Fuel 312 (2022) 122844
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Fuel
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Full Length Article
A sustainable method for germanium, vanadium and lithium extraction
from coal fly ash: Sodium salts roasting and organic acids leaching
Homa Rezaei a, Sied Ziaedin Shafaei a, Hadi Abdollahi a, *, Alireza Shahidi b, Sina Ghassa a
a
b
School of Mining, College of Engineering, University of Tehran, Tehran 1439957131, Iran
Geological Survey of Iran, Tehran 1387835841, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords:
Coal fly ash
Roasting
Organic acids
Leaching
Kinetic
Recycling
The paper proposes an environmentally friendly process for recovering germanium, lithium, and vanadium from
coal fly ash (CFA) using thermal pretreatment and hydrometallurgy. To this end, three sets of experiments were
conducted: parameter screening, optimization, and kinetic modeling. The Taguchi method was employed to
develop screening tests and ascertain the most influential parameters. The salt type (NaCl, NaNO3, Na2CO3,
Na2SO4), the CFA/salt ratio, the organic acid type (malic, oxalic, citric, and acetic acids), the acid concentration,
and the processing time were investigated. The results indicated that while the acid type has the most significant
effect on the leaching process, the salt/CFA ratio has the least effect on metal recoveries. The second set of
experiments used response surface methodology (RSM) to optimize the dissolution process and obtain the highest
recoveries of Ge, Li, and V. Additionally, mathematical models were suggested to predict the metals recoveries.
Citric acid and NaCO3 were selected as the optimal leaching and roasting agents. The optimum condition to
obtain maximum recoveries was 0.5 M citric acid, a CFA/NaCO3 ratio of 1:0.5, and a leaching time of 60 min.
During the final set of experiments, four tests were conducted under optimal conditions at various temperatures
for kinetic modeling and activation energy calculation. At 30 ◦ C, the highest recoveries for Ge, V, and Li were
98.15%, 75.31%, and 97.30%, respectively. The “interfacial transfer and diffusion across the product layer”
model was discovered to govern leaching kinetics. The activation energies for Ge, V, and li were 24.50 kJ/mol,
34.16 kJ/mol, and 49.82 kJ/mol, respectively.
1. Introduction
It may be necessary to utilize all available fuel sources in the future to
meet the world’s energy demand [3]. Despite efforts and commitments
to develop sustainable energy sources, coal remains a significant portion
of the fuels used for energy generation [1]. Coal-fired power plants
generate considerable amounts of coal ash during the combustion pro­
cess [50]. The global annual coal ash production is estimated to be be­
tween 600 and 800 million tons [17]. Coal power plants typically
generate two types of coal ash: 1) coal bottom ash (CBA), which settles
to the bottom of the combustion chamber and is removed after cooling,
and 2) coal fly ash (CFA), which exits the combustion chamber via
outgases and is caught by filters. Both sections of coal ash have a
chemical composition dependent on the feed coal [11].
Currently, waste CFAs are disposed of in landfills or basic stacks
[47]. Due to CFA’s solubility, radioactivity, and toxicity, these disposal
methods are not environmentally friendly [18]. As a result, CFA deposits
pose a threat to human health. CFAs contain significant amounts of
heavy metals such as arsenic, antimony, cadmium, and lead. Heavy
metals leaching from deposited CFA can contaminate groundwater and
soils [51]. Drinking contaminated water or consuming plants grown in
contaminated soil exposes humans to heavy metals, leading to severe
diseases such as cancer, Parkinson’s disease, and Alzheimer’s [16]. This
has prompted researchers to develop appropriate techniques for CFA
reuse.
This have encouraged researchers to find appropriate techniques for
reusing CFA. Coal ash has been used in various applications for many
years, including raw materials in cement-based products, asphalt,
Abbreviations: ANOVA, Analysis of variance; CFA, Coal Fly Ash; DX7, Design Expert 7 software; EDS, energy-dispersive X-ray spectroscopy; ICP-OES, Inductively
coupled plasma optical emission spectroscopy; PLS, Pregnant leach solution; REE, Rare Earth Elements; RSM, response surface methodology; SEM, Scanning Electron
Microscopic; Sqrt, Square root; TGA, Thermogravimetric analysis; XRD, X-ray diffraction.
* Corresponding author.
E-mail address: h_abdollahi@ut.ac.ir (H. Abdollahi).
https://doi.org/10.1016/j.fuel.2021.122844
Received 11 September 2021; Received in revised form 27 November 2021; Accepted 4 December 2021
Available online 15 December 2021
0016-2361/© 2021 Elsevier Ltd. All rights reserved.
H. Rezaei et al.
Fuel 312 (2022) 122844
concrete pavements, soil stabilization, road foundations, filling struc­
tures, ceramics, mine rehabilitation, and fertilizers [58]. However, it is
estimated that only 25% of fly ash is utilized in the industries mentioned
above [4,17,22]. On the other hand, recovering valuable metals from
CFA, a significant industrial waste, has garnered considerable attention
over the last decade [15,58].
CFA is composed of fine spherical particles ranging in size from 1 to
100 µm. CFA is an excellent source of various valuable elements such as
Fe, Al, Ni, Zn, V, Ga, Ge, REE, and Li. Recovering valuable metals from
CFA is critical for environmental protection and supplying metals to
industries [32]. This means that CFA has the potential to become a
reliable source of valuable metals in the near future if economic methods
for metal extraction are developed [59].
Among the metals existent in CFA, germanium, vanadium, and
lithium can be categorized as critical. While most of germanium is
currently recovered as a byproduct of zinc production, coal fly ash
represents an exciting new source of germanium [8]. Furthermore,
because vanadium is rarely found in pure form and is typically recovered
from titaniferous magnetite ores, the presence of a significant amount of
vanadium in coal fly ash represents a potential source of this critical
element [46]. Finally, the steady depletion of primary lithium resources
throughout the world has become a significant source of concern. This
issue can be resolved by recovering Li from secondary sources, such as
CFA.
In CFAs, Ge, V, and Li can appear in different phases, including
aluminosilicates, iron minerals (such as hydrogoethite, goethite, and
jarosite), and polymineral phases [5,21]. Many studies have demon­
strated that the aluminum-based phase and amorphous aluminosilicate
phase may be capable of containing trace metals such as Ge, V, and Li.
Intensive extraction methods are required to break down the glassy
phase of aluminosilicates, the main phase of fly ash [2]. Although CFA
has vast potential, limited research has been conducted on extracting
Ge, V, and Li from this valuable resource.
Hydrometallurgy is the primary method for recovering metals from
CFA [56,59,60]. Two distinct methods for recovering metals from fly
ash’s aluminosilicate phase have been proposed: direct inorganic
leaching (like hydrochloric acid [45] or sulfuric acid [40,53]) and salt
roasting (using CaO, NaOH, Na2CO3, CaSO4, and (NH4)2SO4) followed
by mineral acids leaching [14,61].
Previous research established that metal extraction from alumino­
silicates matrix is possible just by decomposing the aluminosilicate
matrix with high concentrations of minerals acids, or bases. Moreover,
this decomposition requires a longer processing time [28,40,42,48,49].
For example, the highest REEs recoveries reported were 8–45% when fly
ash samples were leached with concentrated nitric acid (6 M) at a high
temperature (90 ◦ C) [34]. Strong acids such as HCl and HNO3 produce
hazardous gases such as Cl2, SO3, and NOx, and their effluents
contaminate the soil and ground waters, endangering the health of
humans, wildlife, and indigenous plants [12]. Additionally, they are
highly corrosive and can cause significant operational issues. To address
these concerns, an increased interest in the leaching of CFA by organic
acids such as acetic acid, citric acid, DL-malic acid, and oxalic acid has
developed. These organic reagents are degradable acids, which may
have a lower environmental impact during and after the leaching pro­
cess, in comparison with mineral acids [27,29].
The current research examines the recovery of valuable elements
(Ge, V, and Li) from fly ash to mitigate the negative environmental
consequences of fly ash disposal while also maximizing economic pro­
cessing. This study developed sequential sodium salts roasting and
organic acid leaching process to recover germanium, vanadium, and
lithium from CFA. Three sets of experiments were conducted to deter­
mine effective parameters, optimize the process, and ascertain the
leaching mechanisms. On Ge, V, and Li recovery, the effects of roasting
reagent, organic acid type as leaching agents, acid concentration, salt/
solid ratio, and leaching duration were investigated, and the main pa­
rameters were optimized using response surface methodology (RSM).
Finally, kinetic modeling was used to determine the leaching mechanism
and calculate the activation energy.
2. Materials and methods
2.1. Coal fly ash sample
CFA sample was prepared by an coal power plant in Iran. Due to the
low concentration of CaO and the high aluminosilicate content, the
sample was classified as class F [54]. Representative samples for char­
acterizations and leaching experiments were obtained using a rotary
tube sample divider (PT 200; RETSCH, Germany). Inductively coupled
plasma optical emission spectroscopy (ICP-OES; Varian 720, USA) was
employed to determine the chemical composition. The chemical
composition of the coal fly ash sample is shown in Table 1. Si (13.5%), Al
(9.4%), and Fe (9.4%) were determined to be the major elements.
Additionally, 250 ppm of germanium, 648 ppm of lithium, and 590 ppm
of vanadium were observed in the sample.
The particle size distribution was measured using a laser particle size
analyzer (Fritsch, ANALYSETTE 22, Germany) (Fig. S1, please refer to
the Supplementary File). The samples d25, d50, and d80 were determined
to be 6 μm, 18 μm, and 41.2 μm, respectively.
2.2. Experiment’s design
A three-step experimental design was used to evaluate the effective
parameters, process optimization, and determination of leaching
mechanism for Ge, V, and Li dissolution. Numerous parameters affect
the salt roasting and leaching process. Due to the scarcity of research on
CFA salt roasting and organic acid leaching, the critical parameters are
unknown. Thus, prior to any process optimization, it is necessary to
conduct screening tests to determine which parameters have the most
significant effect. The Taguchi method, L16 orthogonal array, was used
to design and analyze data obtained from screening tests. This array
investigated the effects of five distinct parameters at four different
levels.
In the first set of experiments, the effective parameters for the salt
roasting stage (salt type and salt/CFA ratio) and the organic acid
leaching stage (acid type, acid concentration, and leaching time) were
screened. Table S1 summarizes the examined parameters and their
levels, in the screening step. The parameters and their levels for Taguchi
experiments were selected based on the preliminary tests and previous
studies [32,44]. The data from Taguchi’s orthogonal array experiments
were analyzed using Minitab software (Minitab, LLC, version 20.3.0.0).
The second set of experiments was conducted to optimize the
leaching process to maximize Ge, Li, and V recoveries, simultaneously.
D-optimal experiment design which is one of the subdivisions of
response surface methodology (RSM) was used for statistical modeling
and optimization of the process. This collection of experiments exam­
ined the effects of three distinct parameters at three different levels. The
parameters and their levels were selected based on the results of
screening tests. Table S2 contains the conditions for optimization tests.
Design Expert 7 software (State-Ease Inc., Minneapolis, MN, USA) was
used to statistically design and analyze the experiments. The simulta­
neous maximization of the recoveries of all three metals was selected as
an indicator of the optimal conditions.
In the third set of experiments, the optimum conditions obtained
from previous steps were used for kinetic modeling and activation en­
ergy calculation. Four tests were carried out at different temperatures
(30 ◦ C, 45 ◦ C, 60 ◦ C and 75 ◦ C), and samples were withdrawn in selected
intervals to calculate the metals recovery versus time.
2.3. Experimental procedure
CFA salt roasting was performed to decompose the aluminosilicates
phase before leaching with organic acids. Based on the experiment
2
H. Rezaei et al.
Fuel 312 (2022) 122844
Table 1
Chemical compositions of fly ash determined by ICP-OES.
Si (%)
Al (%)
Fe (%)
Ti (%)
K (%)
Ca (%)
Mg (%)
Na (%)
13.51
Pb (ppm)
6090
Ni (ppm)
418
La (ppm)
88.7
Be (ppm)
13.9
9.39
Ba (ppm)
1630
Cu (ppm)
343
Nd (ppm)
70.2
Sm (ppm)
13.5
8.91
Zr(ppm)
1173
Ge (ppm)
250
Ga (ppm)
64.1
Dy(ppm)
12.5
4.04
P (ppm)
654
Cr (ppm)
227
Nb (ppm)
62.0
Tl(ppm)
10.0
1.43
Li (ppm)
648
Ce(ppm)
206
Y (ppm)
60.5
Te (ppm)
6.55
0.69
V(ppm)
590
Zn (ppm)
157
Se(ppm)
41
Cd(ppm)
6
0.40
Sr(ppm)
454
Co (ppm)
103
As (ppm)
39.85
Yb (ppm)
5.91
0.36
Mn(ppm)
425
Sc (ppm)
89.4
Er (ppm)
24.8
Ag(ppm)
4.56
design (Tables S1 and S2), 1.000 g fly ash was thoroughly mixed with
roasting salts (NaCl, NaNO3, Na2CO3, and Na2SO4), then transferred to
crucibles and processed in a furnace for 2 h [45]. For all tests, the
thermal pretreatments at the screening and optimization steps were
carried out at 850 ◦ C [23]. The roasted samples were grinded again
before leaching. The leaching experiments were performed with 100 ml
of DL-malic acid (C4H6O5), citric acid monohydrates (C6H8O7⋅H2O),
oxalic acid dehydrate (C2H2O4⋅2H2O), and acetic acid (C2H4O2). All
tests were conducted using 100 ml of leaching reagent at a 450 rpm
agitation rate. After each experiment, the pregnant leach solutions (PLS)
were filtered, and the Ge, Li, and V contents were measured using ICPOES to determine the metal recoveries. The leaching recovery (effi­
ciency) was calculated based on Eq. (1):
C×V
R = C0
× 100
×M
100
ranges. The effective parameters will be used for process optimization,
while the interactions between the parameters are analyzed by the
response surface methodology (RSM). Analysis of variance (ANOVA) is a
strong statistical tool for data analysis. ANOVA can be used to determine
the effect of each parameter on processes and to develop mathematical
models for predicting a response based on a set of parameters and their
interactions [31]. The results of ANOVA for the three metals considered
in this study are listed in Table S3.
The p-value (calculated probability) is a statistical index that in­
dicates the significance of models and parameters when a hypothesis test
is carried out [25,52]. This index, which is calculated by Fisher’s exact
test, can determine the effect of each parameter on the process. Pa­
rameters with lower p-values have a stronger effect on the process [26].
Each parameter’s contribution percentage can be calculated based on
Eq. (2) [35] as follows:
(1)
FactorEffect =
where C denotes the metal concentration (in g/l) in PLS, V denotes the
volume (in l) of the leaching solution, C0 represents the metal concen­
tration (%) in roasted CFA, and M represents the initial mass (in g) of the
roasted CFA added into the reactor. The experiment procedure is sche­
matically depicted in Fig. S2.
All leaching experiments were performed inside a 250 ml thermo­
static Pyrex reactor with three necks and a glass condenser, placed inside
a water bath (Memmert WNB 14; Germany). A mixer controlled the
agitation rate through a Teflon impeller and a speed controller (Hei­
dolph HPS-55; Japan). All organic acids and salts were analytical grade
and supplied by Merck and Sigma-Aldrich.
SoS
∑ SoS
DoF × DoF
(2)
The parameters’ percentages of contribution on the leaching of
target elements are demonstrated in Fig. 1. Based on the results of the
screening tests, the parameters with the strongest effect, in descending
order, are as follows: leaching reagents, roasting reagents, leaching
time, acid concentration, and solid/salt ratio. The percentages of
contribution for acid types are 53.68%, 70.07%, and 51.14% for Ge, V,
and Li dissolution, respectively. In addition, the p-values for the acid
types are 0.038, 0.007 and 0.041, respectively. This means that acid type
has a significant impact on the leaching of all metals.
The main effect plots for means are detailed in Fig. 2, illustrating the
effects of the five parameters on the recovery of the three metals
investigated in this study (i.e. Ge, V, and Li). As can be observed, pa­
rameters with a wider range of recovery have a stronger effect on the
process. In other words, when the range between the lowest and stron­
gest recoveries for a parameter grows, the parameter’s effect on the
process becomes more significant.
According to Fig. 2 acid type has the strongest effect on the process.
Among the four acids investigated in this study, citric acid and acetic
acid were identified as the best leaching reagents for CFA dissolution to
obtain the highest Ge, V, and Li recoveries, simultaneously. It should be
noted that citric acid dissociates in three steps [33] as follows:
2.4. Characterization
Thermogravimetric analysis (TGA) was used to investigate the
thermal decomposition behavior of CFA in the presence of salts with
maximum metals recoveries (Na2CO3 and NaNO3). For this purpose,
salts were mixed with CFA and used in TGA tests. The ratio of salts to
CFA was selected based on the results of screening tests. Furthermore,
CFA analysis was performed without adding salt, to compare and clarify
the effect of salts in the roasting process. Moreover, X-ray diffraction
(XRD), scanning electron microscopy (SEM), and energy-dispersive Xray spectroscopy (EDS) were also used to determine the changes in the
morphology and chemical composition of the samples following CFA
roasting. SEM, EDS, and XRD analyses were performed on samples ob­
tained before and after roasting with selected salts (Na2CO3 and
NaNO3). A mixture of CFA and salts was treated in a furnace (at 850 ◦ C
for 2 h) and used for analysis.
(3)
H3C6H5O7 ↔ H2C6H5O7− + H+ ka1 =7.4 × 104
H2C6H5O7− ↔ HC6H5O72− + H+
HC6H5O7
2−
↔ C6H5O7
3−
+H
+
(4)
ka2 = 1.7×10− 5
(5)
− 7
ka3 = 4.0 × 10
As a result, one mole of citric acid yields three moles of H . It can
thus be inferred that citric acid can produce sufficient H+ ions [38].
Citric acid is widely considered as one of the most effective chelating
agents, capable of forming complexes with several elements [55].
The dissociation of DL-malic acid occurs in two stages [6] as follows:
+
3. Results and discussion
3.1. Screening tests
H2C4H4O5 ↔ HC4H4O5− + H+
As previously mentioned, the Taguchi method was employed to
design and analyze the screening tests. This set of experiments were
aimed at determining the influential parameters and their effective
−
HC4H4O5 ↔ C4H4O5
3
2−
+H
+
ka1 = 4 × 10− 4
(6)
− 6
(7)
ka2 = 9 × 10
H. Rezaei et al.
Fuel 312 (2022) 122844
Fig. 1. The contributions (%) for each parameter on leaching of Ge (a), V (b) and Li (c).
Fig. 2. The main effects plots for means which shows the effect of parameters on the recovery of three investigated metals simultaneously.
4
H. Rezaei et al.
Fuel 312 (2022) 122844
Acetic acid, on the other hand, dissociates in a single step [19] as
follows:
HC2H3O2 ↔ C2H3O2− + H+
of target elements. The RSM not only reduces the costly performances of
numerical simulation but also advances the natural process of optimi­
zation which is often nonlinear. However, it should be noted that the
RSM uses statistical models and even the most sophisticated, accurate
statistical models cannot perfectly reflect the reality. At this stage, using
a D-optimal experimental design, 18 experiments were designed to
optimize the process, as detailed in Table S4.
In order to accurately monitor the effects of the parameters on the
dissolution of the three metals, a separate instance of ANOVA was per­
formed on each metal and the mathematical models governing each
metal were presented along, with their statistical indicators. All statis­
tical analyses were performed using Stat-Ease’s statistical software
Design-Expert® 7 (DX7). Table 2 lists the mathematical models in terms
of coded factors to predict the metals’ recoveries based on the investi­
gated parameters. In the table, the letters A, B, and C represent time,
leaching reagent, and type of roasting additives, respectively. As per the
software’s recommendations, germanium recoveries were left un­
changed, vanadium recoveries were shifted to the inverse square root
format using lambda 0.5 (1/Sqrt), and lithium recoveries were shifted to
inverse squares using lambda 1 (1/Sqrt), to ensure that the best possible
models were created.
The models predicting Ge, V, and Li recoveries were significant due
to having a p-value<0.0001, indicating that proper model terms had
been selected. R-squared values of 0.9898, 0.9891, and 0.9162 were
determined for Ge, V and Li models, respectively, indicating that the
models are well-fit to the experimental data. In order to have an accurate
model, the measure adequate precision (a.k.a. signal-to-noise ratio)
should be larger than 4. Adequate precisions of the Ge, V, and Li models
were 30.739, 26.982, and 11.769, respectively. Five repetition tests
were performed at the center points, revealing lack-of-fit values of
0.1637, 0.7694, and 0.7906 for Ge, V, and Li, respectively. Therefore,
the lack-of-fit values are not significant, which point to the repeatability
of the experiments.
To monitor and optimize the leaching of elements, it is necessary to
calculate the contributions in order to better understand the impact of
each variable on the process response. The contributions of the variables
were calculated using ANOVA [26], with the results summarized in
Table S5. The contributions were determined using the adjusted sum of
squares to total sum of squares ratio. Among the variables chosen for the
models in the selected range, acid type influenced recoveries the most,
with 97.91%, 99.64%, and 89.56% for Ge, V, and Li, respectively. On
this basis, acid type has a stronger effect than dissolution time and salt
type on the leaching process.
As mentioned earlier, the DX7 software was used to determine the
optimum conditions for obtaining the highest Ge, V, and Li recoveries,
simultaneously. As optimum conditions, based on statistical models
(Eqs. (14) to (16)), DX7 recommends citric acid as the leaching agent,
Na2CO3 as the roasting agent, and a leaching time of 60 min. A valida­
tion test was carried out under optimum conditions to compare the re­
sults of the experiments and the predicted recoveries based on statistical
models. Under optimum conditions, Ge, V and Li recoveries were 89.2%,
(8)
ka1 = 1.8×10− 5
Hence, DL-malic and acetic acids liberate two moles of H and one
mole of H+, respectively [57]. As Eq. (9) indicates, although the con­
centration of hydrogen ions (H+) has a significant impact on leaching
efficiency [13], acetic acid forms a highly soluble complex with Ge:
+
GeO2 + 4CH3COO− + 4H+ → Ge[(CH3)COO]4 + H2O
(9)
The dissolution chemistry of germanium in the presence of acetic
acid has been thoroughly investigated in the literature [36,37]. Thus,
acetic acid may be a superior leaching reagent compared to DL-malic
acid. This is in agreement with the results of the Taguchi tests. The
other leaching agent, oxalic acid, undergoes dissociation in the
following two stages [20]:
ka1 = 5.6 × 10− 2
(10)
HC2O4- ↔ C2O42− + H+ ka2 = 1.5 × 10− 4
(11)
H2C2O4 ↔ HC2O4− + H+
As can be seen, oxalic acid dissociates in two steps. Although it re­
leases two moles of H+, it is an inappropriate reagent due to the for­
mation of insoluble germanium oxalate complexes [43].
Salt type is the second important factor, as shown both in Figs. 1 and
2. The aluminosilicate phases are the major phases of coal ash [53].
Therefore, the reactions of mullite (3Al2O3.2SiO2), aluminum oxide, and
silicon oxides with the roasting additives are essential to the decompo­
sition of silicate phases. As Fig. 2 shows, Na2CO3 and NaNO3 have the
most positive effects on leaching efficiency. Therefore, the authors
decided to conduct a more intensive study on these two particular salts.
Under ideal conditions, NaAlSiO4 is produced during aluminosilicate
roasting with Na2CO3 and NaNO3, which is soluble in water and acid,
based on Eq. (12) [24] and Eq. (3) [39]. The phase shifts are discussed
further in Section 3.2.
3Na2CO3 + 3Al2O3⋅2SiO2(mullite) + 4SiO2 → 6NaAlSiO4 + 3CO2
(12)
4NaNO3 + 2Al2O3⋅SiO2(mullite) + 3SiO2 → 4NaAlSiO4 + 4NO2 + O2(13)
As Figs. 1 and 2 indicate, the effect of acid concentration and salt/
CFA ratio on the process is negligible. Therefore, these two parameters
will be excluded from the next set of experiments. For this purpose, the
optimum levels of these parameters were determined based on screening
tests and remain fixed in all the tests performed in subsequent experi­
ments. Decreasing the roasting reagent to ash mass ratio from 2:1 to
0.5:1 did not have a significant impact on the recovery of the elements.
Consequently, the lowest amount of salt was chosen to minimize salt
consumption. In accordance with the results of the Taguchi screening
tests, a fixed acid concentration of 0.5 M was determined for all the other
tests. It was observed that, although the leachate acidity is important in
the leaching process, increasing the H+ concentration by more than 0.5
M does not have a significant impact on recovery.
To sum up, the screening tests showed that leaching reagent type,
roasting reagent type, and leaching duration are the most influential
factors in the process. In the end, acetic acid, citric acid, Na2CO3, NaNO3
and leaching time were selected using response surface methodology
(RSM) for statistical modeling and process optimization.
Table 2
Mathematical equations for metals recoveries in term of coded factors (A:
leaching time, B: leaching reagent type, and C: roasting additive type).
3.2. Optimization
Ge
As previously mentioned, parameters with insignificant effects were
excluded from the second set of experiments. This strategy was adopted
with the purpose of decreasing the number of experiments performed by
the RSM. In order to investigate the influential parameters (acid type,
salt type, optimal process time) and their interactions throughout the
dissolution process, statistical modeling was performed on the recovery
V
Li
5
Mathematical models
RSquared
Fvalue
p-value
Ge = 47.42 + 1.34 × A- 28.81 × B + 4.24
× C + 1.59 × AB − 1.28 × AC − 7.20 ×
BC (14)
Sqrt(V) = 4.85 + 0.069 × A − 3.47 × B +
0.21 × C + 0.035 × AB − 0.033 × AC −
0.46 × BC (15)
Sqrt(Li) = 6.51 + 0.27 × A − 2.26 × B +
0.77×+0.37 × AB + 0.21 × AC-0.23 × BC
(16)
0.9898
178.04
<0.0001
0.9891
79.81
<0.0001
0.9162
35.47
<0.0001
H. Rezaei et al.
Fuel 312 (2022) 122844
80.8%, and 93.4%, respectively. A comparison between the predicted
and actual recoveries indicated that errors were only 2.3%, 1.69% and
3.68% for Ge, V, and Li, respectively. This also points to the high ac­
curacy of the statistical modeling carried out in this study. TGA, XRD,
and SEM were employed to characterize the CFA before and after
roasting to explain the rationale behind the stated optimum conditions.
TGA was used to study the thermal decomposition behavior of fly ash
in the absence of salts and in the presence of Na2CO3 and NaNO3. An­
alyses were carried out within a temperature range between 25 ◦ C and
1000 ◦ C. Fig. 3 illustrates the weight losses based on temperature in­
crease. In the TGA graph for Na2CO3 roasting, weight loss started at
~650 ◦ C and the weight stabilized at ~850 ◦ C. This weight loss occurred
due to the decomposition of the aluminosilicate minerals, as expressed
in Eq. (12). On the other hand, weight loss in CFA roasting in the
presence of NaNO3 started at around 850 ◦ C, while the stabilization
point was not reached at temperatures lower than 1000 ◦ C. This means
that complete decomposition of aluminosilicates for Na2CO3 roasting
occurs at a lower temperature compared to NaNO3 roasting. On the
other hand, there was no significant weight loss in the absence of salt.
The weight loss started from ~450 ◦ C and continued beyond 1000 ◦ C.
While final weight losses for salt roasting with Na2CO3 and NaNO3 were
26% and 25%, respectively, it was 22% for roasting without salt. The
results of TGA show that using sodium salts increases the efficiency of
CFA roasting and decreases the aluminosilicates decomposition
temperature.
XRD analysis was also used to evaluate the crystallography of the
samples before and after roasting (Fig. 4). For this purpose, the fly ash
samples were analyzed before and after roasting with Na2CO3 and
NaNO3. The results showed that fly ash is mainly made from mullite
(3Al2O3.2SiO2), hematite (Fe2O3), quartz (SiO4), and rutile (TiO2). After
roasting the CFA with Na2CO3, all the aforementioned phases vanished.
Mullite, quartz, and rutile decomposed and sodium aluminum silicate
(NaAlSiO4) and paranatisite (Na2(TiO(SiO4)) phases formed. In addi­
tion, hematite shifted to magnetite (Fe3O4).
Although all minerals shifted to new phases as a result of roasting
with Na2CO3 salt, some minerals remained unreacted after roasting with
NaNO3. For instance, after roasting with NaNO3, the CFA contained
quartz and rutile. A sodium aluminum silicate phase with a new
chemical composition (Na6(AlSiO4)6) appeared in this sample, as well.
Na6(AlSiO4)6 can be an intermediate phase for the formation of NaAl­
SiO4. As the TGA diagram for CFA roasting in the presence of NaNO3
indicates, the weight loss starts at around 800 ◦ C and continues beyond
1000 ◦ C. The XRD analysis results also show that the roasting reaction in
the presence of NaNO3 is not completed at the selected roasting tem­
perature (850 ◦ C). The lower recovery of metals in the tests with this salt
is caused by incomplete roasting.
SEM was used to study the surface morphology of the particles before
and after roasting. The results (Fig. S3) indicated that the mineral’s
Fig. 4. The XRD diffractogram for CFA before roasting (a), after roasting with
Na2CO3 (b) and NaNO3 (c).
surface was smooth before roasting and corroded after roasting. Energydispersive X-ray spectroscopy (EDS) showed that Si, Al, Fe, Ti, and O are
the major elements in untreated CFA. In contrast, Na was added to the
EDS spectrum after roasting due to the formation of the sodium
aluminum silicate phase (Fig. S4).
3.3. Kinetic modeling and activation energy calculation
A set of experiments were conducted at four different temperatures
to study the effect of temperature, determine the reaction mechanisms,
and calculate the activation energies. For this purpose, leaching tests
were performed under the optimum conditions defined in the previous
subsection. The PLS samples were withdrawn from the reactor at a
certain time and metals recoveries were depicted based on time (Fig. 5).
The maximum dissolution of metals was obtained at 30 ◦ C and
increasing the temperature caused a decrease in recoveries. The inverse
relation between temperature and reaction efficiency suggests that the
reaction in this case is exothermic. This means that the leaching should
be carried out for 60 min at 30 ◦ C with 0.5 M citric acid after roasting
with sodium carbonate (with CFA/NaCO3 ratio of 1:0.5) to obtain the
highest metals recoveries. Under optimum conditions, the highest re­
coveries for Ge, V, and Li were 98.15%, 75.31%, and 97.30%,
Fig. 3. The TGA analysis for coal fly ash roasting in the absence and presence of
sodium salts.
6
H. Rezaei et al.
Fuel 312 (2022) 122844
Table 3
The coefficient of determination and kd for best-fitted models for different
metals at different temperatures.
Element
Reaction
temperature
(◦ C)
Mechanism
Coefficient of
determination
kd
Ge
T = 30 ◦ C
T = 45 ◦ C
T = 60 ◦ C
T = 75 ◦ C
Interfacial transfer
and diffusion across
the product layer
0.9951
0.9991
0.9878
0.9769
0.0087
0.006
0.0038
0.0025
V
T = 30 ◦ C
T = 45 ◦ C
T = 60 ◦ C
T = 75 ◦ C
Interfacial transfer
and diffusion across
the product layer
0.9946
0.9858
0.9364
0.9855
0.002
0.0014
0.0011
0.0003
Li
T = 30 ◦ C
T = 45 ◦ C
T = 60 ◦ C
T = 75 ◦ C
Interfacial transfer
and diffusion across
the product layer
0.9774
0.97
0.9643
0.9683
0.0084
0.0027
0.0015
0.0006
Fig. 5. Germanium (a), vanadium (b) and lithium (c) recoveries based on time.
respectively. The metal contents in PLS under optimum conditions are
shown in Table S6. The most abundant impurities in PLS are Si, Fe, Al,
Ti, and K.
As previously mentioned, kinetic modeling is used to study the re­
action mechanism and calculation of activation energies. Dissolution
reactions can be followed by three different mechanisms: chemical re­
action control, diffusion control, and mixed control. Two chemical re­
action control, four diffusion control, and one mixed control models
were examined to determine the reaction mechanism. The mathematical
models developed for these mechanisms are detailed in Table S7. For
more information on the model types, please refer to our previous papers
[41,9,10].
The kinetic models were fitted to the existing data for the metals at
different temperatures. In the end, the model with the largest corre­
sponding coefficient (R2) was selected to describe the reaction
mechanism.
The selected models along with their corresponding coefficients and
Kd are listed in Table 3. In addition, Kt versus time were depicted in
Fig. 6. The interfacial transfer and diffusion across the product layer
(
)
− 1
mechanism, where kt = 13 ln(1 − X) + (1 − X) 3 − 1 is the best-fitted
Fig. 6. Plot of Kt versus time for germanium (a), vanadium (b) and lithium (c)
leaching based on Interfacial transfer and diffusion across the product
layer model.
control mechanism. The model was developed based on the shrinking
core model by Dickinson and Heal [7]. According to the diffusion con­
trol model, one of the reactants (leaching reagent) must penetrate
another reactant’s surface (minerals) through the film between the two
model for the dissolution of all elements at all temperatures. The
values of the coefficients corresponding to the fittings are close to 1,
indicating the high accuracy of the curve fitting. Interfacial transfer and
diffusion across the product layer is a modified version of the diffusion
7
H. Rezaei et al.
Fuel 312 (2022) 122844
phases. The film can be a contracting interface and/or a product layer.
The selected mechanism shows that CFA leaching follows reagent
diffusion through a contracting interface and a product layer,
simultaneously.
SEM and EDS were employed to determine the morphology of CFA
particle surfaces for leaching residues (for test at 30 ◦ C). The SEM images
with 10 KX and 50 KX magnifications are displayed in Fig. S5. Particles
with a crystalline shape within the 20 and 200 nm size range were
detected on the CFA surface. The EDS analysis indicates that the
leaching residues were mainly made from Si and O. This is in agreement
with the findings of prior researches [32,45]. Since these particles are
not seen in the SEM image of the ash sample before leaching, it can be
concluded that the layer created by these particles limits the diffusion of
the reagent to the CFA surface. This may confirm the accuracy of the
kinetic modeling approach.
Activation energies for metals dissolution from roasted CFA in
presence of citric acid are calculated using the Arrhenius equation (Eq.
(17)) [30].
(
)
− Ea
k = Aexp
(17)
RT
solution.
CRediT authorship contribution statement
Homa Rezaei: Investigation, Methodology, Software, Formal anal­
ysis, Validation, Data curation, Writing – original draft, Writing – review
& editing, Visualization. Sied Ziaedin Shafaei: Supervision, Funding
acquisition, Resources. Hadi Abdollahi: Conceptualization, Methodol­
ogy, Validation, Supervision, Project administration, Funding acquisi­
tion, Writing – review & editing. Alireza Shahidi: Resources, Funding
acquisition. Sina Ghassa: Methodology, Validation, Writing – review &
editing.
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.
Acknowledgements
where Ea is the activation energy, kd the rate constant, A the frequency
factor, T the temperature (in Kelvin), and R is the gas constant (8.3145
J⋅K− 1⋅mol− 1).
Ea = a × R
The authors acknowledge with thanks the Staff of Geological Survey
of Iran for their technical support. In addition, the supports by Zohreh
Boroumand are highly appreciated.
(18)
Eq. (18) is a simplified version of the Arrhenius equation where R is
the gas constant and a the slope of lnK versus 1000
graph (Fig. S6). The
T
activation energies for Ge, V, and Li are 24.50 kJ/mol, 34.16 kJ/mol,
and 49.82 kJ/mol, respectively.
Appendix A. Supplementary data
4. Conclusions and prospects
References
The current research examined the extraction of germanium, vana­
dium, and lithium from coal fly ash (CFA) using a sequential sodium salt
roasting and organic acid leaching process. To this end, three sets of
experiments were performed. In all experiments, CFA was roasted with
salt at 850 ◦ C and then leached with an organic acid. The first set of
experiments was conducted to determine the process’s influential pa­
rameters, using a Taguchi L16 orthogonal array. The results indicated
that acid type, salt type, and dissolution time are the most important
process parameters. Among investigated acids, citric and acetic acids
and among salts Na2CO3 and NaNO3 had the highest effect on leaching
efficiency.
RSM was used to optimize the process, with effective parameters
obtained from the screening step. After 60 m leaching with 0.5 M citric
acid, and a NaCO3/CFA ratio of 1:0.5, the highest metal recoveries are
obtained. Four tests were conducted under optimal conditions, and at
various temperatures to determine the leaching mechanism and calcu­
late the activation energy via kinetic modeling. The results indicated
that the optimal temperature is 30 ◦ C. At optimum conditions, the
highest recoveries were 98.15%, 75.31%, and 97.30% for Ge, V, and Li,
respectively. The findings indicated that the process is governed by the
interfacial transfer and diffusion across the product layer model. The acti­
vation energies for germanium, vanadium, and lithium leaching are
24.50 kJ/mol, 34.16 kJ/mol, and 49.82 kJ/mol, respectively.
This research established an efficient method for extracting germa­
nium, vanadium, and lithium from CFA through thermal pretreatment
followed by diluted organic acid leaching. However, because organic
acids are more expensive than inorganic acids, their use on a large scale
may increase operating costs. Future research may focus on evaluating
the application of fungal and bacterial bioleaching using species capable
of producing organic acids to develop a more cost-effective process.
Moreover, it would be beneficial to evaluate the effect of other elements
in PLS (such as Si, Fe, Al, and Ti) on metals recovery from a leaching
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