Coupled Structural and Kinetic Model of Lignin Fast Pyrolysis - Energy

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A Coupled Structural and Kinetic Model of Lignin Fast Pyrolysis Abraham J. Yanez-McKay, Pradeep Natarajan, Wenjun Li, Ross Mabon, and Linda J. Broadbelt Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b03311 • Publication Date (Web): 11 Jan 2018 Downloaded from http://pubs.acs.org on January 12, 2018

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A Coupled Structural and Kinetic Model of Lignin Fast Pyrolysis Abraham J. Yanez1, Pradeep Natarajan2, Wenjun Li3, Ross Mabon3, and Linda J. Broadbelt1. ⃰ 1

Department of Chemical and Biological Engineering, Northwestern University, Evanston Illinois 60208, United States 2

Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India 3

Corporate Strategic Research, ExxonMobil Research and Engineering Company, Annandale New Jersey 08801, United States

Abstract Lignocellulosic biomass is a promising feedstock for renewable fuels and chemical intermediates; in particular, lignin attracts attention for its favorable chemical composition. One obstacle to lignin utilization and valorization is the unknown chemical mechanism that gives rise to the complex product distributions observed upon deconstruction. Among possible deconstruction chemistries, fast pyrolysis is promising due to its short residence time, thus enabling high volume production. However, the chemistry is inherently complex, thereby hampering the creation of detailed kinetic models describing pathways to specific low molecular products. To this end, we have created a detailed kinetic model of lignin decomposition via pyrolysis comprised of 4313 reactions and 1615 species based on pathways suggested by pyrolysis of model compounds in the literature. Using a rule-based reaction network generation approach, a pathways-level reaction network is proposed to predict the evolution of macromolecular species and the formation of various low molecular weight products identified from experimental studies. This reaction network is coupled to a structural model of wheat straw lignin via a kinetic Monte Carlo framework to simulate lignin fast pyrolysis. The mass yields of

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and speciation within four commonly-observed fractions, viz., light gases, an aqueous phase containing water and small oxygenates, char, and a highly complex aromatics fraction, are compared to an experimental report of a putatively similar biomass source. Additional capabilities of the model include the time-resolved prediction of volatilization profiles and the evolution of the molecular weight distribution, which may assist in efforts to valorize lignin to a higher degree than that achieved by current approaches.

1. Introduction Lignocellulosic biomass is an abundant, sustainable, and non-edible material that has attracted attention for its potential use as a renewable feedstock for production of fuels, chemicals, and materials.1–3 Lignocellulose is the term applied to the structural polymer of the plant cell wall and is constituted by three intimately-connected polymers: cellulose, hemicellulose, and lignin. Techno-economic analyses of biomass conversion have highlighted that it is imperative to valorize all components of lignocellulose to the highest degree.4 However, lignin has traditionally remained undervalorized despite its attractive chemical composition. Due to its importance as the largest potential renewable source of aromatics, which are common platform molecules in the chemical industry, many studies of lignin have been conducted to understand this phenomenon.5–7 Biomass conversion directed towards fuels and chemicals requires a deconstruction step that transforms macromolecules into low molecular weight species that can be subsequently upgraded. Deconstruction strategies include biological8 (micro-organismal or enzymatic), thermochemical9, and catalytic routes.10 Fast pyrolysis is a thermochemical route that is promising for its short residence time, which facilitates high production volumes, and potentially low external energy requirement for certain reactor configurations.


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Lignin pyrolysis studies typically reveal the formation of complex product distributions comprising numerous aromatic species.11,12 Traditional kinetic models make use of semiempirical correlations to capture the rate of lignin conversion into volatile species.13–16 These models correctly capture experimental mass-loss curves of non-isothermal thermogravimetric analyses (TGA), but they are limited in scope as they can predict the individual yields of only a small number of species. Examples of kinetic models describing the pyrolysis of lignin at a greater level of mechanistic detail include efforts of Klein and Virk17, followed more recently by the work from Faravelli et al.18 and Hough et al.19 These kinetic models consider the reactions occurring on simplified structural representations of lignin at a semi-detailed mechanistic level and are able to reproduce not only TGA mass-loss curves, but also can predict the yields of lumped fractions to a reasonable degree by making use of pseudocomponents, e.g. a “heavies” fraction. Benefits of models involving a greater level of product speciation include an ability to be applied to different feedstocks without parameter adjustments, prediction of compounds in the product distribution not yet detected by experiments, the possibility of reaction engineering and optimization to maximize individual or groups of species of interest, and the ability to be extended to include new experimental data as it becomes available. Two of the major roadblocks to the development of these models are recognized to be the complexity and heterogeneity of lignin structures, in addition to the diverse chemical reaction network that operates on those structures. Recent advances in experimental characterization and reactor configurations have helped overcome these challenges to some extent. Improved methods of lignin characterization have helped identify and quantify key structural elements, thereby enabling the development of comprehensive structural models.53-56 Concomitantly, new reactor


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configurations have also been developed to study the primary pyrolysis chemistry.20,21, 22,52,57,62 The key operating principle of these configurations is that small sample sizes are used to limit transport limitations, very fast heating rates are applied to bring the sample to the desired reaction temperature, and high sweep gas rates are used to cool and minimize the occurrence of secondary gas-phase reactions. Building on the advances in both structural characterization of lignin and fast pyrolysis studies, the data generated from these experiments can be combined with computational models to gain more insight into lignin pyrolysis. To this end, we have constructed a model of the fast pyrolysis of lignin by combining the structural model presented in our recent publication22 with a chemical reaction network that captures the key reaction families underlying lignin pyrolysis. The detailed distribution of low molecular weight products and char quantified by the model is compared to the major fractions that have been identified in experimental studies for model validation. The current model unravels major pathways that result in products of interest such as 2-hydroxy-3-methyl-4(propenaldehyde)phenol and p-vinyl phenol. In addition, the model also predicts the evolution of the molecular weight distribution (MWD) of lignin, which can aid in specialty applications that require pre-defined MWDs. Finally, the detailed product distribution described by the model includes species that have not yet been quantified or identified in experimental reports, thereby providing targets for future analytical efforts to fully characterize unidentified products.

2. Methodology There are three major components to the pyrolysis model: the structural model of lignin, the automated reaction network generation, and the kinetic Monte Carlo framework used to


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quantify the temporal changes in the structural representations of lignin, when the reaction network and particular reaction conditions are applied. Each component will be described in turn. 2.1 Lignin Structural Model The structural model of lignin is presented in a recent publication.22 This model produces a library of wheat straw lignin comprised of 100 complex molecular structures that is statistically verified to conform to four experimental bulk properties: monomer composition, bond composition, molecular weight distribution, and branching coefficient (Table 1, adapted from Yanez et al. 22). From experimental characterization of diverse biomass sources, it has been recognized that lignin characteristics are particular to each biomass source, while similarities have been noted within biomass classes, viz., softwood, hardwood, and herbaceous sources.23 While one biomass source is the focus of the present work, it can be envisioned how the modeling framework developed herein would be used to explore the direct consequences that different lignin properties may have on the product distribution obtained upon deconstruction, thereby linking macromolecular structures to the formation of low molecular weight products observed upon deconstruction. Table 1. Properties of wheat straw lignin library used in this work. Details about monomer structures (S, P and G), bonding types (β-O-4, β-5 and 5-5), branching coefficient, and lignin structure generation are provided in Yanez et al. 22 Target Type

Details

Abundance (%) 29.61

Monomer Percentage

Syringyl (S) p-Hydroxyphenyl (P) Guaiacyl (G)


6.01 64.38

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β-O-4 β-5 5-5 Number-average Weight-average

Bond Percentage Molecular Weight (Units: Dalton) Branching Coefficient

78.97 10.82 10.22 1847 4263 0.2259

2.2 Automated Reaction Network Generation Despite the diversity in lignin linkage types,24,22,52 what is conserved across biomass sources is the presence of one or more ether linkage types, with an additional variety of carboncarbon bonds, including biphenyl bonds, that are commonly observed. Overall, it is observed that four fractions of components are typically formed, including gases (CO, CO2, CH4, H2, and small C1-3 partially oxidized species), aqueous (mainly water), char, and a highly complex fraction of aromatic species having diverse substitution patterns.11,25 To connect pyrolysis products with lignin structural features, i.e. monomers, bonds, and their dyads or higher order moieties comprised of monomers and bonds, reaction families governing their transformations during pyrolysis are specified based on experimental and theoretical work of lignin model compounds and patterns derived from product slates observed during lignin pyrolysis.26,27 Model compounds, or small molecules that have one or few reactive features that are posited to have analogous reactivity to substructures present in native lignin, have been studied reasonably extensively.26,28–37 For the bond types of interest here, viz., β-O-4, β-5 and 5-5, selected reports were used to specify reaction families. The work of Brezny et al.38 reports the non-isothermal decomposition of a model compound containing β-O-4, 1-(4-hydroxy-3methoxyphenyl)-2-(2-methoxyphenoxy)propane-1,3-diol, at temperatures up to 275 °C to produce a variety of phenols having double bonds and carbonyl groups, in addition to 2-methoxy phenol (Figure S1). Kuroda et al.39 studied a model compound containing β-5, 4-[(3-


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(hydroxymethyl)-5-[3-hydroxyprop-1-en-1-yl]-7-methoxy-2,3-dihydro-1-benzofuran-2-yl]-2methoxyphenol, and observed the formation of a wide variety of alkylated phenols (Figure S2). Lastly, Kawamoto et al.40 exposed two derivatized biphenyls to a temperature of 400 °C and did not detect any volatile products, instead reporting only THF-insoluble products, which may correspond to an amorphous material analogous to char, which is universally observed in lignin pyrolysis25 (Figure S3). In the work from Klein and Virk,17 the pseudo-first order rate parameters in Arrhenius form, governed by a frequency factor A and an activation energy Ea, were derived from experimental data for 16 different model compounds pyrolysis. These kinetic parameters are used in this work as an initial estimation to guide the rate constants for different reaction families. However, the frequency factors, A, are optimized here within reasonable ranges to best fit the lignin pyrolysis data from Patwardan et al.21 (Table S1). Based on collating the results from the literature,17,38–40,58-61 the reaction families at the pathways level are summarized in Figure 1. All structures with wavy lines represent optional or variable functional groups that may exist at a specific carbon atom, and may also represent a linkage to a larger macromolecule of lignin. For several of the reaction families, e.g. alcohol oxidation or ether cleavage, it is necessary to add or substract hydrogen atoms or hydroxyl groups as “co-factors” to maintain atom balance for the reactions. Since some reaction families consume and others produce these co-factors, the overall reaction network is also atom-balanced such that any co-factors that are active in the network are originally derived from the initial lignin reactant or its progeny. Thus, the model is initialized to have a zero concentration of the co-factors, but they are quickly created as reactions that form them occur. The exact mechansim by which these co-factors are produced or consumed is not explicitly stipulated in the model, but


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the overall pathways as shown are supported by both theoretical and experimental reports.18,41,42,58-61 The left column of Figure 1 is dedicated to reactions that cleave specific carbon-oxygen bonds, while the right column includes carbon-carbon bond chemistry, redox reactions, an isomerization reaction, and char formation. More granularity in the carbon-oxygen chemistry that operates on the propanoid region is introduced to better capture the variety of products observed in pyrolyzates, whose structural diversity is located precisely in the propanoid region. The reaction families in both columns allow the formation of various light C1-3 species such as carbon monoxide, carbon dioxide, acetaldehyde, acetic acid, and water, which are important components of pyrolyzates. The final reaction family, char formation, is a transformation that stoichiometrically transforms char precursors into carbon (char), carbon dioxide, water, and hydrogen gas. The char precursors are defined as all dimers and trimers of lignin that possess at least one β-5 or biphenyl bond; this designation is based on the experimental observation of charring of β-5 and biphenyl model compounds. The stoichiometric coefficients are informed by experimental measurements of the relative abundance of char, gases, and water, and can be varied to correspond to specific biomass sources.


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Figure 1. Reaction families describing basis set of chemical transformations.


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Having established the reaction families in Figure 1, the reaction network is unfurled by applying each reaction type to the starting lignin macromolecules and their progeny iteratively until no new reactions and species are further produced. To tally the reactive moieties in the starting lignin and the products, a molecular fingerprint (Table 2 and Table 3) is introduced. This fingerprint is a mathematical representation of the space of all chemical structures that can be generated, given the starting macromolecules and the basis set of chemical transformations. The same fingerprint that encodes structures is also used to encode reaction families as mathematical operators. These operators search for prerequisite features in a structure and apply the specified chemical transformation, e.g. an oxidation operator transforms an aliphatic hydroxyl group into a carbonyl group. By iteratively applying these operators to the pool of reactant substrates, a comprehensive reaction network is produced that spans the chemical space of lignin pyrolysis, for a given set of chemical transformations. In the particular application here, 4313 reactions and 1615 distinct species result from the application of the 21 reaction families to the lignin library of 100 molecules initially containing S, P and G monomers linked by β-O-4, β-5 and 5-5 bonds. It is worth noting that with different initial lignin libraries or number of molecules produced from our lignin structure generation approach,22 one could envision that the complete reaction network that is generated could have some slight differences. However, we ensured that a sufficient number of molecules was chosen such that the kinetic modeling framework developed here is not sensitive to the lignin structural library. The only requirement was that it conform to the same bulk properties, i.e. monomer, bond type and molecular weight distribution, and branching coefficient, with the statistical accuracy specified in our earlier work22. This critically demonstrates the consistency and robustness of our coupled lignin structural and kinetic modeling framework.


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Table 2. Molecular fingerprint of phenylpropanoid structures obtainable during pyrolysis. The traditional numbering scheme in the lignin literature is used, where the carbon 1 is defined to be the phenolic carbon, and α, β, and γ refer to carbon atoms in the propanoid region (Figure S4). Fingerprint Ar C2_methyl C3_ome C3_oh C3_methyl C4_ether C4_oh C4_ome

Indication Aryl ring (benzene) Methyl group at C2 Methoxy group at C3 Hydroxyl group at C3 Methyl group at C3 Etherified C4 (a β-O-4 bond) Hydroxyl group at C4 Methoxy group at C4

Fingerprint Ca Ca_sat Ca_oh Ca_ald Ca_ket Ca_acid Cb Cb_sat

Indication Cα, α-C of propanoid region Saturated Cα Hydroxyl group at Cα Aldehyde at alpha Cα Ketone at Cα Carboxylic acid at Cα Cβ, β-C of propanoid region Saturated Cβ

C4_hyd C5_ome C5_oh C5_methyl C5_hydeth C5_ethanal

Etherified hydroxypropyl group at C4 Methoxy group at C5 Hydroxyl group at C5 Methyl group at C5 Hydroxyethyl group at C5 Ethanaldehyde at C5

Cb_eth Cg Cg_sat Cg_oh Cg_ald Cg_carb

C5_acoh

Carboxy methyl group at C5

Cab_olef

C5_vinyl C5_ethyl C6_methyl

Vinyl group at C5 Ethyl group at C5 Methyl group at C6

Cbg_olef

Etherified Cβ (β-O-4) bond Cγ, γ-C of propanoid region Saturated Cγ Hydroxyl group at Cγ Aldehyde at Cγ Carboxylic acid at Cγ Double bond between Cα and Cβ Double bond between Cβ and Cγ


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Table 3. Molecular fingerprint of coumaran structures (part of β-5 bond chemistry) obtainable during pyrolysis. The numbering scheme follows IUPAC rules for the coumaran bicyclic structure (Figure S4). Fingerprint

Fingerprint

Indication

Cou Cou_c7_ome

Indication Dihydrobenzofuran (coumaran) Methoxy group on C7

Cou_cb Cou_cb_sat

Cou_c3_hydmeth Cou_c3_formal Cou_c3_form_acid Cou_ca Cou_ca_sat Cou_ca_oh

Hydroxymethyl at C3 Formaldehyde at C3 Carboxylic acid at C3 Cα, α-C at C6 Saturated Cα Hydroxyl group at Cα

Cou_cb_eth Cou_cg Cou_cg_sat Cou_cg_oh Cou_cg_ald Cou_cg_carb

Cou_ca_ald

Aldehyde at alpha Cα

Cou_cab_olef

Cou_ca_ket Cou_ca_acid

Ketone at Cα Carboxylic acid at Cα

Cou_cbg_olef

Cβ, β-C Saturated Cβ Etherified Cβ (β-O-4) bond Cγ, γ-C Saturated Cγ Hydroxyl group at Cγ Aldehyde at Cγ Carboxylic acid at Cγ Double bond between Cα and Cβ Double bond between Cβ and Cγ

2.3 Kinetic Monte Carlo Simulation In the structural model of lignin used, wheat straw lignin is described by a set of one hundred unique molecules whose average properties (Table 1) conform to experiment. During the course of reaction and fragmentation of these macromolecules, the original one hundred molecules can give rise to numerous unique chemical species of various chain lengths and complexities. Because of this expansive chemical space containing a large number of distinct species and possible reactions, a kinetic Monte Carlo (kMC) framework43 is implemented to simulate lignin pyrolysis. In kMC, the space of chemical events that operate upon molecules (i.e. the reaction network) is simulated according to reaction probabilities derived from relative reaction rates, and for each chemical event selected, the set of structures is updated accordingly. The exploration of the reaction network is governed through the use of two random variables, µ and τ, which are used to select the identity of the specific transformation executed and the time


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increment during which the transformation occurred, respectively. The values for µ and τ are computed by using two uniform random numbers y and z (Eq. 1 and Eq. 2)44: ∑

=

< ≤

∑

Eq. 1 Eq. 2

Here, ai denotes the propensity of reaction channel i, and atotal denotes the total propensity. To specify the reaction propensities, Eq. 3 is used, as all reactions are formulated according to pseudo-first-order kinetics: =

Eq. 3

where ki is the rate constant for the ith reaction, and Ni is the number of reactant species for the ith reaction. Each rate constant is specified according to a single value for each reaction family. Because the kMC framework simulates each reaction and the effect it has on the macromolecules, the explicit identity of each macromolecule and low-molecular weight species is known as a function of time. In order to compare the product distribution quantified by the model to experimental data collected in a semi-batch reactor as used by Patwardhan et al.,21 it is necessary to select a model of phase equilibrium that partitions species between the reactive melt phase and a non-reactive vapor phase. As a simplified yet reasonable approach, the normal boiling point is used as the criterion to determine which species remain in the melt phase and which are shunted to the vapor phase; on this basis, light gases such as CO, CO2, H2, water, acetaldehyde, acetic acid, and monoaromatic species of any substitution pattern (i.e. alkylated, methoxylated, and hydroxylated benzenes) are removed from the melt phase as soon as they are formed and not allowed to react further, i.e., only reactions in the solid phase are allowed, with no secondary reactions in the gas phase permitted. Monoaromatic species are included in the list of volatile species based on experimental measurements of species within this class, because


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their boiling points are much lower than reactor temperature (e.g. syringol Tb = 261.05 °C, eugenol Tb = 253.25 °C, and cinnamaldehyde Tb = 248.05 °C).45 It is important to note that the experimental data to which our modeling results are compared had high mass balance: the overall mass balance for the pyrolysis experiments, including GC-detectables, char, and water vapor, which are all accounted for as products in the model, was in excess of 92%. However, the authors do note that the remaining 8% could include non-volatile lignin oligomers, which could exist as fine aerosols in the pyrolysis gas stream. Our model does include the formation of lignin oligomers as the natural consequence of the decomposition of lignin macromolecules, and if an appropriate physical model for the aerosolization rate as a function of the reaction conditions used became available, the results reported below could potentially be improved further. While the model predicts the time evolution of volatile species, the experimental report from Patwardan et al.21 to which the model results are compared lists only the distribution of the final products at a single time point. Another factor further complicating the comparison between model and experiment is the experimental uncertainty of the exact time-scale of reaction within the melt phase. Based on photographic recordings from other studies of lignin pyrolysis46 and empirical observation from the study to which the results are directly compared21, the reaction time scale is estimated to be approximately ten seconds, which is consistent with the known thermal recalcitrance of lignin compared to cellulose and hemicellulose, which have reaction times of approximately five and three seconds, respectively.47,48

3. Results and Discussion The recent experimental pyrolysis study of Patwardan et al.21 is used for model comparison purposes, based on its merits of including detailed product identification and


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quantification, and careful control of experimental conditions to ensure only primary pyrolysis reactions under a kinetically-controlled regime. The mass yield profiles of the four major fractions quantified by the model at 500 °C are presented in Figure 2. During the first 0.2 s, the gas fraction dominates the others, while relatively quickly char becomes the predominant fraction. The last two fractions, aromatics and the aqueous phase including light oxygenates, trail the dominant fractions. 45

Yield - weight percentage

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40 35

0

30

0

0.5

25 20 15 10 5 0 0

2

4

6

8

10

Time - seconds Gases

Aromatics

Char

Aqueous

Figure 2. Simulated yield profile of the four major fractions in lignin pyrolysis. Yields are expressed as weight percentages of the original mass of lignin. The inset in the upper left corner shows the behavior of the product fractions at times less than 0.5 s.

To further highlight the model’s speciation capabilities, the aromatics fraction is investigated in greater detail. The aromatics fraction at 10 s is composed of 124 species contributing 19.5 wt% to the total pyrolysis yield. There is great structural diversity within this fraction; however, it is possible to sort molecules into principal categories based on key structural features, namely: methyl, ethyl, vinyl, propenyl, aldehyde, acetyl, and monolignols. Delving into further detail within these categories, the predicted species exhibit different substitution patterns of hydroxyl and methoxy groups on the aromatic ring (Figure S5). While


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the structural model used in this work is of wheat straw, which may differ from the corn stover lignin used by Patwardan et al.21 (in particular, the monomer composition may differ49), thus hindering comparison at the level of individual species, it is instructive to compare the predicted and observed yields of the various fractions (Figure 3).

Yield (wt. %)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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45 40 35 30 25 20 15 10 5 0

Model

Experiment

Figure 3. Comparison of simulated yields at a residence time of 10 s of various fractions of molecules to experimentally observed yields from lignin pyrolysis by Patwardan et al.21 Reasonable agreement between the predicted and experimental yields is observed across the large majority of the product fractions; the absolute deviation between the modeling results and the experimental yields is less than 5 wt% for all product fractions except for the oxygenates fraction of acetaldehyde and acetic acid, which has an absolute deviation of approximately 9 wt%. A possible explanation for this difference could be attributed to the fact that the full set of structural characteristics of corn stover lignin were not available, as pyrolysis oils from other biomass sources typically report lower yields of acetic acid50. The yield of char is overpredicted by approximately 5 %, and the yields of the aromatics subfractions are in reasonable agreement


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as well. The last fraction reported, other aromatics, contains various aromatic species that do not fall within one of the delineated aromatics fractions. This is a model prediction that could inform future additional analytical efforts to characterize species in pyrolysis oils whose identity is not yet known. Of the molecules present in the other aromatics fraction, the dominant one is 2-hydroxy3-methyl-4(propenaldehyde)phenol, with a predicted yield of 2.74 wt%. After performing a flux analysis at 0.01 s and 5 s, the dominant pathways resulting in the formation of this molecule are identified and are presented in Figure 4. At 0.01 s, the major pathways are demethoxylation and methyl shift, both reactions that modify the substitution pattern of the aromatic ring. At 5 s, C-O cleavage events have higher fluxes leading to the formation of m151, which is the final molecule still etherified through the phenolic group to a lignin macromolecule. The accumulation of m151 thus increases the flux through the final C-O cleavage reaction that ultimately liberates the final molecule. An additionally relevant result that highlights the current model’s capabilities is the prediction of the evolution of the molecular weight distribution (MWD), characterized by the number-average and the weight-average molecular weights (Figure 5). Quantification of the evolution of the MWD could be useful to identify reaction conditions leading to MWDs tailored for specific lignin applications, such as the production of nanofibers of uniform length.51 In the current application of lignin for fuels and chemicals, however, the desired target is monomer production.


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Figure 4. Reaction flux analysis at (a) 0.01 and (b) 5 s of the major pathways leading to the formation of 2-hydroxy-3-methyl-4(propenaldehyde)phenol, the dominant molecule in the other aromatics fraction. The weight of the arrows is proportional to their reaction fluxes at the specified time.


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Figure 4 continued. Reaction flux analysis at (a) 0.01 and (b) 5 s of the major pathways leading to the formation of 2-hydroxy-3-methyl-4(propenaldehyde)phenol, the dominant molecule in the other aromatics fraction. The weight of the arrows is proportional to their reaction fluxes at the specified time.


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4500

Average molecular weight (Da)

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4000 3500 3000 2500 2000 1500 1000 500 0 0

2

4

6

8

10

Time (s) Mn

Mw

Figure 5. Simulated temporal evolution of the molecular weight distribution, characterized by the number-average and weight-average molecular weights. A final demonstration of the model’s strengths is presented in Figure 6, where the reaction pathways leading to p-vinyl phenol, a molecule of interest for its two functional groups (hydroxyl and vinyl), is presented. The starting points of the pathways are two β-O-4-containing substructures, one based on the P monomer (not abundant in wheat straw), and one based on the G monomer (abundant). The initial steps of the pathway involve oxidation of the terminal hydroxyl group to a carboxylic acid through an aldehyde intermediate, followed by decarboxylation to yield a C2 fragment of the original propanoid region. A final C-O bond cleavage event, either the reaction termed ether cleavage or the one termed aliphatic C-O cleavage, delivers the final product. Having identified oxidation as the first step in pathways forming p-vinyl phenol, this observation could inspire the development of selective oxidation catalysts to maximize the yield of p-vinyl phenol.


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Figure 6. Reaction flux analysis at (a) 0.01 and (b) 5 s of the major pathways leading to the formation of p-vinyl phenol. The weight of the arrows in the figure is proportional to their reaction fluxes at the specified time.


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Figure 6 continued. Reaction flux analysis at (a) 0.01 and (b) 5 s of the major pathways leading to the formation of p-vinyl phenol. The weight of the arrows in the figure is proportional to their reaction fluxes at the specified time.


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4. Conclusions Two of the major roadblocks to lignin valorization are its structural complexity and the fact that the nature of the pathways that lead to low molecular weight species is currently unknown. In this manuscript, we present a coupled model that uses a lignin structural model of wheat straw that we reported previously along with a reaction network that was newly constructed to capture the major pathways of lignin pyrolysis. This coupled model describes the chemical pathways transforming lignin macromolecules into a variety of light gases, aqueous compounds, char, and numerous substituted aromatic species. The yields of four major fractions are compared to experimental reports of corn stover pyrolysis, a lignin type putatively similar to wheat straw, and numerous species in the aromatics fraction are proposed to contribute to currently unidentified species in experimental reports. The evolution of the molecular weight distribution is an additional novel model prediction, which could aid efforts at valorizing lignin for applications other than fuels and chemicals. Major strengths of the framework developed are its extensible and modular components, which allow the introduction of additional complexity, as necessary.

Supporting Information. Product distributions observed from experimental studies of model compounds containing the bond types included in this work. Numbering scheme for the molecular fingerprint. Identity of moieties and small molecules in the reaction network.

6. Acknowledgements The authors express thanks to Dr. Xiaowei Zhou, Lauren Dellon, Hanyu Gao, and Lindsay Oakley from Northwestern University for useful discussions. The authors are grateful


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for financial support by the Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) through the Office of Biomass Program, grant number DEEE0003044, and ExxonMobil Research and Engineering Company. Funding from the Institute for Sustainability and Energy at Northwestern (ISEN) Funding and the National Science Foundation (CBET-1435228) is also gratefully acknowledged.

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8. Table of Contents Graphic


Coupled Structural and Kinetic Model of Lignin Fast Pyrolysis - Energy

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