University of Groningen
Experimental and modelling studies on the synthesis of 5-hydroxymethylfurfural from
sugars
van Putten, Robert-Jan
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van Putten, R-J. (2015). Experimental and modelling studies on the synthesis of 5-hydroxymethylfurfural
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|341
6 Experimental and modelling studies
on the solubility of D-arabinose, Dfructose, 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.
* Published as ‘Experimental and modelling studies on the solubility of D-arabinose, D-fructose, Dglucose, 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
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 chemocatalytic 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 Avantium12 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
Galactose
Solvent
Ethanol/water
Temperature range
(K)
273, 288 and 303
Model type
UNIFAC
13
Xylose and mannose
Ethanol and ethanol/water
278, 288 and 298
UNIFAC
14
Glucose, fructose, xylose,
galactose, mannose,
sucrose
Methanol, ethanol, npropanol, n-butanol, tpentanol
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
Reference
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
Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |345
D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures
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
ln( i xi ) 
H f ,i
R
(
T f ,i
T f ,i
1
1 Cpi
 )
(1 
 ln
)
T f ,i T
R
T
T
(1)
where  i and x i denote the activity coefficient and the molar fraction of the solute in the
liquid phase. Cpi denotes the difference of the solute's specific heat in the solid and liquid
phases, T is the actual temperature and T f ,i 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 Cpi is independent of the temperature in the range of T
to T f ,i .
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) ln  i is calculated:
ln  i  1  i  ln i  5qi (1 
x j j i, j
i

 ln i )  qi (1  ln Si  
)
i
i
Sj
j
(2)
where  and  depend on the volume and surface area parameters ri and q i , and S also on the
binary parameters  ij (see list of symbols).The values of the parameters ri and q i were taken
from Poling et al.27 (Table 2).
The adjustable binary parameters are defined in terms of binary energy interaction
parameters Aij :
 ij  exp(  Aij / T )
(3)
where in general Aii  A jj  0 and Aij  A ji . The values of the parameters Aij were obtained by
fitting the calculated solubilities of equations (1)-(2) to the measured ones using a standard
Newton routine for non-linear optimisation.
Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |347
D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures
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 ri and q i 27
M [g/mol]
ri
qi
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
H f
Tf
[K]
[kJ/mol]
C p
[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
a
estimated by Ferreira et al.15
b
estimated following Ferreira et al.15
120a
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
Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |349
D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures
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
Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |351
D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures
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.
Aij
methanol
Methanol
water
arabinose
fructose
glucose
mannose
sucrose
xylose
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
Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |353
D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures
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.
0.3
fructose
0.25
10%
x [-]
0.2
0.15
0%
0.1
0.05
0
290
300
310
320
330
T [K]
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.
354| Chapter 6
0,1
glucose
20%
0,08
10%
x [-]
0,06
0,04
0,02
0%
0
300
310
320
330
340
350
360
T [K]
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.
0.05
sucrose
20%
0.04
x [-]
0.03
10%
0.02
0.01
0%
0
290
300
310
320
330
340
350
360
T [K]
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.
Experimental and modelling studies on the solubility of D-arabinose, D-fructose, D-glucose, |355
D-mannose, sucrose and D-xylose in methanol and methanol-water mixtures
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
Ai, j
UNIQUAC binary energy interaction parameter, K
C p
solid-liquid difference of specific heat, J mol-1 K-1
H f
heat of fusion, J mol-1
qi
UNIQUAC surface area parameter
ri
UNIQUAC volume parameter
Si
UNIQUAC parameter, S i   xk  k k ,i
k
Tf
temperature of fusion, K
x
molar fraction, -
i
UNIQUAC parameter, i  ri /  xk rk
k
i
UNIQUAC parameter, i  qi /  xk qk
k
 i, j
UNIQUAC binary interaction parameter
356| Chapter 6
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(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
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Ishikawa, Y.; Saka, S., Chemical conversion of cellulose as treated in supercritical methanol.
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(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)
(28)
(29)
(30)
(31)
(32)
(33)
Peres, A. M.; Macedo, E. A., Measurement and Modeling of Solubilities of d-Glucose in
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mixtures: Comparison of UNIQUAC 1-based models. Carbohydr. Res. 1997, 303, 135-151.
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based on original experimental data. AIChE J. 2007, 53, 2411-2418.
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Fluid-Phase Equilibria. 2 ed.; Prentice Hall Inc.: Englewood Cliffs, N.J., 1986.
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