Batch Conversion of Methane to Methanol Using Copper Loaded Mordenite: Influence of the Main Variables of the Process

Abstract

Due to the demands of oxygenated derivatives of hydrocarbons for the industry, the methane (CH4) to methanol (MeOH) conversion through solid-state catalysis is a current topic, with definite questions and specific challenges. This work shows a statistical model that predicts the quantity of methanol produced through a batch conversion process employing copper-exchanged mordenite in accordance with a full factorial experimental design. Synthesis was performed through solid-state ion exchange from Cu(acac)2 and NH4-Mordenite, obtaining weight percentages (%Cu) of 1%, 3%, and 5%, which was followed by activation through calcination at a range of temperatures (Tcal) between 300-500 ◦C, as well as a reaction with methane under 2-10 bar pressure (P) in static conditions employing a batch reactor. The quantities of MeOH produced, and their yields were determined through a gas chromatography and mass spectrometry analysis of the reaction samples. Finally, the role and contribution of each of the variables considered in the conversion process were analyzed. By using a nonlinear model, a quadratic dependence with %Cu and P in the studied range of the variables was found, as well as a linear dependence with Tcal. Finally, for this experiment, the highest yields (µmol/g) were obtained with the following conditions: %Cu = 3 %, P = 6 bar, and Tcal = 400 ◦C.

Full text

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(e87537)
Research Article / Chemical, Food, and Environmental Engineering https://doi.org/10.15446/ing.investig.v41n3.87537
Batch Conversion of Methane to Methanol Using Copper
Loaded Mordenite: Influence of the Main Variables of the
Process
Conversión de metano a metanol por lotes usando mordenita
intercambiada con cobre: influencia de las variables principales del
proceso
Hebert Rodrigo Mojica Molina 1, Marlene González Montiel 2, and Amado Enrique Navarro Frómeta 3
ABSTRACT
Due to the demands of oxygenated derivatives of hydrocarbons for the industry, the methane (CH
4
) to methanol (MeOH) conversion
through solid-state catalysis is a current topic, with definite questions and specific challenges. This work shows a statistical model
that predicts the quantity of methanol produced through a batch conversion process employing copper-exchanged mordenite in
accordance with a full factorial experimental design. Synthesis was performed through solid-state ion exchange from Cu(acac)
2
and
NH
4
-Mordenite, obtaining weight percentages (%Cu) of 1%, 3%, and 5%, which was followed by activation through calcination at a
range of temperatures (Tcal) between 300-500
◦
C, as well as a reaction with methane under 2-10 bar pressure (P) in static conditions
employing a batch reactor. The quantities of MeOH produced, and their yields were determined through a gas chromatography and
mass spectrometry analysis of the reaction samples. Finally, the role and contribution of each of the variables considered in the
conversion process were analyzed. By using a nonlinear model, a quadratic dependence with %Cu and P in the studied range of the
variables was found, as well as a linear dependence with Tcal. Finally, for this experiment, the highest yields (
µ
mol/g) were obtained
with the following conditions: %Cu = 3 %, P = 6 bar, and Tcal = 400 ◦C.
Keywords: methane, methanol, mild conditions, copper mordenite, solid-state ion exchange, activation temperature, methane
pressure, copper weight percent
RESUMEN
Debido a la demanda de derivados oxigenados de hidrocarburos para la industria, la conversión de metano (CH
4
) a metanol (CH
3
OH)
por medio de catálisis en estado sólido es una cuestión de actualidad, precisa y con retos específicos. Este trabajo muestra un
modelo estadístico que predice la cantidad de metanol producido por un proceso de conversión por lotes empleando mordenitas
intercambiadas con cobre de acuerdo con un diseño experimental factorial. La síntesis fue realizada por intercambio iónico en estado
sólido a partir de Cu(acac)
2
y NH
4
-Mordenita, obteniendo porcentajes de intercambio en peso de Cobre ( %Cu) de 1 %, 3 % y 5 %,
seguido de una activación por calcinación en el rango de temperaturas (Tcal) de 300-500
◦
C, así como una reacción con metano en
el rango de presiones de 2-10 bar (P) bajo condiciones estáticas con un reactor por lotes. Las cantidades de MeOH producidas y
sus rendimientos fueron determinados usando análisis de cromatografía de gases y espectrometría de masas de las muestras de la
reacción. Finalmente se analizaron el papel y la contribución de cada una de las variables consideradas en el proceso de conversión.
Usando un modelo no lineal, se encontró una dependencia cuadrática del %Cu y P en el rango estudiado de cada variable, así como
una dependencia lineal con Tcal. Finalmente, para este experimento, los mayores rendimientos (
µ
mol/gr) se obtuvieron con las
siguientes condiciones: %Cu=3 %, P = 6 bar y Tcal = 400 ◦C.
Palabras clave: metano, metanol, condiciones suaves, mordenita de cobre, intercambio iónico en estado sólido, temperatura de
activación, presión de metano, porcentaje de peso de cobre
Received: May 22nd, 2020
Accepted: March 20th,2021
1
Bachelor in Physics, BUAP, México. Ph.D. Candidate in Advanced Technology,
Centro de Investigación en Ciencia Aplicada y Tecnolog´ıa Avanzada. Affiliation:
Ph.D. student, IPN, México. E-mail: hmojicam1900@alumno.ipn.mx
2
Bachelor in Physics, UPITA-IPN. Master in Advanced Technology, CICATA-IPN.
Ph.D. in Advanced Technology, Centro de Investigación en Ciencia Aplicada y
Tecnolog´ıa Avanzada, México. Affiliation: CONACyT-Fellowship, CICATA, U.
Legaria, IPN, México. E-mail: mgonzalezmo@conacyt.mx
3
Bachelor in Chemistry, Universidad de la Habana, Cuba. Ph.D. in Chemical
Sciences in Petroleum and Chemistry Azizbekov Institute, Azerbaijan. Affiliation:
Research Professor, UTIM, México. E-mail: navarro4899@gmail.com
Introduction
MeOH is a very important raw material for the chemical
industry, with an increasing demand for a wide variety of
How to cite: Mojica-Molina, H. R., González-Montiel, M., and Navarro-
Frómeta, A. E. (2021). Batch Conversion of Methane to Methanol Using Copper
Loaded Mordenite: Influence of the Main Variables of the Process. Ingenier
´
ıa e
Investigación,41(3), e87537. 10.15446/ing.investig.v41n3.87537
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Batch Conversion of Methane to Methanol Using Copper Loaded Mordenite: Influence of the Main Variables of the Process
applications (Dalena et al., 2018; Hammond, Conrad, and
Hermans, 2012). CH
4
is one of the most important fossil
fuels in the planet, which is not fully exploited because of
the economical unsuitability of the synthetic pathway from
syngas to produce the required quantities of MeOH (Burnett
et al., 2019; Jovanovic et al., 2020). Therefore, the direct
conversion of CH
4
to MeOH has been a long-standing chal-
lenge in the field of catalysis (Tomkins, Ranocchiari, and
van Bokhoven, 2017). Normally, MeOH is synthesized in
different ways, such as CO
2
hydrogenation and synthesis gas
production (Abashar and Al-Rabiah, 2018; da Silva, 2016).
Many efforts have been made to control the completion of
the reaction up to the exact level of MeOH formation, in
order to avoid overoxidation and its byproducts, e.g., car-
bon oxides or formic acid (Narsimhan et al., 2015; Schwarz,
2011). It is assumed that a continuous process for direct
methane to methanol conversion will ultimately be limited
to achieving high methanol selectivity at low methane con-
versions (Latimer, Kakekhani, Kulkarni, and Nørskov, 2018).
Thus, the search for catalysts and reaction conditions for
this process is a topical issue. A wide variety of materials
have been developed as catalysts for this reaction, includ-
ing copper-exchanged zeolites and mordenite (Burnett et
al., 2019; Lomachenko et al., 2019; Tomkins et al., 2016;
Wulfers, Teketel, Ipek, and Lobo, 2015). These kinds of
materials are synthesized from different copper precursors.
Zeolites with different frameworks and physical-chemical
properties are used, as well as a variety of ionic exchange
methods (aqueous, solid, and gaseous) (Zakaria and Ka-
marudin, 2016). These kinds of materials are inspired by
the activity of Particulate Methane Monooxygenase (pMMO),
which is a metalloenzyme found in methanotrophs, capable
of oxidizing CH
4
with very high efficiency at room condi-
tions (Banerjee, Proshlyakov, Lipscomb, and Proshlyakov,
2015; Sharma, Poelman, Marin, and Galvita, 2020). The
functioning of these enzymes is given by a combination of
specific copper active sites and biological-chemical processes
(Balasubramanian and Rosenzweig, 2007; Yoshizawa and
Shiota, 2006). Copper active sites have been broadly stud-
ied. Based on pMMO, it has been proposed that stable
copper monomers, dimers, and trimmers in different config-
urations are able to activate methane C-H bonding due to
the favourable electronic environment generated (Grundner
et al., 2015; Newton, Knorpp, Sushkevich, Palagin, and van
Bokhoven, 2020; Palagin, Knorpp Pinar, Ranocchiari, and van
Bokhoven, 2017; Sushkevich, Palagin, and van Bokhoven,
2018). Furthermore, through various reaction mechanisms,
these sites assist methane oxidation up to its specific point.
Even though it has been possible to almost reproduce spe-
cific, reactive to CH
4
, copper active sites in the framework
structure of exchanged zeolites, their yield and selectivity are
still low (Jovanovic et al., 2020; Newton et al., 2020). In
this context, different reaction conditions and steps within
the processes have been studied to increase the quantity of
MeOH produced.
Copper mordenite has been reported as one of the most
efficient inorganic materials, with applications in CH
4
to
MeOH conversion under mild conditions (
´
Alvarez, Marín,
and Ordóñez, 2020; Burnett et al., 2019; Tomkins et al.,
2016; Wulfers et al., 2015). Furthermore, it has been linked
to the solid-state ion exchange synthesis method with a
higher amount of MeOH production (Sainz-Vidal, Balmaseda,
Lartundo-Rojas, and Reguera, 2014). Normally, experiments
are focused on continuous flow reaction systems (Grundner
et al., 2015; Sushkevich et al., 2018; Tomkins et al., 2017,
2019), but a static system (batch reactor) still offers a broad
field to be studied, relating to the material behavior at differ-
ent reaction conditions. Besides describing an experiment
and predicting the results, the construction of a mathematical
model allows understanding of the role played by variables,
which can be modified in every reaction. It is possible to un-
derstand if and how much they are related, as well as the way
in which they affect experiment performance. Herein, with
the objective of elucidating the role of variables on MeOH
yields, the influence of the percentage of copper charge, the
temperature of activation, and methane reaction pressure in
a batch process are studied using a full factorial experiment
design. The choice of these variables was made according to
previously reported studies, in which, starting with different
copper ion exchange amounts, specific activation tempera-
tures produce different reactive sites with various populations
in the mordenite framework where the conversion process
takes place. Furthermore, methane pressure is a manageable
variable in batch experiments that indirectly allows the study
of the interaction of a material with a gas phase, thus influ-
encing MeOH yields (
´
Alvarez et al., 2020; Kim et al., 2017;
Tomkins et al., 2016). sectionExperimental section
Ammonium mordenite with a 20:1 (Silicon: Aluminium, SiO
2
:
Al
2
O
3
) mole ratio from Alfa Aesar, denoted as NH
4
-Mordenite,
and copper (II) acetylacetonate (
>
99,9%) from Sigma Aldrich
as a metal ion source, denoted as Cu-(acac)
2
, were employed
in the synthesis of materials.
Synthesis of materials: The solid-state ion-exchange method
was chosen, using a planetary ball mill, to obtain homoge-
neous samples and control the energy applied to the samples.
This is very difficult to reach if an Agate mortar and arm force
are used. 0,9 grams of NH
4
-Mordenite with 0,0387 grams,
0,1269 grams, and 0,2334 grams of Cu-(acac)
2
were grinded
for 60 minutes to obtain three samples with a copper weight
percentage of 1, 3, and 5, labeled as CuMor 1%, CuMor 3%,
and CuMor 5%, respectively. These weight percentages were
corroborated in the activated samples by inductively coupled
plasma-optical emission spectrometry (ICP-OES).
Activated materials: The milled samples were dried at 70
◦
C
for 24 hours, kept in a silica desiccator, and calcined in a
muffle with airflow. Two samples (CuMor 1% and CuMor
5%) were calcined separately at 200
◦
C and 500
◦
C, thus
obtaining four activated samples, labeled as CuMorO 1% 200,
CuMorO 1% 500, CuMorO 5% 200 and CuMorO 5% 500.
Also, CuMor 3%, calcined at 400◦C (CuMorO 3% 400), was
obtained.
Experimental design: A complete
2k
factorial experimental
design with the variables and levels shown in Table 1 was
used.
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Table 1. Definition of variables according to the experimental design
Variable -1 0 1
Calcination Temperature, ◦C 300 400 500
Cu wt % in the catalyst 1 3 5
Methane pressures, bar 2 6 10
Source: Authors
Methanol obtention: Reactions were conducted in a 500
mL batch reactor (Parr Instruments, USA). 0,25 grams of
each sample sieved through a
<
200
µ
m mesh reacted with
methane at the design pressure. The reactor was heated
at a rate of 2
◦
C/min up to 200
◦
C and maintained at this
temperature for 2 hours. After this time, it was cooled to
room temperature.
Sample analysis: The resulting material was dispersed in 1 mL
of water and stirred vigorously for 30 minutes to extract the
formed methanol. The liquid phase was centrifuged, filtrated,
and preserved in vials at 4
◦
C until the chromatographic
analysis was carried out. Before the analysis, 1
µ
L of isopropyl
alcohol (IOH) was added to each sample as an internal
standard. The GC-MS analysis was carried out in a Clarus
SQ 680 GC, coupled to a Clarus SQ 8T MS, using a PE-
WAX (Perkin Elmer, Boston, MA, USA) capillary column
(50 m x 0,25 mm i.d. x 0,25
µ
m phase thickness). The
temperature program was set as follows: 40
◦
C for 8 min,
20
◦
C/min up to 80
◦
C, 40
◦
C/min up to 90
◦
C, and 50
◦
C/min up to 180
◦
C. The injection temperature was 150
◦
C, and helium was used as carrier gas (1,5 mL min
−1
).
The mass spectrometer was operated in electron impact
mode (70 EV), with selective ion monitoring (m/z 29, 31,
43, 45, 46, 58, 59, and 60; dwell time 0,05 s), keeping the
chromatograph interphase and the source temperature at
280
◦
C. Quantitation of MeOH (mz 29+31) and IOH (m/z 45)
was performed using a five-point calibration curve (R
>
0,99)
and areas of specific ions mass-chromatograms.
Statistical Analysis: Analysis of the experimental design, gen-
eral analysis of variance (ANOVA), and nonlinear regres-
sions were performed with Statistica V 13.3 (TIBCO Software
Inc., 2017).
Results and discussion
Starting with a
2k
factorial experimental design, as mentioned,
two central points and a random point were included. In
Table 2 is summarized the experimental conditions employed
and methanol yields obtained according to the experimental
design described in previous section.
It should be noted that no other compounds besides MeOH
were detected in the chromatographic analysis. The obtained
amount of MeOH is in the same order of magnitude with
those reported in the literature (Tomkins et al., 2016, 2019;
Wulfers et al., 2015), but much less compared with cycling
continuous flow processes (
´
Alvarez et al., 2020; Burnett et
al., 2019; Jovanovic et al., 2020; Ma et al., 2020). Likewise,
a dependency on Cu wt % of catalyst was observed (Figure
1) with a similar tendency to the work reported by Le et
al. (2017) and Oord, Schmidt, and Weckhuysen (2018).
Particularly, the samples with 1% and 3% Cu wt reported
an increasing tendency of MeOH obtention, but samples
with 5% Cu wt showed a decreased performance. This
can be explained by stoichiometric calculations, in which a
maximum ion exchange between 4 and 5 copper percent
is evident, depending on the chemistry of mordenite and
copper precursor used. More copper ions than are possible
to insert into the mordenite framework tend to form different
species that, at the temperatures employed in this work,
did not contribute to forming MeOH (Tomkins et al., 2017).
Similarly, the results in Table 2, expressed in
µ
mol/
µ
mol Cu,
show a behavior similar to that reported in the literature,
that is, lower quantities of copper loaded into the mordenite
framework display a better performance in MeOH obtention
(Le et al., 2017).
Table 2. Experimental design and amount of methanol obtained in
moles per gram of catalyst (
µ
mol/g) and moles per copper loaded moles
(
µ
mol Met/mol Cu)
Samples Cu wt % Activation
Temperature
Pressure
µ
mol/g
µ
mol Met/mol Cu
cu1t300p10 -1 -1 1 11,215 0,0713
cu1t300p2 -1 -1 -1 4,732 0,0301
cu1t500p10 -1 1 1 20,732 0,1317
cu1t500p2 -1 1 -1 17,289 0,1099
cu5t300p10 1 -1 1 15,636 0,0199
cu5t300p2 1 -1 -1 3,098 0,0039
cu5t500p10 1 1 1 21,206 0,0270
cu5t500p2 1 1 -1 8,460 0,0108
cu3t400p6a 0 0 0 24,745 0,0524
cu3t400p6b 0 0 0 29,840 0,0632
cu1t500p6 -1 1 0 6,596 0,0419
Source: Authors
Figure 1. Mean values and standard errors of MeOH obtained against
copper weight percent.
Source: Authors
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Batch Conversion of Methane to Methanol Using Copper Loaded Mordenite: Influence of the Main Variables of the Process
The complex dependency of
µ
mol/g yield is shown in Figure
2, in which partial minimal square adjusted surface graphics
can be appreciated. It is observable that quadratic relations
are needed. Certainly, the analysis of complete factorial
experimental design
2k
, with a pair of central points, does
not show a significative influence by any variable, nor a
suitable adjustment, if the curvature (Curv) parameter is not
considered in the analysis design. After eliminating the non-
significative effects, a model was obtained with an intercept
in which only the variables Tcal, P, and Curv were significant.
Figure 2. Yield dependence with respect %Cu, Tcal, and P.
Source: Authors
It must be noted that, at this stage, comparing the obtained
and predicted data, the model does not offer sufficient cer-
tainty. At this point, a main effects ANOVA shows the global
influence of variables in MeOH obtention in the studied range.
The different behavior of each variable can be appreciated in
Figure 3.
Figure 3. Yield dependence with different values of variables used in the
experiment, according to the experimental design (vertical bars denote
0,95 confidence intervals).
Source: Authors
3 %Cu wt, Tcal = 500
◦
C, and P = 10 bar are the best
conditions in each of the global analysis per variables. 3
%Cu wt has a maximum yield because it contains a greater
population of active sites than 1% Cu wt, which is made
evident by the amount of copper ion charged in the materials.
Also, it is greater than 5 %Cu wt due to a stoichiometric
limitation (Dyballa et al., 2019; Pappas et al., 2017) that favors
the formation of copper species which do not participate in
methane oxidation. Considering the molecular shape of NH
4
-
mordenite and the substitution of ammonium ions by copper
(II) ions, the stoichiometric rate of maximum ion exchange is
between 4 and 5%, depending on the mordenite’s molecular
formula.
Tcal = 500
◦
C favors the oxidation of the material, the elimi-
nation of residual organic compounds, and the production
of specific active sites (Groothaert, Smeets, Sels, Jacobs, and
Schoonheydt, 2005; Sheppard, Hamill, Goguet, Rooney, and
Thompson, 2014). Temperatures of 300, 400, and 500
◦
C
follow a growing trend, suggesting that higher temperatures
favor the formation of a higher populations of reactive copper
sites in mordenite (Sainz-Vidal et al., 2014; Vanelderen et
al., 2014), associated with the amount of MeOH obtained.
Activation at higher temperatures allowed us to confirm that
organic material from acetylacetonate disappears, and that
activation at lower temperatures does not remove it at all.
The MeOH yield has a peculiar dependence on P, showing a
minimum at P = 6 atm. Pressures of 10 bar are related to a
higher concentration of CH
4
and greater interaction between
CH
4
and the material. In our experiments, a better yield
was obtained at 2 bars, rather than at a 6 bar CH
4
pressure.
Higher CH
4
conversion at lower pressures is a desired feature,
and the better response at high pressures is an expected and
usual aspect.
As mentioned above, the experimental model with curvature
does not offer a good enough explanation to the influence of
the variables on the MeOH yield. Therefore, a polynomial
regression was employed with the coded values of the vari-
ables, in which the quadratic influence of %Cu and P was
observed with a good description of results. The quadratic
term for Tcal was zeroed with the statistical package. Figure
4 shows how far away the experimental data are from those
predicted by the proposed model.
Figure 4. Predicted vs. obtained results by the polynomial regression
model.
Source: Authors
Finally, to verify the goodness of the quadratic model, and to
obtain the model coefficients based on the real values of the
variables, a nonlinear estimation was performed using Equa-
tion 1, considering that the yield has a quadratic dependence
on %Cu and P, and a linear dependence on Tcal, as it was
obtained with the quadratic regression. It was observed, as
expected, that residuals are like those obtained with the pre-
viously discussed model. The coefficients A, B, C, L, Q, and
R in Table 3 were obtained using the Levenberg-Marquardt
estimation method of the statistical package.
Yield =A+B%Cu +C(%Cu)2+L Tcal +Q P +R P2(1)
All terms were considered because the entire p-values of the
coefficients were under 0,05. In Table 3, the estimate values
of coefficients according to the nonlinear model, which are
represented in Equation 1, can also be observed.
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Table 3. Estimated values of coefficients accordingly to the nonlinear
model
Estimate Standard error t-valued f = 5 p-value
A -12,7498 4,753433 -2,68224 0,043703
B 35,8928 4,635228 7,74348 0,000574
C -5,9719 0,788338 -7,57536 0,000636
L 0,0331 0,008428 3,92458 0,011131
Q -7,7347 2,106922 -3,67107 0,014427
R 0,5358 0,174697 3,06712 0,027875
Source: Authors
This nonlinear model allows a good description of the ex-
perimental points in terms of the real values of the studied
variables.
Conclusions
A nonlinear statistical model of a batch reaction of methane to
methanol direct conversion employing copper mordenite as
the catalyst is proposed, which describes with good accuracy
the experimental results of the yield of methanol production.
The model adequately confirms what is seen in Figures1 and
3, that is, very high or very low values of a copper-loaded
percent can lead to a decrease in the yield, which is a fact
that has been observed in practice. The maximum value at
3% Cu wt can be explained according to the formation pro-
cess of copper mordenite, with the maximum stoichiometric
copper percent ranging between 4 and 5%, depending on the
exact mordenite molecular framework unity and precursor
of copper employed. Higher amounts of Copper-loaded
into mordenite (
>
5% Cu wt) may have a negative effect
on methanol production with the conditions applied in the
experiments. This suggests the important role of the cata-
lyst stoichiometry in the yield of methanol production. On
the other hand, pressure behaved as expected, and higher
pressure means bigger concentration and contact between
methane and material and, consequently, a major probability
of interaction of methane with the mordenite’s active sites.
At lower pressures, the interaction is reduced and, as a result,
decreases the amount of methanol produced. However, the
material still produces good enough quantities, suggesting
that pressure does not have such a great impact in methanol
production within the range studied. Within global analysis,
the mordenite’s calcination temperature had a linear behavior
at higher temperatures, and a higher yield of methanol was
obtained. However, in relation to the copper-loaded amount,
this variable seems to have a low influence on the experimen-
tal results, as is suggested by its linear contribution to the
model. To the best of our knowledge, a nonlinear model that
describes the yields of the batch direct conversion of methane
to methanol has not been proposed, which gives practical
value to the results herein obtained from an engineering point
of view. More experimental data are needed to improve the
accuracy of the model and to expand the studied variable
ranges.
Acknowledgements
The authors thank the Energy Conversion and Storage National
Laboratory (LNCAE-IPN) and the University of Izúcar de
Matamoros (UTIM) for the equipment support and materials
provided, as well as the National Council for Science and
Technology of the Mexican government (CONACyT) for the
doctorate student scholarship provided.
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