bio-FLASHCHAIN® theory for rapid devolatilization of biomass 1. Lignin devolatilization

Fuel 263 (2020) 116649

Contents lists available at ScienceDirect

Fuel journal homepage: www.elsevier.com/locate/fuel

Full Length Article

bio-FLASHCHAIN® theory for rapid devolatilization of biomass 1. Lignin devolatilization

T

Stephen Niksa Niksa Energy Associates LLC, 1745 Terrace Drive, Belmont, CA 94002 USA

ARTICLE INFO

ABSTRACT

Keywords: Devolatilization Lignin Molecular weight Reaction mechanism FLASHCHAIN®

This study validates a modeling framework to accurately predict the total and tar yields from any lignin at any devolatilization conditions. The original reaction mechanism and depolymerization kinetics in bio-FLASHCHAIN® are slightly modified for pure lignin, and used to accurately interpret how the molecular weight distribution (MWD) of a lignin governs its primary devolatilization behavior. The only sample-specific input requirements are an ultimate analysis and a weight-average molecular weight (MW), although a measured MWvalue is not needed for lignin in whole biomass. Predicted total and tar yields are validated with measured values from 20 diverse lignins for heating rates from 10 °C/min to 5000 °C/s to temperatures from 430 to 950 °C with contact times of several seconds. Predicted tar yields increase for progressively lighter lignin MWDs, and accurately depict how variations in lignin MWD affect total volatiles yields. The strong impact of lignin MWD on its primary devolatilization behavior is accurately interpreted by the flash distillation analogy without any adjustments to the reaction kinetics for a diverse assortment of lignins. The entire reaction mechanism in bio-FLASHCHAIN® is required with non-pyrolytic lignins, whose predicted total and tar yields increase in inverse proportion to reductions in MW. For pyrolytic lignins, bridge scission is superfluous because the whole lignin falls within the size range for tar precursors initially and throughout devolatilization. Consequently, neither total nor tar yields are affected by MW variations because the tar vaporization rates are maximized throughout this MW range. Devolatilization shifts toward hotter temperatures for progressively faster heating rates, in good agreement with data, whereas predicted total and tar yields are insensitive to heating rate variations. Total yields increase for progressively hotter temperatures due to the transition from tar production to char decomposition in the reaction mechanism. Tar yields saturate to an asymptotic ultimate value at approximately 550 °C for fast heating rates.

1. Introduction

yields of gas, tar, and char, but do not resolve volatiles compositions. More important, lumped analyses contain no fuel constitution submodel to relate reactant compositions and concentrations to the reaction kinetics, so data on every sample must be recorded to specify kinetic parameters. This requirement is too cumbersome for routine design work. Detailed mechanisms overcome these drawbacks, but are susceptible to inordinate and opaque adjustments to their large families of rate parameters. For example, when mineral catalysis comes into play, which rate parameters must be adjusted and by how much? We simply have no theoretical basis for such adjustments, so they cannot yet be distinguished from the unbridled parameter adjustments in global schemes. Over time, reliable protocols may yet be devised to manage such issues, and elementary reaction mechanisms provide the best guidance for less comprehensive approaches. But the list of issues yet to be resolved is neither short nor straightforward [8]. This study validates a modeling framework to accurately predict the

Lignin is important as both the second-most abundant major component of whole biomass, and also as a major byproduct of paper and pulp manufacturing. Both waste lignin and the lignin in biomass are currently burned to generate electricity, and have also been characterized as feedstocks for gasification and pyrolysis technologies. In all these applications, lignin devolatilization partitions most of the fuel mass into the gaseous fuels that ignite, stabilize, and sustain flames near fuel injectors, and determines the amount of char that must be converted on much longer time scales. Accurate predictions for both mass and elemental partitioning are important prerequisites for engineering design computations. The available mechanisms for lignin devolatilization can be grouped into lumped schemes [1–4] and detailed reaction mechanisms [5–7]. The lumped schemes can always be tuned in to match laboratory datasets on

E-mail address: [email protected]. https://doi.org/10.1016/j.fuel.2019.116649 Received 16 September 2019; Received in revised form 6 November 2019; Accepted 12 November 2019 Available online 04 December 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.

Fuel 263 (2020) 116649

S. Niksa

Nomenclature

MWj NTOT

Pseudo-frequency factor for reaction process k Scaled molar concentration of bridges Stoichiometric coefficients for gas production during bridge scission C Scaled molar concentration of char links D Scaled molar concentration of dehydrated bridges E Scaled molar concentration of uncapped chain ends %E Mass percentage of element E in lignin E0 Mean activation energy in a Gaussian energy distribution, kJ/mol Ei Number of element E in structural component i (=B, C, D, E) Function to determine an average value of any molar g(Θi) property, Θi, over all macromolecules in lignin Gi Noncondensable gases released during bridge scission j Index for the degree of polymerization in lignin macromolecules and tar MW Weight-average molecular weight of lignin, g/mol Average molecular weight of a monomer in lignin, g/mol MWE Molecular weight of element E, g/mol

Ak B bi

p pB pD WPY xj

Average molecular weight of xj Molar concentration of connected nodes in lignin, moles/ cm3 Probability for bridge and char connections among nodes in biomass component k Probability for bridges among nodes in biomass component k Probability that a chain end holds a dehydrated bridge Total volatiles yield from a devolatilization test, daf wt. % Moles of chains with j connected nodes

Greek Symbols β ρ0 σB

p – pB Bulk lignin density, kg/m3 Std. dev. in the energy distribution for bridge scission, kJ/ mol

Subscripts i

total and tar yields from any lignin at any devolatilization conditions. It is based on the version of FLASHCHAIN® for biomass, which is called bio-FLASHCHAIN® (bio-FC) [9]. The original reaction mechanism and depolymerization kinetics are slightly modified for pure lignin, and used to accurately interpret how the molecular weight distribution (MWD) of a lignin governs its primary devolatilization behavior. The only sample-specific input requirement is an ultimate analysis for the lignin and a weight-average molecular weight (MW), although it remains to be established whether or not a measured MW-value will be needed for each lignin sample. The next two sections briefly review the theory and develop the constitution submodel for lignins. Then the validation section evaluates predicted total and tar yields from 20 diverse lignins for heating rates from 10 °C/min to more than 5000 °C/s to temperatures from 430 to 950 °C with contact times of several seconds.

“B, C, D, and E” denote bridges, char links, dehydrated bridges, and uncapped chain ends

spontaneously converted into charred ends throughout devolatilization. Char links are unbreakable connections formed by extensive decompositions of both whole and dehydrated bridges. Due to their complex chemical structures, bridge conversions are concerted chemical processes involving numerous steps and many reaction species, not unimolecular scissions. The details of this chemistry are not elaborated in the theory; instead, two distributed energy rate laws represent only the thermal response of bridge conversion. A broad energy distribution for bridge conversion ensures a broad thermal response during primary devolatilization. Conversion of a bridge initiates two distinct reaction pathways: scission and spontaneous charring. Scission generates smaller chain fragments, including tar precursors, and also noncondensable gas mixtures of mostly water with minor amounts of CO2, alcohols, and ketones. Spontaneous charring forms a new refractory char link plus the gaseous products of bridge scission plus additional CO, CO2, H2, hydrocarbons, alcohols, aldehydes, and ketones. This pathway to char links depletes the bridge population without inducing fragmentation, thereby suppressing the production of tar precursors. Dehydrated bridges also decompose into charred ends while releasing noncondensables. Spontaneous charring comprises the decomposition of both bridges and dehydrated bridges. As an analog to crosslinking, additional char links and gases may also form by bimolecular recombination between the ends of smaller fragments. The slowest process in the mechanism is char decomposition, which releases CO and H2 from char links at high temperatures. The model contains three sets of separate distributed energy reaction rates for scission, spontaneous charring, and char decomposition for the cellulose and lignin components, plus an Arrhenius rate constant for bimolecular recombination. These reactions are independently applied to each major biomass component, and the product distributions are weight-averaged to determine the complete product distribution. All the results in this paper are based on only lignin decomposition kinetics. Many forms of biomass contain mineral matter that catalyzes the conversion of bridges into char links. An abundance of ash tends to promote faster devolatilization rates but suppresses tar production, while the total volatiles yields remain approximately constant. But ash levels were not reported for most of the lignins in the validation database, although the few reported ash contents vary from 0.7 to 10.6 dry wt. %. Potential impacts of mineral catalysis will be noted as they arise, but such catalysis is not formally incorporated into the lignin

2. bio-FLASHCHAIN® theory 2.1. Overview of the theory In bio-FC, biomass is represented as chain copolymers of cellulose and a lignin-like component [9]. The masses of xylans and hemicelluloses are folded into the cellulose component, even though the actual composition of cellulose is implemented. To partially compensate for this approximation, a broad energy distribution for cellulose bridge conversion expresses the faster decomposition kinetics of xylans and hemicelluloses compared to pure cellulose. The composition of the lignin-like component is assigned from the ultimate analysis of the biomass. Since only pure lignins are analyzed in this study, lignin compositions match the reported ultimate analyses. The diverse assortment of structural components in biomass is rendered coarsely with four generic structural components: bridges (B), dehydrated bridges (D), and char links within fragments (C) and on fragment ends (E). Note that refractory aromatic nuclei, which are central to coal constitution, are absent for biomass. In biomass bridges connect to other bridges, either whole or dehydrated. Char links and charred ends are added during devolatilization, and none of the components is refractory throughout devolatilization. A whole bridge is the monomer unit in the chains, whereas dehydrated bridges are the condensed-phase products of bridge scission that only appear on chain ends. Initially all chain fragments are capped with dehydrated bridges, which are 2

Fuel 263 (2020) 116649

S. Niksa

devolatilization kinetics. In bio-FC, the macromolecular structure of biomass is modeled as a mixture of chain fragments ranging in size from a monomer to the nominally infinite chain. A j-mer fragment contains j nodes that interconnect any of the four structural components together such that bridges and char links only occur within the first and last nodes and dehydrated bridges and uncapped ends only occur on the first and last nodes. As seen in Fig. 1, the chain fragments are classified by size into three groups: reactant, intermediate, and metaplast. Reactant fragments are too large to participate in bimolecular recombination due to hindered mobility but are subject to scission, spontaneous charring, and char decomposition. Intermediate fragments participate in these processes and also contain the products of bimolecular recombinations among metaplast fragments. But only metaplast fragments are light enough to actually recombine. Most important, all tar precursors are in the metaplast size class. Throughout devolatilization, fragments in the condensed phase disintegrate as bridges break and reintegrate as char links form. The changing amount of metaplast sustains tar vaporization until it vanishes, then devolatilization transitions into gas production from spontaneous charring of residual bridges and dehydrated bridges and from char decomposition. When primary devolatilizaiton goes to completion, all macromolecules in the condensed phase are continuously transformed from chains of labile bridges into chains of char links. However, at intermediate extents of devolatilization, chains contain bridges and char links and have dehydrated bridges or char links attached to fragment ends. As long as whole and dehydrated bridges are present, the condensed phase will release additional volatile products if heated further. Consequently, the distinction between lignin and char cannot be precisely drawn. This paper uses “char” to denote the condensed phase after some appreciable amount of volatile matter has been released – say 2–4 daf wt. %. From that point on, char yields equal 100 minus the percentage of volatiles on a daf mass basis. The original chain statistics developed for FLASHCHAIN® [10] are used in bio-FC. They incorporate the chemical kinetic rates of bridge scission, spontaneous charring, and recombination to describe the changing fragment size distributions, including that for tar precursors. Tar formation proceeds by the flash distillation analogy (FDA) [11], in which a phase equilibrium relates the instantaneous mole fractions of like fragments in the vapor and condensed phases. No finite-rate mass

transport phenomena are involved for the particle sizes and operating conditions in the validation database and all volatiles are presumed to escape in a convective flow that is initiated by the chemical production of noncondensable gases. Throughout this paper, condensable liquid products are denoted as “tar” instead of “bio-oil” or “biofuel” because both latter products reflect substantial extents of secondary decomposition of primary tars, and all the testing used to validate the model predictions took effective measures to recover pristine primary tars. 2.2. Fuel constitution submodel The submodel for lignin constitution is developed in terms of the element numbers for the four structural components (B, D, C, and E) and probabilities that, on average, linkages are either bridges or char links and fragment ends are either capped with dehydrated bridges or uncapped by open half-char links. The element numbers are denoted by Ei, where i is an index on the structural component (B for bridge; C for char link; D for dehydrated bridge; and E for uncapped end). For example, the average number of carbon atoms in a bridge within lignin chains is CB. The three probabilities that specify the relative proportions of the structural components are: p(t), the instantaneous fraction of all potential nodes which are connected; pB(t), the fraction of all potential nodes which are labile bridges; and pD(t), the fraction of all chain ends with a dehydrated bridge. These probabilities are evaluated from the following expressions:

p=

pB =

B+C jx j=1 j B j=1

pD =

(1a)

jxj

(1b)

2x j

(1c)

D j=1

where B, C, and D are molar concentrations of the structural components; and xj is the moles of chains with j connected nodes normalized by the node concentration. Since the half-bridges on all chain ends are included in the population of potential linkages, the number of potential linkages equals the number of nodes. The denominator for pD is simply twice the number of chains. The initial proportion of char links in any biomass normally vanishes for both major biomass components,

Fig. 1. Reaction mechanisms in bio-FC. 3

Fuel 263 (2020) 116649

S. Niksa

where the three reactants are the three canonical lignins proposed by Faravelli et al. [5] and used as the basis set in the reaction mechanisms of Hough et al. [6] and Furutani et al. [7]. These groups explicitly describe the decompositions of these lignins with detailed mechanisms, whereas this development regards the entire lignin monomer as a bridge, and considers only one among many plausible products without regard for the mechanistic conversion process. Spontaneous charring is envisioned as concerted chemistry that eliminates the aliphatic and ether linkages that bind the two rings in the bridge, and replaces them with one carbon double bond in conjugation with both rings. In losing the aliphatics and heteroatoms, on average a char link retains 85% of the carbon, 70% of the hydrogen, and 55% of the oxygen in the original bridge. Only two aromatic carbons are added, which increases aromaticity by only 12% (although the total aromaticity of the char link approaches unity). The relations for dehydrated bridges are based on element balances applied to the bridge scission process, which for a bridge within a j-mer is

1.6

1.4

H/C

1.2

1.0

0.8

0.6 0.2

0.3

0.4

0.5

0.6

O/C

xj

Fig. 2. Van Krevelen diagram for (●) lignins in the validation database in Sec. 3 and (○) for lignins assigned for woods with the constitution submodel.

)

B

+

C

+ (1

p)[

C

+ pD (

D

C )]

= g ( i)

(2)

B

where β is p – p . Values specified from Eq. (2) are average values for a nominal monomer unit. The operations in function g(Θi) will be applied to molecular weight and the element numbers for C/H/O in the ensuing derivation. The major component constitutions are subject to element balances on C, H, and O, which are 0 %E

MWE

= NTOT g (Ei )

(3a)

where %E is the mass percentage of the element in lignin; ρ0 is bulk density; NTOT is the moles of nodes per unit volume; and MWE is the molecular weight of element E. As explained in more detail elsewhere [10], Eq. (3a) can be simplified by introducing the average molecular weight of a lignin monomer, < MW > , with 0 = MW = g (MWi ),and the average element number, E, with NTOT E = g(Ei). With substitution, the element balances become

%E E NTOT = E= MWE < MW > 0

B

xk

D·xj

k 1

+ D·xk + bi Gi

where the isolated bridge disintegrates into two dehydrated bridges and releases noncondensable mixtures of mostly water with minor amounts of CO2, alcohols, and ketones. The stoichiometric coefficients for the gases, bi, determine which bridge elements are retained in dehydrated bridges. The values in this study were set during the introduction of bio-FC [9] and used without modification. Among the 20 lignin samples in the database, the compositions are generally within the ranges recommended by Faravelli et al. [5], as seen in the van Krevelen diagram in Fig. 2. The minor exceptions are that three have slightly higher H/C, and Furutani et al. [7] also found a sample beyond the recommend range of H/C. The spread in the values for lignins in woods assigned with the constitution submodel is discussed after the Results section, below. Another important variation among the lignins in the validation database are their molecular weight distributions (MWDs), primarily because Marathe et al. [12] monitored the devolatilization of four sets of different lignins, each prepared with low, moderate, and high number-average molecular weights, plus two different samples with known MW. Values for two other lignins in the database are also known, so that only four of the 20 samples have unknown MW-values. As introduced in FLASHCHAIN® [10], the initial MWD of lignin is specified from p(0) with the statistical theory of runs, as follows:

for which pB(0) equals p(0). Only lignins with very low H/C and O/C ratios have any char links initially, and these are at very low levels. For any molar property, Θ, the following relation gives the average value over all nodes in lignin macromolecules:

= (p

k 1

x j = p (0) j

1 (1

p (0)) 2

(4a)

A j-mer is recognized as a sequence of j − 1 intact connections, with a broken half-link on each end. The mer-size distribution specified by Eq. (4a) is applied to the three fragment lumps within the size ranges defined in Fig. 1. The weight-average molecular weight is evaluated as

(3b)

Simultaneous solution of the three versions of Eq. (3b) for C, H, and O determine CB, HB, and OB. The mathematical system is closed with two sets of additional stipulations that relate the compositions of char links and dehydrated bridges to bridges. For char links in lignin: EC = 0.85CB; 0.70HB; 0.55OB. These relations are based on the following representative charring process:

MW =

j=1

MW 2j xj

j=1

MWj xj

(4b)

where MWj is the average molecular weight of j-mers which varies with chain length because the proportions of bridges and dehydrated bridges are different for different chain lengths. Most important, MW for the

4

Fuel 263 (2020) 116649

S. Niksa

12000

1.0

10000

0.8

0.6 6000

p(0)

MW, g/mol

8000

0.4

4000

0.2

2000

0 0.0

0.2

0.4

0.6

0.8

0.0

1.0

0.9

1.0

1.1

p(0)

1.2

1.3

H/C

Fig. 3. (Left) Assigned values of p(0) that matched reported MW-values for the validation database in Sec. 3; and (right) p(0) as a function of lignin H/C.

initial feedstock is a function of only the elemental composition and p (0), although the dependence on p(0) is much stronger than on composition. This is apparent in Fig. 3 which shows the assigned values of p (0) that matched reported MW-values for the lignins in the database. Reported MW-values vary from 590 to 10,200 g/mol. They increase in near-proportion to changes in p(0) from 0.2 to 0.6, then surge for higher p(0), becoming acutely sensitive for p(0) greater than 0.9. Since the values fall on a continuous curve defined by eq. (4a) with only slight deviations, p(0) largely determines MW. This insensitivity to lignin composition is illustrated in Fig. 3 in different terms in the plot of p(0) vs. lignin H/C, which are completely uncorrelated.

nos. 7 and 8 in the dataset at 0.1 MPa. Furutani et al. [7] calculated thermal histories for each of the five temperatures in their dataset. They were implemented as uniform heating rates from 1000 to 7000 °C/s for progressively hotter temperatures with a uniform 10 s reaction time. In principle, the measurement uncertainties on a total volatiles yield (WPY) and on tar yields (YTAR) in this database are indicated by the reproducibility of char and tar yields, respectively. (Throughout this paper “tar” is used as a synonym for “bio-oil.”) However, Marathe et al. [12] reported independent determinations of gas, tar, and char yields that have total mass balance closures which varied from 80 to 88 dry wt. %. The authors attribute the breaches to undetected noncondensable gases, but this author suspects that portions of the bio-oils and tars also were undetected. For these tests predicted values will be compared within the uncertainties on mass closures for each lignin sample. The converse situation pertains to the dataset reported by Furutani et al. [7] which precisely close mass and C/H/O balances for all samples at every test condition. Stefanidis et al. [15] reported a comparable mass closure of 99.8 wt%, whereas Worasuwannarak et al. [14] reported product distributions that also identically closed the material balance. Only Panangulapati et al. [13] omitted sufficient information to evaluate a material balance. In deference to the impossibility of precisely closing material and element balances with such complex fuels as lignin, the predictions for these tests are evaluated with nominal uncertainties on total weight loss of ± 2 wt% and on tar yields of ± 3 wt%. Individual simulations were run for each test in the validation database. The simulations implemented the reported reaction temperatures and periods and the particle thermal histories described above. Reported ultimate analyses were processed through the fuel constitution submodel to specify all reactants and their properties. Each simulation took no more than a few seconds on an ordinary microprocessor. All lignins were analyzed with the kinetic parameters in Table 1 except for the pyrolytic lignins in the tests of Marathe et al. [12], which

3. Validation database, simulation protocol, and kinetic parameter assignments The qualified validation database was compiled from five lab studies [12–16]. All test series used pure lignin, although a variety of lignin preparation methods are represented, including organosolv, enzymatic hydrolysis, pyrolysis/water condensation, milled wood, alkali, and Klason processing. Three test series used TGA’s [13–15], one used an electrically heated wire mesh reactor (WMR) [12], and one used a two-stage reactor [16]. In the latter reactor, 100 µm lignin particles were dropped into a preheated zone and held for 10 s, while the vapors were swept into a tubular flow reactor to monitor tar decomposition. Ultimate analyses were reported for all lignins in the database. However, ash contents were only reported by Stefanidis et al. [15] and Worasuwannarak et al. [14]. Both Pasangulapati et al. [13] and Worasuwannarak et al. [14] used an alkali lignin distributed by Sigma Aldrich Corp. They did not report MW, but the company website [17] gives MW ≅ 10,000 g/mol, which was implemented in both test simulations. Particle thermal histories for individual tests must be incorporated into the chemistry simulations. Thermal histories are unambiguous for the TGA tests. Marathe et al. reported a uniform heating rate of 5000 °C/s to all temperatures and a uniform reaction time of 5 s. These conditions were incorporated into the devolatilization simulations without modification. Marathe et al. reported datasets for 500 Pa and 0.1 MPa. However, the chemistry simulations would not converge below 0.01 MPa. The predicted total and tar yields were greater at 0.01 MPa, as expected, but still much lower than those reported for 500 Pa. This dataset will not be considered further, pending a resolution of the numerical issues. Also, there are no measured yields for lignin

Table 1 Rate parameters for lignin.

5

Reaction

A-Fac, s−1

Bridge scission Spontaneous charring Bimolecular Recombination Char decomposition

8.0 6.0 8.0 3.0

× × × ×

108 1012 1013 109

E0, kJ/mol

σ, kJ/mol

149 167 212 230

25.1 25.1 – 12.5

Fuel 263 (2020) 116649

S. Niksa

are labeled as nos. 7–12. None of the kinetic parameters were adjusted for any of the non-pyrolytic lignins in this paper unless noted explicitly. Whenever possible, p(0) was adjusted to match reported MW-values. Consequently, 10 of 14 non-pyrolytic samples were simulated with identical kinetic parameters, so that the only differences were the C/H/ O compositions and the assigned value of p(0) that matched the reported MW. In the four tests without MW-values, the same kinetics were implemented but p(0) was adjusted to match the reported char yields, as indicated below. For the pyrolytic lignins, bridge scission was turned off (AB = 4 × 103 s−1) because the MW-values were so low that, with scission, these light MWDs were rapidly evaporated away, and total yields approached 100%. MW-values were reported for all pyrolytic lignins and matched in the simulations by adjusting p(0) in the constitution submodel.

100

WPY & YTAR, daf wt.%

Lignin 13 5000 °C/s; 5 s; 0.1 MPa 80

WPY

60

YTAR

40

4. Results

375

This section compares measured and predicted total and tar yields for the devolatilization of lignins with a primary focus on accurately depicting how variations in the lignin MWD affect primary devolatilization. Since no kinetic parameters were adjusted to improve the agreement among predicted and measured yields, and since reported MW-values were matched in most test cases, the comparisons among predicted and measured yields are uncommonly stringent. The most stringent evaluation of all appears in Fig. 4, where the yields for three lignins with three MW specifications each are evaluated. These series are Nos. 1–3; 4–6; and 10–12, and they are arranged in order of midMW, low-MW, and high-MW from left to right. The reported MW-values appear along the top axis. Three other lignins in this dataset each have one MW specification, but nos. 7 and 8 have no yields for 0.1 MPa. The left panel contains lignins prepared by any method except pyrolysis/ water condensation and the right panel contains only pyrolytic lignins. The predicted total volatiles yields are remarkably accurate for all samples except nos. 12 and 14. They are well within the measurement uncertainties and accurately depict how variations in lignin MWD affect devolatilization. For non-pyrolytic lignins nos. 1–6, they accurately track the inverse proportionality between total yield and MW. For the MW series with pyrolytic lignins, the predicted and measured yields are insensitive to MW, except for the predicted yield for no. 12. Provided that bridge scission is omitted, the predictions for the pyrolytic lignins

450

525

600

675

750

825

Temperature, °C Fig. 5. Evaluation of (● and solid curves) total volatiles yields and (○ and dashed curves) tar yields from non-pyrolytic lignin no. 13 heated at 5000 °C/s to different temperatures for 5 s [12].

are as accurate as the rest, albeit for much greater total and tar yields. The predicted impact of MW variations on total volatiles yields closely mimics the variations in tar yields. Marathe et al. estimated uncertainties on tar yields of 1–7 wt% [12]. These tolerances are satisfied in only two of the predictions (nos. 3 and 9). However, three cases have discrepancies of half or less of the breaches in the mass balances (nos. 1, 4, and 6), and all other discrepancies are within the mass balance breaches, except nos. 11 and 12. There is no conspicuous reason that the predictions for nos. 12 and 14 gave the greatest discrepancies. The MW-value for no. 12 is the heaviest among the pyrolytic lignins, which puts it within the range for non-pyrolytic lignins, so the omission of bridge scission could be responsible. That for no. 14, at 3600 g/mol, is toward the upper end of the range for non-pyrolytic lignins, although predictions for lignins with 10,000 g/mol are accurate (cf. Fig. 6). It is unlikely due to measurement error because MW would need to be reduced to 2170 g/mol to bring no. 14′s predicted yields into agreement. Since ash levels were not

MW , g/mol 3460

1590

1450

725

3600

670

1050

620

590

1240

WPY

WPY

80

YTAR 60

60

40

40

YTAR

20

1

2

3

4

5

6

7

8

20

Pyrolytic Lignins Only 5000 °C/s to 530 °C; 0.1 MPa

5000 °C/s to 530 °C; 0.1 MPa 0

100

1860

2600

2045

80

WPY & YTAR, daf wt.%

MW , g/mol

9

10

11

12

13

14

7

Sample Number

8

9

10

WPY & YTAR, daf wt.%

100

2515

11

12

0

Sample Number

Fig. 4. Evaluation of (● and solid curves) total volatiles yields and (○ and dashed curves) tar yields from (left panel) non-pyrolytic and (right panel) pyrolytic lignins heated at 5000 °C/s to 530 °C for 5 s [12]. Measured MW-values for lignins appear on the top axis. 6

Fuel 263 (2020) 116649

S. Niksa

reported for these lignins, it is difficult to attribute these discrepancies to distinctive kinetics, especially since no. 12 is part of a MW-series that has the same nominal composition and the predictions are accurate for both other members. The temperature dependence in the devolatilization of non-pyrolytic lignin no. 13 is evaluated in Fig. 5. Predicted total volatiles yields are accurate across the entire temperature range. Marathe et al. reported measurement uncertainties of 1–2 wt% for char yields, but breaches in the mass balances were 13–18 wt% for this test series [12]. All the discrepancies in the predicted total yields are within half the mass balance breaches. But only the tar yield at 433 °C satisfies this tolerance. Predicted tar yields reach their asymptotic value at about 550 °C whereas the measured tar yields have saturated by 433 °C, which is much cooler than the bulk of reported temperatures for the saturation of tar yields from biomass. It is also cooler than the predicted and measured saturation temperature for tar release from a lignin for a much slower heating rate in Fig. 6, below, which is incorrect because saturation temperatures shift toward cooler temperatures for progressively slower heating rates. Pasangulapati et al. [13] and Worasuwannarak et al. [14] independently tested the same lignin with an MW of 10,000 g/mol in TGAs with different heating rates and ultimate reaction temperatures. This sample has the heaviest MWD of all, yet evaluations in Fig. 6 are satisfied for both heating rates. For the slower heating rate, predicted total and tar yields are within measurement uncertainties from 300 through 600 °C, whereas the discrepancies in total yields approach 5 wt % at the coolest temperatures. For the faster heating rate, predicted yields are within measurement uncertainties throughout, and clearly depict the transition from tar release through 450 °C to char decomposition above 650 °C. These results exhibit the expected shift toward hotter saturation temperatures for tar release for progressively faster heating rates; that temperature shifts from 375 to 450 °C for the factorof-eight increase in heating rate in these tests, and the predictions accurately depict the shift. Stefanidis et al. [15] also reported total yields from TGA tests at 10 °C/min but for a lignin whose MW is unknown. As seen in Fig. 7, this evaluation is staged with three options on the predictions. The baseline predictions implemented the baseline kinetics and a p(0) of 0.735, which is the nominal average for all non-pyrolytic lignins whose MWvalues were reported. The baseline MW-value is 2400 g/mol. Compared with the measured devolatilization history, the baseline predictions are shifted toward cooler temperatures by 75 °C. The direction of the shift is

70

70 60

WPY, daf wt.%

40

Hi MW & Baseline Kinetics Baseline MW & Slow Kinetics

30 20 10

10 °C/min 0 0

150

300

450

600

900

Fig. 7. Total volatiles yields for 10 °C/min to 800 °C [15] compared to predictions for (solid curve) baseline kinetics and MW; (dotted curve) baseline kinetics and heavy MW; and (dashed curve) slow kinetics and baseline MW.

consistent with a lower ash level of 2.3 wt% compared with the 10.6% ash in the sample in the tests in Fig. 6 (because weaker mineral catalysis shifts the measured devolatilization behavior toward hotter temperatures during heat-up [9]). But the shift’s magnitude is relatively large for heating rates as slow as 10 °C/min. To clarify the discrepancy, two additional predictions were run, one with a heavier MW and baseline kinetics and another with slow kinetics and the baseline MW. Increasing MW to 3280 g/mol with baseline kinetics eliminated one-third of the temperature shift below 375 °C and brought the predictions within measurement uncertainties for hotter temperatures. Slowing the kinetics with the baseline MW gave accurate predictions throughout the temperature range except from 400 to 475 °C. Both adjustments at once could bring the predictions within measurement uncertainties throughout the entire test. A final evaluation uses datasets for three lignins whose MW-values are unknown. The three lignins were rapidly heated to 650 °C with 10 s contact times, and one of the lignins (EHL) was tested from 500 through 950 °C [7,16]. Unfortunately, two of the samples tested at 650 °C could only be used to match the reported char yields to determine MW-values,

70

MW = 10,000 g/mol 80 °C/min

60

50

50

40

40

WPY YTAR

30

30

WPY - YTAR

20

WPY, daf wt.%

WPY, daf wt.%

750

Temperature, °C

MW = 10,000 g/mol 10 °C/min

60

Baseline MW & Kinetics

50

20

YGAS

10

10

0 0

150

300

450

600

750

900 0

Temperature, °C

150

300

450

600

750

0 900

Temperature, °C

Fig. 6. Devolatilization of the same lignin for (left panel) 10 °C/min to 600 °C [14] and (right panel) 80 °C/min to 800 °C [13]. Left panel shows (● and solid curves) total volatiles yields and (○ and dashed curves) gas yields, so that tar yields appear as the difference between the respective curves and data points. 7

Fuel 263 (2020) 116649

S. Niksa

80

100

1590 g/mol

70

1000 °C/s

WPY

Tar Precursors, daf wt.%

WPY & YTAR, daf wt.%

80

60

YTAR 40

20

0

MW = 2170 g/mol 1000 - 7000 °C/s, 10 s 500

600

700

60 50

2530 g/mol

40

3430 g/mol

30 20 10

800

900

1000

0

Temperature, °C

0

200

400

600

800

1000

Temperature, °C

Fig. 8. Evaluation of (● and solid curves) total volatiles yields and (○ and dashed curves) tar yields from lignin EHL heated at 1000–7000 °C/s to different temperatures for 10 s [7].

Fig. 9. Predicted amounts of tar precursors (metaplast) during heatup at 1000 °C/s from sample (solid curve) no. 1, (dashed curve) no. 2, and (dotted curve) no. 3 in Fig. 4.

because these tests were designed to resolve the dynamics of tar decomposition, not primary devolatilization. The assigned MW -values of 2530 and 3100 g/mol for lignins OEL and KL reflect respective p(0) specifications of 0.75 and 0.79. Both pairs of assignments are firmly in the expected range. The predicted temperature dependence for lignin EHL is evaluated in Fig. 8. The value of p(0) was adjusted to match the volatiles yield at 650 °C, then tests for the other temperatures were simulated with baseline kinetics. The assigned values for p(0) and MW are 0.72 and 2170 g/mol, which are within expectations. Predicted volatiles yields are within measurement uncertainties across the entire temperature range. But predicted tar yields are accurate only for 500 °C. This is because 500 °C is the only test temperature cooler than the threshold for tar decomposition, and explains why the disparities among predicted and measured tar yields grow for progressively hotter temperatures. For this heating rate and contact time, predicted tar yields saturate at about 600 °C.

1590 g/mol. As soon as noncondensable gases are generated by bridge scission and spontaneous charring, the lightest of the tar precursors vaporizes into tar. Above about 350 °C, tar precursors are consumed in a competition between tar vaporization and bimolecular recombination into larger fragments, which removes some chains from the size range of tar precursors. According to the FDA, the mole fraction of tar vapor is equilibrated with like-size chains of tar precursors in the condensed phase according to a version of Henry’s Law for continuous mixtures [11]. Predicted tar yields grow for progressively lower lignin MW because the higher levels of tar precursors in the condensed phase release more tar into the escaping stream of noncondensables. A higher concentration of escaping tar accumulates into a greater tar yield. Pyrolytic lignins have MW-values from 590 to 1260 g/mol. All but the heaviest values are light enough that the entire lignin is within the size range for tar precursors. Neither total nor tar yields are affected by MW variations within this range, because the tar vaporization rates are maximized throughout. The insensitivity to MW variations is apparent in the measured yields for nos. 10–12 in Fig. 4, and in the predictions for nos. 10 and 11. The underpredicted yields for no. 12 signal that the MW transition from 100% tar precursors in lignin to some smaller portion is inaccurate, which is not surprising considering the idealizations underlying Eq. (4a). The FDA is governed by the instantaneous MWD of tar precursors and temperature, whereas the chemical reaction kinetics depend on reactant compositions and temperature. Despite the broad range of lignin compositions in the validation database (cf. Fig. 2), the impact of MW variations on volatiles yields has been accurately interpreted with the same kinetics. This clearly demonstrates a predominant role for the FDA in lignin devolatilization, and poses a formidable challenge to interpretations based on chemical kinetics alone, regardless of their sophistication. Of course, lignin compositions will strongly affect the product distributions for devolatilization, particularly the noncondensable gas distributions. But they do not determine total and tar yields. The FDA has been firmly established as the only means to accurately interpret the reductions in total and tar yields for progressively higher pressures during coal devolatilization that does not introduce an incorrect dependence on particle size [18]. The accuracy of the predictions in this study demonstrates that the FDA also accurately interprets how lignin MWD affects its primary devolatilization behavior,

5. Discussion Among the 16 lignins whose MW-values were known in advance, volatiles yields were predicted within the measurement uncertainties in 12 cases, and two cases had no devolatilization data for comparison (nos. 7 and 8 in Fig. 4). The discrepancies were substantial only for nos. 12 and 14 in Fig. 4. Predicted tar yields are somewhat more ambiguous, primarily due to large breaches in the material balances of most tests. Notwithstanding, predicted tar yields increase for progressively lighter lignin MWDs, and accurately depict how variations in lignin MWD affect devolatilization. This finding is especially secure because the same reaction kinetics were implemented in the chemistry simulations with all non-pyrolytic lignins. For pyrolytic lignins, the rate parameters were also the same, except that bridge scission was omitted. Only one mechanism in bio-FC determines how the MWD affects lignin devolatilization: the FDA. Predicted amounts of tar precursors (a.k.a. metaplast in Fig. 1) during heatup at 1000 °C/s with three lignins of different MW appear in Fig. 9. These are the levels of tar precursors in the condensed lignin phase in the original lignin and throughout devolatilization. The initial levels (from Eq. (4a)) grow from one-third of the lignin mass to nearly three-fourths as MW diminishes from 3430 to

8

Fuel 263 (2020) 116649

S. Niksa

without any parameter adjustments whatsoever. The strong impact of MW on volatiles yields from lignins could impede the validation of models like bio-FC for routine design calculations, because it is impractical to require a measured MW-value for each lignin sample, and even harder to extract the lignin from whole biomass before the MW determination. Early indications are from this validation database that the p(0) values assigned to match measured MW vary from 0.67 to 0.82, with a mean of 0.735. For pure lignin devolatilization this range is associated with a spread in predicted volatiles yields of 20 daf wt. % and in tar yields of 15 daf wt. %. But the spreads would be much lower for the much lower lignin mass fractions in whole biomass, which typically vary from 15 to 35 wt%. Predicted volatiles yields for several woods are reported in Part 2 of this series [19], based on the mean value of p(0) and the uniform rate parameters for non-pyrolytic lignins in Table 1. The assigned compositions of the lignins in these woods are seen in Fig. 2 to largely overlap the compositions of the actual lignins in the validation database, except for the two wood lignins whose H/C ratios exceed 1.4. The overlap bodes well for the applicability of the reaction kinetics assigned in this study in simulations of whole biomass.

transition from tar production to char decomposition in the reaction mechanism. Tar yields saturate to an asymptotic ultimate value at approximately 550 °C for fast heating rates. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] Petrocelli FP, Klein MT. Model reaction pathways in Kraft lignin pyrolysis. Macromolecules 1984;17:161–9. [2] Nunn TR, Howard JB, Longwell JP, Peters WA. Product compositions and kinetics in the rapid pyrolysis of milled wood lignin. Ind Eng Chem Process Des Develop 1985;24:844–52. [3] Klein MT, Virk PS. Modeling of lignin thermolysis. Energy Fuels 2008;22:2175–82. [4] Caballero JA, Font R, Marcilla A. Study of the primary pyrolysis of Kraft lignin at high heating rates: yields and kinetics. J Anal Appl Pyrol 1996;36:159–78. [5] Faravelli T, Frassoldati A, Migliavacca G, Ranzi E. Detailed kinetic modeling of the thermal degradation of lignins. Biomass Bioenergy 2010;34:290–301. [6] Hough BR, Schwartz DT, Pfaendtner J. Detailed kinetic modeling of lignin pyrolysis for process optimization. Ind Eng Chem Res 2016;55:9147–53. [7] Furutani Y, Kudo S, Hayashi J-I, Norinaga K. Predicting molecular composition of primary product derived from fast pyrolysis of lignin with semi-detailed kinetic model. Fuel 2018;212:512–22. [8] Anca-Couce A. Reaction mechanisms and multi-scale modelling of lignocelluosic biomass pyrolysis. Prog Energy Combust Sci 2016;53:41–79. [9] Niksa S. Predicting the rapid devolatilization of diverse forms of biomass with bioFLASHCHAIN. Proc Combust Inst 2000;28:2727–33. [10] Niksa S, Kerstein AR. Flashchain theory for rapid coal devolatilization. part 1: formulation. Energy Fuels 1991;5(5):647–64. [11] Niksa S. Rapid coal devolatilization as an equilibrium flash distillation. AIChE J 1988;34(5):790–802. [12] Marathe PS, Westerhof RJM, Kersten SRA. Fast pyrolysis of lignin with different molecular weight: Experiments and modelling. Appl Energy 2019;236:1125–37. [13] Pasangulapati V, Ramachandriya D, Kumar A, Wilkins MR, Jones CL, Huhnke RL. Effects of cellulose, hemicellulose, and lignin on thermochemical conversion characteristics of the selected biomass. Bioresource Technol 2012;114:663–9. [14] Worasuwannarak N, Sonobe T, Tanthapanichakoon W. Pyrolysis behaviors of rice straw, rice husk, and corncob by TG-MS technique. J Anal Appl Pyrolysis 2007;78:265–71. [15] Stefanidis SD, Kalogiannis KG, Iliopoulou EF, Michailof CM, Pilavachi PA, Lappas AA. A study of lignicellulosic biomass pyrolysis via the pyrolysis of cellulose, hemicellulose and lignin. J Anal Appl Pyrolysis 2014;105:143–50. [16] Yang H-M, Appari S, Kudo S, Hayashi J-I, Norinaga K. Detailed chemical kinetic modeling of vapor-phase reactions of volatiles derived from fast pyrolysis of lignin. Ind Eng Chem Res 2015;54:6855–64. [17] www.sigmaaldrich.com/catalog/product/aldrich/471003?lang=en®ion=US. [18] Niksa S, Liu G-S, Hurt RH. Coal conversion submodels for design applications at elevated pressures. Part I. Devolatilization and char oxidation. Prog Energy Combust Sci 2003;29(5):425–77. [19] Niksa S. bio-FLASHCHAIN® Theory for rapid devolatilization of biomass. 2. Predicting total yields for torrefied woods. Fuel 2019; submitted.

6. Conclusions 1. Given a measured value for MW and an ultimate analysis, total volatiles yields from almost any lignin under any thermal history can be predicted within measurement uncertainties with bio-FC. Predicted tar yields increase for progressively lighter lignin MWDs, and accurately depict how variations in lignin MWD affect devolatilization yields. But the accuracy of predicted tar yields is currently subject to large breaches in material balances that can only be rectified with additional testing. 2. The strong impact of lignin MWD on its primary devolatilization behavior is accurately interpreted by the flash distillation analogy without any adjustments to the reaction kinetics for a diverse assortment of lignins. 3. The entire reaction mechanism in bio-FC is required with non-pyrolytic lignins, whose predicted total and tar yields increase in inverse proportion to reductions in MW. For pyrolytic lignins, bridge scission is superfluous because the whole lignin falls within the size range for tar precursors initially and throughout devolatilization. Consequently, neither total nor tar yields are affected by MW variations because the tar vaporization rates are maximized throughout this MW range. 4. Devolatilization shifts toward hotter temperatures for progressively faster heating rates, in good agreement with data, whereas predicted total and tar yields are insensitive to heating rate variations. Total yields increase for progressively hotter temperatures due to the

9