Physicochemical Modeling for Hot Water Extraction of Birch Wood

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Physico-chemical modeling for hot water extraction of birch wood Waqar Ahmad, Susanna Kuitunen, Marc Borrega, and Ville Alopaeus Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b02987 • Publication Date (Web): 03 Oct 2016 Downloaded from http://pubs.acs.org on October 9, 2016

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Industrial & Engineering Chemistry Research

Physico-chemical modeling for hot water extraction of birch wood Waqar Ahmad 1,*, Susanna Kuitunen 2, Marc Borrega 3, Ville Alopaeus 1 Department of Biotechnology and Chemical Technology, Aalto University, Aalto, P.O. Box 16100, FI-00076 Finland 1

2

Neste Jacobs, P.O. Box 310, 06101 Provo, Finland

Department of Forest Products Technology, Aalto University, Aalto, P.O. Box 16300, 00076 Espoo, Finland 3

* Corresponding author, E-mail: [email protected]

Abstract This paper presents a model developed for hot water extraction of birch wood meal. Besides solids, two liquid phases are assumed in the system: liquid bound to wood fiber wall and the other remaining external liquid. True chemical species, their reactions and diffusion between the liquid phases are considered in the model. The breakdown of hemicellulose into short-chain polymers and monomeric sugar units are modeled by applying accurate and computationally efficient population balance approach. State-of-theart correlations and equations are used thus aiming to a truly predictive model. Several thermodynamic and kinetic sub-models are integrated to achieve additional information as compared to models already presented in the literature. The presented model is capable of reproducing the measured concentration profiles of chemical species and molecular weight distribution of hemicellulose polymers as a function of process conditions. The output concentration data is further utilized to calculate the dissolved species and pH in the two liquid phases. Eventually, it could be utilized in optimizing batch hot water extraction process to maximize either the yield of long chain hemicellulose or their monomeric sugars. Keywords: Hot water extraction; Birch; wood fractionation; Population balance; Modeling; Xylan degradation; Delignification

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1. Introduction The utilization of woody biomass for the production of useful chemicals, food products, textile fibers and energy has gained remarkable interest in the recent years due to its renewability and abundance in nature. Traditionally, wood has been utilized in the production of timber, board, paper and tissue products. The chemical pulp, produced from wood by Kraft pulping process, is an essential constituent in board, paper and tissue products. The main drawback of this process is the degradation of hemicelluloses leading to yield and chemical losses. Thus, hot water extraction (HWE) of wood to recover hemicelluloses prior to pulping has been considered as an interesting option for efficient use of all wood constituents.1-4 The extracted hemicelluloses may be used in production of value added products.3, 5 The produced pulps with low hemicellulose contents could be used as dissolving pulp for making textiles and cellophane but not for paper and tissue products. Ordinarily, the water extractions are introduced as steam treatments on industrial scale.6 HWE creates problems in downstream processing of liquid extract due to formation of lignin condensation products.7 The use of liquid water in HWE can be now applied commercially as a result of the development of new continuous digester system by Andritz.8 In HWE, the reactions are typically catalyzed by hydronium ions initially produced from the autoionization of water. Hydronium ions catalyze the polymer-degradation reactions in HWE, remove acetyl groups present on hemicelluloses, and convert them to acetic acid.9, 10 The produced acetic acid lowers the pH by producing more hydronium ions. Presence of uronic acids in wood make fiber wall acidic and also contribute in lowering the pH during HWE.11, 12 Long chain hemicelluloses in raw material are depolymerized to short chain polymers, oligomers and monomers. In case of xylan containing raw materials, the major fraction of degradation products is furfural10, 13 which is formed from xylose monomers.3, 9 Small fractions of lignin are hydrolyzed apart from hemicelluloses during HWE.14 The lignin degradation occurs mainly by homolytic scission of aryl-ether bonds.7, 15 These ether bonds are present between lignin phenyl-propane units that are most susceptible for degradation. About 74% of bonds between units of birch wood lignin are ether bonds.16 Lignin degradation is followed by condensation reactions resulting in lignin products which could precipitate back to wood fibers.7, 17 A number of mathematical models have been used for describing hemicellulose dissolution and degradation kinetics.9, 11, 18, 19 Most of HWE studies dealt with xylan containing raw materials (hardwoods) due to presence of higher amounts of acetyl linkages to xylan which facilitates HWE by producing acetic acid. Additionally, hardwoods are favored over softwood because lignin in softwoods has much higher tendency for condensation due to their chemical structure.6 Several model strategies for xylan dissolution and degradation have been proposed in the previous studies to explain mechanism involved. Most of the developed models used two fractions of xylan (fast hydrolyzing and slow hydrolyzing xylan) to fit the experimental data.3, 11, 13, 20 In another used mechanism, degradation of xylan involves the formation of high and low molecular weight oligomers as intermediates with


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different degradation rates.21 Modeling of delignification kinetics can be carried out by considering two fractions of lignin with different reactivity.1, 22 In this article, a comprehensive physicochemical model for HWE is presented. Most of involved reactions are included as catalyzed by hydrogen ion (H+). The presented model combines previous modeling efforts conducted in our group to model the outcome from HWE of wood.12, 23, 24 In order to account for ion exchange effect and mass transfer restrictions, system of wood meal and water is modeled using two liquid phases, i.e. fiber wall liquid and external liquid. Utilizing the knowledge from the literature, comprehensive sets of reversible and irreversible chemical reactions are included to model degradation and dissolution of hemicellulose and lignin.1, 13 Degradation of hemicellulose polymers are modeled using population balances. Derived properties such as pH is obtained from the calculated composition of the liquid phases. This HWE model is more advanced and detailed than the previous models presented in the literature, enabling more detailed studies of the phenomena and optimization of the process to produce either polymeric xylan or xylose monomers.

2. Experimental data The major part of experimental data used in the model development was published earlier.1, 13 Extractives-free wood meal from silver birch (Betula pendula) was used as raw material. High liquid to wood ratio i.e. 40:1 g/g was applied in all the experiments for better dissolution of wood fibers and its extracted components. Information about the experimental setups is given in Table 1. Table 1. Conditions of the experimental setups for birch hot water extraction. Setup temperature (C) 180 200 220 240

Isothermal treatment duration (min) 0-180 0-180 0-60 0-30

At the start of experiments, the system was preheated to raise the temperature to desired setup temperatures. After the preheating, the batch reactor was operated at isothermal conditions for certain periods. The detailed description of used equipment and analysis procedure is available in the previous studies.1, 13

3. Modeling of composition and phenomena In order to model the ion exchange, the wood-water system is considered to consist of two liquid phases.12, 25 Fibers in wood are porous in nature and water is absorbed by the fiber wall. The water bound by the fiber wall is called fiber wall liquid. The initial amount of fiber wall liquid is assumed to be 0.3 kg water per kg of dry wood.26 This value increases when a part of material is removed from wood during HWE. The remaining liquid external to fiber wall, including water in fiber lumen, is 3 ACS Paragon Plus Environment


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termed as external liquid. The wood fibers are assumed to be present in fiber wall liquid making a homogeneous phase and there is no direct contact between wood fibers and external liquid. Both liquid phases are separated by thin liquid film comprising all the mass transfer resistance. The concentration of species in the external liquid correspond to concentration of liquid extract. Modeling strategies for physicochemical phenomena and other important aspects are discussed in this section.

3.1.

Initial composition of wood material

The components included in the simulations were reactants, products, and inert materials that affect the behavior of model (for instance pH). The experimental data for initial composition of untreated birch wood was collected from various literature sources. The uncertainties related to the chemical composition of wood sets up challenges for the modeling since the outcome of the simulations is sensitive to the initial composition of the raw material. For modeling of the HWE, the initial composition of relevant chemical constituents in wood are listed in Table 2. Table 2. Chemical composition of birch wood meal. Species

Reference

Acetyl groups (OAc)

Initial concentration (mmol/kg wood) 822.3

Total uronic acids in wood

250

Accessible uronic acids in wood ‘MeGlcSoluble(f)’

110

Amount of total carboxyl groups in hardwood is 250-350 mmol/kg wood.27 Adjusted to get better fit for pH behavior.

Inaccessible uronic acids in wood ‘MeGlcMeOH(f)’ Ca2+ Mn2+ Total divalent ions K+

140

Adjusted to get better fit for pH behavior.

Xylan (extractive free wood) Xylan (wood with extractives) Lignin (extractive free wood) Lignin (wood with extractives)

1, 13

16.7 1.2 17.9 13.0 wt.% of wood 20.93 21.29 22.75 22.36

28

1, 13

The acetyl groups in the wood are important due to their strong influence on the pH evolution in HWE. The uronic acids present in wood contribute to the ion exchange phenomena. A fraction of uronic acid is present at inaccessible locations in fiber wall.27 Initial composition of accessible uronic acids (MeGlcSoluble) in the wood was adjusted to satisfy the pH values measured from the liquid extract. The remaining inaccessible uronic acids are included as uronic acid methyl esters (MeGlcMeOH) that will remain in fiber wall. The adjusted values of uronic acids are within the range of total carboxylic content of hardwood. 4 ACS Paragon Plus Environment

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Metal ions act as counter ions to the dissociated uronic acids in the fiber wall liquid thus increasing the pH by neutralizing a part of acids. In order to accurately model the initial pH in the fiber wall liquid, it is very important to know the amounts of metal ions in the wood raw material. Metal ion amounts reported by Saltberg et al.28 were used. All divalent ions were included as Ca2+ due to their similar behavior in HWE process. Xylan is considered as the major hemicellulose extracted during the process. Xylan is considered as a straight chain polymer with xylose as monomer. The side groups on xylan polymer (uronic acids, acetyl groups etc.) are included separately in the simulations. The initial MWD of xylan utilized in this work was taken from the experimental data by Penttilä et al..4 A lognormal distribution function was used as initial MWD. More details about initial molecular weight distribution of xylan is given in the Supporting information. Figure 1. demonstrates resulting initial distribution curve as a continuous line. For implementation of population balance approach, the initial MWD was discretized into categories by conserving the first three moments of measured distribution.23 The discretized categories are shown as points in Figure 1. Further explanation about polymer categories is provided in the section 3.3.

Figure 1. Initial xylan distribution and discretized categories. In the model, the initial total lignin amount (see Table 2.) was divided into easily dissolvable and hard to dissolve lignin factions. The initial fraction of easily dissolving lignin fraction was determined by parameter optimization performed in this work. The initial amount of lignin fractions were assumed to be independent of temperature in each experimental setup.

3.2.

Initial heating period

The heating period at the start of experiments was quite long and it was evident from experimental data that degradation reaction have taken place during this period. For this reason, the preheating period was considered in the modeling work. The duration of heating period was assumed to be same (15 minutes)



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in all four experimental setups. The exponential temperature profile for preheating period was computed by the simplified equation below: dT  Rt arg et  Tt arg et  T  dt

T

t arg et

(1)

 T  = Difference between target temperature of experiment and process temperature (°C)

Rtarget = Rate at which temperature is driven towards target temperature (s-1) The details about derivation of equation 1 are reported in the Supporting information. The value of Rtarget is obtained by integration of equation (1), assuming that temperature reaches to 99.5% of target temperature in 15 minutes. The temperature profiles within the reactor obtained from the model are presented in Figure S1 (Supporting information). Time period from -15 to 0 minutes represents the initial heating period at the start of each experiment. Temperature increased exponentially at the start and after fifteen minutes it reached the isothermal temperature conditions in each of the four experiments.

3.3.

Modeling of changes in molecular weight distribution of hemicellulose

The evolution of MWD of xylan is modeled using population balances.29 The modeling approach is the same as used earlier by Visuri et al.23 and Liu et al.24 for degradation of hemicelluloses and by Ahmad et al.30 for degradation of cellulose. The idea in using population balances is to reduce the computational time for the simulations by discretizing the xylan distribution into categories. In experimental MWD of xylan, the molecular weight of the longest polymer with considerable weight faction is about 120000 g/mol (Degree of polymerization (DP) ≈ 900). Without the usage of categories (each representing certain DP range), the evolution of 900 variables needed to be solved while solving the ordinary differential equations. However, by population balance approach, it was found that only 18 categories (variables) were sufficient for describing the experimental MWD accurately. The categories representing the DP ranges and their initial amounts in birch wood are given in Table 3. The initial differential mass fraction of first nine categories are zero i.e. their initial amount in wood raw material is zero. Table 3. The categories chosen to represent the xylan molecular weight distribution and their initial amounts. Diffusion coefficients were estimated by Wilke-Chang method.31 Category no.

Characteristic DP of the category

XYL1 XYL2 XYL3 XYL4 XYL5 XYL6

1 2 3 5 8 12

Molecular weights (g/mol) 132.1 264.2 396.3 660.5 1056.8 1584

DP range

1 2 3 4-6 7-9 10-14

Diffusion coefficient at 25C (m2/s) 9.34  10-10 6.17  10-10 4.83  10-10 3.56  10-10 2.68  10-10 2.10  10-10

Initial amount (g/kg wood) 0 0 0 0 0 0 6

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XYL7 XYL8 XYL9 XYL10 XYL11 XYL12 XYL13 XYL14 XYL15 XYL16 XYL17 XYL18

3.4.

18 26 38 55 80 116 168 242 349 502 723 1041

2377.8 3434.6 5019.8 7265.5 10568 15323.6 22192.8 31968.2 46102.9 66314.2 95508.3 137516.1

15-21 22-30 31-45 46-64 65-95 96-136 137-199 200-284 285-413 414-590 590-855 856-1226

1.65  10-10 1.32  10-10 1.05  10-10 8.44  10-11 6.74  10-11 5.39  10-11 4.32  10-11 3.47 10-11 2.79  10-11 2.24  10-11 1.80  10-11 1.45  10-11

0 0 0 0.21 4.96 25.57 56.90 66.44 41.52 14.38 2.69 0.35

Activity coefficient model

The activities (a) of chemical species (solutes) were used instead of molalities (m) for reaction and dissolution rates calculations. The utilization of activities take into account the non-idealities in the solution.32 The activity of each chemical compound can be calculated by multiplying the molality with its activity coefficient (γ).

ai   i mi

(2)

The Davies model was implemented for estimation of activity coefficients.33 The validity of this model is up to ionic strength (I) of 0.1 mol/kg water. The ionic strength at the start of HWE simulation was close to zero and it reached the value of ≈ 10-3 mol/kg water at the end of experiment. For ions, the activity coefficients are obtained from equation:

  I log 10  i   Az i2   0.2 I  1 I 

(3)

A is osmotic coefficient of water34 and z is charge number of ionic species. For neutral species, the activity coefficients are obtained from the equation:35

log10  i  0.1I

(4)

The ionic strength of the mixture is obtained from:

I

1 mi zi2  2 i

(5)

By this approach, the reaction rates obtained from dilute electrolyte solutions can be extended to concentrated solutions because the effect of ionic strengths on the rate constant has been taken into account in activities.



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3.5.

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Equilibrium reactions

Equilibrium reactions included in the simulations are assumed to be very fast. The reversible reactions involved in HWE are ionization of uronic acids, acetic acid and water. Those included to the model and their corresponding pKa values are listed in the table 4. The equilibrium constants are dependent on the temperature. Table 4. Equilibrium reactions and the pKa (-log10K) values at the reaction temperatures. Temperature (C) Equilibrium reactions

Reversible equilibrium reaction

25

180

200

220

240

References

pKa Values

ER1

ER2

ER3

MeGlc(f) ↔ MeGlc(-f) + H+ ; MeGlcSoluble(f) ↔ MeGlcSoluble(-f) + H+ ; MeGlcSoluble(aq) ↔ MeGlcSoluble(-aq) + H+

3.14

3.78

3.92

4.05

4.19

AcOH(aq) ↔ Acetate(-aq) + H+

4.77

5.42

5.57

5.74

5.92

H2O ↔ OH + H+

13.99

11.42

11.32

11.26

11.25

At 25C, the pKa value was taken from Teleman et al..36 The temperature dependency of pKa value similar to acetic acid was assumed.* 37

38

*Thermodynamic parameters (enthalpy, heat capacity and entropy) used in computing pKa values of ER1 were fitted to have similar temperature dependence as pKa values of ER2.37

3.6.

Irreversible reactions

A set of irreversible reactions were introduced to model the reaction kinetics and dissolution phenomena in HWE. Stoichiometric equations for all irreversible reactions are listed in Table 5. Arrhenius equation was used for modeling of the temperature dependency of reactions and dissolution rates. Values of rate constants (k) at average temperature and activation energy (Ea) are obtained from parameter optimization. The reaction rates for hydrolysis of acetyl groups bound to fiber wall and dissolved during HWE, were assumed equal (R1 in Table 5.). The mechanism is based on the first order reaction kinetics with respect to both H+ and acetyl group concentration as reported by Vos et al..39 H+ catalyzed hydrolysis of uronic acid esters (R2) and formation of acetic acid (R1) were considered to have the same reaction kinetics. Apart from acetic acid formation from acetyl groups, it was assumed that a small fraction of acetic acid is formed from degradation of cellulose (R11).40 Equal breakage rate of glycosidic bonds was assumed for xylan in fiber wall liquid and xylan dissolved into the external liquid. The breakage rate is proportional to H+ concentration and the number of bonds in xylan polymer as in previous work by Visuri et al..23 It was assumed that xylan monomer (XYL1) is 8 ACS Paragon Plus Environment

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converted to furfural by H+ catalyzed dehydration, but there was no further degradation from furfural under these reaction conditions.13 Instead, xylose directly converts to complex degradation products.41 Lignin in wood was assumed as sum of two fractions with different degradation/dissolution rates. A straightforward modeling approach was chosen based on the reactions scheme presented by Borrega et al..1 The difference between these two fractions can be either explained on the basis of different carbohydrate-lignin linkages or the types of ether bonds present.42 The re-polymerization/condensation rates of degraded products back to fiber wall from both types of lignin are the same (R9). The chemical modification of the lignin units was not considered in this modeling work i.e. the dissolved lignin units have the same structure as the native lignin units. Further, the physical changes in lignin structure by HWE were not taken into account.43 Dissolution kinetics of acetyl groups (R3), uronic acids (R4) and xylan polymers (R10) from fiber wall are included as irreversible phenomena. Acetyl group dissolution was also catalyzed by H+. Furthermore, the dissolution rate of xylan polymers from fiber wall is assumed to be independent of their degree of polymerization. Table 5. Reactions and regressed parameters for modeling hot water extraction of birch. 95% confidence limits are shown after . Chemical reactions

Stoichiometry

R1

OAc(f) or OAc(aq) + H+ + H2O  AcOH(aq) + H+

R2

MeGlcMeOH(f) + H+  MeGlc(f) + CH3OH(aq) + H+

R3 R4

R5 R6 R7 R8 R9

OAc(f) + H+  OAc(aq) + H+ MeGlcSoluble(f)  MeGlc(aq) MeGlcSoluble(-f)  MeGlc (-aq) H+ catalysed breakage of glycosidic bonds in xylan XYL1(aq)  Furfural (aq)+ 3 H2O LigninEasy(f) + H+  LigninEasy(aq) + H+ LigninHard(f) + H+  LigninHard(aq) + H+ LigninEasy(aq) +H+ LigninCondensed(f) + H+ LigninHard(aq) + H+ LigninCondensed(f) + H+

Rate law

Activation Energy (Ea) (kJ/mol)

Reaction rate constant (k) at 210C

Frequency factor (A)

aOAc  aH+

158  13

6.8  0.9 M-1s-1

8.1  1017 M-1s-1

Same as in R1

Same as in R1

Same as in R1

229  41

45  30 s-1

2.9  1026 M-1s-1

aMeGlcSoluble

92  182

8.9  10-3  0.29  10-3 s-1

8.3  107 s-1

abond  aH+ aXYL1  aH+

134.4  11.6

11.1  1.8 M-1s-1

3.7  1015 M-1s-1

131.5  12.5

3.2  0.4 s-1

191  249

56  250 M-1s-1

5.27  1014 M1 -1 s 2.7  1022 M-1s-1

173  119.7

1.8  2.6 M-1s-1

9.7  1018 M-1s-1

79.5  56.5

59.2  32.9 M-1s-1

2.3  1010 M-1s-1

aMeGlcMeOH  aH+ aOAc  aH+

aLigninEasy  aH+ aLigninHard  aH+ aLignin(aq)  aH+



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

R10

Dissolution of xylan polymers: XYL(f)  XYL(aq) Cellulose unit(f) + H+ + H2O  3 AcOH(aq) + H+

R11

XYL1(aq) + H+  Degproduct(aq) + H+

R12

3.7.

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aXYL

100  13.5

4  10-3  0.83  10-3 s-1

6.1  108 s-1

aCellulose  aH+

157.9  87

1.76  10-2  1.3  10-2 M-1s-1

2.0  1015 M-1s-1

aXYL1  aH+

156.3  15.5

3.9  0.6 s-1

3.2  1017 M-1s-1

Modeling of mass transfer

The mass transfer between the liquid phases was modeled by assuming a thin liquid film between fiber wall liquid and external liquid. The liquid film was assumed stagnant and comprised of the total mass transfer resistance between two liquid phases. The Nernst-Plank equation is used to model the mass transfer between the liquid phases44 that assumes electroneutrality in both liquid phases at all conditions.

ri , MT 

AMT .w fibers . water .Di ,eff l

  n    z j .D j ,eff  a j   j 1  ai  ai z i  n    z 2j .D j ,eff  a j   j 1 

      

(6)

The values for specific mass transfer area (AMT) and diffusion length (l) assumed in the simulation are 10 m2/kg wood and 2 x 10-6 m respectively as previously used for pulp suspension by Tarvo et al..45 The activation energies of reactions in Table 5. are high, making the effect of mass transfer restrictions negligible. Therefore, mass transfer parameters for pulp were used. More details about modeling of activities in both liquid phases and at the interface are included in the Supporting information. The diffusion coefficients for most of the species involved in the reactions can be found in literature.46, 47 Wilke-Chang estimation method31 was used for calculating diffusion coefficients of xylan polymers in polymer categories, as presented by Liu et al..24 The calculated values of diffusion coefficients at 25C are reported in the Table 3. The diffusion coefficients of solute ‘A’ in solvent ‘B’ at the process temperatures (T) were calculated by equation:31

D AB T  

 B 25C   D AB 25C   T 298.15K  B T 

(7)

Viscosity of solvent (B) water as a function of temperature was estimated by the correlation proposed by Laliberte.48 The diffusion in the fiber wall pores caused additional resistance as compared to diffusion in the free liquid. The effective diffusion coefficients were calculated by multiplying the diffusion coefficient (DAB(T)) with fiber porosity (ε) and then dividing it by tortuosity (τ). Di ,eff 

 D T   AB

(8) 10 ACS Paragon Plus Environment

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The value of fiber wall porosity was calculated from fiber saturation point value. The tortuosity value used in the simulations was 7.49

3.8.

Mass balance equations

Mass balance for chemical species in the system were solved for both liquid phases. The concentrations of species are affected by chemical reactions producing and consuming them, reaction equilibrium, i.e. acid-base equilibrium of acetic acid, uronic acid and water, and mass transfer between the liquid phases. The mass balance equations used for fiber wall liquid and external liquid are presented in the Supporting information.12 The ordinary differential equations were solved numerically by using solver developed by Petzold.50 For mass balance of xylan, concentration of each discretized polymer category was calculated by solution of population balance equation.23 Separate population balance equations were used for fiber wall liquid and external liquid.

3.9.

Optimization of kinetic parameters

The optimization of kinetic parameters was carried out with KinFit software.51 The objective function used in the optimization was weighted sum of squares of relative difference between experimental and simulated results. The number of total parameters optimized were 23 (activation energies and rate constants for 11 irreversible reaction and fraction of easily dissolving lignin). Transpose temperatures were used to reduce the correlation between parameters during optimization.23, 52 The average temperature used was 210°C at which values of rate constants and activation energies of irreversible reactions were optimized. The cross correlation of fitted parameters is listed in Table S1 (Supporting information). The results showed significantly weak correlation among most of parameters. However, the activation energies and rate constants for R4 and R7 were found strongly correlated.

4. Results and discussion Comparison of modeling results with experimental measurements as a function of time are presented in this section. In all the figures, time period from -15 to 0 minutes represents the initial pre-heating period at the start of each experiment. After time 0, the remaining time durations of experiments are at isothermal conditions.

4.1.

Deacetylation of wood and acetic acid formation

Evolution of acetyl groups and acetic acid in HWE was modeled successfully except at lowest temperature. Optimized parameters for hydrolysis and dissolution reactions are reported in Table 5. The resulted value of activation energy for deacetylation was 158 kJ/mol which is twice to the results from previous studies.12, 39 The parameter values were different because results obtained in previous studies were from deacetylation of dissolved xylan and cellulose acetate instead of untreated wood. Therefore, the parameters reported in the current study are considered more reliable for HWE simulations. The output concentrations of acetyl groups and acetic acid in different phases are plotted against experimental data in Figure 2. The results showed very good agreement between experimental and model results. The results also clarified the importance of including the preheating period in the 11 ACS Paragon Plus Environment


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simulations. At high temperatures, most of acetyl groups in wood were dissolved to liquid extract during the preheating (before isothermal temperature duration). Amounts of uronic acid in wood and extract were not measured in the experiments and kinetic parameter were optimized to fit only the pH behavior. As a consequence, the confidence limits for activation energy of uronic acid dissolution (R4 in Table 5.) were quite wide. Additionally, activation energy and rate constant of R4 were found highly correlated in the optimization. This means that the Arrhenius rate parameters for uronic acid dissolution were not identified very well in the optimization. Experimental data for uronic acid is needed for better identification of parameters.

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Figure 2. Acetyl groups in wood (■), acetyl groups in extract (▲) and acetic acid in extract (♦) at (a) 180, (b) 200, (c) 220 and (d) 240°C. Lines are outcome of modeling work and points represent experimental observations. The behavior of pH during HWE is affected by the presence of acetic acid, uronic acid and metal ions. The pH in the both liquid phases is calculated by negative logarithm of hydrogen ion activity (-log10 aH+) in that phase. Figure 3. shows the pH behavior in fiber wall liquid and external liquid at 12 ACS Paragon Plus Environment

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experimental conditions. Initially, the pH in the fiber wall liquid is less than external liquid due to the presence of uronic acid attached to fiber wall. The pH was measured experimentally at 25°C. Thus, the simulated pH values at process temperatures were converted to corresponding values at 25° C. The behavior of pH was predicted very accurately.

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Figure 3. The pH values in extract at 25°C (■ and solid black line), and at process temperatures (solid grey line) and in fiber wall liquid (dotted grey line) during HWE at (a) 180, (b) 200, (c) 220 and (d) 240°C. Lines are outcome of modeling work and points represent experimental observations.

4.2.

Breakage of glycosidic bonds in xylan and conversion

The breakage of glycosidic bonds in xylan polymers was assumed completely random and catalyzed by H+. Based on this assumption, monomers can be formed from any chain length including very long chain polymers. Equal breakage rate for all polymer lengths was assumed, resulting short chain polymers, oligomers and monomers at the same time. Optimized value of activation energy for xylan degradation is 134.4 kJ/mol which is within the range 119-250 kJ/mol presented for stepwise xylan degradation by other mechanisms.13, 53, 54 The confidence limits for optimized parameters is small 13 ACS Paragon Plus Environment


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which means the values were truly identified. The comparison between experimental results and model predictions for xylan concentration in wood fiber and extract is shown in Figure 4. In the results comparison, the term ‘xylan’ includes all polymers, oligomers and xylose monomers. Almost all xylan was removed from wood during the initial preheating period at severe temperature conditions (220°C and 240°C). At 180°C (Figure 4a.), the experimental results showed that a portion of xylan was not dissolved from wood which was not predicted by the model. It shows that there is a portion of xylan which is only dissolved at high temperatures. The undissolved portion of xylan might be linked to lignin fraction that was not dissolved at 180°C. Small deviation in prediction might also result from the simplified reaction mechanism selected for xylan hydrolysis.

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Figure 4. Xylan in residual wood (▲ and dotted line) and in extract (■ and solid line) at (a) 180, (b) 200, (c) 220 and (d) 240°C. Lines are outcome of modeling work and points represent experimental observations The major benefit of using polymer categories with different DPs was outcome of average molecular weights as a function of time and evolution of MWDs of xylan. The model predicted the concentration 14 ACS Paragon Plus Environment

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of xylan polymers in discretized categories as a function of time. These concentration values were further utilized to evaluate average molecular weights and the MWD of dissolved xylan polymers. Few experimental values of number average molecular weight (Mn) and weight average molecular weight (Mw) of xylan polymers in extract were available for model validation. Although, experimental values of average molecular weights were not used in parameter optimization still model outcome is comparable to experimental data. Figure 5. shows the comparison between simulated and experimental results for Mn and Mw. It is clear that xylooligomers with very small DP are present at the end of HWE periods. Simulation results were able to predict Mn quite accurately, but Mw showed some deviation from experimental values. There was no experimental data available to compare the MWD of xylan polymers in the extract or residual wood. The results for MWD for the first experiment at different time periods is shown in Figure S2 (Supporting information). The predictions of average molecular weights and MWD can be improved further if sufficient experimental data is available for model development.

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Figure 5. Number average molecular weight Mn (▲ and dotted line) and weight average molecular weight Mw (■ and solid line) of dissolved xylan in extract at (a) 180, (b) 200, (c) 220 and (d) 240°C. Lines are outcome of modeling work and points represent experimental observations 15 ACS Paragon Plus Environment


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Figure 6. demonstrates the changes in the concentrations of xylose and furfural in the liquid extract as a function of time. The simulated results were able to predict the yield of xylose and furfural accurately. The optimized value of activation energy for xylose dehydration to furfural was 131.5 kJ/mol which is very close to previously reported values 129.91 kJ/mol and 122.5 kJ/mol13, 54 because same reaction mechanism was used. Similarly, activation energy for xylose conversion to degradation products are in close agreement with values reported by Borrega et al..13

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Figure 6. Xylose (■ and solid line) and furfural (▲ and dotted line) in extract at (a) 180, (b) 200, (c) 220 and (d) 240°C. Lines are outcome of modeling work and points represent experimental observations.

4.3.

Delignification and condensation of lignin back to wood fibers

Several studies concerning the changes in lignin structure during auto-hydrolysis proposed that both de-polymerization and polymerization of lignin takes place simultaneously.55, 56 In this work, a simple mechanism for lignin dissolution from fiber wall and condensation back to wood fibers was assumed. All kinetic parameters for lignin reactions (R7-9 in Table 5.) and the fraction of more soluble/reactive lignin were optimized. According to the optimization result, the amount of more reactive or easy to 16 ACS Paragon Plus Environment

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dissolve lignin is 50.06 ± 30 % of the total lignin in wood. This value is close to the amount of β-O-4 ether linkages in lignin found in hardwood i.e. 60%.16 The optimized values of activation energies were compared to the values reported by Borrega et al.1 as similar mechanism of lignin dissolution and condensation was implemented. For dissolution reaction of more reactive lignin, the optimized value of activation energy was 191 kJ/mol which was considerably higher than 103 kJ/mol1 and for less reactive lignin the optimized value was 173 kJ/mol, which is quite less compared to 219 kJ/mol.1 The activation energy for condensation reactions of both types of lignin was 79.5 kJ/mol in comparison to 120 kJ/mol.1 In this model, same initial fractions for more and less reactive lignin in raw material was assumed in all setups in contrast to model by Borrega et al.1 which assumed different initial fractions for different setups. In addition, the dissolution and condensation was catalyzed by H+. For lignin reactions, confidence limits for parameters are wide, indicating that parameter values were not identified well from the experimental data. Due to these reasons, the results were different from previous studies in terms of reaction kinetic parameters. More experimental data at lower temperatures should be included in parameter optimization to improve identification of parameters. The comparison between simulated and experimental results is shown in Figure 7. The simulation results showed good agreement with experimental data for shorter time periods but, notable deviations were present for extended time periods.

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(d)

Figure 7. Lignin in extract (■ and solid line) and in residual wood (▲ and dotted line) at (a) 180, (b) 200, (c) 220 and (d) 240°C. Lines are outcome of modeling work and points represent experimental observations. Since the model for lignin degradation is not based on actual reaction mechanisms, it is possible that it is not truly predictive and might not work very well if the L:W is changed significantly.

5. Conclusions A comprehensive kinetic model for HWE of hardwood was developed based on physico-chemical phenomena. The model is able to reproduce the evolution of pH, average molecular weights of hemicellulose and lignin degradation as a function of time. Additionally, it is able to provide the evolution of MWD for hemicellulose in liquid extract and residual wood. A set of reversible and irreversible reactions was collected to accurately describe the kinetics involved in the process. Most of the irreversible reactions during HWE were included as H+ catalyzed. The simulated concentration profiles have shown good agreement with experimental data used in the optimization. The optimized kinetic parameters from modeling were comparable to earlier modeling studies on HWE of wood. The model is considered more reliable in predicting the behavior of xylan degradation than for deacetylation and lignin degradation. The experimental data at wide temperature ranges and with different liquid to wood ratios are required for parameter optimization to improve the reliability of model. The additional analyses (e.g. uronic acids, metal ions and MWD of hemicellulose) of raw material, liquid extract and residual wood should also be included in the experimental studies for development of such detailed chemistry model. The developed model can be utilized to describe the phenomena involved in HWE and in optimizing extraction processes to maximize the yield of desired products.


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Acknowledgments Finnish Bioeconomy Cluster (FIBIC) and the Finnish Agency for Technology and Innovation (Tekes) is acknowledged for providing funding. Paavo A. Penttilä from University of Helsinki, Finland is thanked for providing the detailed experimental data of xylan MWD in wood. Supporting information: Lognormal distribution function for initial distribution of xylan, Parameter correlation matrix of optimized parameters, Molecular weight distribution of hemicellulose in the extract as a function of time

Nomenclature A = Osmotic coefficient of water a = activity AcOH = Acetic acid D = Diffusion coefficient (cm2/s) I = Ionic strength (mol/kg water) LigninEasy(f) = Easy to dissolve lignin in fiber wall LigninEasy(aq) = Easy dissolve lignin in water LigninHard(f) = Hard to dissolve lignin in fiber wall LigninHard(aq) = Hard to dissolve lignin in water m = Molality (mol/kg of water) MeGlc(f) = Uronic acid group in xylan attached to fiber wall MeGlc(-f) = Ionized uronic acid group in xylan attached to fiber wall MeGlcSoluble(f) = Uronic acid in fiber wall, soluble in hot water extraction conditions MeGlcSoluble(-f) = Ionized uronic acid in fiber wall, soluble in hot water extraction conditions MeGlcSoluble(aq) = Uronic acid in water, soluble in hot water extraction conditions MeGlcSoluble(-aq) = Ionized uronic acid in fiber wall, soluble in hot water extraction conditions MeGlcMeOH(f) = Methyl ester of uronic acid in fiber wall, insoluble in hot water extraction conditions 19 ACS Paragon Plus Environment


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MW = Molecular weight (g/mol) Mw = Weight average molecular weight (g/mol) Mn = Number average molecular weight (g/mol) n = Value for lognormal distribution OAc(f) = Acetyl group in xylan attached to fiber wall OAc(aq) = Acetyl group in xylan dissolved in water R = rate (1/s) t = Time (s) T = Temperature (K) w = Mass (kg) XYL1 = Xylan monomer (Xylose) XYL2-XYL18 = Xylan polymers z = Electric charge on ion Greek symbols

 = Activity coefficient ε = Porosity of wood particles

 = liquid viscosity (Pa·s) Subscript A = Solute (xylan) B = Solvent (Water) Eff = Effective i, j = component i, j MW = Molecular weight R = chemical reactions w = water 20 ACS Paragon Plus Environment

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TOC/Abstract Graphics


Physicochemical Modeling for Hot Water Extraction of Birch Wood

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