Physico-chemical modeling for pressurized hot water extraction of

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Kinetics, Catalysis, and Reaction Engineering

Physico-chemical modeling for pressurized hot water extraction of spruce wood Waqar Ahmad, Susanna Kuitunen, Andrey Pranovich, and Ville Alopaeus Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b05097 • Publication Date (Web): 16 Nov 2018 Downloaded from http://pubs.acs.org on November 23, 2018

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Industrial & Engineering Chemistry Research

Physico-chemical modeling for pressurized hot water extraction of spruce wood Waqar Ahmad 1,*, Susanna Kuitunen 2, Andrey Pranovich3, Ville Alopaeus 1 Department of Chemical and Metallurgical Engineering, School of Chemical Engineering, Aalto University, P.O. Box 16100, FI-00076 Finland 1

2

Neste, P.O. Box 310, 06101 Porvoo, Finland

3

Laboratory of Wood and Paper Chemistry, Åbo Akademi University, Porthansgatan 3, FI-20500 Turku/Åbo, Finland * Corresponding author, E-mail: [email protected]

Abstract The study of reaction kinetics and process modeling plays a vital role in the development and optimization of industrial processes. In this study, a physico-chemical model is presented for pressurized hot water extraction of spruce wood meal to facilitate process development and optimization. Model takes into account several relevant phenomena during the extraction process, including acid-base reaction equilibrium, irreversible reaction kinetics, ion exchange, and mass transfer. The system is modeled by assuming two liquid phases i.e. liquid external to wood fibers and fiber bound liquid to enable inclusion of ion exchange. The non-ideal behavior of system (due to presence of ions) is considered by utilizing activities instead of concentrations. The scission of hemicellulose polymers into oligomers and monomers is modeled by population balance approach of discretized categories. Model parameters are optimized by fitting the model output to experimental data reported in the literature. The presented model aims to be the most comprehensive description of the phenomena taking place during the hot water extraction of softwood to date. Such physicochemical models are able to provide better understanding for parts of process, which cannot be followed directly due to tediousness of analytical methods. It also reveals gaps in the present knowledge and provides one-step towards further development. Keywords: Hot water extraction; Spruce; Wood fractionation; Population balance; Modeling; Galactoglucomannan degradation; Delignification

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1. Introduction The demand of energy and chemicals, still produced mostly from petroleum, has been increasing continuously with improved living standards while fossil resources are diminishing. There is need for implementation of alternative chemical and energy resources that are renewable and sustainable. Wood biomass is considered as renewable, environmental friendly and sustainable chemical and energy source. Utilization of woody biomass for production of fuels, energy and value added products, requires efficient fractionation of its comprising components (cellulose, hemicellulose and lignin). Extraction of hemicelluloses from wood has gained significant interest because of their potential in production of not only biofuels, but also value added products.1 In the conventional kraft pulping processes, the aim is to remove only lignin. However, hemicelluloses are dissolved and degraded simultaneously, resulting in yield losses. Major part of hemicellulose (≈ 80% of the initial amount) is lost in production of chemical pulp.2 The extraction of hemicellulose with water at elevated temperatures and pressures, even before kraft pulping, has gained remarkable interest in past few years due to novel applications of hemicellulose in the modern wood-based biorefinery.3-6 The galactoglucomannan (GGM) is principle softwood hemicellulos and constitutes 15-23% of softwood.7 The amounts of GGM in wood varies by the type of wood species and the specific part of tree from which sample is collected. The approximate degree of polymerization (DP) of GGM in wood is 100-150.8 During hot water extraction (HWE) process, long chain hemicelluloses are depolymerized to short chain oligomers and monomers. The extracted galactoglucomannans have variety of potential applications.9, 10 Hydronium ions produced during HWE of wood act as catalyst for most of the reactions taking place during the process.11 Hydronium ions are produced by auto-ionization of water at elevated temperatures. They facilitate the hemicellulose depolymerization as well as deacetylation, which leads to formation of acetic acid. Acetic acid in the reaction mixture further lowers the pH and more hydronium ions are produced. Uronic acids present in wood may also contribute to formation of hydronium ions and reduce the pH during HWE.12, 13 The presence of hydroxyl ions can also act as catalyst for hydrolysis of acetyl groups and uronic acids in alkaline conditions.14 In HWE, small fraction of lignin is also hydrolyzed along with dissolution of hemicellulose.15, 16 A part of hydrolyzed lignin may also contribute to condensation products that may end up in precipitates along with wood fibers.17 Normally, HWE is preferred process for hardwoods due to higher amounts of acetyl groups present on hardwood hemicellulose, i.e. xylan.18 Moreover, lignin structure present in softwood is more susceptible to condensation reactions. Most of the hemicellulose extraction models have been developed for processes using hardwood as raw material.12, 19-24 The use of severity factor to combine the effects of reaction time, temperature, and catalyst concentration in a single parameter is also common in such models.11, 25 Delignification kinetics during HWE was previously modeled by assuming two fractions of lignin with different reaction rates in case of hardwood.26, 27 Modeling of kinetics involved in hemicellulose dissolution from softwood has been carried out in a few previous studies.5, 28 These models focus only on the kinetics of hemicellulose dissolution and no other physico-chemical effects were taken into account.


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In the present study, a comprehensive physico-chemical model is developed for water extraction of spruce. Previously, modeling strategies for HWE have been presented applying hydrogen ion (H+) catalyzed chemistry and assuming two liquid phases in system consisting of water and wood meal.13, 27 In these studies, the reactions influencing the pH evolution, degradation of birch hemicellulose and delignification in the hot water extraction were modeled. In the present work, the previous model is extended to consider the dissolution and degradation of GGM and softwood lignin. The starting material properties in the simulation are modified to match those of spruce. The effects of added chemicals (buffer solution and NaHCO3) for controlling HWE kinetics are also taken into account. Model aims to describe the extraction of GGM and other components during HWE of softwood. Experimental data, needed in the model parameter regression, was taken from the literature.

2. Experimental Data The experimental data applied in the model parameter regression have been published in the previous studies.15, 29, 30 Experiments were performed in accelerated solvent extractor (ASE) (Dionex, Sunnyvale, CA, USA). Information about experimental setups is presented briefly in Table 1. Further details regarding operational procedure (Temperature maintenance, sample collection etc.) was reported earlier, 31 and briefly described in supporting information. Table 1. Experimental setups used in the model development. (L:W is liquid to wood ratio) Setup Reactor

Pressure

L:W

(MPa)

Temperature Duration (°C)

(min)

Additives

Reference

A

ASE-200

13.8

6:1

160, 170, 180

5, 20, 60, 100

None

15

B

ASE-300

10

4:1

170

20, 60, 100

0.1 M potassium phthalate (initial pH 3.8, 4, 4.2 adjusted with NaOH or HCl)

29

C

ASE-200

13.8

6:1

170

60, 100

NaHCO3 added 0,

30

2.5, 5, 12.5, 25, 50, 100, 150 mM

In all three cases, grounded fresh spruce wood (Picea abies) with particle size smaller than 1 mm was used as the raw material. The liquid extract samples were analyzed for carbohydrates (monomers and polymers), pH (after cooling the samples to room temperature), lignin, acetic acid, acetyl groups, and average molecular weight of dissolved hemicelluloses. The residual amounts of non-cellulosic carbohydrates in the extracted wood samples were also analyzed. The detailed explanation about the experimental setups and analyses methods have been reported in the publications describing the experimental work.15, 29, 30 3 ACS Paragon Plus Environment


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3. Simulation model The modeling approach used in this study integrates ion exchange equilibrium, mass transfer and chemical reaction kinetics during hot water extraction. The details of different applied theories and sub-models are explained in this section.

3.1.

Chemical composition of spruce raw material

The outcome of the HWE simulation is dependent on the model itself as well as the initial composition of the raw material. For modeling of the ion exchange in aqueous extraction of wood, the contents of acetyl groups, uronic acids and metal ions are needed. Chemical composition of hemicelluloses as well as their molecular weight distribution in wood is also required in order to model their degradation and dissolution properly. In Table 2, the different types of uronic acids included into the model are listed.32 The total uronic acids, 130 mmol/kg wood, is the amount of carboxyl groups in wood that may participate in ion exchange after certain treatment by which ester and lactone linkages are hydrolyzed. The initial accessible amount, 70 mmol/kg wood, is the amount of uronic acids that is assumed soluble in liquid extract during HWE. The inaccessible amount of uronic acids is considered not contributing to ion exchange between the liquid phases and will remain attached to the fiber wall. In the simulation, this inaccessible amount is introduced as uronic acid methyl ester (MeGlcMeOH). For Picea abies, Fengel and Wegener33 did not provide acetyl group content, but for Picea glauca and Picea mariana, it is 1.2 w-% (280 mmol/kg wood) and 1.3 w-% (302 mmol/kg wood), respectively. These values are ~25 % smaller than the maximum amount of acetic released in the experiments, 390 mmol/kg wood. The reason for difference in amount of acetyl content is due to either different softwood raw material (i.e. Picea abies instead of Picea glauca and Picea mariana) or additional acetic acid formation from degradation of cellulose during HWE. 34 The average of the literature values, 290 mmol/kg wood, was chosen as the initial amount of acetyl groups in wood. Table 2. Amount of uronic acids32 and acetyl groups33 in spruce wood raw material. Species

mmol/kg wood

Total uronic acids

130

Accessible uronic acids, MeGlcSoluble

70

Inaccessible uronic acids, MeGlcMeOH

60

Acetyl groups

290

The metal content in wood varies a great deal depending upon the location of the tree due to effects of surrounding soil and atmosphere. The analyzed amounts of calcium and potassium in softwoods, reported by Pranovich et al.35 and Saltberg et al.36 are quite close to each other, but much lower than the values reported by Hakkila and Kalaja.37 Since the former analysis was more comprehensive (including also magnesium and sodium), the values reported by Pranovich et al.35 are used in the


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present study. The initial amounts of metal ions in softwood from different literature sources are listed in the Supporting Information Table S1. The initial amount of lignin in softwood (Picea Abies) is 24.72%.18 In the simulations, the total initial amount of lignin was defined as two fractions based on their dissolution rates. The fraction of fast dissolving lignin was determined by parameter optimization in this work. The rest of the lignin was considered as slow dissolving lignin.

3.2.

Modeling of chemical composition and molecular weight distribution of galactoglucomannan

Population balance approach of discretized categories was used for modeling the evolution of molecular weight distribution of galactoglucomannan. The implementation of population balance reduces the computational time by reducing the number of variables to be solved.38 This modeling approach has been previously used for degradation of cellulose and hemicellulose polymers.27, 39, 40 According to Sjöström,7 galactoglucomannans are linear polymers consisting of (1-4)-linked β-Dglucopyranose and β-D-mannopyranose units. The amount of galactoglucomannans in softwood is approximately 15-23 w-%. Galactoglucomannans are classified into two fractions based on their different galactose content. In the low galactose (glucomannan) content fraction, the ratio of the units Gal:Glu:Man is 0.1:1:4 whereas in the high galactose (galactoglucomannan) content fraction, the ratio is 1:1:3. Molecular weight distribution of glucomannan fraction was generated using log10 normal distribution. The values of average and standard deviation were obtained by weight average and number average molecular weights.41 Similarly, a molecular weight distribution of galactoglucomannan fraction was generated using log10 normal distribution. The values of average and standard deviation were determined by the weight average molecular weight of galactoglucomannan42 and assuming same polydispersity index as for glucomannan. Based on the relative average amounts of galactoglucomannan and glucomannan in softwood,7 a combined distribution of both fractions was implemented in the simulations. The initial GGM content of wood was set to be the total maximum amount, 13.7 w-% of wood, i.e. maximum amount of glucose and mannose units released in the experiments. The side groups present in the form of acetyl groups were included as separate components. The remaining side groups in the form of galactose were neglected. All glycosidic bonds present in the linear chain of galactoglucomannan polymers were assumed equally reactive.43 The molecular weight distribution was discretized into 22 categories with characteristic DP, are listed in Table S2 in supporting information.

3.3.

Phase and reaction equilibria

In order to model ion exchange or Donnan phenomena, two liquid phases are assumed in the system.44, 45 Water bound to the porous fiber wall is the first liquid phase. All wood fibers are assumed to be present in this homogeneous liquid phase. The amount of water in the fiber bound liquid phase is 0.3 kg/kg of dry wood.2 The remaining liquid is termed as external liquid phase. In the model, there is no direct contact between solid wood fibers and the external liquid phase. When compounds dissolve from the solid wood fibers, they first dissolve to the fiber bound liquid and



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later they diffuse to the external liquid phase depending on the deviation from phase equilibrium i.e. driving force in the mass transfer. In the system involving electrolytes, the behavior of the solution becomes non-ideal due to interaction between the ions. The non-idealities of solution are taken into account by using activities (a) instead of molalities (m). The activity of chemical species is calculated as:

ai   i mi

(1)

In the present study, activity coefficient (γ) was calculated using Davies model.27, 46 Activities of the chemical species are used in the mass transfer, reaction and dissolution rate calculations. The mass transfer between the liquid phases is modeled by Nernst Plank equation as reported in our previous work (most recently in 27). The values for specific area of mass transfer and diffusion lengths were assumed same as of pulp suspension.27, 47 Table 3. Equilibrium reactions and the pKa (-log10K) values at the reaction temperatures. pKa values at the temperatures (C) Equilibrium reactions

ER1

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

25

3.14

160

3.66

170

3.72

180

3.78

References

At 25C, the pKa value was taken from Teleman et al..48 The temperature dependency of pKa value similar to acetic acid was assumed.*

ER2

AcOH(aq) ↔ Acetate(-aq) + H+

4.77

5.28

5.35

5.42

49

ER3

H2O ↔ OH + H+

13.99

11.56

11.48

11.42

50

ER4

HCO3(-aq) ↔ CO3(-2aq) + H+

10.33

10.25

10.30

10.36

50

ER5

CO2(aq) ↔ HCO3(-aq) - H2O + H+

ER6

oPhthalicAcid(aq) ↔oPhthalicAcid(-aq) + H(+aq)

ER7

oPhthalicAcid(-aq) ↔ oPhthalicAcid(-2aq) + H+

49

6.35

6.83

6.93

7.03

2.95

3.40

3.44

3.48

5.41

6.47

6.58

6.70

49

49

*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.


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The evolution of pH during HWE is important as most of the reactions are catalyzed by hydrogen ions (H+) that exists in the solution as hydronium ions. The effect of temperature on the reaction equilibrium constants is considered. The pKa values of equilibrium constants at experimental temperatures are listed in Table 3. The first three equilibrium reactions are involved during HWE with pure water i.e. ionization of uronic acids, acetic acid and water. The remaining four reactions are included in order to consider experiments performed using additives i.e. sodium bicarbonate and phthalate buffers.

3.4.

Irreversible reactions

Temperature dependency of irreversible reactions and dissolution is defined using Arrhenius equation. The stoichiometric equations of irreversible reactions included in the simulation are listed in Table 4. The values of rate constants (k) at average temperature and activation energies (Ea) for reactions were optimized against experimental data. H+ catalyzed hydrolysis of acetyl group (R1) was assumed equal in both fiber bound liquid and external liquid. The same assumption was used for OH- catalyzed hydrolysis of acetyl groups. Kinetics of hydrolysis of uronic acid esters was assumed equal to the deacetylation kinetics for both H+ and OH- catalyzed reactions, i.e. kinetic parameters of R2 and R4 are equal to kinetic parameters of R1 and R3 respectively. The assumption of equal deacetylation kinetics and uronic acid hydrolysis is adopted from work of Inalbon et al.14 for wood impregnation. The mechanisms for both H+ and OH- catalyzed hydrolysis of acetyl groups are adopted from the work of Vos et al..51 In the simulations, small amount of acetic acid is formed from degradation of cellulose (R11).34 Breakage rate of glycosidic bonds in GGM is assumed proportional to H+ activity and number of bonds present in the polymer. The GGM polymers attached to the fiber wall seem to degrade at a rate lower than that in the liquid phase due to their lower accessibility. The degradation rates of GGM polymers in fiber phase were calculated by multiplying with factor (FGGM) to the degradation rate in the external liquid. The value of FGGM was obtained by parameter optimization given at the end of Table 4. Lignin present in spruce wood is assumed to consist of two fractions with different reaction rates. Both lignin reactions (R8 and R9) were catalyzed by H+ ion. In R8, exponent for activity of H+ in reaction rate was also optimized as shown in Table 4. The differentiation between easily dissolving and hard to dissolve lignin can be explained by difference in the type of degrading ether bonds in lignin 52 and types of carbohydrate-lignin linkages. 53, 54 Both types of lignin also have possibility to condense back to the fibers at equal reaction rate (R10). The chemical modification of the lignin units and the physical changes in lignin structure may take place during HWE.55 In this work, the chemical modification in lignin structures are not taken into account. The initial value for fraction of easy to dissolve lignin was also optimized. Dissolution of acetyl groups (R6) and uronic acid (R7) from fiber wall are modelled as irreversible reactions with Arrhenius equation of temperature dependency. Dissolution of GGM polymers is discussed in the next section.



3.5.

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Dissolution of galactoglucomannan from fiber wall

Dissolution of GGM from fiber wall to external liquid is also included as an irreversible reaction with effect of DP on dissolution rate. Monomers and oligomers of GGM in first five categories (DP = 1-8) were assumed readily soluble in the external liquid phase. The dissolution rate of long chain GGM polymers from the fiber wall is implemented using Arrhenius type equation below:

Rate constant for dissolution = ri  Ai  e



Where, Ai  Ao  e ( Bo DPi )

  Eo  1 1     R T T o  

   



(2) (3)

DPi is the degree of polymerization of that characteristic polymer category i. Ao and Bo are constants and their values are optimized along with Eo. T and To are reaction temperature and average temperature (175 ˚C), respectively. Thus, dissolution rate for long chain polymers is slower than that for short chain polymers as can be seen from the Figure 1. In HWE, small amounts of deacetylated GGM may condense back to fibers.56 Although for simplification, this effect is not included in the model. The values of optimized parameters are shown in Table 5.

Figure 1. Dissolution rate (s-1) of polymers as a function of DP at reaction temperatures

3.6.

Mass balances in liquid phases

Mass balance equations for chemical species are solved for both liquid phases. Amounts of chemical species in both phases are affected by: 1. Presence in acid-base equilibrium reactions 2. Involvement in irreversible reactions (dissolution reactions also included as irreversible reactions) and 3. Mass transfer between two liquid phases. The ordinary differential equations for mass balances in two liquid phases have been presented in earlier study.27 The equations are solved numerically in the simulation for all chemical species and GGM polymer categories by using a DDASL solver.57 Concentration of each discretized category of GGM was calculated individually by solution of population balance equations in both fiber and external liquid phase.27, 43


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3.7.

Parameter regression

KINFIT (in-house software) was used for optimization of the kinetic parameters and other model parameters mentioned above. Weighted sum of squares of relative difference between model and experimental values (mentioned in chapter "Experimental data") was used as an objective function in optimization routine. In total 23 parameters were optimized for kinetics of irreversible reactions, fraction of easily dissolving lignin, GGM dissolution parameters and multiplication factor for difference in degradation rate of GGM in the two liquid phases. The activation energy of R3 was taken from Kuitunen et al.13 as experimental data was insufficient to obtain the temperature dependency of reaction rate. The average temperature of 175°C was used at which values of rate constants were optimized. Transpose temperatures were used instead of actual reaction temperatures to reduce the correlation between the parameters in the optimization.58 The cross correlations of optimized parameters is given in the supporting information Table S3.

4. Results and discussions The fitted model outputs are compared to the experimental data from three reactor setups. In the first setup, experiments were performed with pure water in the absence of additives and only effect of temperature on the extraction kinetics was studied. In the second setup, buffer solutions with different pH were used and temperature was kept constant at 170˚C. In the last setup, different amount of NaHCO3 in mM were used to control the pH of HWE at 170˚C. The regressed values of kinetic parameters for model are provided in Tables 4 and 5. Table 4. Reactions and regressed parameters for modeling hot water extraction of spruce. 95% confidence limits are shown after . Chemical reactions

Stoichiometry

Rate law

Activation Energy (Ea) (kJ/mol) 50.6  143.4

Reaction rate constant (k) at 175C

Frequency factor (A)

0.07  0.14 M-1s-1

5.8  104 M1 -1 s

Same as for R1

Same as for R1 7.24  108 M1 -1 s Same as for R3

R1

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

aOAc  aH+

R2

MeGlcMeOH(f) + H+  MeGlc(f) + CH3OH(aq) + H+ OAc(f) or OAc(aq) + OH- + H2O  AcOH(aq) + OHMeGlcMeOH(f) + OH-  MeGlc(f) + CH3OH(aq) + OHH+ catalyzed breakage of glycosidic bonds in GGM (external liquid phase) OAc(f) + H+  OAc(aq) + H+

aMeGlcMeOH  aH+ aOAc  aOH-

Same as in R1 52.20*

aMeGlcMeOH  aOH-

Same as in R3

Same as for R3

abond  aH+

175.1  36.9

2.13  0.55 M-1s-1

5.5  1020 M1 -1 s

193.6  311.3

7.61  10.8 M-1s-1

2.8  1023 M1 -1 s

0.57  1.3

7.9  10-5  5.7  10-5 s-1

9.1  10-5 s-1

R3 R4

R5

R6 R7

MeGlcSoluble(f)  MeGlc(aq) MeGlcSoluble(-f)  MeGlc (-aq)

aOAc  aH+ aMeGlcSoluble

596  305 M-1s-1



LigninEasy(f) + H+  aLigninEasy  165.4  LigninEasy(aq) + H+ 100.6 aH+0.4 0.1 R9 LigninHard(f) + H+  aLigninHard  23.6  LigninHard(aq) + H+ aH+ 49.5 R10 LigninEasy(aq) +H+ 245.9  LigninCondensed(f) + H+ aLignin(aq)  351 LigninHard(aq) + H+ aH+ LigninCondensed(f) + H+ R11 Cellulose unit(f) + H+ + H2O aCellulose  114.2   3 AcOH(aq) + H+ aH+ 88.8 Multiplication factor for GGM polymer breakage in fiber phase 0.36  0.16 FGGM R8

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0.021  0.026 M0.4 -1 s 0.005  0.009 M1 -1 s 0.014  0.023 M1 -1 s

4.04  1017 M-0.4s-1 2.67 M-1s-1

0.012  0.005 M1 -1 s

2.48  1011 M-1s-1

6.39  1026 M-1s-1

*Value of activation energy for alkali-catalyzed deacetylation is reported by Kuitunen et al..13

Table 5. Parameters for dissolution kinetic model of GGM polymers with different degree of polymerization. Parameter Name

Value from optimization

Ao

0.000353  0.00016

Bo

0.00889  0.00038

Eo

57.62  89.38

4.1.

Hydrolysis of acetyl groups and pH evolution

Figure 2. illustrates the comparison of model fitting and experimental results for acetyl groups in extract. Limited experimental data was available for optimizing reaction kinetics involving acetyl groups. The data was not present for acetyl groups in the presence of buffer solution and only single points after 60 minutes were available for experiments with NaHCO3 addition. This issue can also be noticed from relatively wide confidence intervals for parameter values of deacetylation kinetics. Additionally, the initial amount of acetyl groups was based on literature values, which was less than total amount of acetyl groups ending up in the extract solution. Higher initial amount of acetyl groups in the simulation may improve the fitting.

(a)

(b) 10 ACS Paragon Plus Environment

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Figure 2. Acetyl groups in the extract with (a) No additives and (b) NaHCO3 (after 60 minutes). In lines chart, lines are for model output and points represent experimental values. The amount of acetyl groups in extract is affected by the dissolution rate of acetyl groups from wood and H+ - OH- catalyzed deacetylation. The value of activation energy for H+ catalyzed deacetylation (R1) is 50.5 kJ /mol, which is close to value of 68.6 kJ /mol reported by Vos et al.51 and 75.3 kJ /mol reported by Kuitunen et al.13 The activation energy value for OH- catalyzed deacetylation (R3) was adopted from Kuitunen et al.13 that resulted in comparable values of frequency factors. The difference in the resulted values might be due to difference in raw materials, wide confidence limits (i.e. parameters not identified well) and implementation of activity coefficient model in the present study. The comparison of model fitting and experimental results for acetic acid present in the extract is shown in Figure 3.

(a)

(b)

(c) Figure 3. Acetic acid in the extract with (a) No additives, (b) Buffer solution and (c) NaHCO3 (after 60 minutes). In lines chart, lines are for model output and points represent experimental values. Evolution of pH in the external liquid phase depends on the concentrations of acetic acid, uronic acid, metal ions and additional amounts of buffer solution and NaHCO3. The inclusion of metal ions is important as it neutralize a part of acidity in the mixture.13 The calculation of pH in the external liquid phase is based on the activity of H+. The model predictions for pH during experiments are accurate as shown in Figure 4. The results reported here for the pH are at 25˚C instead of values at reaction temperatures. The experimental measurements were made at 25˚C and simulation results at 11 ACS Paragon Plus Environment


Page 12 of 26

reaction temperatures were converted to corresponding values by solving reaction equilibrium equations at 25˚C.

(a)

(b)

(c)

(d)

Figure 4. The pH values in the extract at 25˚C with (a) No additives, (b) Buffer solution, (c) NaHCO3 (after 60 minutes) and (d) NaHCO3 (after 100 minutes). In lines chart, lines are for model output and points represent experimental values.

4.2.

Galactoglucomannan dissolution and degradation

Dissolution of GGM polymers from the fiber wall and its degradation in both liquid phases take place simultaneously during HWE. The optimized value for activation energy of liquid phase degradation rate is 175.13 kJ/mol, which is much higher than 102 kJ/mol reported by Visuri et al..43 The difference in the values is due to the definition of reaction rates in two liquid phases and molecular weight of monomer unit for GGM. Molecular weight of anhydrous mannose (162.1 g/mol) is used in the present study instead of normal mannose monomer (180.2 g/mol). Moreover, in the previous work, kinetic parameters were optimized with ideal solution assumption in a single homogeneous phase and activity coefficients were not taken into account. The degradation rates of GGM polymers are different in both liquid phases in contrast to the case of xylan degradation in birch wood case where equal rates were assumed.27 The outcome from fitted model showed good agreement against experimental data for total dissolved GGM content as shown in Figure 5. The confidence limits of optimized parameters for GGM degradation are narrow which means breakage model fits well to experimental data.


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(a)

(b)

(c)

(d)

Figure 5. Total amount of GGM in the extract solution with (a) No additives, (b) Buffer solution, (c) NaHCO3 (after 60 minutes) and (d) NaHCO3 (after 100 minutes). In lines chart, lines are for model output and points represent experimental values. The outcome of fitted model for concentration of GGM monomer showed very good agreement with experimental data, when HWE was carried out with pure water. When additional chemicals (buffer solution and NaHCO3) were added, the amount of GGM monomers of linear chain are underestimated by the model. Probably the initial distribution used for GGM in wood did not contain enough low molecular weight material. The initial molecular weight distribution of GGM is crucial for accurate prediction of oligomers and monomers in the extract. The comparison of model output to experimental data for GGM monomers is shown in Figure 6.



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(a)

(b)

(c)

(d)

Figure 6. Total amount of GGM monomers from linear part in the extract solution with (a) No additives, (b) Buffer solution, (c) NaHCO3 (after 60 minutes) and (d) NaHCO3 (after 100 minutes). In lines chart, lines are for model output and points represent experimental values. The weight average molecular weight of GGM was not quite well modeled. The modeling results are illustrated in Figure 7. The side chains of GGM in the form of galactose were ignored in the model. Moreover, the analysis method for measurement of average molecular weight itself is quite challenging. The assumed initial molecular weight distribution of GGM in the model has also strong effect on the outcome of model.

(a)

(b)


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(c) Figure 7. Average molecular weight of GGM linear part in the extract solution with (a) No additives, (b) Buffer solution and (c) NaHCO3 (after 60 minutes). In lines chart, lines are for model output and points represent experimental values.

4.3.

Delignification

A simplified mechanism for delignification or lignin dissolution is implemented in this work. The value obtained from optimization for percentage of fast dissolving lignin is 31.7  9.4 %. This percentage of fast dissolving lignin in softwood case is lower than that of hardwood (i.e. 50%).27 For the reaction rate calculation of fast dissolving lignin, the exponent for activity of hydrogen ion was also optimized (R8). Parameters for all lignin reactions are optimized and reported in Table 4 (R8-R10). The comparison of fitted model and experimental measurements for lignin concentration in the extract solution is shown in Figure 8. Model predicted the experimental results very well for most of experimental points. Few notable deviations were observed at longer reaction times when buffer solution was used. The lignin contents utilized here were measured by UV light absorption at 280 nm that are approximate extractives and carbohydrate derived compounds can also absorb UV light at 280 nm.

(a)

(b)



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(c) Figure 8. Amount of lignin in the extract solution with (a) No additives, (b) Buffer solution and (c) NaHCO3 (after 60 minutes). In lines chart, lines are for model output and points represent experimental values. The confidence limits of some final optimized kinetic parameters are wide. It means that for such a comprehensive model (having high number of parameters) additional experimental data at wide temperature range and additional data points at the selected temperatures are required. In other words, this kind of modeling approach can guide further experimental design so that relevant parameters are better identified and the physicochemical system better understood in the future. Moreover, improved experimental methods for analysis in the initial raw material and final product could be implemented. It could also suggest that the model is too complex and does not fit to the experimental data very well and different model equations could be tested for further improvements in the model in the future.

4.4.

Improved modeling methods

In this work, non-idealities of the system are taken into account by implementing Davies model for estimation of activity coefficients. The phenomenon of interaction between the ions becomes highly important at higher ionic strengths. The ionic strengths were comparatively low (Max ~ 10-3 M) when extractions were performed with plane water. The values were in higher range (Max ~ 0.1 M) when buffer solution and NaHCO3 were added to maintain the pH of the system. The Davies model is applicable up to ionic strength of 0.1 M.46 The implementation of activities instead of molalities has improved the model by extending its range to different ionic strengths. Furthermore, the inclusion of two liquid phases and considering metal ions in the model provides the better prediction of ionic species and their effect on reactions.13 Accurate prediction of H+ concentration is essential in both liquid phases as it acts as catalyst in many irreversible reactions during HWE.

5. Conclusions A comprehensive model for HWE of softwood was developed. The model is based on physiochemical phenomena and takes into account phase and reaction equilibrium, mass transfer and reaction kinetics. It describes the degradation of glycosidic bonds in GGM, deacetylation, pH evolution and delignification during HWE of softwood. A detailed set of reversible and irreversible 16 ACS Paragon Plus Environment

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reactions are combined to describe the chemical changes taking place during the process. Most of the irreversible chemical reactions are assumed to be catalyzed by H+. Non-idealities in the system are taken into account by implementation of Davies model. A large number of kinetic parameters and limited experimental data has resulted in wide confidence limits of some optimized parameters. Additional experimental data at wide temperature range and at different liquid to wood ratios are important to narrow down the confidence limits and to make results more reliable. Another necessity is a sufficiently detailed characterization of the wood used as the starting material in the experiments. The present modeling approach provides a platform, which can be consistently improved as more experimental data with improved analytics, will be available. It also allows testing of various sets of model equations to improve the overall model performance with increasing experimental data quantity and quality. The developed model can provide a better understanding for HWE process of softwood and can be implemented in optimizing the operating conditions and amounts of added chemicals for desired outcome from the extraction process.

Acknowledgments Finnish Bioeconomy Cluster (FIBIC) and the Finnish Agency for Technology and Innovation (Tekes) is acknowledged for providing funding. Supporting information: Hot water extractions in ASE, Amounts of metal ions in soft wood, Discretized categories of GGM polymer, Parameter correlation matrix of optimized parameters

Nomenclature a = activity AcOH = Acetic acid ASE = Accelerated solvent extractor Ao, Bo, Eo = Constants for dissolution rate equation DP = Degree of polymerization Ea = Activation Energy (kJ/mol) ER1-ER7 = Equilibrium reactions FGGM = Multiplication factor for GGM polymer breakage in fiber phase GGM = Galactoglucomannan GGM1= Galactoglucomannan monomer GGM2-GGM22 = Galactoglucomannan polymers k = Reaction rate constant 17 ACS Paragon Plus Environment


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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 L:W = Liquid to wood ratio m = Molality (mol/kg of water) MeGlc(f) = Uronic acid group attached to fiber wall MeGlc(-f) = Ionized uronic acid group attached to fiber wall MeGlc(aq) = Uronic acid dissolved in water MeGlc(-aq) = Ionized uronic acid dissolved in water 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 OAc(f) = Acetyl group in xylan attached to fiber wall OAc(aq) = Acetyl group in xylan dissolved in water R1-R11 = Reversible chemical reactions r = rate of dissolution (s-1) t = Time (s) T = Temperature (K) To = Average temperature (K) w = Mass (kg) Greek symbols  = Activity coefficient

Subscript


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i = category i

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TOC/Abstract Graphics


Physico-chemical modeling for pressurized hot water extraction of

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