBR) and a plug-flow thermal cracker (TC) reactor. A series of downstream processes cleaned and conditioned the gas. The gasifier has been described in detail in other papers;51-53 the process flowsheet is shown in Figure 1. A quadrupole molecular beam mass spectrometer (MBMS) provided real-time mass spectra of the gasification products. The MBMS has a detection range of 10-400 amu, and the resulting spectra provide qualitative data for the range of species present in the gas. The MBMS is also calibrated for key species

650 650

3:4 750 750

1:1 600

875 750

650 650

750 750

800

875 875

including benzene, toluene, naphthalene, phenanthrene, phenol, and cresol. A more-detailed description of this instrument is given elsewhere.52 2.2. Experimental Design and Feedstocks. Three gasification conditions were chosen as the independent variables for this study: 8FBR temperature, TC temperature, and the ratio of steam to biomass fed to the fluidized bed. Three levels of each independent variable were chosen based on logical considerations and system constraints. For example, there is no benefit to operating the TC at a temperature cooler than that of the 8FBR. Experiments were conducted at 8FBR temperatures of 600, 650, and 710 °C; TC temperatures of 600, 650, 750, and 875 °C; and steam-to-biomass ratios of 1:3, 1:2, and 1:1. A grid that represented the experiments that were run is shown in Table 1. The 8FBR temperature is shown in the first column, the steam-to-biomass ratio is across the first two rows, and the TC temperatures are in the table cells. The residence time and fluidization severity were kept constant by supplementing the steam with nitrogen for the lower steam-to-biomass ratios. The residence time is ∼10 s in the 8FBR and ∼1 s in the TC. Several analytical techniques were used to analyze samples of the four feedstocks. Ultimate, proximate, and ash analyses were conducted offsite by Hazen Research, Inc., and detailed wet chemistry and MBMS analysis were conducted to determine biopolymer concentrations. Relevant results are given in Tables 2 and 3. The four feedstocks used included three herbaceous species (wheat straw, corn stover, and switchgrass) and one mixed wood species (Vermont wood). Vermont wood has a composition of 25% red oak, 15% white pine, 15% maple, 15% ash, and 10% poplar, and the remaining 20% is a mixture of cherry, birch, and cedar.


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Table 2. Proximate and Ultimate Analyses for Samples of Each Raw Feedstock Proximate Analysis (wt %, dry) fixed carbon

ash

C

H

N

Oa

S

85.4 71.6 71.84 76.02

14.15 15.68 16.72 15.95

0.45 12.72 11.43 8.03

54.23 50.90 46.28 46.47

6.16 5.60 5.37 5.35

0.15 0.36 0.88 0.71

38.98 30.33 35.97 39.35

0.03 0.09 0.06 0.09

Vermont wood wheat straw corn stover switchgrass a

Ultimate Analysis (wt %, dry)

volatiles

The oxygen amount in the ultimate analysis is calculated by subtracting the weight fraction of the sum of the other components from unity.

Table 3. Wet Chemistry Analysis for Samples of Each Raw Feedstock Wet Chemistry Analysis (wt %, dry)

Vermont wood wheat straw corn stover switchgrass

ash

structural protein

extractives

lignin

cellulose

hemicellulose

acetate

total

0.56 11.44 13.41 8.54

1.04 1.9 1.83

5.92 11.96 10.09 11.97

27.82 14.69 13.63 16.76

40.56 32.51 35.13 31.06

21.39 22.89 19.62 22.95

3.31 2.31 1.48 2.26

99.56 96.84 95.26 95.37

2.3. Multivariate Data Analysis Theory. The biomass gasification experiments completed during this study produced almost 12 000 mass spectral intensities of MBMS52 data in total. This dataset was reduced to several factors for more-thorough analysis using a multivariate data analysis tool called Interactive Self-Modeling Multivariate Analysis (ISMA). ISMAsand the more-recent version, SIMPLISMAshave been used previously to extract information about global chemistry and kinetics from a range of spectral data and for organic species, biomass, and biopolymers.12,13,44,47,54-72 ISMA is one of the pioneering curve resolution methods that enables the user to identify physically important information from a dataset without requiring a reference set of data.73 This is useful for biomass gasification on the pilot scale because it is difficult and time-consuming to calibrate for each compound that might affect variance in the product composition. In addition to this, there is a significant amount of mixing between components in the stream that make it difficult to fit the data to a predictive model. Curve resolution methods uncover the “true” source of data variation called a pure component, but do not produce unique solutions. That is, ISMA is incapable of identifying the total variance for one single variable like a principal component analysis (PCA) would. However, that single variable, or principal component, does not have any physical meaning. ISMA groups mass spectra that are highly correlated within a pure component. This allows us to gain better understanding of the underlying global chemical processes of biomass gasification. A pure component is defined as the longest vector projected in a particular direction on the Vardia plot shown in Figure 2.74 All of the masses contained within this pure component are a positive, linear combination of this pure component. To determine pure masses, it is necessary to calculate the purity of each mass within a data matrix, D. The purity is equivalent to the length of the vectors.

Figure 2. Vardia plots in ISMA show the loading projections of all of the masses analyzed.

Therefore, the longest vector is the mass with the highest purity. In ISMA, the user chooses pure masses manually by placing an asterisk on the longest vector in a direction that contains a high number of masses. As shown in Figure 2, there are three clear directions and, therefore, three pure masses. The purity is defined as the ratio of the standard deviation σi of a variable i to the mean µi of a variable i.74 The mean (µi) is calculated using eq 1 and the standard deviation is calculated using eq 2. µi )

σi )

1 m

1 m

m

∑d

i,j

(1)

j)1

m

∑ (d

i,j

- µi)2

(2)

j)1

To determine the purity, the data matrix D of size (m × n) with m spectra and n masses is decomposed into its concentration and spectra profiles.74 Equation 3 shows the relationship between D and the desired concentration C and spectra S profiles. DT ) CS

(3)

ISMA has a data limit of 5000 points, and, therefore, only masses of 25-290 amu were examined. Data were entered into a Lotus 123 program and sorted by variance using a macro. The first 150 masses in the sorted data were normalized to unity and reloaded into ISMA. The concentration matrix C of size (m × p) and the spectra matrix S of size (p × n), where p is the number of pure components were found by completing a factor analysis on the normalized data. Seven factors were found from this analysis and loaded into the Vardia program, which calculated the purity of all of the masses contained in this factor. Loadings that were calculated using the Vardia program were then rotated and projected onto a Vardia plot, as shown in Figure 2.54,73 After choosing pure components from the Vardia plot, ISMA then grouped the loadings contained within these pure components and calculated adjusted loadings for each mass. These loadings were plotted as reconstructed mass spectra for each pure component. Each of the masses in these mass spectra are highly correlated to each other. In addition to calculating loading data, ISMA also calculated scores data. The scores data describe correlations between samples. This is used to identify differences in gasification conditions such as the TC temperature.

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Figure 3. Comparison of main factors found using ISMA (left) and MCR (right). The left column shows the pure masses from ISMA found using the same dataset. The right column shows the pure components from MCR found using the MBMS dataset from the range of 25-290 amu. The masses are repeated almost exactly. There is a higher density of masses in the MCR analysis, because we did not limit the dataset to those 150 masses with the most variance.

ISMA provides compact information about global chemistry and the effect of gasification conditions on the overall chemistry in the system. However, ISMA operates under DOS, does not have a graphical user interface, and is no longer supported. In addition to this, ISMA cannot accommodate our entire dataset, and it requires the user to choose pure masses manually. This adds error because pure masses are chosen subjectively and not using any type of statistical method. To circumvent limitations associated with ISMA, the same analysis for the same dataset using multivariate curve resolution75,76 (MCR-Unscrambler 9.7) was conducted. The matrix math behind MCR-ALS is essentially the same as that used in ISMA and has been reported in greater detail elsewhere.75,77,78 In particular, De Juan and Tauler79 and Tauler et al.80 have worked extensively on MCR and MCR-ALS (alternating least squares) methods for soft-modeling data analysis of agricultural (biomass) samples. A soft model explains the physical progression of the chemistry without being capable of quantitatively describing it like a hard model would.75 This is due to the lack of a unique solution. Similar to ISMA, MCRALS has been used to compress spectral data for plant-based materials, because it does not require any prior knowledge of the spectra for those materials.78,81 The main difference between ISMA and MCR in the Unscrambler is ease of use. For reasons mentioned previously, it is difficult to learn and operate ISMA. The Unscrambler runs on more-current operating systems and has a wide variety of import/export options, smoothing and data handling options, and various statistical tasks. Another important difference between ISMA and MCR is that the Unscrambler was able to handle all 12 000 points of data that were produced during these experiments. MCR in the Unscrambler requires that the user impose external constraints, which bound the analysis.79 Two constraints were used to bind the analysis for this set of data. The first is the closure constraint, which requires that the sum of the total spectra be unity. To achieve closure, the spectrum is normalized

to unity with known peaks of nonvariance removed (i.e., argon, helium). The second constraint is the non-negativity of both the concentrations and the pure signal profiles. This constraint is imposed to reconstruct mass spectra, so that we can make direct comparisons to the ISMA analysis completed on the same set of data, which also requires non-negativity. Negative loadings for both ISMA and MCR have no physical meaning and, therefore, are omitted from the overall statistical dataset. The Unscrambler reports one more than the optimal number of pure components. The user can adjust the number of pure components manually, and the Unscrambler adjusts the loadings and scores automatically. Using this feature, we are able to both mimic the three factors identified using ISMA and to re-evaluate the dataset to discover new factors that were difficult to discern from the Vardia plot. This allows us to fine-tune the model for the global chemistry in the biomass gasification system. 3. Results and Discussion 3.1. Validation of MCR by Comparison with ISMA. The mass spectra for all four feedstocks together (∼90) were analyzed using both ISMA and MCR. Because ISMA has data size limitations, the dataset was reduced to 25-290 amu and normalized so that the range summed to unity. The remaining data, from 291 amu to 390 amu, was eliminated from the analysis. Three representative pure components appeared to elute from the Vardia plot of the 150 masses with the most variance in ISMA. Relative loadings were calculated for each pure mass and are graphed in Figure 3 on the left. The same dataset from the range of 25-290 amu that was normalized in the same way was loaded into MCR and run with constraints on closure and non-negativity. The MCR data were not cut down to the 150 masses with the most variance. Initial guesses consistent with the three pure masses found using ISMA (m/z 139, 155, 173) were used for the MCR analysis. The resultant main factors found using the MCR are plotted in Figure 3 on the right-hand


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Figure 5. Proposed global chemistry for the biomass gasification system considered here. Statistical analysis showed a consistent breakdown of the data into three main factors. Factor 1 contains primary pyrolysis products that, for the most part, contain two O atoms. Factor 2 contains secondary pyrolysis products and cracking products that are alcohols or precursors to alcohols. Factor 3 contains tertiary pyrolysis products that are ring species, such as benzene or indene and PAHs.

Figure 4. MCR analysis results for all of the data taken during this study. This is meant to give an overall perspective of the data distribution for all of the feedstocks.

side. Note that the scales for the ISMA and MCR factors are very different and cannot be directly compared. No quantitative understanding of the product stream composition can be obtained from these data. Instead, the relative intensities may be used to interpret which masses are more or less important within a particular factor. The masses contained within the three factors designated to MCR and ISMA are very similar. The most prominent discrepancy between these analyses is the relative intensities within each factor for certain masses. For example, for Pure Mass 2 in ISMA, the relative intensities for 91 and 94 are almost identical, whereas in MCR, they are different. This may be due to the fact that more masses were eliminated from the previous ISMA dataset than the MCR dataset. This indicates that ISMA may have overestimated the relative importance of certain masses. The ISMA and MCR analyses correlate the masses contained within Factor 2 more differently than the other two factors. This is probably because it was most difficult to choose this factor manually from the Vardia plot. The masses were not clearly concentrated in any particular direction. This also might indicate that one factor is not sufficient to properly describe the relationship between all of the masses that are contained within this factor. For pyrolysis, this second factor contains secondary cracking products.12,34-36,82,83 Because gasification in this case was two-stage and involved significantly higher temperatures than pyrolysis, the second factor might actually represent several smaller factors that contain fragmented cracking products and secondary pyrolysis products. 3.2. Global Chemistry by Identification of Factors. After confirming that MCR and ISMA give very similar results, MCR was rerun for all of the combined data collected during this study (∼90 spectra). Results from this analysis are shown in Figure 4. MCR was run with the entire dataset from the range of 25-390 amu normalized to sum to one with inert gases (helium, argon) and overlapping masses (e.g., m/z 28, 44)

removed. Initial guesses for main factors were not entered, so that MCR could statistically determine the optimal number of principal components (PCs). For the analysis reported henceforth, MCR was allowed to report the optimal number of PCs. The optimal number of PCs was then manually reduced to three factors for a more-direct comparison to the work performed by Evans and Milne.12 The three factors that were identified from these data agree with factors that describe primary, secondary, and tertiary pyrolysis products identified by Evans and Milne.12 A schematic of the global chemistry for this system is given in Figure 5. Generally, the three factors represent primary pyrolysis products, secondary pyrolysis and cracking products, and tertiary pyrolysis products. There are several minor discrepancies between the Evans and Milne products and our products, and they are discussed in detail in the following sections. Pyrolysis vapors are evolved from the feedstock. Initially, these species contain carbohydrate fragments, anhydrosugars, and lignin fragments. These are what were termed primary pyrolysis products by Evans and Milne,12 and some of these species are contained in the mass spectra in Factor 1. Because these are directly evolved from the biomass, they show the strongest dependence upon feedstocks. At higher temperatures, these molecules are cracked to smaller molecules with fewer O atoms. These species are contained in the secondary pyrolysis products of Evans and Milne12 and in Factors 1 and 2 here. These species have less dependence upon feedstock because they are further removed from the feedstock. At higher temperatures, the cracking products break into small hydrocarbons and radicals.84 These species recombine through wellknown molecular weight growth reactions to produce PAHs, which are found in Evans and Milne’s tertiary products and Factor 3 here. Because these compounds are far removed from the pyrolysis products, there is essentially no variation with feedstock. 3.2.1. Factor 1: Primary Pyrolysis Products. This study was conducted at atmospheric pressure and at gasification temperatures in the range of 600-710 °C. Because the product stream is sent immediately through a TC at temperatures in the range of 650-875 °C, some of the previous studies on biomass gasification and pyrolysis are not directly comparable. However, known reaction pathways for the well-known primary, secondary, and tertiary pyrolysis products are compared here to attempt to identify possible reaction pathways for gasification. Results for the first main factor (Factor 1) from the MCR analyses for each of the four feedstocks gasified separately during this study are shown in Table 4. Most peaks have possible products associated with them; however, some do not. In lieu of assigning

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Table 4. Species Assignments for Masses Contained with Factor 1 for Each of the Four Feedstocksa m/z 29 30 31 39 41 43 55 58 60 68 84 91 94 96 110 120 122 124 138 146 162 210 224

possible product(s) aldehydes formaldehyde CH3O+ C2HO+ aldehydes, pyruvaldehyde, acetyl cation CH2CHCO+ acetone, acrolein acetic acid, methyl formate, hydroxyacetaldehyde furan C4H4O2+ phenol furfural, 2-methyl-2-cyclopentene-1-one catechol, methyl furfural vinylphenol dimethylphenols, ethyl phenol, benzoic acid guaiacol 4-methylguaiacol 1-hydroxy-2-butanone acetate levoglucosan sinapyl alcohol propiosyringone, alpha-oxy

Relative Loading Intensityb corn stover switchgrass Vermont wood wheat straw 3.9 1.8 1.2 2.9 5.5 3.0 2.4 0.4 0.4 1.0 0.3 1.8 2.0 0.7 1.1 0.8 0.8 0.8 0.4 0.91 0.36 0.27 0.22

7.8 3.7 1.5 5.2 8.0 4.6 3.0 1.2 1.2 1.1 0.4 3.1 0.9 0.7 1.0 0.6 0.6 0.6 0.3 0.07 0.11 0.00 0.00

2.0 1.3 0.9 0.7 2.4 3.0 2.2 2.5 2.5 1.2 0.6 0.2 0.7 1.4 4.9 3.6 3.6 3.6 1.9 1.39 1.09 0.40 0.50

2.2 1.0 0.8 0.3 2.4 3.5 2.7 1.4 1.4 1.4 0.9 0.0 0.5 1.6 3.2 2.3 2.3 2.3 1.4 1.41 0.89 0.38 0.45

suspected precursor(s) 18, 15, 15 G lignin, protein 29, 15, levoglucosan 12, levoglucosan 19 glucose, fructose 12, levoglucosan 12, 12, 18 protein 30 12, levoglucosan 12, 12, 12 12 12, 12, 12 cellulose 12 12 31

reference(s) 19, 22 20, 24 31 20 15, 18, 20-22 16, 18, 19, 21, 28 21 18 26 24 25 17, 23 17, 23

a Masses m/z 43, 55, 110, and 162 are strongly correlated for Vermont wood and wheat straw. This seems to be affected by the formation of levoglucosan. Masses m/z 29, 41, 94, and 110 are strongly correlated for switchgrass and corn stover. This seems to be affected by the decomposition of G lignin. b The relative loading intensity refers to the normalized loading intensity. All of the loadings for this factor were summed, and each loading was then divided by the sum of all of the loadings.

peak names to these species, we have included possible precursors that are believed to be associated with those peaks. Factor 1 contains peaks that have been identified in the literature to be primary pyrolysis products. These products include a combination of volatiles, levoglucosan, and peaks associated with the breakdown of levoglucosan, which are derived from cellulosic ring fragmentation, depolymerization, and dehydration.12,34,41,85,86 Typical oxygenated species that are associated with the primary pyrolysis products of carbohydrates and lignin such as phenol (m/z 94), 5-methylfurfural or catechol (m/z 110), guaiacol (m/z 124), and 4-methylguaiacol (m/z 138) are evident in all of the feedstocks at varying relative loadings.12,23 Because Factor 1 contains the most peaks at relatively low intensities (see the Supporting Information), it is hypothesized that it contains species that are easily broken down into morestable tar compounds such as PAHs. Peaks at 43, 55, 60, and 96 amu are found in the secondary pyrolysis products of levoglucosan at 650 and 700 °C.12,29 Other possible assignments for these peaks include furfural (m/z 96), the acetyl cation (m/z 43), formic acid or hydroxyacetaldehyde (m/z 60), and methyl furfural (m/z 110).12 Vermont wood and wheat straw have prominent peaks at m/z 43, 96, and 110, which means that the decomposition of these feedstocks is strongly affected by a mechanism that contains all of these masses. As mentioned previously, that mechanism is probably associated with levoglucosan.12 Switchgrass and corn stover have peaks at m/z 39, 41, and 55 that are strongly correlated. These peaks are all associated with G lignin and more specifically with guaiacol derivatives.31 These peaks could also be indicative of 4-methylpentanamide, which could also be a molecular source.29 This means that the grassy feedstocks might be more affected by the degradation of lignin rather than cellulose or hemicellulose. It does not mean that they contain more or less of these components; it means the lignin fraction is more important statistically, with respect to the global chemistry.

Corn stover shows a relatively strong peak for phenol (m/z 94), which is normally a secondary pyrolysis product. Based on the wet chemistry analysis shown in Table 3, corn stover contains less sugar than all of the other feedstocks. This might mean that the sugar in corn stover breaks down more quickly than the other feedstocks. If this is true, then the primary pyrolysis products produced from the breakdown of cellulose in corn stover would have more time in the gasifier and, therefore, might react further to form phenolics. 3.2.2. Factor 2: Secondary Pyrolysis Products and Cracking Products. Important results for the MCR analyses for each feedstock examined during this study for the second main factor (Factor 2) are shown in Table 5. Factor 2 contains the second-highest number of masses and has the lowest loading intensities. The reconstructed mass spectra for this factor are given with the Supporting Information. Similar to the secondary pyrolysis products outlined by Evans and Milne,12 Factor 2 contains phenolics, such as phenol (m/z 94), cresol (m/z 108), and p-vinylphenol (m/z 120), as well as smaller hydrocarbons such as cyclopentadiene (m/z 66) C3 compounds (m/z 39) and C4 compounds (m/z 54). These are likely intermediates between the breakdown of compounds contained in Factor 1 and compounds contained in Factor 3. Factor 2 also contains alkenes that are known to recombine with radicals to create tars via a molecular weight growth mechanism. The first part of this study outlines the product distribution from the gas chromatography (GC) for all of the gasification parameters presented here.51 These data include quantitative data from ethylene and propene, which both contribute to molecular weight growth. The feedstocks are more similar, with regard to observed peaks and relative intensities, in Factor 2 than they were in Factor 1. The separation of feedstocks in Factor 1 was based around the decomposition of the lignin, cellulose, and hemicellulose fractions in the native feedstock. Because there is very little difference between the feedstocks for Factor 2, we can conclude that the chemistry governing the formation and breakdown of the phenolic compounds is not dominated by


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a

Table 5. Species Assignments for Masses Contained in Factor 2 for Four Feedstocks m/z 26 27 29 30 39 41 54 66 91 94 108 120 132 144 158 182 208

Relative Loading Intensityb

possible product(s) C2H2+ C2H3+ CHO+, aldehydes ethane, formaldehyde C2HO+ butadiene, pyridine derivative cyclopentadiene phenol cresol p-vinylphenol methylbenzofuran 3,5-dihydroxy-6-methyl-2,3-dihydro-[4H]-pyran-4-one(?) syringaldehyde C11H12O4, sinapyl aldehyde

corn stover

switchgrass

Vermont wood

wheat straw

2.2 2.8 3.0 2.5 5.7 5.7 1.6 2.3 3.7 5.6 3.0 2.6 0.9 1.2 0.6 0.53 0.29

0.0 0.4 1.3 1.8 4.5 4.5 1.5 1.4 2.3 4.0 3.0 2.3 1.3 1.3 0.9 0.56 0.37

0.2 0.9 1.7 1.8 4.0 4.0 1.8 1.5 2.7 4.6 4.0 1.3 1.7 1.9 1.4 0.93 0.66

0.1 1.1 1.9 2.0 4.6 4.6 1.9 1.7 3.0 5.7 3.6 2.0 1.3 1.6 1.1 0.80 0.55

suspected precursor(s)

protein protein protein protein

reference(s) 18, 18, 15, 12, 29 21 12, 12, 30 26, 26, 12 12 33

19, 19, 18, 15,

21 21 19, 21 18, 19, 21, 24

29 29 32 32

12, 26, 32 12, 28

a Vermont wood, wheat straw, and switchgrass are very similar in two main ways: (1) relative to the masses that are correlated with each other and (2) relative to the relative intensities of those masses. b The relative loading intensity refers to the normalized loading intensity. All of the loadings for this factor were summed, and each loading was then divided by the sum of all of the loadings.

Table 6. Major Species Contained within Factor 3 (Most Are PAH Species or Species That Are Often Related to the Formation of PAHs) m/z

possible product(s)

26 78 92 116 128 152 178 202

acetylene benzene toluene indene naphthalene acenapthylene anthracene/phenanthrene pyrene

Relative Loading Intensitya corn stover

switchgrass

Vermont wood

wheat straw

9.0 11.6 2.8 2.4 6.2 2.0 2.0 1.2

7.2 14.0 3.1 2.6 6.6 2.2 2.4 1.6

5.1 13.6 3.4 3.3 6.8 2.5 2.7 1.9

5.6 13.4 3.2 2.7 6.5 2.2 2.6 1.7

reference(s) 12, 12, 12, 12 12, 12 12, 12

16 27 27 27 27

a The relative loading intensity refers to the normalized loading intensity. All of the loadings for this factor were summed, and each loading was then divided by the sum of all of the loadings.

feedstock. Instead, it must be controlled by kinetics and, more specifically, probably by temperature. Because Factor 2 shares some major peaks with Factor 1 (m/z 41, 91), we might also conclude that, because of this overlap, there should be more than three factors. Overlap between the factors indicates that there must be more than one purity vector being calculated for each mass. This indicates that there are some compounds that are not fully broken down in the primary pyrolysis step. Therefore, there may be a fourth factor between the primary and secondary factors that represents cracking products that cannot break past the energy barrier required to create the phenolics. Based on this, the overlap between factors should be examined more closely on a smaller-scale system with more-intermediate temperatures. 3.2.3. Factor 3: Tertiary Pyrolysis Products. Results from the MCR analyses of each feedstock for Factor 3 are shown in Table6.Generally,polynucleararomatichydrocarbons(PAHs)ssuch as naphthalene (m/z 128), anthracene or phenanthrene (m/z 178), and pyrene (m/z 202) and benzene (m/z 78)sare contained within Factor 3. These compounds are commonly known as tertiary pyrolysis products.12,13,50 It is not surprising to find these species strongly correlated to each other. These molecules cannot be easily cracked or steam-reformed, and their refractory nature makes these tars particularly troublesome in gasification. There is almost no difference between the relative intensities and masses contained within Factor 3 for all of the four feedstocks. This indicates that, no matter the feedstock, the product stream from biomass gasification tends toward terminal products such as PAHs.

3.3. Comparison of Experimental Data with Statistical Findings. To get a sense for how well the statistical factor analysis represents the quantitative data, the relative loading concentrations for the three main factors are presented in Figure 6 in the right column. In the left column, actual concentrations in the stream of guaiacol, phenol, and benzene are shown. Concentrations were calculated using calibration factors that were found from calibrations done on the MBMS for these specific species. This is discussed more thoroughly in the first part of this study.51 The data were obtained at an 8FBR temperature of 600 °C and at a steam-to-biomass ratio of 1:3. Guaiacol (m/z 124) seems to be a representative species for the primary pyrolysis products contained within Factor 1. The most reactive species are contained within this factor, and the sharp downward trend is repeated by the actual concentration of guaiacol. Unlike Factor 1, guaiacol is not completely diminished at 875 °C. This means that the MCR analysis indicates that the highly oxygenated species are no longer important for the global chemistry at that temperature. The concentration of guaiacol varies for the four feedstocks, which is expected, because the relative lignin, cellulose, and hemicellulose compositions also vary, based on feedstock. Factor 2 is represented by phenol (m/z 94). There is a large difference in the total concentration of phenol in the stream for the four feedstocks. Despite large differences in actual concentration, all of the feedstocks follow the same trend where the phenol concentration is lower at the lowest TC temperatures, highest at the midrange TC temperatures, and lowest at the highest TC temperatures. This trend is repeated for the Factor 2 scores. The Factor 2 scores indicate that corn stover is most

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Figure 6. Comparison of calculated concentrations of species in the product stream for four feedstocks (left) with scores of the three factors identified using MCR (right). These data were measured at a steam-to-biomass ratio of 1:3 and an 8FBR temperature of 600 °C.

strongly affected by the phenolic compounds, and this is corroborated by the high concentration of phenol in corn stover. Similarly, Vermont wood is least affected by phenolics and has the lowest concentration. The PAHs and benzene are contained in Factor 3, and the trends for benzene concentration and Factor 3 are very similar. There is some difference between the feedstocks for benzene, but this separation is well within experimental error51 and probably has no significant statistical meaning. 3.4. Effects of Gasification Parameters. An examination of the scores plots that are generated concurrently with the loading plots helps to explain and validate interpretations of the loadings plots by indicating clustering patterns based on process variables. In this study, the variable process parameters were the TC temperature, FBR temperature, and steam-tobiomass ratio. There was no clustering based on steam-tobiomass ratio, and the FBR temperature caused moderate clustering at low TC temperatures for corn stover and Vermont wood. The TC temperature caused the most significant clustering, as can be seen in the scores plot presented in Figure 7. Because the loadings plots for the primary and secondary pyrolysis products and cracking products contain more masses than the tertiary pyrolysis products, scores for those factors are more dispersed. There is more information about the separation of feedstocks based on gasification parameters contained within these factors. Markers in the scores plot are colored based on feedstock and sized based on TC temperature. Cluster 1 contains Vermont wood at TC temperatures of 600 and 650 °C. This is probably because Vermont wood contains significantly more lignin than the other feedstocks (see Table 3) and, therefore, has a product distribution closer to lignin pyrolysis at lower TC temperatures. Cluster 2 contains mostly wheat straw and switchgrass at a TC temperature of 600 °C and is probably due solely to the TC temperature. Cluster 3 contains mostly scores for a TC temperature of 650 °C. There is some feedstock separation in this cluster, and corn stover scores at a TC temperature of 600 °C are also contained in this cluster. This is consistent with what

Figure 7. Scores plot for Factor 1 versus Factor 2 from the MCR results for all feedstocks together (∼90 mass spectra). Clustering is based primarily on the TC temperature, which is indicated by the size of the markers. The largest markers indicate runs conducted at a TC temperature of 875 °C and they are tightly clustered near the origin. The smallest markers indicate runs conducted at a TC temperature of 600 °C and are loosely clustered based on feedstock.

we had seen for the loadings and scores for corn stover in Factor 1, because corn stover decomposes more quickly than the other feedstocks and moves more quickly to secondary and tertiary pyrolysis products. There is clustering behavior for the highest TC temperatures 750 and 875 °C in clusters 4 and 5, respectively. Switchgrass is completely divided from the other three feedstocks in cluster 4, and Vermont wood, corn stover, and wheat straw are separated based on a higher 8FBR temperature (710 °C). There is no noticeable division based on feedstock in cluster 5. The clustering patterns are strongly correlated with the TC temperature, and the indicated trend suggests that, to create a truly feed insensitive model for biomass gasification, the TC temperature should be >750 °C.


4. Conclusions A multivariate curve resolution (MCR) technique that was offered as part of the Unscrambler software package was shown to give the same results as an Interactive Self-Modeling Multivariate Analysis (ISMA) analysis of mass spectra. MCR was then used determine the main statistical factors that correlate specific masses and reveal relative importance for those masses. The loadings that were found for this set of data were tabulated, and the four feedstocks used for this study were compared. The results of this analysis show that, with respect to tars, there is feedstock flexibility of biomass gasification at the higher temperatures. As the temperature of gasification increases, the types and quantities of tars are similar for the feedstocks examined here. To best relate the complex large-scale data to more simple small-scale data, MCR techniques were used to abridge these data and compress it into several main factors. These factors represented primary, secondary and cracking, and tertiary pyrolysis products. This is consistent with a model of gasification and tar formation in which oxygen-containing molecules are continuously cracked to small hydrocarbon species and radicals, which recombine to form polynuclear aromatic hydrocarbons (PAHs). Higher temperatures favor the formation of PAHs, and because these molecules are further removed from the primary pyrolysis vapors of the biomass, they are less sensitive to feedstock. This work imparts supporting data and analysis from the large-scale to smaller-scale studies, which are designed more carefully to deduce kinetic parameters. Acknowledgment We gratefully acknowledge funding for this research, which was sponsored by the Office of Biomass Programs under the U.S. Department of Energy (Contract No. DE-AC36-99GO10337 with the National Renewable Energy Laboratory). We also gratefully acknowledge Justin Sluiter, Dr. Angela Ziebell, Robert Sykes, Dr. Eun-Jae Shin, and Dr. Robert J. Evans for wet chemistry data and analysis, training on ISMA and Unscrambler, and helpful discussions. Supporting Information Available: Graphical representations of the data presented in Tables 4, 5, and 6 are given as supplemental material. (PDF) These graphical representations are reconstructed spectra for the three main factors found using MCR. Comparing the reconstructed spectra for the four feedstocks allows the reader to quickly discriminate the major differences between each of the four feedstocks used during this study. This is also a useful tool for those wishing to apply similar statistical analysis to mass spectral data. This information is available free of charge via the Internet at http://pubs.acs.org. Literature Cited (1) Hoekman, S. K. Biofuels in the U.S.-Challenges and opportunities. Renew. Energy 2009, 34, 14–22. (2) Patterson, T.; Dinsdale, R.; Esteves, S. Review of energy balances and emissions associated with biomass-based transport fuels relevant to the United Kingdom context. Energy Fuels 2008, 22, 3506–3512. (3) Perlack, R. D.; Wright, L. L.; Turhollow, A. F.; Graham, R. L.; Stokes, B. J.; Erbach, D. C. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply; U.S. Department of Energy: Washington, DC, 2005. (4) Saxena, R. C.; Adhikari, D. K.; Goyal, H. B. Biomass-based energy fuel through biochemical routes: A review. Renew. Sustain. Energy ReV. 2009, 13, 167–178. (5) Orecchini, F.; Bocci, E. Biomass to hydrogen for the realization of closed cycles of energy resources. Energy 2007, 32, 1006–1011.

10699

(6) Phillips, S. D. Technoeconomic analysis of a lignocellulosic biomass indirect gasification process to make ethanol via mixed alcohols synthesis. Ind. Eng. Chem. Res. 2007, 46, 8887–8897. (7) Phillips, S.; Aden, A.; Jechura, J.; Dayton, D. Thermochemical Ethanol via Indirect Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass; Technical Report; National Renewable Energy Laboratory: Golden, CO, 2007. (8) Larson, E. D. Technology for electricity and fuels from biomass. Ann. ReV. Energy EnViron. 1993, 18, 567–630. (9) Larson, E. D.; Consonni, S.; Katofsky, R. E.; Iisa, K.; Frederick, W. J., Jr. A Cost-Benefit Assessment of Gasification-Based Biorefining in the Kraft Pulp and Paper Industry; Technical Report, U.S. Department of Energy: Washington, DC, 2007. (10) Bonk, D. L. Coal Gasification as AlternatiVe Fuel for Glass Industry; National Energy Technology Laboratory: Toledo, OH, 2005. (11) Yung, M. M.; Jablonski, W. S.; Magrini-Bair, K. A. Review of catalytic conditioning of biomass-derived syngas. Energy Fuels 2009, 23 (4), 1874–1887. (12) Evans, R. J.; Milne, T. A. Molecular characterization of the pyrolysis of biomass. 1. Fundamentals. Energy Fuels 1987, 1 (2), 123–137. (13) Shin, E.-J.; Nimlos, M. R.; Evans, R. J. Kinetic analysis of gas phase pyrolysis of carbohydrates. Fuel 2001, 80, 1697–1709. (14) Bjorkman, A. Projects on coal characterization. Fuel 2001, 80, 155– 166. (15) Jakab, E.; Faix, O.; Till, F.; Szekely, T. Thermogravimetry/mass spectrometry study of six lignins within the scope of an international round robin test. J. Anal. Appl. Pyrolysis 1995, 35, 167–179. (16) Hoffman, D.; Hoffman, I. Tobacco smoke components. Beitr. Tabakforsch. Int. 1998, 18, 49–52. (17) Shafizadeh, F.; Lai, Y. Z. Thermal degradation of 1,6-anhydrobeta-D-glucopyranose. J. Org. Chem. 1972, 37 (2), 278–284. (18) Varhegyi, G.; Antal, M. J., Jr.; Szekely, T.; Till, F.; Jakab, E. Simultaneous thermogravimetric-mass spectrometric studies of the thermal decomposition of biopolymers. 1. Avicel cellulose in the presence and absence of catalysts. Energy Fuels 1988, 2, 267–272. (19) Varhegyi, G.; Antal, M. J., Jr.; Szekely, T.; Till, F.; Jakab, E.; Szabo, P. Simultaneous thermogravimetric-mass spectrometri studies of the thermal decomposition of biopolymers. 2. Sugar cane bagasse in the presence and absence of catalysts. Energy Fuels 1988, 2, 273–277. (20) Varhegyi, G.; Antal, M. J., Jr.; Szekely, T.; Szabo, P. Kinetics of the thermal decomposition of cellulose, hemicellulose, and sugar cane bagasse. Energy Fuels 1989, 3, 329–335. (21) Varhegyi, G.; Jakab, E.; Till, F.; Szekely, T. ThermogravimetricMS characterization of the thermal decomposition of sunflower stem. Energy Fuels 1989, 3, 755–760. (22) Szabo, P.; Varhegyi, G.; Till, F.; Faix, O. Thermogravimetric/mass spectrometric characterization of two energy crops, Arundo donaz and Miscanthus sinensis. J. Anal. Appl. Pyrolysis 1996, 36, 179–190. (23) Shafizadeh, F. Introduction to pyrolysis of biomass. J. Anal. Appl. Pyrolysis 1982, 3, 283–305. (24) Pavlath, A. E.; Gregorski, K. S. Carbohydrate pyrolysis: I. A continuous mass spectrometric method for the analysis of the resulting gaseous mixture. J. Anal. Appl. Pyrolysis 1987, 11, 341–353. (25) Thomas, S.; Wornat, M. J. Effects of acetylene addition on yields of the C1-C10 hydrocarbon products of catechol pyrolysis. Energy Fuels 2008, 22, 976–986. (26) Branca, C.; Di Blasi, C.; Elefante, R. Devolatilization of conventional pyrolysis oils generated from biomass and cellulose. Energy Fuels 2006, 20, 2253–2261. (27) Fisher, T.; Hajaligol, M.; Waymack, B.; Kellogg, D. Pyrolysis behavior and kinetics of biomass derived materials. J. Anal. Appl. Pyrolysis 2002, 62, 331–349. (28) Ranzi, E.; Cuoci, A.; Faravelli, T.; Frassoldati, A.; Migliavacca, G.; Pierucci, S.; Sommariva, S. Chemical kinetics of biomass pyrolysis. Energy Fuels 2008, 22, 4292–4300. (29) Page, D. W.; van Leeuwen, J. A.; Spark, K. M.; Mulcahy, D. E. Pyrolysis characterisation of plant, humus and soil extracts from Australian catchments. J. Anal. Appl. Pyrolysis 2002, 65, 269–285. (30) Van Arendonk, J.; Niemann, G.; Boon, J. The effect of enzymatic removal of proteins from plant leaf material as studied by pyrolysis-mass spectrometry: Detection of additional protein marker fragment ions. J. Anal. Appl. Pyrolysis 1997, 42, 33–51. (31) Faix, O.; Meier, D.; Fortman, I. Thermal degradation products of wood. Holz Roh Werkst. 1990, 48 (9), 351–354. (32) Branca, C.; Di Blasi, C. Multistep mechanism for the devolatilization of biomass fast pyrolsis oils. Ind. Eng. Chem. Res. 2006, 45, 5891– 5899. (33) Niemann, G.; Pureveen, J.; Eijkel, G.; Poorter, H.; Boon, J. Differences in relative growth rate in 11 grasses correlated with differences

10700


in chemical composition as determined by pyrolysis mass spectrometry. Oecologia 1992, 89, 567–573. (34) Antal, M. J., Jr. Biomass pyrolysis: a review of the literature. Part 1sCarbohydrate pyrolysis. AdV. Solar Energy 1983, 61–111. (35) Antal, M. J., Jr. Biomass pyrolysis: A review of the literature. Part 2sLignocellulose pyrolysis. AdV. Solar Energy 1985, 2, 175–255. (36) Antal, M. J., Jr. A review of the vapor phase pyrolysis of biomass derived volatile matter. In Fundamentals of Thermochemical Biomass ConVersion; Overend, T. A., Milne, R. P., Mudge, L. K., Eds.; Elsevier: New York, 1983; pp 511-537. (37) Antal, M. J., Jr.; Varhegyi, G. Cellulose pyrolysis kinetics: The current state of knowledge. Ind. Eng. Chem. Res. 1995, 34, 703–717. (38) Antal, M. J., Jr.; Varhegyi, G.; Jakab, E. Cellulose pyrolsis kinetics: revisited. Ind. Eng. Chem. Res. 1998, 37, 1267–1275. (39) Babu, B. V.; Chaurasia, A. S. Modeling for pyrolysis of solid particle: kinetics and heat transfer effects. Energy ConVers. Manage. 2003, 44 (14), 2251–2275. (40) Boroson, M. L.; Howard, J. B.; Longwell, J. P.; Peters, W. A. Heterogeneous cracking of wood pyrolysis tars over fresh wood char surfaces. Energy Fuels 1989, 3, 735–740. (41) Bradbury, A. G. W.; Sakai, Y.; Shafizadeh, F. A kinetic model for pyrolysis of cellulose. J. Appl. Polym. Sci. 1979, 23 (11), 3271–3280. (42) Di Blasi, C. A concentrated parameter model for fluid-bed flash pyrolysis of biomass. In Biomass Gasification and Pyrolysis: State of the Art and Future Prospects, Stuttgart, Germany, April 9-11, 1997; Kaltschmitt, M., Bridgwater, A. V. P. O., Eds.; CPL Press: Stuttgart; Germany, 1997; pp 382-391. (43) Fagbemi, L.; Khezami, K.; Carpat, R. Pyrolysis products from different biomasses: Application to the thermal cracking of tar. Appl. Energy 2001, 69, 293–306. (44) Goodacre, R.; Neal, M. J.; Kell, D. B. Rapid and quantitative analysis of the pyrolysis mass spectra of complex binary and tertiary mixtures using multivariate calibration and artificial neural networks. Anal. Chem. 1994, 66, 1070–1085. (45) Green, A. E. S.; Chaube, R. P. A unified biomass-coal pyrolysis reaction model. Int. J. Power Energy Syst. 2004, 24 (3), 215–223. (46) Kersten, S. R. A.; Wang, X. Q.; Prins, W.; van Swaaij, W. P. M. Biomass pyrolysis in a fluidized bed reactor. Part 1: Literature review and model simulations. Ind. Eng. Chem. Res. 2005, 44 (23), 8773–8785. (47) Kleen, M.; Lindblad, G.; Backa, S. Quantification of lignin and carbohydrates in kraft pulps using analytical pyrolysis and multivariate data analysis. J. Anal. Appl. Pyrolysis 1993, 25, 209–227. (48) Lv, P. M.; Chang, J.; Wang, T. J.; Wu, C. Z.; Tsubaki, N. A kinetic study on biomass fast catalytic pyrolysis. Energy Fuels 2004, 18 (6), 1865– 1869. (49) Mohan, D.; Pittman, C. U., Jr.; Steele, P. H. Pyrolysis of Wood/ Biomass for Bio-oil: A Critical Review. Energy Fuels 2006, 20, 848–889. (50) Morf, P.; Hasler, P.; Nussbaumer, T. Mechanisms and kinetics of homogenous secondary reactions of tar from continuous pyrolysis of wood chips. Fuel 2002, 81, 843–853. (51) Carpenter, D. L.; Feik, C. J.; Gaston, K. R.; Jablonski, W. S.; Bain, R. L.; Phillips, S.; Nimlos, M. R. Pilot-scale gasification of corn stover, wood, switchgrass and wheat straw. Ind. Eng. Chem. Res., in review. (52) Carpenter, D. L.; Deutch, S. P.; French, R. J. Quantitative measurement of biomass gasifier tars using a molecular-beam mass spectrometer: comparison with traditional impinger sampling. Energy Fuels 2007, 21, 3036–3034. (53) Bain, R. L.; Dayton, D. C.; Carpenter, D. L.; Czernik, S. R.; Feik, C. J.; French, R. J.; Magrini-Bair, K. A.; Phillips, S. D. Evaluation of catalyst deactivation during catalytic steam reforming of biomass-derived syngas. Ind. Eng. Chem. Res. 2005, 44, 7945–7956. (54) Windig, W.; Haverkamp, J.; Kistemaker, P. G. Interpretation of sets of pyrolysis mass spectra by discriminant analysis and graphical rotation. Anal. Chem. 1983, 55, 81–88. (55) Mansueto, E. S.; Wight, C. A. Pure component analysis of chain length distributions from solid-state polymerization of formaldehyde. Appl. Spectrosc. 1992, 46, 1799–1803. (56) Windig, W.; Stephenson, D. A. Self-modeling mixture analysis of second-derivative near-infrared spectral data using the SIMPLISMA approach. Anal. Chem. 1992, 54, 2735–2742. (57) Guilment, J.; Markel, S.; Windig, W. Infrared chemical microimaging assisted by interactive self-modeling multivariate analysis. Appl. Spectrosc. 1994, 48 (3), 320–326. (58) Cuesta Sanchez, F.; Toft, J.; van den Bogaert, B.; Massart, D. L. Orthogonal projection approach applied to peak purity assessment. Anal. Chem. 1996, 68, 79–85. (59) Gargallo, R.; Cuesta Sanchez, F.; Izquierdo-Ridorsa, A.; Massart, D. L. Application of eigenstructure tracking analysis and SIMPLISMA to

the study of the protonation equilibrium of cCMP and several polynucleotides. Anal. Chem. 1996, 68 (13), 2241–2247. (60) Toft, J.; Cuesta Sanchez, F.; Van den Bogaert, B.; Libnau, F. O.; Massart, D. L. Resolution of overlapping mid-infrared spectra using SIMPLISMA and a second-order derivative approach. Vibr. Spectrosc. 1996, 10, 125–138. (61) Windig, W.; Antalek, B.; Lippert, J. L.; Batonneau, Y.; Bremard, C. Combined use of conventional and second-derivative data in the SIMPLISMA self-modeling mixture analysis approach. Anal. Chem. 2002, 74, 1371–1379. (62) Tummers, P. H. G.; Houben, E. J. E.; Jansen, J. F. G. A.; Wienke, D. Evaluation of advanced data analysis techniques on time resolved FTIR spectroscopy data of photo-copolymerization reactions. Vibr. Spectrosc. 2007, 43, 116–124. (63) Vosough, M.; Salemi, A. Second-order standard addition for deconvolution and quantification of fatty acids of fish oil using GC-MS. Talanta 2007, 73, 30–36. (64) Windig, W.; Meuzelaar, H. L. C.; Shafizadeh, F.; Kelsey, R. G. Biochemical-analysis of wood and wood products by pyrolysis massspectrometry and multivariate-analysis. J. Anal. Appl. Pyrolysis 1984, 6 (3), 233–250. (65) Windig, W.; Meuzelaar, H. L. C.; Haws, B. A.; Campbell, W. F.; Asay, K. H. Biochemical differences observed in pyrolysis mass-spectra of range grasses with different resistance to Labops-Hesperius Uhler attack. J. Anal. Appl. Pyrolysis 1983, 5 (3), 183–198. (66) Windig, W.; Kistemaker, P. G.; Haverkamp, J.; Meuzelaar, H. L. C. Factor-analysis of the influence of changes in experimental conditions in pyrolysis-mass spectrometry. J. Anal. Appl. Pyrolysis 1980, 2 (1), 7–18. (67) Windig, W.; Kistemaker, P. G.; Haverkamp, J. Chemical interpretation of differences in pyrolysis mass-spectra of simulated mixtures of biopolymers by factor-analysis with graphical rotation. J. Anal. Appl. Pyrolysis 1982, 3 (3), 199–212. (68) Windig, W.; Jakab, E.; Richards, J. M.; Meuzelaar, H. L. C. Selfmodeling curve resolution by factor-analysis of a continuous series of pyrolysis mass-spectra. Anal. Chem. 1987, 59 (2), 317–323. (69) Windig, W.; Haverkamp, J.; Kistemaker, P. G. Interpretation of sets of pyrolysis mass-spectra by discriminant-analysis and graphical rotation. Anal. Chem. 1983, 55 (1), 81–88. (70) Windig, W. Self-modeling mixture analysis of spectral data with continuous concentration profiles. Chemom. Intell. Lab. Syst. 1992, 16 (1), 1–16. (71) Vandemeent, D.; Deleeuw, J. W.; Schenck, P. A.; Windig, W.; Haverkamp, J. Quantitative-analysis of bio-polymer mixtures by pyrolysis mass-spectrometry. J. Anal. Appl. Pyrolysis 1982, 4 (2), 133–142. (72) Meuzelaar, H. L. C.; Windig, W.; Harper, A. M.; Huff, S. M.; McClennen, W. H.; Richards, J. M. Pyrolysis mass-spectrometry of complex organic materials. Science 1984, 226 (4672), 268–274. (73) Windig, W.; Lippert, J.; Robbins, M.; Kresinske, K.; Twist, J. P.; Snyder, P. Interactive self-modeling multivariate analysis. Chemom. Intell. Lab. Syst. 1990, 9, 7–30. (74) Windig, W.; Guilment, J. Interactive self-modeling mixture analysis. Anal. Chem. 1991, 63 (14), 1425–1432. (75) Amigo, J. M.; de Juan, A.; Coello, J.; Maspoch, S. A mixed hardand soft-modelling approach for the quantitative determination of oxipurines and uric acid in human urine. Anal. Chim. Acta 2006, 567, 236–244. (76) Duponchel, L.; Elmi-Rayaleh, W.; Ruckebusch, C.; Huvenne, J. P. Multivariate curve resolution methods in imaging spectroscopy: Influence of extraction methods and instrumental perturbations. J. Chem. Inf. Comput. Sci. 2003, 43, 2057–2067. (77) Garrido, M.; Rius, F. X.; Larrechi, M. S. Multivariate curve resolution-alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes. Anal. Bioanal. Chem. 2008, 390, 2059–2066. (78) Schoonover, J. R.; Marx, R.; Nichols, W. R. Application of multivariate curve resolution analysis to FTIR kinetics data. Vibr. Spectrosc. 2004, 35, 239–245. (79) De Juan, A.; Tauler, R. Multivariate curve resolution (MCR) from 2000: Progress in concepts and applications. Crit. ReV. Anal. Chem. 2006, 36, 163–176. (80) Tauler, R.; Lacorte, S.; Barcelo, D. Application of multivariate selfmodeling curve resolution to the quantitation of trace levels of organophosphorus pesticides in natural waters from interlaboratory studies. J. Chromatogr. 2006, 730, 177–183. (81) Azzouz, T.; Tauler, R. Application of multivariate curve resolution alternating least squares (MCR-ALS) to the quantitative analysis of pharmaceutical and agricultural samples. Talanta 2008, 74, 1201–1210. (82) Varhegyi, G.; Jakab, E.; Antal, M. J. Is the Broido-Shafizadeh model for cellulose pyrolysis true? Energy Fuels 1994, 8 (6), 1345–1352.

Ind. Eng. Chem. Res., Vol. 48, No. 23, 2009 (83) Varhegyi, G.; Antal, M. J., Jr.; Jakab, E.; Szabo, P. Kinetic modeling of biomass pyrolysis. J. Anal. Appl. Pyrolysis 1997, 42, 73–87. (84) Vasiliou, A.; Nimlos, M. R.; Daily, J. W.; Ellison, G. B. Thermal decomposition of furan generates propargyl radicals. J. Phys. Chem. A 2009, 113 (30), 8540–8547. (85) Zhang, J.; Toghiani, H.; Mohan, D.; Pittman, C. U.; Toghiani, R. K. Product Analysis and Thermodynamic Simulations from the Pyrolysis of Several Biomass Feedstocks. Energy Fuels 2007, 21 (4), 237–313.

10701

(86) Wooten, J. B.; Seeman, J. I.; Hajaligol, M. R. Observation and characterization of cellulose pyrolysis intermediates by C CPMAS NMR. A new mechanistic model. Energy Fuels 2004, 18 (1), 1–15.

ReceiVed for reView April 13, 2009 ReVised manuscript receiVed August 25, 2009 Accepted September 5, 2009 IE900596V

Pilot-Scale Gasification of Corn Stover, Switchgrass, Wheat Straw, and

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