BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
FACULTY OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY
GEORGE OLÁH DOCTORAL SCHOOL
Vapor-Liquid Equilibrium Studies on Bio-oxygenate Liquid Mixtures
Thesis booklet
Author: Munaf Al-Lami
Supervisor: Prof. Dr. László T. MIKA
Consultant: Dr. Dávid HAVASI
Department of Chemical and Environmental Process Engineering
Budapest University of Technology and Economics
Catalysis Research Group
Budapest
2023
1
Introduction and literature review
The global transition towards a more sustainable economy is driving the demand for
renewable and environmentally friendly chemicals. Among the various classes of
renewable chemicals, biomass platform chemicals have emerged as a promising alternative
to petroleum-based chemicals. Biomass platform chemicals are derived from
lignocellulosic materials, which are abundant and renewable feedstocks.
The availability of expanded usage of renewable energy is contingent upon
advancements in technology, long-term planning, realization of integration strategies, and
enough expenditure. Biomass resources can be converted into fuels and energy barriers
(e.g. ethanol, bioethanol, methane, methanol, dimethyl ether, and hydrogen) by using
suitable thermal or biochemical conversion technologies.1 Knowledge of the many types
of bioenergy, the different kinds of biomass materials and where they come from, and the
multiple types by-products coming from converting biomass resources is essential for
grasping the full breadth of bioenergy. Bioenergy feedstocks typically come from three
distinct forms of biomass: sugars/starches, lipids, and cellulose/lignocellulose. The first
generation of biofuels is derived from the sugars and starches present in food crops like
corn grain. Fats, oils, and waxes are all examples of lipids, which are energy-dense, waterinsoluble molecules. Animal fats, vegetable oils, and algal lipids are all potential
feedstocks. Several common seed crops are used to produce oils for biodiesel, including
soybeans, oil palms, and sunflowers. Cellulosic/lignocellulosic biomass is made up of
complicated carbohydrates and non-carbohydrate compounds present in plants' leaves and
stems. Cellulose/lignocellulose has little or no nutritional benefit in humans. As a result,
advanced biofuels provide a chance to utilize these remarkably low resources in the
production of valuable energy products.2
One of the most promising classes of biofuels is bio-oxygenates, which are oxygencontaining compounds that are derived from renewable biological sources, such as plants,
algae, or waste products. They have gained significant attention due to their potential as
sustainable alternatives to traditional fossil fuel-based chemicals. Bio-oxygenates have
various environmental benefits, including reduced greenhouse gas emissions and increased
energy security. Among the bio-oxygenates, -valerolactone, 2-methyltetrahydrofuran and
1,4-pentanediol are identified as promising materials due to their renewable nature and
1
potential as sustainable alternatives to traditional fossil fuels. They have a wide range of
applications in various industries, including biofuels, solvents, and precursors for the
production of biodegradable polymers and plastics.3
The production of bio-oxygenates including GVL, 1,4-PDO, and 2-MeTHF from
levulinic acid often results in a mixture of these compounds.7 These mixtures have distinct
properties and behaviors that can greatly influence their potential uses in different
industries. Therefore, it is critical to study and investigate these mixtures to gain a
comprehensive understanding of their properties and potential applications including
efficient product.3
To understand the thermodynamic behavior of bio-oxygenate liquid mixtures, it is
important to measure the vapor-liquid equilibrium (VLE) and obtain the binary interaction
parameters. VLE is a crucial parameter in the design and optimization of separation
processes and plays an essential role in understanding the physical and chemical properties
of the mixture. Measuring the VLE data of bio-oxygenate liquid mixtures can provide
insight into their thermodynamic behavior and help to optimize the production process.
Obtaining the binary interaction parameters can enable the development of predictive
models, which can be used to estimate the thermodynamic properties of bio-oxygenate
mixtures. These models can be particularly useful in the absence of experimental data and
can significantly reduce the time and cost required for the design and optimization of
separation processes. Therefore, understanding the VLE behavior of bio-oxygenate liquid
mixtures and obtaining the binary interaction parameters is critical for the efficient and
sustainable production of biofuels.
2
Experimental work
The modified Gillespie still, redesigned by Havasi4 in 2016, was used to perform the
the vapor-liquid equilibrium experiments. The still was connected to a pressure regulation
system via the top condensers. The temperature-dependent vapor pressure measurements
were conducted for five compounds: GVL, 2-MeTHF, THF, toluene, and 1,4-PDO. The
VLE experiments were performed under different pressures for six mixtures containing the
bio-oxygenate, GVL, or 1,4-PDO. The analysis of the samples was performed by
employing the refractometer and gas chromatography (GC). The data were further treated
using ChemCad Aspen Plus software.
2
To analysis and measure the refractive indices, both type G Carl Zeiss Abbe
refractometer (with an integrated thermostat, and A670 auto digital refractometer, which
has a CCD light-sensitive part based on the Abbe principle were utilized. Also, due to the
lack of data regarding the refractive indices of pure 1,4-PDO in the literature, the refractive
indices of the corresponding chemical were measured at different temperatures. Some
samples were also tested using gas chromatography and 1H NMR to verify the accuracy of
the refractometry results, where no significant differences were identified, which gave
validity to the refractometer to be used for the analysis of the samples.
The VLE samples from these mixtures were analyzed using HP 6890N
chromatography apparatus, which was run in a splitless configuration, using H2 as the
carrier gas, and an HP INNOWax capillary column (15 m × 0.25 μm × 0.25 μm) fitted with
an FID detector. The analyses were performed using 10 mL of VLE sample dissolved in 1
mL of dichloromethane and took advantage of toluene as an internal standard. In addition,
a Bruker Avance-250 NMR spectrometer was used to confirm the purity of the used
material. Deuterated chloroform was used to dissolve the materials, and benzene was used
as a standard.
A rolling ball viscometer (Lovis 2000, Anton Paar) was used to measure the viscosity
of 1,4-PDO at an angle of 70 degrees. Capillary and ball diameters were 1.8 and 1.5 mm,
respectively. Anton Paar DMA 4500 M apparatus was employed to measure the densities.
The viscometer and the densimeter were calibrated using water.
2.1
Vapor pressure measurement
Knowledge of the vapor pressure for the applied chemicals is essential for the
calculation of the activity coefficients. For that reason, Antoine's adjustable parameters are
required for the investigated materials. The temperature-dependent vapor pressure of the
GVL, THF, 2-MeTHF, toluene, and 1,4-PDO were experimentally determined employing
the equilibrium still The modified Gillespie still, redesigned by Havasi4. The vapor pressure
of the examined materials was experimentally measured in the equilibrium still to guarantee
that the results are based on data provided for the relevant temperature range. The
measurements started by filling the still with pure substance. Pressures of different values
were applied to the system during the measurement process, and once the temperature had
stabilized, the corresponding pressure and temperature pairs were recorded. The three
3
adjustable parameters of the Antoine equation were obtained using those pairs by
employing the Excel solver function.
3
Results and discussion
3.1
Vapor pressure measurements
The temperature-dependent vapor pressure of GVL, 2-MeTHF, THF, toluene, and
1,4-PDO were experimentally measured at the specified range, and the results were
regressed with the Antoine equation. The adjustable parameters of the corresponding
equation were obtained using Excel Solver and presented in Table1. For the all measured
vapor pressure data the consistency were tested using the Oonk method,5,6 the results are
presented in Figure 1.
Table1: Antoine parameters for the materials investigated at the specified temperature
range.
chemical
temperature
range /K
GVL
377.85 – 480.55
2-MeTHF
296.70 − 353.30
THF
302.00 – 339.25
Toluene
321.27 − 383.45
1,4-PDO
413.10 − 496.50
A
B
C
6.4314
± 1888.0173
± –53.9478
0.0291
22.8921
2.3781
5.15780
± 761.7718
± −111.7206
0.0614
27.3455
3.9526
5.2177
± 747.0081±
-106.8035
0.0427
23.2826
2.8213
5.3778
± 922.3461
± 110.0198
0.0385
19.6080
2.6790
5.8942
± 1210.9794
± −185.0325
0.0604
34.0884
3.8818
4
±
±
±
±
±
Figure 1: Oonk consistency test.
3.2
Heat of vaporization
Using the pressure-temperature pairs that were obtained experimentally, the heat of
g,id
vaporization (∆l
0
𝐻m
/ kJ·mol–1) for the studied material was computed, in which the data
were regressed using the Calusius −Clapeyron equation, by assuming the gas phase to
g,id
behave ideally, where ∆l
0
𝐻m
/ J·mol–1 is a heat of vaporization for an ideal system, R =
8.314462 J·K–1·mol–1 is the molar gas constant, T / K is temperature, and po / kPa is vapor
pressure.
g,id
∆l
0
𝐻m
=
dln(𝑃0 /kPa)
𝑅(𝑇/K)2
d(𝑇/K)
5
(1)
g
0
∆l 𝐺m
𝑃0
1 1
𝑔 0
(𝜃) ( − )
𝑅ln ( 𝑠 ) = −
+ ∆l 𝐻m
𝑃
𝜃
𝜃 𝑇
(2)
𝜃
𝑇
𝑔 0
(𝜃) [( ) − 1 + ln ( )]
+ ∆𝑙 𝐶𝑝,m
𝑇
𝜃
Furthermore, the Clarke–Glew relation,7
g
0
eq (2), where ∆l 𝐻m
/ J·mol–1 is the
g
0
enthalpy of vaporization at a reference temperature ( ), ∆l 𝐺m
/ J·mol–1 is the variation in
molar Gibbs energy between the liquid and the gas phase at a reference pressure (the
gaseous phase is supposed to have characteristics of an ideal gas at the pressure ps), and
g
0
∆l 𝐶𝑝,m
/ J·mol–1·K–1 is the variation between the heat capacities of the ideal gas and the
condensed phase. The regression was applied with a ps = 105 Pa and a  =   as a
reference pressure and temperature, respectively, to calculate the corresponding
thermodynamic parameters of the investigated chemicals, which are shown in Table 2.
Table 2: Parameters of Clausius−Clapeyron and Clarke−Glew equations
Reference temperature  =   and Pressure Ps
material
g,id
Tmin – Tmax
∆l
/K
/ kJ·mol–1
= 105 Pa
0
𝐻m
g
0
∆l 𝐻m
kJ·mol–1
GVL
2-MeTHF
THF
377.85
– 47.43
480.55
0.12
296.7
353.3
302.0
339.3
321.27−
Toluene
1,4-PDO
–
–
±
g
g
0
∆l 𝐶𝑝,m
/
0
/ ∆l 𝐺m
/
kJ·mol–1
J·mol–1·K–1
51.51 ± 0.14 35.76 ± 0.02
-31.86 ± 1.10
33.9 ± 0.2
37.0 ± 0.2
5.343 ± 0.005
–112.83 ± 5.46
32.1 ± 0.2
34.6 ± 0.8
3.963 ± 0.003
–105.13 ± 3.62
37.31
±
42.72 ± 0.19
383.45
0.21
413.10
− 66.42
± 95.
496.50
1.14
1.89
61
6
±
8.6815
0.0146
49. 16 ± 0.40
±
–97.06 ± 3.04
–187.41 ± 12.1
3.3
Refractive indices, density, and viscosity of 1,4-PDO
Due to the limited number of reported data regarding the refractive index of 1,4-
PDO,8,9 and the fact that some of those data showed large differences even when held at
the same temperature, the refractive indices were determined across the range of 291.2–
333.2 K (Figure 2). Using eq (3), we found a highly significant linear relationship between
the temperature and the refractive index (R2 = 0.993).
𝑇/K
𝑛𝐷
= – 2.88285 × 10−4 ∙ 𝑇/K + 1.53154
(3)
Figure 2: The temperature-refractive index for 1,4-PDO. Blue • : experimentally
determined data series, blue – regressed linear equation, red ◆ and green ▲: literature
data.9
Since there is no density information for 1,4-PDO at the National Institute of
Standards and Technology (NIST), we measured its temperature-dependent density from
293.1 to 363.1 K. The results are shown in Table 3, and they fall within the typical range
for linear C5 diols. A linear correlation was used to describe the relationship between the
data as shown in eq (4).
𝜌/g ⋅ cm−3 = – 6.5209 × 10−4 ∙ 𝑇/K + 1.17503
7
(4)
Table 3: Density and viscosity for 1,4-PDO in 293.1–363.1 Ka at P = 101.3 kPa
a
T/K
r / g⋅cm–3 h / mPa⋅s
T/K
r / g⋅cm–3
h / mPa⋅s
293.1
0.9833
243.867
333.1
0.9582
24.640
303.1
0.9773
124.967
343.1
0.9516
16.030
313.1
0.9710
69.067
353.1
0.9447
11.093
323.1
0.9647
39.883
363.1
0.9378
7.731
Standard uncertainties u are: u(T) = 0.1 K and u(r) = 0.0003 g⋅cm–3 and u(h) = 1 mPa⋅s.
The temperature-dependent viscosity of 1,4-PDO was also determined in the range
of 293.1 – 361.1 K, as viscosity is an important factor in heat transfer calculation and
modeling. The results and regression analysis were determined using equations of the
Arrhenius eq (5) and the Vogel-Fulcher-Tammann eq (6).
The activation energy of viscous flow (Ea,h / kJ⋅mol–1) for 1,4-PDO was 47.55
kJ⋅mol–1 with a preexponential constant Ah = 8.15044⋅10–10
lnAh = –20.9278. The
parameters of Vogel–Fulcher–Tammann equation are A = –5.2063 ± 0.2736, B = 1570.6375
± 97.9622, T0 = 146.5395 ± 5.5228.
ln 𝜂 = 𝐴𝜂 +
𝜂/mPa ∙ s = exp (𝐴 +
𝐸𝑎,𝜂
𝑅𝑇
(5)
𝐵
)
𝑇/K − 𝑇0 / K
(6)
8
3.4
Vapor-liquid equilibrium data
The VLE data for the investigated mixtures were performed under isobaric
conditions. The results were correlated using Wilson, NRTL and UNIQUAC models. The
T-x-y diagrams are presented by Figure 3.
Figure 3: T-x-y diagrams for the investigated mixtures. Experimental data (•, ○), green- - -: Raoult’s law calculations, black ⋯⋯: Wilson, black − − −: NRTL, black –––:
UNIQUAC activity coefficient Models.
3.5
Regression of the experimental VLE date
The gathered experimental vapor-liquid equilibrium data were correlated using three
activity coefficient models; Wilson, NRTL, and UNIQUAC. The binary interaction
parameters for the 2-MeTHF+GVL, THF+GVL and toluene+GVL systems were obtained
using ChemCad software v.7.1 by employing the objective function given in eq(7). The
software attained the interaction parameters for the necessary activity coefficient models
using 138 iterations. The findings are presented in Table 4.
9
𝑁
𝑁
2
𝑂𝐹ChemCad = ∑ (𝑇calc. ;𝑖 − 𝑇exp ;𝑖 ) + ∑ (𝑦calc. ;𝑖 − 𝑦exp. ;𝑖 )
𝑖=1
2
(7)
𝑖=1
Since 1,4-Pentandeiol is not available in ChemCad database and because of the
regression flexibility of the VLE data in Aspen Plus v.11 software, the latter was utilized
to compute the BIPs for the 2-MeTHF+1,4-PDO and GVL+1,4-PDO mixtures. Aspen Plus
also provided a better regression for the VLE of GVL+1,2-EDO mixture. The regression
was performed using the maximum likelihood objective function of Britt Luecke,198 which
is given by eq (8), where σ is the standard deviation of the indicated data, NP: the number
of points in data group n, and NC is the number of components present in the data group.
It is worth noting that the original form of the three activity coefficients models has been
extended with extra parameters which offer more flexibility in the phase equilibrium fitting.
The regression results are collected in Table 4.
𝑁𝑃
𝑇calc − 𝑇meas 2
𝑃calc − 𝑃meas 2
) +(
)
𝑂𝐹Aspen = ∑ [(
𝜎𝑇
𝜎𝑃
𝑖=1
𝑁𝐶−1
2
(8)
𝑁𝐶−1
2
𝑥calc − 𝑥meas
𝑦calc − 𝑦meas
) + ∑ (
+ ∑ (
) ]
𝜎𝑥
𝜎𝑦
𝑗=1
𝑗=1
Table 4: Binary interaction parameters (BIPs) obtained by ChemCad.
system
GVL+THF
GVL+2MeTHF
GVL+Toluen
e
Wilson
Aij
230.403
3
NRTL
Aji
Bij
-
-
202.001
6.89709
1
3
-
63.8628
97.5906
63.8310
8
–416.07
1409.50
7583.80
0
0
UNIQUAC
Bji
αij
17.3670
0.300
2
4
153.347
0.316
202.573
2
3
4
–
280.280
0
10
0.300
0
ΔUij
76.5112
7
803.40
ΔUji
58.2168
8
134.097
3
–408.63
Table 5:Binary interaction parameters (BIPs) obtained by Aspen Plus.
system
model
aij / –
aji / –
bij / K
bji / K
αij
2-MeTHF
NRTL
–1.8493
0.175955
1048.518
59.88137
0.3
+
UNIQUAC 0.715366
–0.40617
–298.023
50.49299
-
1,4-PDO
Wilson
0.257791
1.831978
-274.287
–1032.5
-
GVL
NRTL
-
-
527.017
-436.117
0.3
+
UNIQUAC -
-
-316.198
247.458
-
1,4-PDO
Wilson
-
-
366.989
-520.857
-
GVL
NRTL
-
-
578.828
123.307
0.3
+
UNIQUAC -
-
-104.994
-131.532
-
1,2-EDO
Wilson
-
-553.155
-217.515
-
4
-
Practical applicability of the results
The practical applicability of the results obtained from the measurements of VLE,
vapor pressure, density, viscosity, and refractive index of bio-oxygenates liquid mixtures
can be significant in various industries. Firstly, the knowledge of VLE is crucial in
designing and optimizing various separation processes such as distillation, absorption, and
extraction. Accurate data on VLE can help to determine the optimal operating conditions,
choice of solvents, and overall behavior of the bio-based chemicals in mixtures, leading to
more efficient and environmentally friendly processes. This can also result in a reduction
in waste, energy consumption, and environmental impact, making the production and use
of these chemicals more sustainable.
Secondly, the vapor pressure data is crucial in understanding the thermodynamic
properties of the substances, which can lead to process improvements and increased
efficiency. The information obtained can also be used to predict the distribution of the
components between the vapor and liquid phases, which is essential for the design and
modeling of separation processes.
Thirdly, the density and viscosity data are important in the design and optimization
of fluid handling equipment and processes. Knowledge of these properties can help in
selecting appropriate materials for pipelines, tanks, and reactors. It can also aid in the design
of efficient mixing and agitation systems and the optimization of heat transfer processes.
11
Finally, the refractive index data is crucial in the development of quality control
procedures and the determination of the purity of substances. The information obtained can
also help to fill the gaps in knowledge and enhance the understanding of the behavior and
characterization of these compounds.
5
Proposed theses
1. The binary systems of gamma-valerolactone (GVL) can be separated from
tetrahydrofuran (THF), 2-methyltetrahydrofuran (MeTHF) or toluene by distillation as
GVL-2-MeTHF, GVL-THF, toluene and GVL are non-azeotropic mixtures and deviate in
a positive direction from Raoult's law across the entire concentration range at pressures of
101.3 and 50.7 kPa, indicating that intermolecular forces in the mixtures are weaker than
in ideal liquid mixtures.
Publication related to proposed thesis: MA4, MA3
2. The density and refractive index of 1,4-pentanediol are in a linear correlation with
temperature in the ranges of 293.1–363.1 K and 291.2–333.2 K, respectively. Instead of the
Arrhenius equation, the VFT equation should be used to calculate the temperaturedependent viscosity in the temperature range of 293.1– 361.1 K.
Publication related to proposed thesis: MA2
4. The binary system of 2-MeTHF and 1,4-PDO is non-azeotropic mixture and it deviates
in a positive direction from Raoult's law across the entire concentration range at pressures
of 101.3 and 50.7 kPa.
Publication related to proposed thesis: MA2
5. The binary system of GVL and 1,4-PDO is non-azeotropic mixtures and it deviates in a
negative direction from Raoult's law across the entire concentration range at pressures of
50.7 and 10.1 kPa allowing straightforward distillation based separation. In the opposite, at
10.1 kPa, the binary system composed of GVL and 1,2-EDO forms a minimal the boiling
point azeotropic mixture with an azeotropic composition in the mole fraction range of 0.55–
0.65.
Publication related to proposed thesis: MA1
12
6
Publications and Lectures
6.1
Publications
AM1. Al-Lami, M.; Pivarcsik, T.; Havasi, D.; Mika, L. T. Isobaric Vapor-Liquid Equilibria
for Binary Mixtures of biomass-derived Gamma-Valerolactone + 1,4–Pentanediol and 1,2–
Ethanediol. J. Chem. Eng. Data 2023. In press. (DOI:10.1021/acs.jced.2c00667)/ 90%; I.F
(2021): 3.119/ (Q1).
AM2. Al-Lami, M.; Szilágyi, A.; Havasi, D.; Mika, L. T. 1,4-Pentanediol: Vapor Pressure,
Density, Viscosity, Refractive Index, and Its Isobaric Vapor-Liquid Equilibrium with 2Methyltetrahydrofurane. J. Chem. Eng. Data 2022, 67 (6), 1450–1459.
(DOI:10.1021/acs.jced.2c00049)/ 100%; I.F (2021): 3.119/ (Q1), Independent citations: 1.
AM3. Al-Lami, M.; Havasi, D.; Koczka, K.; Mika, L. T. Isobaric Vapor-Liquid Equilibria
for Binary Mixtures of Gamma-Valerolactone + Toluene. J. Chem. Eng. Data 2021, 66 (1),
568–574. (DOI:10.1021/acs.jced.0c00791)/ 100%; I.F (2021): 3.119/ (Q1), Independent
citations: 4.
AM4. Al-Lami, M.; Havasi, D.; Batha, B.; Pusztai, É.; Mika, L. T. Isobaric Vapor–Liquid
Equilibria for Binary Mixtures of Biomass-Derived γ-Valerolactone +
Tetrahydrofuran
and 2-Methyltetrahydrofuran. J. Chem. Eng. Data 2020, 65 (6), 3063–3071. (DOI:
10.1021/acs.jced.0c00084)/ 90%; I.F (2019): 2.49/ (Q1), Independent citations: 3.
6.2
Conference lectures
1. Al-Lami, M.; Mika, L. T. 1,4-Pentanediol: Preparation, characterization, and its phase
equilibria with 2-methyltetrahydrofuran. Proceedings of the 48th International Conference
of Slovak Society of Chemical Engineering, High Tatras, Slovakia. 25th May 2022.
2. Al-Lami, Mika, L. T. Separation studies on binary mixtures of biomass-derived γvalerolactone + toluene. 12th International Council of Environmental Engineering
Education “Global Environmental Development & Sustainability: Research, Engineering
& Management”. Online. 22 April 2021
3. Al-Lami, M.; Mika, L. T. Separation studies on binary mixtures of biomass-derived γvalerolactone + tetrahydrofuran and 2-methyltetrahydrofuran. 3rd George Olah
Conference. BME, Budapest, Hungary; 28th September 2020.
13
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