Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Contents lists available at ScienceDirect
Journal of Industrial and Engineering Chemistry
journal homepage: www.elsevier.com/locate/jiec
Development of correlations between deasphalted oil yield and Hansen
solubility parameters of heavy oil SARA fractions for solvent
deasphalting extraction
Jun Woo Park a,1, Min Yong Kim a,b,1, Soo Ik Im a, Kang Seok Go b, Nam Sun Nho b,⇑, Ki Bong Lee a,⇑
a
b
Department of Chemical and Biological Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Climate Change Research Division, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
a r t i c l e
i n f o
Article history:
Received 19 October 2021
Revised 8 December 2021
Accepted 11 December 2021
Available online 17 December 2021
Keywords:
Solvent deasphalting
Solvent extraction
Deasphalted oil
Hansen solubility parameters
Heavy oil upgrading
SARA analysis
a b s t r a c t
Solvent deasphalting (SDA) is a heavy oil upgrading process that selectively extracts deasphalted oil
(DAO) and rejects asphaltenes. In this study, a quantitative analysis was conducted to predict DAO yields
in the SDA process using relative energy difference (RED); the RED was calculated from Hansen solubility
parameters (HSPs) of the feedstock and extraction solvent along with the extraction conditions, such as
temperature and solvent-to-oil ratio (SOR). SDA extraction experiments were performed in a continuous
bench-scale unit using vacuum residue (VR) and a mixture of bunker C fuel oil (BC) and VR as feedstocks.
The HSPs of saturate, aromatic, resin, and asphaltene fractions derived from the VR and BC were measured using solubility tests, wherein the fractions were dissolved in 37 different solvents. Finally, simple
and accurate correlations between the DAO yield and corresponding modified RED were acquired and
used to explain the effects of temperature and SOR on the DAO yield.
Ó 2021 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights
reserved.
Introduction
The International Energy Agency expects petroleum demands to
increase from 96.8 mbbl/day in 2017 to 111.1 mbbl/day in 2040
[1]. As the reserves of crude oil have reduced and its quality has
degraded, many attempts to utilize unconventional oils, such as
heavy oil, extra-heavy oil, and oil sands bitumen, are ongoing to
balance the increasing demand for oil and its supply [2,3].
Although the reserves of heavy oil are sufficiently large, the
asphaltenes constituents contain significant amounts of sulfur
and heavy metals (V and Ni), which are attributable to coke formation and catalyst poisoning. Therefore, unconventional oils cannot
be directly applied to the refining processes designed for conventional oils, necessitating more advanced heavy oil upgrading processes [4–7].
Heavy oil upgrading processes can be classified into hydrogen
addition and carbon rejection. Hydrogen addition processes, such
as catalytic and non-catalytic hydrogenation, increase the H/C ratio
of hydrocarbon feedstock, require harsh reaction conditions (i.e.,
high temperature and high pressure), and impose high operating
⇑ Corresponding authors.
1
E-mail addresses: nsroh@kier.re.kr (N.S. Nho), kibonglee@korea.ac.kr (K.B. Lee).
These authors contributed equally.
costs. In comparison, carbon rejection processes, such as thermal
cracking and solvent extraction, are based on H/C rearrangement
without hydrogen supply; some fractions are converted to have
higher H/C ratios, while others are converted to have lower H/C
ratios. Carbon rejection also has advantage of relatively low capital
and operating costs [8,9].
One particular carbon rejection process, solvent deasphalting
(SDA), has been receiving attention owing to its relative simplicity,
low operating costs, and moderate operating conditions [10]. In the
SDA processes, C3–C6 alkane solvents are traditionally used for
treating heavy feedstocks, such as atmospheric and vacuum residue (VR). The feedstock is separated into deasphalted oil (DAO)
and pitch. The DAO is soluble in the solvent and has higher H/C
ratios than the feedstock, and the pitch is insoluble in the solvent
and has lower H/C ratios than the feedstock. The produced DAO
with very low asphaltene content can be used as the feedstock
for upgrading processes, such as fluid catalytic cracking, hydrocracking, and lubricant manufacturing [10,11]. The SDA process is
advantageous for the selective removal of asphaltenes and requires
lower energy consumption than other reaction-based upgrading
processes. However, the liquid–liquid extraction mechanism in
the SDA process is complex, and has not yet been clearly explained,
despite extraction being the main step in determining the yield
and quality of DAO. The optimal operating conditions of the SDA
https://doi.org/10.1016/j.jiec.2021.12.015
1226-086X/Ó 2021 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
test the applicability of the proposed correlations for blended
heavy oil feedstock. n-Pentane (HPLC grade, Duksan Chemical
Co.) was used as the SDA extraction solvent. Column chromatography was used to prepare SAR fractions from VR and BC; n-heptane,
toluene, dichloromethane, and methanol (HPLC grade, Daejung
Chemical & Metals Co.) were selected as the mobile-phase solvents,
and activated alumina (0.05–0.15 mm, neutral, Brockmann Activity
I, Sigma Aldrich) was the stationary-phase adsorbent.
The elemental compositions of the feedstock were measured
using an organic elemental analyzer (Thermo Scientific FLASH,
EA-2000) and elemental analyzer (Thermo Scientific FLASH, EA1112). The nickel and vanadium contents were measured using
an X-ray fluorescence spectrometer (Oxford Instruments, XSupreme 8000, ASTM D4294). The micro-carbon residue of the
feedstock was detected using a micro-carbon residue tester (Alcor,
MCRT-160, ASTM D4530). The viscosity of the feedstock was measured using a rheometer (Thermo Scientific, HAAKE RheoStress
6000).
The boiling point distribution and American Petroleum Institute
(API) gravity of the feedstock and DAO were measured using simulated distillation (SIMDIS, ACG2123, ASTM D7500) analysis and a
density meter (Anton Paar, DMA 4500, ASTM D5002), respectively.
The molecular weight of the feedstock and DAO was measured
using gel-permeation chromatography (Waters, 515 HPLC pump).
Here, tetrahydrofuran (HPLC grade, J.T. Baker) was used as the solvent at a flow rate of 1 mL/min; the measurement was carried out
at 40 °C.
process required to produce the desired DAO are generally determined based on repeated experiments, because predictive models
for the yield and quality of DAO are not well established.
Previous studies on the SDA process have focused on testing
various operating conditions in the extraction [12–18] and solvent
recovery steps [19–21], and most research only presented experimental results of the yield and quality of DAO. Although some
researchers have attempted to obtain a fundamental understanding of the extraction by introducing a solubility parameter, the
relationship between the DAO yield and solubility parameter was
only qualitatively analyzed [17,18]. Al-Sabawi et al. showed that
the DAO yield was related to the Hildebrand solubility parameter
of n-pentane/modifier mixtures; however, this relationship did
not reflect all the DAO yields and solubility parameters in the npentane/modifier mixtures [17]. Long et al. found that the DAO
yield increased with increasing feedstock ratio of coal tar to VR
because the Hildebrand solubility parameter difference between
the extraction solvent and coal tar was smaller than that between
the extraction solvent and VR [18]. This indicates that the Hildebrand solubility parameter difference between two components
is inversely (negatively) correlated to their mutual solubility. However, this explanation did not account for the trend in the DAO
yield in some feedstock ratios.
The explanations for the solution phenomena that only use the
Hildebrand solubility parameter are inappropriate for polar systems [22,23]. To overcome this limitation, Hansen restructured
the Hildebrand solubility parameter into dispersion, polar, and
hydrogen bonding solubility parameters [24]. Furthermore, the
concept of relative energy difference (RED) as a mutual solubility
index was introduced; RED values of less than 1.0 indicate complete dissolution, and values greater than 1.0 indicate partial dissolution or insolubility. However, the comparison of systems that
have different compositions but identical components was still
impossible, because the theory based on the original Hildebrand
solubility parameter and Hansen solubility parameters (HSPs) did
not consider the compositional effect of the solution. Later, a modified RED was developed, which enabled HSPs to be used in the
comparison of systems having various compositions [25].
In this study, a quantitative analysis was conducted by applying
the modified RED to predict the DAO yield in the SDA process at
various operating conditions, including temperature and solventto-oil ratio (SOR). To the best of our knowledge, the application
of the modified RED to heavy oil extraction was first proposed in
this study. VR and bunker C fuel oil (BC) were used as heavy oil
feedstocks, which were separated into saturate, aromatic, resin,
and asphaltene (SARA) fractions. The HSPs of the fractions were
obtained from the solubility test by dissolving the fractions in various solvents. To account for the effects of temperature and SOR on
the DAO yield, a modified RED was introduced, which was calculated from the HSPs of the extraction solvent and SAR fractions
of the feedstock at various temperatures and SORs. Finally, quantitative correlations were made between the DAO yield and corresponding modified RED for the application to SDA extraction to
decide operating variables such as temperature, pressure, and
SOR for different types of feedstocks to produce desired DAO.
Asphaltene precipitation and SAR analysis of feedstock and DAO
Asphaltene precipitation was performed by following a modified ASTM D4124 method. First, 1 g of VR or BC sample and
100 mL of n-heptane were mixed in an Erlenmeyer flask, and
heated to 90 ± 5 ℃, while being stirred for 30 min; the temperature
of the mixture was kept constant for an additional 30 min. The
mixture was then cooled without stirring under ambient conditions for 1 h, followed by vacuum filtering through filter paper
(32–34 mm diameter, 1.5 lm pore size). The asphaltene component that did not pass through the filter paper was collected, and
the maltene component was obtained from the filtered solution
after removing n-heptane by heating.
The modified ASTM D4124 method for SAR analysis was performed to further characterize the maltene component using
open-column chromatography. First, 110 g of activated alumina,
after thermal treatment at 200 °C for 24 h, was wet-packed with
n-heptane in a glass column with 1.5-mm diameter and 1-m
length. Then, 0.9 g of DAO, 0.5 g of VR-derived maltene, or 0.5 g
of BC-derived maltene was dissolved in 5 mL of dichloromethane
and transferred through the column. After confirming that the oil
sample was adsorbed on the alumina, the elution solvents, which
are presented in Table 1, were changed sequentially. The mixture
of the eluted fraction and solvent was continuously drained from
the bottom of the column and collected in stainless steel beakers.
After evaporating the eluted solvents, each eluted SAR fraction
was obtained, as presented in Fig. 1. The weight of each fraction
was measured, and its proportion was calculated based on the
weight of the original oil sample. To analyze the DAO samples,
the DAO was assumed to comprise saturate, aromatic, and resin
fractions. Moreover, the weight ratio of each SAR fraction in the
DAO to the feedstock (DAO_SAT, DAO_ARO, and DAO_RES yields)
was calculated as follows:
Experimental section
Materials and analytical methods used for characterizing feedstocks
and products
DAO SAT yield ðwt%Þ ¼ DAO yield ðwt%Þ
Two types of heavy oils—VR and 50% w/w BC and VR (BC/VR)—
were used as the feedstocks for the SDA extraction. Oil blending is
common in oil refining; therefore, the BC/VR mixture was used to
Proportion of saturate fraction in DAO ðwt%Þ
457
ð1Þ
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
respectively. The HSP for a mixture of several solutes can be calculated as follows [26]:
Table 1
Sequence of eluant solvents in SAR column chromatography.
Sequence
Eluant solvent
Volume (mL)
1
2
3
4
5
n-Heptane
Toluene
Toluene
Toluene/methanol (50:50)
Dichloromethane/methanol (90:10)
150
33
67
75
150
Eluate
dðd;p;hÞ;mixture ¼ Rni¼1 dðd;p;hÞ;i ui
Saturate
where d(d,p,h),i is the HSP, and ui is the volume fraction of solute i. Eq.
(7) was used for calculating the HSP of the mixed BC/VR feedstock.
The solubility is changed by the properties of the material as
well as pressure, temperature, and the solute and solvent compositions. n-Pentane possesses dp and dh of zero and the extraction
was conducted at supercritical and subcritical conditions. Thus,
the following empirical correlation was used to calculate dd [27]:
Aromatic
Resin
dd;solvent ¼ dd;ref ðV ref =V Þ1:13
ð7Þ
ð8Þ
Because the original RED did not consider the compositional
effect among components, a modified RED was proposed, which
includes the contribution of the compositions to the entropy of
mixing (DSmix) and enthalpy of mixing (DHmix) for the radius of
the HSP sphere [25]. The modified RED is calculated by substituting
Reff for R0 as follows:
DSmix ðxÞ=DHmix ðxÞ
Reff ðx ¼ 0:5Þ
DSmix ðx ¼ 0:5Þ=DHmix ðx ¼ 0:5Þ
Fig. 1. Photograph of asphaltene and fractions eluted from the SAR column
chromatography.
Reff ðxÞ ¼
DAO ARO yield ðwt%Þ ¼ DAO yield ðwt%Þ
Rsolvent ðx ¼ 0:5Þ ¼
ð9Þ
1600
VM
ð10Þ
1
1
Reff ðx ¼ 0:5Þ ¼ 1=
þ
R0 Rsolvent ðx ¼ 0:5Þ
ð11Þ
DSmix ¼ R½xlnx þ ð1 xÞlnð1 xÞ
ð12Þ
HSP theory
DHmix ¼ bxð1 xÞ
ð13Þ
The Hildebrand solubility parameter, defined as the square root
of the cohesive energy density, was introduced to account for solution phenomena; it can be described as follows [25]:
1
b ¼ ½ðdd1 dd2 Þ2 þ ðdp1 dp2 Þ2 þ ðdh1 dh2 Þ2 2
ð14Þ
Proportion of aromatic fraction in DAO ðwt%Þ
ð2Þ
DAO RES yield ðwt%Þ ¼ DAO yield ðwt%Þ
Proportion of resin fraction in DAO ðwt%Þ
d2 ¼
EV DHV RT
¼
V
V
ð3Þ
where Reff is the modified HSP radius of the solute, which is
obtained by applying DHmix, DSmix, and the mole fraction (x).
The x value was calculated from the number-average molecular
weight, which was measured using gel-permeation chromatography, and the composition of each SARA fraction derived from the
VR or the mixture of VR and BC. Parameter Rsolvent is the HSP radius
of the solvent, which was not defined in the original HSP theory; VM
is the molar volume (cm3/mol); and b is the interaction parameter
between the solute and solvent. The modified RED is expressed as a
function of the HSP of the solute and solvent, temperature, pressure,
and composition of a system.
ð4Þ
where d is the Hildebrand solubility parameter, EV is the molar
energy of vaporization, V is the molar volume, DHV is the molar
enthalpy of vaporization, R is the gas constant, and T is the temperature. However, the Hildebrand solubility parameter alone cannot
adequately describe the dissolution of polar substances. To overcome this limitation, Hansen reconfigured the Hildebrand solubility
parameter to include dispersion (dd), polarity (dp), and hydrogen
bonding (dh) solubility parameters, resulting in a threedimensional parameter system that can provide better predictions
for equilibrium, where polar and hydrogen bonding interactions
are significant factors [24]. This three-parameter system is called
the HSP. Additionally, to quantify the solvation ability of a solvent,
the RED was defined, as presented in Eq. (6). When RED < 1, the system is stabilized in one phase (i.e., a solution); when RED > 1, the
system is partially miscible or immiscible [26].
d2 ¼ d2d þ d2p þ d2h
Ra
¼
RED ¼
R0
Measurement of HSPs
The HSPs of widely used solvents can be obtained from previous
studies [28]. However, it is difficult to obtain the parameters for
calculating theoretically defined HSPs of oil samples. Therefore,
the HSPs of oil samples are best determined through solubility
tests [27–31]. For the initial screening of the candidate solvents,
previous studies were referred to, and the HSP of bitumenderived maltene was obtained as dd = 17.5 Mpa0.5, dp = 5.8
Mpa0.5, dh = 2.5 Mpa0.5, and R0 = 6.7 [27,29]. Most solvents were
selected based on the RED range between the solvent and
bitumen-derived maltene of 0.8–1.2. Following the solubility test
with the first group of solvents, the HSP of each SARA fraction
was updated. Additional solvents were also selected based on the
updated RED between the solvent and SARA fractions in the range
of 0.8–1.2. Additionally, solvents well-known for their ability to
dissolve asphaltene were added. In total, 37 solvents were tested
in this study.
ð5Þ
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
2
4ðdd1 dd2 Þ2 þ dp1 dp2 þ ðdh1 dh2 Þ2
R0
ð6Þ
where Ra is the Hansen distance, the distance between HSPs of
solute and solvent in the 3D space, which represents the solubility
parameter difference between the solute and solvent; and R0 is the
radius of the virtual sphere centered on the solute HSP and represents a cutoff value that is used to distinguish between good and
bad solvents. Subscripts 1 and 2 indicate the solute and solvent,
458
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
higher density than BC/VR, and the boiling point range of the VR
was more concentrated at high temperatures compared to that of
the BC/VR. Moreover, the VR contains more resins and asphaltenes
than the BC/VR mixture, which explains why VR has a higher density and viscosity than BC/VR.
In the solubility test, 0.1 mL of solvent was added to approximately 0.01 g of SAR fractions derived from the VR or BC. In the
case of asphaltene, approximately 0.01 g of asphaltene fraction
derived from the VR or BC was mixed with 0.2 mL of solvent
because of the high viscosity of asphaltene. The mixture was
immersed in a sealed vial, and allowed to stand for approximately
3 h to determine the solubility of asphaltene. The solvents were
then classified into three categories based on their ability to dissolve the SARA samples: (1) completely soluble, (2) partially soluble, or (3) insoluble, which were assigned values of 1, 0.5, and
0, respectively.
The HSPs of the SARA samples were calculated using an opensource software devised by the HSP developers [32]. After setting
R0, the software adjusted a solute HSP with the lowest fitting error,
so that the HSPs of solvents in the ‘‘completely soluble” category
were placed (as much as possible) inside the sphere and those of
the solvents in the other categories outside the sphere. The fitting
error is defined as follows:
Fitting Error ¼ 1 n
Y
Effect of extraction temperature and SOR on the yield and properties of
DAO
Fig. 3 shows the DAO yield obtained in the SDA extraction
experiments conducted at different temperatures and SORs. The
decrease in the extraction temperature and increase in the SOR
caused an increase in the DAO yield for both the VR and BC/VR
feedstocks. The DAO yield is known to be highly dependent on
the extraction temperature because temperature significantly
affects the solvation ability of a solvent [20,33,34]. The temperature increase entails increasing molar volume and decreasing surface tension [31], resulting in a lower value of the dispersion
energy parameter (dd) of n-pentane and higher modified RED
between n-pentane and SAR fractions derived from both feedstocks, owing to the lower dd value of n-pentane compared to that
of the feedstocks. Previous studies also reported that the DAO yield
was improved by increasing the SOR [33,34]; however, the effect of
the SOR on the DAO yield cannot be explained using the original
HSP theory. This trend could be theoretically described using the
modified RED. An increase in SOR (x in Eq. (9)) increases both DSmix
and DHmix; however, the increasing rate of DSmix is higher than
that of DHmix, leading to an increase in the radius of the HSP sphere
(Reff), which corresponds to a decrease in the modified RED (because Reff is the denominator of RED). This explains why the DAO
yield increases with increasing SOR.
Fig. 4 depicts the inverse relationship between the yield and API
gravity of the DAO produced at various extraction temperatures
and SORs of both the VR and BC/VR feedstocks, implying that the
heavier components were extracted into DAO in the case of higher
DAO yield [18,33]. Notably, for the same SDA feedstock, although
the operating conditions of the temperature and SOR differed,
the density of the DAO products was similar in the case of identical
DAO yields.
Fig. 5 shows the boiling point distribution obtained using SIMDIS analysis for the DAO products. The operating conditions presented in the abscissa are listed in the order of increasing
modified RED between n-pentane and SAR fractions derived from
the feedstocks. The DAO produced at higher extraction temperatures and lower SORs, corresponding to relatively higher modified
RED values and lower DAO yields, tends to have a lower boiling
point distribution.
Fig. 6 shows the molecular weight distribution measured via
gel-permeation chromatography of the feedstocks and DAO produced at various temperatures and SORs. At higher extraction temperatures, DAO with low average molecular weights could be
attained, implying that high-molecular-weight components were
excluded from the DAO. The molecular weight distribution of the
DAO also narrowed and shifted to lower values as the extraction
temperature was increased. The analytical results of the API gravity, boiling point distribution, and molecular weight distribution
revealed that higher-quality DAO could be attained at higher
extraction temperatures and lower SORs in the SDA process.
Fig. 7 shows the proportions of SAR fractions in the DAO derived
from the VR and BC/VR feedstocks for different DAO yields. As the
DAO yield increased, the proportions of the aromatic and resin
fractions increased, but that of the saturate fraction decreased. This
trend is consistent with the API gravity decrease that accompanied
higher DAO yields. For the same DAO yield of approximately 70 wt
%, the proportion of the saturate fraction was higher and that of the
aromatic fraction was lower in the BC/VR-derived DAO compared
!1=n
ð15Þ
Ai
i
Ai ¼
8
>
<
>
:
ðgood solventsÞ
1
expðRa R0 Þ
expðR0 Ra Þ
ðwrong-in bad solventsÞ
ð16Þ
ðwrong-out bad solventsÞ
where n is the number of tested solvents. Notably, if there is an outlier with a fairly large difference between Ra and R0, the fitting error
increases sharply because of its exponential form. Each HSP and fitting error were calculated with varying R0 values to one decimal
place. Among the fitting errors calculated by changing R0, the HSP
of the solute and R0 were finally determined based on the result
yielding the lowest fitting error.
SDA extraction experiment in a bench-scale unit
The SDA extraction experiments were performed on a 0.1 barrel
per day scale using the continuous extraction unit, as presented in
Fig. 2. The feedstock oil flowed to the top of the extraction column,
while the solvent flowed to the bottom, resulting in countercurrent extraction. The flow rate of the feedstock was fixed at
0.7 kg/h; the weight-based SOR was 7, 5, and 3 for the VR feedstock, and 8 and 6 for the BC/VR mixture feedstock. A small amount
of solvent was mixed with the feedstock to ensure fluidity. For the
VR feedstock, both the feedstock and solvent were heated to
extraction temperatures of 160, 170, 180, and 190 ℃ using an electrical heating jacket. For the mixed BC/VR feedstock, the extraction
temperatures were set to 185, 190, and 200 ℃. Both the feedstock
and solvent were pressurized to 43 bar. Overall, 12 and 6 extraction experiments were conducted for the VR and mixed BC/VR
feedstocks, respectively. For each extraction experiment, the temperature, pressure, and solvent and feedstock flow rates in the column were kept constant for 1 h to maintain steady-state operation,
followed by the production of DAO and pitch for 1 h. The weight of
the extracted DAO was measured after the evaporation of the solvent at 150 °C for 24 h.
Results and discussion
Characteristics of feedstock
The properties of the VR and mixed BC/VR feedstocks are listed
in Table 2. Based on the API gravity, viscosity, and heteroatom (N, S,
and O) and heavy metal (Ni and V) contents, both VR and BC/VR
can be classified as heavy oil. The VR has a lower H/C ratio and
459
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Fig. 2. Schematic of experimental apparatus for SDA extraction.
HSPs of SARA fractions derived from feedstocks
Table 2
Characteristics and SARA fractions of the feedstock.
Properties
VR
BC/VR (50% w/w)
Elemental analysis (wt%)
C: 83.7
H: 10.0
N: 0.6
O: 0.7
S: 5.5
4.1
56
320
20.53
382.8–745.6
9.53/30.71/
40.37/19.39
240–46,400
1025
C: 84.8
H: 10.3
N: 0.4
O: 0.6
S: 5.2
8.9
27
137.5
17.04
157.8–745.2
9.95/35.51/
38.58/15.95
124–24,460
845
API gravity (°)
Ni (wt. ppm)
V (wt. ppm)
MCR (wt.%)
Boiling point distribution (℃)
SARA analysis (wt%): saturate/
aromatic/resin/asphaltene
Viscosity (mPas)
Molecular weight (Mn)
The experimental solubility-levels for SARA fractions were measured and REDs between the solvents and SARA fractions were calculated for the VR and BC/VR feedstocks, as listed in Tables 3 and 4,
respectively. Here, the abbreviations for the SARA fractions derived
from VR and BC are based on the feedstock and the first three letters of the SARA fractions; for example, VR_SAT represents the saturate fraction obtained from VR. Based on the calculated solubility
levels, the HSP of each SARA fraction was obtained, as shown in
Table 5. The HSPs and R0 values of VR_SAT, VR_RES, and VR_ASP
are identical to those of BC_SAT, BC_RES, and BC_ASP, respectively,
although there are slight differences between the results for the
‘‘partially soluble” and ‘‘insoluble” categories. This is because the
HSPs were determined using only the ‘‘completely soluble” category of the solvents. The HSPs of VR_ARO and BC_ARO exhibit
minor differences because the solvents isobutyl acetate and
dichloromethane have different solvation abilities. Notably, the
VR and BC have similar HSPs despite having different physical
properties. The HSPs of the SARA fractions derived from different
heavy oils are expected to have similar values.
In both the VR and BC feedstocks, dd tends to increase in the
order of saturates < aromatics < resins < asphaltenes. In particular,
with the VR-derived DAO, indicating that the BC/VR-derived DAO
was of higher quality than the VR-derived DAO, despite having
identical yields.
Fig. 3. DAO yield at different extraction temperatures and SORs of (a) VR and (b) BC/VR feedstocks.
460
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
Fig. 4. Relationship between the yield and API gravity of DAO produced at various extraction temperatures and SORs from (a) VR and (b) BC/VR feedstocks.
Fig. 5. Boiling point distribution of feedstock and DAO produced at various extraction temperatures and SORs from (a) VR and (b) BC/VR feedstocks. SORX_Y refers to the DAO
produced at an operating condition of Y °C and SOR X.
Fig. 6. Molecular weight distribution curves of feedstock and DAO produced at various extraction temperatures and SORs from (a) VR with SOR 7, (b) VR with SOR 5, (c) VR
with SOR 3, (d) BC/VR with SOR 8, and (e) BC/VR with SOR 6.
461
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Fig. 7. Proportion of SAR fractions derived from (a) VR and (b) BC/VR feedstocks for various yields of DAO.
Table 3
Solubility-levels and REDs between solvents and SARA fractions derived from VR.
Solvent
SAT
ARO
RES
ASP
RED_SAT
RED_ARO
RED_RES
RED_ASP
Heptane
Benzene
Toluene
Diethylamine
Isopropyl ether
o-Xylene
1,2,4-Trichlorobenzene
2-Ethylhexyl acrylate
Dimethyl phthalate
Ethyl ether
Chloroform
Butyl acetate
Isobutyl acetate
Isoamyl acetate (3-methylbutyl acetate)
Acetone
Ethyl acetate
1,4-Dioxane
Methyl acetate
Tetrahydrofuran
Isopropyl acetate
Methyl acrylate
1-Decanol
Dimethyl sulfoxide
N,N-Dimethylformamide
Diethylene glycol monomethyl ether
Isooctyl alcohol
Benzyl alcohol
2-Chlorophenol
Triethylamine
1,2-Epoxy butane
Diethylene glycol monoethyl ether acetate
1,2,3,4-Tetrahydronaphthalene
Butyraldehyde
Cyclopentanone
N,N-dimethyl acetamide
Methyl chloride
Dichloromethane
1
1
1
1
1
1
0.5
1
0
1
1
1
1
1
0
1
0.5
0
1
0.5
0.5
1
0
0
0
0
0
0
1
1
0
1
0
0.5
0
1
1
1
1
1
1
0.5
1
0.5
1
0
1
1
1
0.5
0.5
0
1
0.5
0
1
0
0
0
0
0
0
0
0
0
1
1
0
0.5
0
0.5
0
1
1
0
0.5
1
1
0.5
1
0.5
0.5
0
0.5
1
0.5
0.5
0.5
0
0
0.5
0
1
0
0
0
0
0
0
0
0
0.5
0
1
0
0.5
0.5
0.5
0.5
0.5
1
0
0.5
0.5
0
0
1
1
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0.5
0
0
0
1
0
0.5
0
0.5
1
0.42
0.58
0.46
0.67
0.79
0.42
1.24
0.64
1.48
0.66
0.67
0.66
0.72
0.74
1.41
0.88
1.00
1.11
1.00
0.99
1.25
1.11
2.37
2.09
1.65
1.72
1.77
1.98
0.44
0.99
1.08
0.89
1.33
1.56
1.75
0.75
1.00
0.84
0.83
0.58
0.37
1.01
0.64
1.18
0.73
1.36
0.97
0.78
0.85
0.96
1.03
1.43
1.01
1.25
1.20
1.10
1.26
1.45
1.42
2.51
2.24
1.92
2.08
2.08
2.32
0.67
0.91
1.29
0.99
1.31
1.47
1.86
0.72
0.96
1.73
1.28
0.99
0.56
1.71
0.99
1.03
1.21
1.10
1.45
0.66
1.01
1.24
1.29
1.44
1.03
1.21
1.21
0.91
1.47
1.50
1.41
2.53
2.21
1.92
2.30
2.02
2.30
1.25
0.98
1.25
1.12
1.31
1.28
1.77
0.98
0.50
2.17
1.29
0.99
1.02
2.35
0.99
1.03
1.76
1.54
1.97
0.62
1.39
1.71
1.71
2.12
1.48
1.15
1.76
1.25
1.98
2.06
1.61
3.30
2.91
2.49
3.01
2.43
2.65
1.35
1.59
1.65
0.99
1.98
1.84
2.40
1.54
0.64
RED between asphaltene and n-heptane is higher than that
between resin and n-heptane. It seems that the asphaltene with
relatively high dispersive interactions in the feedstock was separated first through asphaltene precipitation, while the resin having
medium dispersive interactions remained in the maltene fraction
[27].
In the HSP calculation, the outlier was defined as a solvent with
a theoretical solubility-level (estimated from the RED) that differed
from the experimental solubility-level. In the solubility test for
VR_SAT and BC_SAT, four outliers were observed: 1-decanol
(RED = 1.11), tetrahydrofuran (RED = 1.00), isopropyl acetate
(RED = 0.99), and 1,4-dioxane (RED = 1.00). Although the saturate
fraction was theoretically partially dissolved in 1-decanol and
tetrahydrofuran, it was completely dissolved in the experiment.
dd increases sharply from the aromatic to resin fractions, signifying
that the resin and asphaltene fractions contain more polycyclic
aromatic compounds than the saturate and aromatic fractions.
Because dd is highly correlated with the number of aromatic rings
per molecule, this trend agrees with well-known facts concerning
the structure of the SARA fractions [35–37].
As shown in Table 1, carrier solvents (n-heptane for saturates,
toluene for aromatics, and toluene/methanol (50:50) for resins)
were used in the order of increasing dp and dh to fractionate the
SAR
fractions.
Therefore,
the
increasing
trend
of
saturates < aromatics < resins that was observed for both dp and
dh is expected. The exception is VR_ARO, which has a lower dp than
VR_SAT and VR_RES. Interestingly, the dp value for VR_ASP and
BC_ASP is lower than that for VR_RES and BC_RES, although the
462
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
Table 4
Solubility-levels and REDs between solvents and SARA fractions derived from BC.
Solvent
SAT
ARO
RES
ASP
RED_SAT
RED_ARO
RED_RES
RED_ASP
Heptane
Benzene
Toluene
Diethylamine
Isopropyl ether
o-Xylene
1,2,4-Trichlorobenzene
2-Ethylhexyl acrylate
Dimethyl phthalate
Ethyl ether
Chloroform
Butyl acetate
Isobutyl acetate
Isoamyl acetate (3-methylbutyl acetate)
Acetone
Ethyl acetate
1,4-Dioxane
Methyl acetate
Tetrahydrofuran
Isopropyl acetate
Methyl acrylate
1-Decanol
Dimethyl sulfoxide
N,N-Dimethylformamide
Diethylene glycol monomethyl ether
Isooctyl alcohol
Benzyl alcohol
2-Chlorophenol
Triethylamine
1,2-Epoxy butane
Diethylene glycol monoethyl ether acetate
1,2,3,4-tetrahydronaphthalene
Butyraldehyde
Cyclopentanone
N,N-dimethyl acetamide
Methyl chloride
Dichloromethane
1
1
1
1
1
1
0.5
1
0
1
1
1
1
1
0.5
1
0
0
1
0.5
0.5
1
0
0.5
0
0
0
0
1
1
0.5
1
0.5
0.5
0.5
1
0.5
1
1
1
1
0.5
1
0.5
1
0
1
1
1
1
0.5
0.5
1
0.5
0
1
0.5
0
0
0
0.5
0
0
0
0
1
1
0.5
0.5
0.5
0.5
0.5
1
0.5
0.5
0.5
1
1
0.5
1
0.5
0.5
0
0.5
1
0.5
0.5
0.5
0.5
0.5
0
0
1
0
0
0
0
0.5
0
0
0
0
0.5
1
0
0.5
0.5
0.5
0.5
0.5
1
0
0.5
0.5
0
0
1
1
0.5
0
0
1
0.5
0
0
0.5
0.5
0.5
0
1
0
0
0
0
0
0
0
0
0
0
0.5
0
0.5
0
0.5
0
0.5
1
0.42
0.58
0.46
0.67
0.79
0.42
1.24
0.64
1.48
0.66
0.67
0.66
0.72
0.74
1.41
0.88
1.00
1.11
1.00
0.99
1.25
1.11
2.37
2.09
1.65
1.72
1.77
1.98
0.44
0.99
1.08
0.89
1.33
1.56
1.75
0.75
1.00
0.99
1.00
0.71
0.39
1.07
0.72
1.39
0.70
1.53
0.95
0.77
0.78
0.91
0.99
1.52
0.97
1.35
1.20
1.09
1.25
1.47
1.47
2.79
2.46
2.02
2.20
2.24
2.55
0.84
0.92
1.28
1.17
1.38
1.63
2.01
0.68
1.00
1.73
1.28
0.99
0.56
1.71
0.99
1.03
1.21
1.10
1.45
0.66
1.01
1.24
1.29
1.44
1.03
1.21
1.21
0.91
1.47
1.50
1.41
2.53
2.21
1.92
2.30
2.02
2.30
1.25
0.98
1.25
1.12
1.31
1.28
1.77
0.98
0.50
2.17
1.29
0.99
1.03
2.35
0.99
1.03
1.76
1.54
1.97
0.62
1.39
1.71
1.71
2.12
1.48
1.15
1.76
1.25
1.98
2.06
1.61
3.30
2.91
2.49
3.01
2.43
2.65
1.35
1.59
1.65
0.99
1.98
1.84
2.40
1.54
0.64
Table 5
Comparison of HSPs among SARA fractions.
Sample
dd (Mpa0.5)
dp (Mpa0.5)
dh (Mpa0.5)
R0
Number of outliers
Fitting error (%)
VR_SAT
VR_ARO
VR_RES
VR_ASP
BC_SAT
BC_ARO
BC_RES
BC_ASP
16.3
16.8
17.7
18.3
16.3
16.6
17.7
18.3
1.1
4.0
5.8
4.7
1.1
4.0
5.8
4.7
2.2
1.4
3.9
4.1
2.2
2.4
3.9
4.1
7.5
6.2
4.9
4.0
7.5
5.4
4.9
4.0
4
3
0
2
4
2
0
2
55
60
0
69
55
38
0
69
tene samples, a small amount of solid asphaltene, such as carboid,
remained undissolved even in solvents categorized as completely
soluble, making it difficult to classify the solubility level of asphaltene. Because the same phenomenon occurred during the solubility
test involving 1,2,4-trichlorobenzene and tetrahydrofuran, these
solvents were classified as completely soluble.
Conversely, isopropyl acetate was calculated to belong to the completely soluble category, but was experimentally observed to partially dissolve both VR_SAT and BC_SAT. In particular, 1,4dioxane was originally calculated to completely dissolve both
VR_SAT and BC_SAT but was experimentally observed to partially
dissolve VR_SAT and did not dissolve BC_SAT to any appreciable
extent.
The solvents included in the outliers for VR_ARO are isobutyl
acetate (RED = 0.96), tetrahydrofuran (RED = 1.10), and tetrahydronaphthalene (RED = 0.99), which have acceptable fitting errors.
The outliers for BC_ARO are tetrahydrofuran (RED = 1.09) and isoamyl acetate (RED = 0.99), which also fall within an acceptable
range. The outliers for VR_ASP and BC_ASP are 1,2,4trichlorobenzene (RED = 1.03) and tetrahydrofuran (RED = 1.25).
These two outliers, which were theoretically predicted to partially
dissolve asphaltene, were classified as completely soluble based on
the solubility test. In the solubility-level classification of asphal-
Correlation between RED and weight ratio of each SAR fraction in DAO
to the feedstock
As explained previously, the modified RED integrated major
process variables, such as the HSPs of the SAR fractions of the feedstocks, HSP of n-pentane, extraction temperature, and SORs. Moreover, the SDA extraction results showed that the yield and
properties of DAO highly depend on the extraction temperature
and SOR. In this respect, the modified RED was expected to correlate with the DAO yield.
463
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Fig. 8. Correlations between (a) DAO_SAT yield and corresponding RED between the HSPs of VR_SAT and n-pentane; (b) DAO_ARO yield and corresponding RED between the
HSPs of VR_ARO and n-pentane; and (c) DAO_RES yield and corresponding RED between the HSPs of VR_RES and n-pentane.
Fig. 9. Correlations between (a) DAO_SAT yield and corresponding RED between the HSPs of BC/VR_SAT and n-pentane; (b) DAO_ARO yield and corresponding RED between
the HSPs of BC/VR_ARO and n-pentane; and (c) DAO_RES yield and corresponding RED between the HSPs of BC/VR_RES and n-pentane.
applied to industrial applications using only the HSPs of the feedstocks and a simple linear-mixing rule based on the compositions
of feedstocks. Moreover, because these correlations only require
the HSP value of the feedstock, they can be applied to the liquid–
liquid extraction process, wherein thermodynamic parameters
are difficult to measure. The HSP has been used in theoretical analysis of phase equilibrium in various separation technologies [40–
43].
To determine the correlations, six modified RED combinations
were calculated using the HSPs of three SAR fractions of the VR
and BC/VR, and the HSP of the extraction solvent at different operating conditions. Likewise, six yield combinations of DAO_SAT,
DAO_ARO, and DAO_RES derived from each of the two feedstocks
were also selected. The modified REDs and yield combinations
are plotted in Figs. 8 and 9. For both feedstocks, note that the
DAO_ARO and DAO_RES yields have an inversely linear relationship with the corresponding RED, and the R-square values of four
correlations exceed 0.89.
Although the two points delineated by the circle in Fig. 8b and c
were obtained at different operating conditions (one at 160 °C/SOR
3, and the other at 170 °C/SOR 5), they exhibited similar REDs and
yields. This implies that a similar DAO yield can likely be obtained
at different combinations of SOR and temperature. More energyefficient and less solvent-demanding operating conditions would
be more attractive if similar performances could be achieved
(e.g., 160 °C and SOR 3 was more favorable than 170 °C and SOR 5).
In the VR-derived DAO_SAT yield, a roughly inversely linear
relationship was obtained, although the data were significantly
scattered. All saturate fractions in the feedstocks were assumed
to be transferred to the DAO because almost none were detected
in the SARA analysis of pitch using thin-layer chromatography.
The scattering of data probably arises because the SAR column
chromatography inherently has an absolute mass error [38,39].
In particular, the absolute data range of the DAO_SAT yield is much
narrower than those of the DAO_ARO and DAO_RES yields. Additionally, a fluctuation of temperature (approximately 2 °C) in the
experiment inevitably introduced errors.
Notably, for the BC/VR-derived DAO, the HSP was calculated
from the respective HSPs of the BC and VR using Eq. (7), and the
correlation was successfully applied to the blended BC/VR feedstock. This suggests that the developed correlations can also be
Conclusions
A new correlation for DAO yield from heavy oil was developed
applying modified REDs with investigating the effects of operating
variables. DAO was produced from two types of feedstocks—VR
and a mixture of VR and BC—using n-pentane as the solvent
through SDA extraction. The experimental and analytical results
showed that the DAO yield was inversely correlated to the DAO
quality and decreased with increasing extraction temperature (a
decrease in solvent density and dd) and decreasing SOR (a decrease
in system entropy/enthalpy and solvent Reff). The HSPs of the SARA
fractions of feedstocks were determined through solubility tests
and used for calculating the modified REDs. The HSPs obtained
for each fraction showed good agreement with the well-known
structure of each fraction, and were used for calculating the modified REDs, which can quantify the solubility. Based on the results,
the weight ratio of each SAR fraction in the DAO to the feedstock
(DAO_SAT, DAO_ARO, and DAO_RES yields) was correlated to the
corresponding modified RED between the n-pentane solvent and
each SAR fraction of the feedstock. Because the modified RED generally increases as the intermolecular interactions between the
extraction solvent and feedstock decrease, inversely linear relationships between the modified RED and each of the DAO_SAT,
464
Journal of Industrial and Engineering Chemistry 107 (2022) 456–465
Jun Woo Park, Min Yong Kim, Soo Ik Im et al.
DAO_ARO, and DAO_RES yields were observed for both the VR and
BC/VR feedstocks. These correlations are expected to be beneficial
for predicting the DAO yield under various operating conditions
upon calculating a modified RED for the particular conditions.
Furthermore, these correlations can be applied to determine the
optimal operating conditions for an SDA process to produce DAO
with desired specifications.
[15] S. Sun, F. Meng, Ind. Eng. Chem. Res. 60 (2021) 652–658, https://doi.org/
10.1021/acs.iecr.0c05766.
[16] R.N. Magomedov, A.V. Pripakhaylo, D.S. Dzhumamukhamedov, T.A. Maryutina,
J. CO2 Util. 40 (2020), https://doi.org/10.1016/j.jcou.2020.101206.
[17] M. Al-Sabawi, D. Seth, T. De Bruijn, Fuel Process. Technol. 92 (2011) 1929–
1938, https://doi.org/10.1016/j.fuproc.2011.05.010.
[18] J. Long, B. Shen, H. Ling, J. Zhao, J. Lu, Ind. Eng. Chem. Res. 50 (2011) 11259–
11269, https://doi.org/10.1021/ie2004169.
[19] J.M. Lee, S. Shin, S. Ahn, J.H. Chun, K.B. Lee, S. Mun, S.G. Jeon, J.G. Na, N.S. Nho,
Fuel Process. Technol. 119 (2014) 204–210, https://doi.org/10.1016/
j.fuproc.2013.11.014.
[20] S. Ahn, S. Shin, S.I. Im, K.B. Lee, N.S. Nho, Korean J. Chem. Eng. 33 (2016) 265–
270, https://doi.org/10.1007/s11814-015-0146-3.
[21] S.I. Im, S. Shin, J.W. Park, H.J. Yoon, K.S. Go, N.S. Nho, K.B. Lee, Chem. Eng. J. 331
(2018) 389–394, https://doi.org/10.1016/j.cej.2017.08.094.
[22] Y. Zhao, F. Wei, Fuel Process. Technol. 9 (2008) 933–940, https://doi.org/
10.1016/j.fuproc.2008.03.010.
[23] J.E. Jolley, J.H. Hildebrand, J. Am. Chem. Soc. 80 (1958) 1050–1054, https://doi.
org/10.1021/ja01538a008.
[24] C.M. Hansen, The three dimensional solubility parameter, Danish Technical
Press, 1967.
[25] M.J. Louwerse, A. Maldonado, S. Rousseau, C. Moreau-Masselon, B. Roux, G.
Rothenberg, ChemPhysChem. 18 (2017) 2999–3006. https://doi.org/10.1002/
cphc.201700408.
[26] L.L. Williams, J.B. Rubin, H.W. Edwards, Ind. Eng. Chem. Res. 43 (2004) 4967–
4972, https://doi.org/10.1021/ie0497543.
[27] P. Redelius, Energy Fuels 18 (2004) 1087–1092, https://doi.org/10.1021/
ef0400058.
[28] C.M. Hansen, Hansen Solubility Parameters: A User’s Handbook, 2nd ed., CRC
Press, 2007.
[29] T. Sato, S. Araki, M. Morimoto, R. Tanaka, H. Yamamoto, Energy Fuels 28 (2014)
891–897, https://doi.org/10.1021/ef402065j.
[30] S. Acevedo, A. Castro, E. Vásquez, F. Marcano, M.A. Ranaudo, Energy Fuels 24
(2010) 5921–5933, https://doi.org/10.1021/ef1005786.
[31] F. Mutelet, G. Ekulu, R. Solimando, M. Rogalski, Energy Fuels 18 (2004) 667–
673, https://doi.org/10.1021/ef0340561.
[32] C.M. Hansen, A handy HSP excel spreadsheet. https://www.hansensolubility.com/ downloads.php/, (accessed 22 June 2021).
[33] F. Cao, D. Jiang, W. Li, P. Du, G. Yang, W. Ying, Chem. Eng. Process. Process
Intensif. 49 (2010) 91–96, https://doi.org/10.1016/j.cep.2009.11.013.
[34] O.D. Onukwuli, I.M. Onyia, E.O. Ekumankama, S.I. Okeke, Pet. Sci. Technol. 17
(1999) 37–49, https://doi.org/10.1080/10916469908949704.
[35] A.Y. León, M. Parra, J.L. Grosso, CTF Cienc Tecnol Futuro 3 (2008) 129–142.
[36] S. Akmaz, O. Iscan, M.A. Gurkaynak, M. Yasar, Pet. Sci. Technol. 29 (2011) 160–
171, https://doi.org/10.1080/10916460903330361.
[37] R.F. Fedors, Polym. Eng. Sci. 14 (1974) 147–154, https://doi.org/10.1002/
pen.760140211.
[38] T. Fan, J.S. Buckley, Energy Fuels 16 (2002) 1571–1575, https://doi.org/
10.1021/ef0201228.
[39] K.K.A. Bissada, J. Tan, E. Szymczyk, M. Darnell, M. Mei, Org. Geochem. 95
(2016) 21–28, https://doi.org/10.1016/j.orggeochem.2016.02.007.
[40] G. Jiang, D. Lv, F. Zhang, A. Ning, G. Gao, Y. Ren, Z. Wang, Sep. Purif. Technol.
272 (2021), https://doi.org/10.1016/j.seppur.2021.118902.
[41] H. He, Y. Wan, R. Sun, J. Sha, P. Zhang, Y. Li, T. Li, B. Ren, J. Mol. Liq. 300 (2020),
https://doi.org/10.1016/j.molliq.2019.112203.
[42] J. Esteras-Saz, Ó. de la Iglesia, C. Peña, A. Escudero, C. Téllez, J. Coronas, Sep.
Purif. Technol. 270 (2021), https://doi.org/10.1016/j.seppur.2021.118793.
[43] A.B. Beltran, G.M. Nisola, E.L. Vivas, W. Cho, W.J. Chung, J. Ind. Eng. Chem. 19
(2013) 182–189, https://doi.org/10.1016/j.jiec.2012.07.022.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared
to influence the work reported in this paper.
Acknowledgments
This work was supported by the Super Ultra Low Energy and
Emission Vehicle Engineering Research Center (grant number
NRF-2016R1A5A1009592) of the National Research Foundation of
Korea, funded by the Korean government’s Ministry of Science
and ICT.
References
[1] OPEC, World Oil Outlook 2040, 2017.
[2] M.R. Gray, Upgrading Oilsands Bitumen and Heavy Oil, The University of
Alberta Press, 2015.
[3] R. Sahu, B.J. Song, J.S. Im, Y.P. Jeon, C.W. Lee, J. Ind. Eng. Chem. 27 (2015) 12–24,
https://doi.org/10.1016/j.jiec.2015.01.011.
[4] G. Nazarova, E. Ivashkina, T. Shafran, N. Belinskaya, MATEC Web Conf. 85
(2016) 01010, https://doi.org/10.1051/matecconf/20168501010.
[5] L. Goual, A. Firoozabadi, AIChE J. 50 (2004) 470–479, https://doi.org/10.1002/
aic.10041.
[6] L.C. Castañeda, J.A.D. Muñoz, J. Ancheyta, Fuel 100 (2012) 110–127, https://doi.
org/10.1016/j.fuel.2012.02.022.
[7] K.S. Go, E.H. Kwon, K.H. Kim, N.S. Nho, K.B. Lee, Energy Fuels 30 (2016) 2076–
2083, https://doi.org/10.1021/acs.energyfuels.5b02860.
[8] A. Shah, R. Fishwick, J. Wood, G. Leeke, S. Rigby, M. Greaves, Energy Environ.
Sci. 3 (2010) 700, https://doi.org/10.1039/b918960b.
[9] R.S. Mohamed, W. Loh, A.C.S. Ramos, C.C. Delgado, V.R. Almeida, Pet. Sci.
Technol. 17 (1999) 877–896, https://doi.org/10.1080/10916469908949754.
[10] J.G. Speight, Heavy and Extra-heavy Oil Upgrading Technologies, Gulf
Professional Publishing, 2013.
[11] J.A. Carrillo, L.M. Corredor, Fuel Process. Technol. 109 (2013) 156–162, https://
doi.org/10.1016/j.fuproc.2012.09.059.
[12] H.L. Cossey, S.E. Guigard, E. Underwood, W.H. Stiver, J. McMillan, S.
Bhattacharya, J. Supercrit. Fluids 154 (2019), https://doi.org/10.1016/
j.supflu.2019.104599.
[13] L.R. Morantes, A.M. Percebom, E. Mejía-Ospino, Fuel 241 (2019) 542–549,
https://doi.org/10.1016/j.fuel.2018.12.028.
[14] P.D. Patil, M. Kozminski, J. Peterson, Fuel 235 (2019) 17–22, https://doi.org/
10.1016/j.fuel.2018.07.084.
465