(600e) Modelling of Diffusion-Reaction Interaction inside the Co-Based Catalyst Particles for the Fischer-Tropsch Synthesis

Authors: 
Bukur, D. B., Texas A&M University at Qatar
Todic, B., Chemical Engineering Program, Texas A&M University at Qatar
Mandic, M., Chemical Engineering Program, Texas A&M University at Qatar
Zivanic, L., Faculty of Technology and Metallurgy, University of Belgrade
Nikacevic, N., Delft University of Technology

1.    
Introduction

Fischer-Tropsch synthesis (FTS) is a
heterogeneous reaction used to convert synthesis gas into a range of
hydrocarbon products. This reaction is a pivotal part of the Gas-to-Liquid
(GTL) process in which natural gas is converted into liquid fuels and
value-added chemicals. As such, FTS is often conducted inside of multi-tubular
fixed bed reactors filled with cobalt-based catalyst particles. In order to
avoid high pressure drops inside of the catalyst bed, relatively large
particles (1-3 mm) are typically applied [1,2]. This
may lead to intraparticle diffusional limitations for
highly active catalysts. This means that the particle size, shape and operating
process conditions may have a profound influence on the catalyst effectiveness
and FTS selectivity. Several literature studies modelled diffusion with
chemical reaction with Co based FTS catalysts [3,4],
but focused mainly on spherical geometry and utilized rate equations based on experiments
with a low activity catalyst [5].

In recent years Velocys [6] developed micro-channel
reactors for FTS. These reactors utilize “super-active” Co catalysts compared
to fixed-bed reactors in order to improve reactor volumetric productivity. It
is expected that transport resistances could play a significant role with these
reactors as well. Models of diffusion-reaction interaction are helpful in
determining an optimal layer thickness that maximizes catalyst effectiveness
and improves FTS product selectivity.

Here we present results from simulations utilizing one-dimensional
particle models for Co-based FTS catalysts. Several critical parameters, such
as the effects of shape (sphere, hollow cylinder and slab), diffusion length,
temperature distribution inside the particle, diffusivity coefficients (gas-
and wax-filled) and catalyst activity, and their influence on catalyst
effectiveness and selectivity have been examined.

2.    
Diffusion-reaction model

FTS reaction can
be simplified using the following stoichiometric relation:


                                     (1)

Our
model assumes that the average hydrocarbon product is n-hexane (i.e. n = 6).
Therefore, the components considered in the model are: carbon-monoxide,
hydrogen, water and n-hexane.

The
equation describing diffusion with chemical reaction inside FTS catalyst particle
can be expressed as:


                                                                                            (2)

where
x is the dimensionless distance,
superscript g stands for geometry (g
= 2, 1 and 0 for sphere, cylinder and slab, respectively), yi is dimensionless
concentration (i
= CO, H2, H2O and n-hexane), stoichiometric coefficient
i = 1, 13/6, 1 and 1/6 for CO, H2,
H2O and n-hexane, respectively), Ψ
is the dimensionless CO consumption rate and φi is the
dimensionless parameter (
). This parameter includes catalyst density (ρp),
characteristic length (L), reactant surface
concentrations (
), and effective diffusivity coefficients (De,i). Set of second
order ordinary differential equations (Eq. 2) was solved in Matlab
using the bvp4c solver.

     Reaction rate was
described using the Yates-Satterfield expression [5]:


                                                                                                            (3)

The
parameters k and a in Eq. (3) were estimated from experimental data with
0.48%Re–25%Co/Al2O3 catalyst [7]. Rates of H2
disappearance and H2O and n-hexane formation are calculated based on
Eq. (3) and stoichiometry of Eq. (1). It is worth noting that this catalyst is
approximately an order of magnitude more active under typical FTS operating
conditions compared to the original Yates-Satterfield catalyst [8], and as such
is more representative of modern Co catalysts used in industrial applications.

            Two approaches to
effective diffusion calculations were used, one assuming wax-filled and the
other gas-filled catalyst pores. The former used correlations for diffusivity
in FTS wax developed by Akgerman
et al. [9], which is a representative of the porous catalyst transport
environment over a significant portion of the reactor bed. For gas-filled
particle we used multi-component diffusion approach, adjusted with Knudsen
diffusion, and this is an extreme representation of segments of the reactor bed
in which catalyst pores are not entirely filled with wax.

            The key aspect of
FTS catalyst selectivity is methane selectivity. Methane formation rate was
calculated using Ma et al. [10] expression:


                                                                                                         (4)

where numerical
values of kinetic parameters were estimated from the same set of data as those
in Eq. (3), i.e. with 0.48%Re–25%Co/Al2O3 catalyst [7]. Solution
of Eq. (2) provides partial pressures needed to evaluate methane formation rate
and calculate methane selectivity as:


                                                                                                             (5)

which is valid for
Co-catalysts due to their low WGS activity, i.e. low CO2 formation.

3.    
Results and discussion

Our simulations show that with very small spherical particles (< 150
μm) diffusional resistance is small, even with highly
active Co catalyst used in our study. This is consistent with literature
results [2-4]. However, with larger particle sizes (e.g. 2 mm) diffusion
resistances are present both with the highly active Co catalyst and low activity
catalyst used in previous studies [3,4]. Figure 1
shows model results for a 2 mm diameter spherical pellet, filled with wax, with
kinetic parameters obtained with 0.48%Re–25%Co/Al2O3 catalyst.
Reaction rate results show that FTS follows “abnormal” kinetics (increase in
rate with conversion followed by decrease at high CO conversions). Ratio of H2
to CO diffusivity is higher than the corresponding consumption ratio. This
means that the ratio of H2 and CO concentrations is increasing
inside the particle. Because of this, as well as the positive reaction order in
H2 and negative order in CO, we get that the reaction rates inside
the particle initially increase starting from the particle surface. However,
for large particles, reactants are consumed relatively fast and reaction rate
drops to zero leaving a large fraction of the particle unutilized (Figure 1).  The calculated effectiveness factor for such a
particle is only 0.52. Even more interesting is the behaviour of selectivity
inside the spherical particle (Figure 1). Because methane selectivity follows
the trend of H2/CO ratio, S(CH4)
increases rapidly inside the particle. As concentration of CO goes to zero, H2/CO
ratio becomes so high that methane selectivity at a certain point inside the
particle becomes 100%. Additional insights were obtained from simulations with
a lower activity catalyst, different size particles and assumption of gas-filled
pores. Results strongly suggest that the eggshell catalyst distribution is the
only viable option for large catalyst particles utilized in multi-tubular fixed
bed reactors.

Similar simulations were performed for a slab geometry present in
wall-coated micro-channel reactors with various slab thickness and reaction
rates. Figure 2 shows results obtained with 300 μm
thickness layer. Catalyst effectiveness for this thickness is 0.65, suggesting
that lower thickness should be used in order to fully utilize the coated
catalyst. Also, methane selectivity is extremely high. This is contrary to the
goal of increasing the reactor productivity by adding more catalyst (i.e. using
thicker layers). Therefore, an optimal solution between high catalyst
effectiveness and reactor productivity has to be found. Selectivity has to be
taken into account also. Our simulation show that, similar to spherical
particles, methane selectivity rises almost exponentially inside of 300 μm catalyst slab and reaches 100% well before FTS rate
drops to zero. Thus, selectivity has to be considered in selection of the
optimal slab thickness.

4.    
Conclusions

A one-dimensional model of diffusion-reaction interaction inside the
FTS cobalt catalyst was developed. A number of simulations for sphere and slab
geometry were performed. Results show that intraparticle
diffusional limitations are severe with a highly active cobalt-based catalyst.
Results suggest that large 2 mm spherical particles should utilize eggshell
design in order to minimize the negative impact of pore diffusion on activity and
selectivity. Also, pore diffusional limitations were found to be significant
for a thin layer of catalyst (300 mm). Good understanding of diffusion-reaction
interplay inside FTS catalyst is required in order to optimize reactor
productivity, catalyst effectiveness and product selectivity.

Figure  SEQ Figure \* ARABIC
1 – Distribution of
dimensionless CO concentration (yCO),
dimensionless CO consumption rate (Ψ) and methane selectivity (S(CH4)) within a spherical particle (dp = 2 mm, T = 473 K, P = 25 bar, bulk H2/CO
= 2).

Figure 2 – Distribution of dimensionless CO
concentration (yCO), dimensionless CO
consumption rate (Ψ) and methane selectivity (S(CH4))
within a slab geometry (slab thickness = 300
mm,
T = 473 K, P = 25 bar, bulk H2/CO = 2).

Acknowledgements

This publication was made possible by NPRP grant 7-559-2-211 from
the Qatar National Research Fund (a member of Qatar Foundation). The statements
made herein are solely the responsibility of the authors.

References

[1] A.P. Steynberg, M.E. Dry, B.H. Davis and B.B. Breman, Stud. Surf. Sci. Catal., 152
(2004) 64.

[2] E. Rytter, N.E. Tsakoumis, A. Holmen, Catal.
Today, 261 (2016) 3.

[3] D. Vervloet, F. Kapteijn, J. Nijenhuis, J.R. van Ommen, Catal. Sci. Technol., 2 (2012) 1221.

[4] S.A. Gardezi, B. Joseph, Ind. Eng. Chem. Res., 54 (2015) 8080.

[5] I.C. Yates,
C.N. Satterfield, Energy Fuels, 5 (1991) 168.

[6] H.J. Robota, L.A. Richard, S. Deshmukh,
S. LeViness, D. Leonarduzzi,
D. Roberts, Catal. Surv.
Asia, 18 (2014) 177.

[7] B. Todic, T. Bhatelia, G.F. Froment, W. Ma, G.
Jacobs, B.H. Davis, D.B. Bukur, Ind. Eng. Chem. Res., 52 (2013) 669.

[8] C. Maretto, R. Krishna, Catal.
Today, 52 (1999) 279.

[9] Akgerman, Final Report DOE AC22-84PC70032, (1984).

[10] W. Ma, G.
Jacobs, T.K. Das, C.M. Masuku, J. Kang, V.R.R. Pendyala, B.H. Davis, J.L.S. Klettlinger,
C.H. Yen, Ind. Eng. Chem. Res., 53 (2014) 2157.