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Experimental and modelling studies on the synthesis of 5-hydroxymethylfurfural from sugarsvan Putten, Robert-Jan

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* Published as ‘Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-

glucose, D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures,’ Van

Putten, R.-J.; Winkelman, J. G. M.; Keihan, F.; Van der Waal, J. C.; De Jong, E.; Heeres, H. J., Ind.

Eng. Chem. Res., 2014, 53, 8285–8290

6 Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures*

Abstract

The solubilities of D-glucose, D-arabinose, D-xylose, D-fructose, D-mannose and sucrose in

methanol and methanol-water mixtures (<25 wt% water) were determined at temperatures

between 295 and 353 K using a unique high-throughput screening technique. The data were

modelled with a UNIQUAC framework with an average error between calculated and

experimental data of 3.7%. The results provide input for the design of efficient chemical

processes for the conversion of these sugars into valuable biobased building blocks in

methanol-water mixtures.

342| Chapter 6

6.1 Introduction

Lignocellulosic biomass is considered an attractive renewable source for the production of

biofuels and biobased chemicals and intense research and development activities have been

performed the last decade to identify techno-economically viable routes. Well-known

examples are the conversion of lignocellulosic biomass to bioethanol as a second generation

biofuel, and the conversion of C6-sugars to platform chemicals like 5-hydroxymethylfurfural

(HMF) and levulinic acid.1-4

For most of these conversions, water was the initial solvent of choice as it is considered

environmentally benign and a good solvent for monomeric sugars. Especially aqueous

fermentation of sugars to biofuels and biobased chemicals will play an important role.5 There

are, however, significant disadvantages for using water as solvent for chemical processes.

General disadvantages of using water as the solvent for any chemical process are the high

cost of its removal due to its high heat of vaporisation (2.4 kJg-1

)6 and its corrosiveness at

elevated temperatures. The importance of a more classical chemocatalytic approach to the

conversions of sugars is expected to gain momentum.7 For some high-potential chemo-

catalytic processes the carbon yields in water cannot meet techno-economic targets due to

excessive by-product formation. Alternative solvents have been explored , especially in the

case of the production of furans from sugars.1

Unfortunately, the solubility of monomeric sugars in common organic solvents is rather

limited and sugars typically only dissolve well in high-boiling polar aprotic solvents such as

DMSO and NMP. However, the relatively high boiling points of these solvents result in

serious issues in the product work-up and therefore lower boiling solvents are favoured. As

such, the use of lower alcohols is currently extensively explored in the development of

carbohydrate (pre-)treatment and conversion processes, such as the organosolv processes

using ethanol8 (boiling point 351 K, heat of vaporisation 0.84 kJg

-1)6 and sugar dehydration to

5-hydroxymethylfurfural derivatives.1, 9

Saka and co-workers have looked extensively into

cellulose and lignocellulosic biomass pre-treatment in methanol10, 11

(boiling point 338 K,

heat of vaporisation 1.11 kJg-1

).6 Research by Bicker et al.

9 and Avantium

12 has shown that

methanol is the solvent of choice for the production of 5-hydroxymethylfurfural (HMF) and

its methyl ether (MMF).

Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |343 D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures

For the development of efficient green processes solvent usage and recycle should be

minimised and as such there is a large incentive for performing reactions in highly

concentrated solutions. For this purpose, detailed solubility data of the sugars in solvents

other than water are required. Limited research has been performed on the solubility of sugars

in alcohols, see Table 1 for an overview. The majority of the studies focus on glucose and

fructose solubilities using methanol and ethanol as the solvent. The emphasis is mainly on

alcohol-water mixtures and less on the pure alcohols. Furthermore the temperature ranges for

individual studies are often very narrow. Limited attention is given to higher alcohols and to

other sugars.

Table 1. Overview of research studies on sugar solubility in alcohols

Sugar Solvent

Temperature range

(K) Model type Reference

Galactose Ethanol/water 273, 288 and 303

UNIFAC 13

Xylose and mannose Ethanol and ethanol/water

278, 288 and 298 UNIFAC 14

Glucose, fructose, xylose,

galactose, mannose,

sucrose

Methanol, ethanol, n-

propanol, n-butanol, t-

pentanol

298-398 UNIFAC 15

Glucose, fructose, sucrose Methanol, methanol/ethanol

and methanol/water and

Ethanol/water

298, 313, 333 UNIFAC 16

Fructose Methanol,

methanol/ethanol,

methanol/water and

Ethanol/water

298, 313, 333 UNIFAC

and

UNIQUAC

17

Sucrose Ethanol and ethanol/water

310 UNIQUAC 18

Fructose and glucose Ethanol/water

303 UNIQUAC 19

Glucose Methanol, ethanol,

methanol/water,

methanol/ethanol

313 and 333 UNIQUAC 20

Glucose, fructose, sucrose Methanol, methanol/ethanol

and methanol/water and

ethanol/water

298, 313, 333 UNIQUAC 21

Glucose, fructose,

galactose

Ethanol and ethanol/water 295, 303, 313 UNIQUAC 22

344| Chapter 6

In this paper, a systematic study is presented on the solubility of a range of C6 and C5

sugars (D-arabinose, D-fructose, D-glucose, D-mannose and D-xylose) and one disaccharide

(sucrose) in methanol and methanol-water mixtures containing up to 25 wt% water. The

sugars are shown in Scheme 1 in which the monosaccharides are presented in their pyranose

forms as it is likely that these will be the most abundant tautomers in water and in

methanol.23, 24

To the best of our knowledge, this is the most extensive study reported in this field so

far, allowing a proper comparison of the solubilities of the sugars under study at a wide range

of conditions. The experiments were performed using a unique Avantium high-throughput

device (Crystal16™). A wide range of temperatures (295-353 K) and methanol-water ratios

(0-25 wt%) were applied. The equipment allows sixteen parallel experiments at temperatures

higher than the boiling point by measuring within a closed vial. The solubility data were

modelled using a UNIQUAC model.

Scheme 1: The sugars tested in this study, with all monosaccharides in their pyranose forms


6.2 Experimental section

6.2.1 Chemicals

Sucrose (99.6%) was obtained from Fluka. All other sugars (pure D-form) were obtained

from Sigma-Aldrich at >99% purity. Methanol was obtained from Fisher and was HPLC

gradient grade. Milli-Q grade water was used for all experiments.

6.2.2 Solubility measurements

All experiments were performed using the Avantium Pharmatech Crystal16™ equipment.

This equipment has been specifically designed for determining clear points and cloud points

for crystallisation studies. It consists of four heating blocks with magnetic stirring, with four

slots each for standard 1.6 mL HPLC vials. Validation of the equipment showed that the

temperature of the liquid in the vial was always within 1 K of the set point temperature for

the complete temperature range of the experiments. Each reactor has a light source and a

sensor. The equipment determines the percentage of light passing through the vials allowing

for automatic turbidity measurements and thus for the determination of the clear points and

cloud points of the solutions under investigation.

The software provided with the Crystal16™ equipment allows for automatic and

regulated temperature increase and cooling sequences. Here, one cycle consisted of heating

up at a rate of 3 K/h to 353 K, followed by cooling at a rate of 1 or 2 K/min to 268 K. The

experiments generally consisted of 3 cycles. Solutions were prepared from weighed amounts

of all components, making the error in sample preparation significantly less than 1%. The

experiments were performed at approximately 1 mL scale. After the experiments the vials

were weighed to check for loss of the solvent by evaporation due to leakage. Significant loss

of solvent was not observed.

346| Chapter 6

6.2.3 UNIQUAC modelling

From thermodynamics the well-known equation for the solubility of a solid in a liquid is

obtained as:25

)ln1()11

()ln(,,

,

,

T

T

T

T

R

Cp

TTR

Hx

ififi

if

ifii

(1)

where i and ix denote the activity coefficient and the molar fraction of the solute in the

liquid phase. iCp denotes the difference of the solute's specific heat in the solid and liquid

phases, T is the actual temperature and ifT , the fusion point temperature. Equation (1) is

obtained with the assumptions that the solid phase that is in equilibrium with the solution

consists of the pure solute and that iCp is independent of the temperature in the range of T

to ifT , .

Equation (1) can be used to evaluate the experimental results if a model for the activity

of the solute is implemented. Here the UNIQUAC model formulation26

was chosen to model

the activity coefficients because of its suitability for the systems under investigation, i.e. a

highly polar, non electrolyte, low pressure mixture that contains both large and small

molecules.27

From Equation (2) iln is calculated:

)ln1()ln1(5ln1ln,

j j

jijjii

i

i

i

iiiii

S

xSqq

(2)

where and depend on the volume and surface area parameters ir and iq , and S also on the

binary parameters ij (see list of symbols).The values of the parameters ir and iq were taken

from Poling et al.27

(Table 2).

The adjustable binary parameters are defined in terms of binary energy interaction

parameters ijA :

)/exp( TAijij

(3)

where in general 0 jjii AA and jiij AA . The values of the parameters ijA were obtained by

fitting the calculated solubilities of equations (1)-(2) to the measured ones using a standard

Newton routine for non-linear optimisation.


The physical properties of the carbohydrates mentioned in equation (1), i.e. the heat of

fusion, the temperature of fusion and the solid-liquid specific heat difference, were taken

from literature when available, or estimated following literature (Table 3).

Table 2. UNIQUAC parameters ir and iq27

M [g/mol] ir iq

methanol 32.04 1.9011 2.048

water 18.01528 0.92 1.40

arabinose 150.13 6.7089 6.492

fructose 180.16 8.1589 8.004

glucose 180.16 8.1558 7.92

mannose 180.16 8.1558 7.92

sucrose 342.30 14.5586 13.764

xylose 150.13 6.7089 6.492

Table 3. Physical properties of the sugars used in this study

sugar fH

[kJ/mol]

fT [K] pC

[J/mol.K]

arabinose 35.7828

43529

120b

fructose 33.030

37830, 31

120a

glucose 3228, 30, 32

42029-32

120a

mannose 24.728

40428, 29

120a

sucrose 46.228

45229, 31

25433

xylose 31.728

42028, 29, 31

120a

a estimated by Ferreira et al.15

b estimated following Ferreira et al.

15

348| Chapter 6

6.3 Results and discussion

6.3.1 Experimental studies

The solubility of the six sugars in methanol and methanol/water mixtures (0-25 wt% water)

was studied at temperatures in the range of 298-353 K. Determination of the solubility of

fructose at a water content higher than 10 wt% proved not possible due to stirring issues as a

results of the high solid loading required by the high solubility under those conditions. The

experimental results, combined with the modelled solubilities (vide infra) are shown in

Figures 1-6. As expected the solubility of the sugars increased with temperature and water

content. Of the sugars tested, sucrose shows by far the lowest solubility in methanol,

followed by glucose and arabinose with almost equal solubility, xylose, mannose and

fructose. The order was the same in methanol containing 10 wt% water, with the exception

that glucose now possessed significantly better solubility than arabinose.

Figure 1. The solubility of glucose in pure methanol and methanol/water mixtures containing up to 25

wt% water


Figure 2. The solubility of arabinose in pure methanol and methanol/water mixtures containing up to

25 wt% water

Figure 3. The solubility of xylose in pure methanol and methanol/water mixtures containing up to 25

wt% water

350| Chapter 6

Figure 4. The solubility of fructose in pure methanol and methanol/water mixtures containing up to

10 wt% water

Figure 5. The solubility of mannose in pure methanol and methanol/water mixtures containing up to

25 wt% water


Figure 6. The solubility of sucrose in pure methanol and methanol/water mixtures containing up to 25

wt% water

6.3.2 Modelling studies

The UNIQUAC binary energy interaction parameters were obtained by matching the

calculated to the measured solubilities and the results are given in Table 4. The data were

modelled on a mass basis to avoid possible complications due to density changes.

Table 4. Modelled UNIQUAC binary energy interaction parameters.

ijA Methanol water arabinose fructose glucose mannose sucrose xylose

methanol 0 -13.88 79.48 -6.64 92.31 13.72 43.73 37.67

water 167.19 0 57.73 65.91 -124.81 6558.79 -127.48 306.31

arabinose 112.39 -1.34 0 - - - - -

fructose 171.59 48.58 - 0 - - - -

glucose 122.22 380.87 - - 0 - - -

mannose 226.14 -225.16 - - - 0 - -

sucrose 187.87 410.38 - - - - 0 -

xylose 152.50 -164.80 - - - - - 0

352| Chapter 6

A parity plot of experimental versus calculated values for 323 datapoints is shown in

Figure 7. This figure clearly shows that the model fits the experimental results very well.

Figure 7. Parity plot of the solubilities of six carbohydrates (323 points)

The modelling results for all sugars are given in Figures 1-6. The measured solubilities

show consistent trends and the data points are all very close to the model lines, again

indicating that the model fits the data very well. Deviations between the model and

experimental data in the figures are mainly due to small differences in the actual water

concentration in the solutions and not due to modelling inaccuracies. For instance, in the

specific case of Figure 1 at 15 wt% water, the experimental points above the line were

actually obtained at 16.1 wt% water in methanol and those below that at 15.6 wt% water,

whereas the line represents the model prediction at a 15 wt% water content. The actual water

contents were used in the model.

The average absolute error between the calculated model and the experimental data was

3.7%, which shows that the obtained models are accurate representations of the solubilities of

these sugars in methanolic mixtures. As such, the high throughput technology is very well

suited for this type of solubility research.

6.3.3 Literature comparison

The solubility of fructose, glucose and sucrose in methanol and methanol-water mixtures has

been reported (Table 1). Figures 8-10 show a comparison of the experimental solubility data


obtained in this study with those previously reported. In addition, the model lines are

provided. For fructose (Figure 8) agreement between our and the literature data is in general

good in the range 310-325K. In addition, the combined data are predicted well using the

model provided in this paper. However, at both the high and low end of the temperature

range, some deviations between our model and the experimental literature data are observed.

We were not able to obtain datapoints for fructose in this temperature regime (max 10 wt%

fructose loading) making comparison cumbersome.

Figure 8. Comparison of fructose solubility vs. temperature in pure methanol (0%) and methanol

with 10% by weight water (10%). Symbols: closed symbols: 0% water; open symbols: 10% water; ▲:

Montañes et al.22

; , : Macedo and Peres17

; □, ■: this work. Lines: calculated according to the model

of this work.

In Figure 9 the experimental glucose data and the model developed in this work are

compared with the results from literature. In pure methanol, our experimental data and those

obtained by Montañes et al.22

and Macedo and Peres20

are in agreement, though the literature

results are consistently slightly higher. The glucose solubilities in methanol with 10 wt%

water at 313 and 333 K reported by Macedo and Peres20

are only slightly above the model

line obtained in this work. At 20 wt% water content the results reported by Macedo and

Peres20

are significantly higher than the model line obtained in this work.

0

0.05

0.1

0.15

0.2

0.25

0.3

290 300 310 320 330

x [-

]

T [K]

fructose

10%

0%

354| Chapter 6

Figure 9. Comparison of glucose solubility vs. temperature in pure methanol (0%) and methanol with

10% and 20% by weight water (10%, 20%). Symbols: closed symbols: 0% and 20% water; open

symbols: 10% water; ▲: Montañes et al.22

, : Macedo and Peres20

; □, ■: this work. Lines:

calculated according to the model of this work.

In Figure 10 the sucrose solubility data and model from this work are compared to results

from literature. At all three water concentrations given, the data reported by Macedo and

Peres20

are exactly in line with the model and data obtained in this work.

Figure 10. Comparison of sucrose solubility vs. temperature in pure methanol (0%) and methanol

with 10% and 20% by weight water (10%, 20%). Symbols: closed symbols: 0% and 20% water; open

symbols: 10% water; , : Macedo and Peres20

; □, ■: this work. Lines: calculated according to the

model of this work.

0

0,02

0,04

0,06

0,08

0,1

300 310 320 330 340 350 360

x [-

]

T [K]

glucose

0%

10%

20%

0

0.01

0.02

0.03

0.04

0.05

290 300 310 320 330 340 350 360

x [-

]

T [K]

sucrose20%

10%

0%


6.4 Conclusions

The solubilities of six sugars in methanol and methanol-water mixtures (< 25 wt% water)

were successfully determined using a unique method employing Avantium high-throughput

technology. This allowed the measurement of many datapoints with high accuracy and

reproducibility at a wide temperature range and even above the boiling point of the solvent.

The data were successfully modelled using the UNIQUAC model. The results provide

valuable input for the development of efficient processes for biomass conversions in

methanol, and methanol water mixtures.

6.5 Symbols

jiA , UNIQUAC binary energy interaction parameter, K

pC solid-liquid difference of specific heat, J mol-1 K-1

fH heat of fusion, J mol-1

iq UNIQUAC surface area parameter

ir UNIQUAC volume parameter

iS UNIQUAC parameter, k

ikkki xS ,

fT temperature of fusion, K

x molar fraction, -

i UNIQUAC parameter, k

kkii rxr /

i UNIQUAC parameter, k

kkii qxq /

ji, UNIQUAC binary interaction parameter

356| Chapter 6

6.6 References

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358| Chapter 6

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