(97f) Improvement of a Kinetic Model for the Hydroconversion of an Atmospheric Residue in a Slurry Semi-Batch Reactor | AIChE

(97f) Improvement of a Kinetic Model for the Hydroconversion of an Atmospheric Residue in a Slurry Semi-Batch Reactor

Authors 

Browning, B., LAGEP
Maurin, J., IRCELYON
Jansen, T., TOTAL
Lacroix, M., TOTAL
Tayakout, M., University of Lyon
Geantet, C., IRCELYON
Pitault, I., University of Lyon

Introduction

In the development of heavy oil upgrading
technologies, such as slurry hydroconversion, the kinetic model plays an
important role together with the hydrodynamic and mass transfer models, Jansen et al. (2014a). However, kinetic modeling
of heavy oil hydroconversion is a complex task. Hydroconversion reactions
described in the literature have various levels of detail, but a common factor:
all reactions are considered non-reversible and light products are formed from
heavy products, Jansen et al (2014b).
Previously, using a batch reactor, Nguyen et al.
(2013) proposed a kinetic model for atmospheric residue hydroconversion (see
Fig. 1) wherein residue consumption rate depends on residue and dissolved
hydrogen concentrations. In the present work, we improve Nguyen’s kinetic model
by introducing a HDS reaction and modeling the outlet gas dynamic in a
semi-batch reactor. Kinetic parameters and the mass transfer coefficient (kLa)
were re-estimated using a nonlinear least-squares
regression on the product yields and the outlet gas dynamic. Moreover, we explain the difference between the values of kinetic
parameters obtained in batch and semi-batch reactors by a free radical kinetic
model taking into account coke formation and we compare also these two reactors
in terms of hydrogen transfer.

Figure 1. Kinetic scheme of Nguyen’s model for atmospheric residue
hydroconversion

Experimental
setup

The properties of the atmospheric residue
feed used for our hydroconversion experiments have already been reported by Nguyen et al (2013). Hydroconversion was
performed in the presence of a Mo octoate precursor (supplied by Shepherd
Chemical Co.). Experiments were conducted in a semi-batch pilot equipped with
an autoclave PARR (250 cm3), a condenser and a cold separator (see
Fig. 2). For all hydroconversion experiments, the catalyst concentration (600
ppm Mo), stirring rate (900 rpm), gas/liquid ratio (60/40, about 100 g of
feed), heating ramp (3°C/min), pressure (15MPa) and H2 inlet flow
(50 NL/h) were kept constant. Kinetic experiments were carried out at different
temperatures (420, 430°C) and reaction times (0, 30, 60 and 120 min). The
reaction time was measured from the instant the reaction temperature was
reached, defined as time zero. The gas outlet flow was measured by Coriolis
flow meter and analyzed by a micro-GC using two modules PoraPLOTU (8m x 0.32 mm
ID) and OV1 (8m x 0.15mm ID). The total liquid products from each experiment
were analyzed by simulated distillation (D-7169 ASTM). To study the
hydrodynamic behavior of the gas phase, residence time distribution (RTD)
experiments were carried out with N2 as tracer under non reacting
conditions (below 300°C).

\Sans titre.jpg

Figure 2. Experimental setup of hydroconversion of atmospheric residue

Results
and discussion

The RTD experimental results show that the
reactor and separator can be modeled as a Continuous Stirred-Tank Reactor and
the rest of the semi-batch pilot (condenser and tubes) as a Plug Flow Reactor.
The hydrodynamic model of the gas phase was coupled to the mass balance of each
unit of the semi-batch pilot. For the HDS reaction, we assumed that the organic
sulphur in the feed is converted into H2S by a first order
non-reversible reaction. Furthermore, the HDS reaction stoichiometry is
considered assuming the HDS of 4,6-DMDBT by partial hydrogenation pathway. In
Fig. 3, we can observe that the improved kinetics of Nguyen’s model coupled to
the semi-batch pilot model (hydrodynamic, mass transfer and thermodynamic
models) predicts the product total masses and the outlet gas dynamic to a large
extent.

Figure 3. Comparison of experimental (Exp) and Simulated (Sim)
product total mass (left) and outlet gas dynamic (right) at 430°C – 60min

The original and improved rate constants of
Nguyen’s model at 430°C are presented in Table 1. We can see significant
differences between the original and improved rate constants of Nguyen’s model.
This is due to the configuration of the batch and semi-batch systems in terms
of hydrogen transfer. In the case of the batch reactor, the low value of kLa
suggests a strong limitation of hydrogen transfer. This causes high rate
constants to be obtained with Nguyen’s model. For the semi-batch reactor, the
hydrogen transfer from gas to liquid is improved (high value of kLa),
the reaction system is under kinetic control and the bulk hydrogen
concentration is close to equilibrium hydrogen concentration. Without
limitation on hydrogen transfer, the rate constants estimated from semi-batch
reactor data correspond to the pseudo-intrinsic rate constants of Nguyen’s
model.

The differences
between the original and improved rate constants of Nguyen’s model can be also
explained from a purely kinetic view point. Nguyen’s kinetic model can be
modified to create a free radical kinetic model. In Fig. 4, reaction 1 of
Nguyen’s model is broken down into 3 reactions: cracking (1), hydrogenation (2)
and condensation (3).

Figure 4. Break down of Nguyen’s kinetic model into a free radical
kinetic model

For the free radical kinetic model, we
suppose that the radical species are produced and consumed at the same rate.
Comparing the rate constants of both kinetic models, we obtain:

For
Residue, R:       

For Vacuum
Gas Oil, VGO:         

If the free radical
compound hydrogenation rate (due to dissolved hydrogen concentration and/or
catalyst activity) is very much higher than the coke formation rate () or the coke
formation rate is intrinsically slow () then the
rate constant of Nguyen’s model K1 is approximated by 

The rate constants of Nguyen’s model
depend on the cracking rate constants and the bulk hydrogen concentration. The
cracking rates are relatively similar for the batch and semi-batch systems,
since they are predominately a function of temperature (see Table 2). Thus, the
rate constant of Nguyen’s model for a specific reaction system (Ki)
depends mainly on the bulk hydrogen concentration in that system. This approach
will be extended to the whole kinetic scheme and all the compounds in a further
study.

Conclusions

A new set of kinetic parameters for
atmospheric residue hydroconversion have been estimated. The accuracy of
kinetic reaction data was improved by modeling the outlet gas dynamic. A HDS
reaction was introduced into Nguyen’s kinetic model. In comparison to Nguyen’s
kinetic study performed in a batch reactor, the results of the present study do
not show any limitation of the transfer of hydrogen from gas to liquid in the
semi-batch reactor. A high value of kLa was obtained demonstrating
the high efficiency of this kind of apparatus for estimation of the chemical
kinetic constants. Using a free radical kinetic model approach, we show how
Nguyen’s kinetic constants depend on dissolved hydrogen concentrations that are
different in the batch and semi-batch reactors.

References

Jansen,
T., Dimitri, G., Gotteland, D., Bacaud, R., Lacroix, M., Ropars, M., Chantal,
L., Christophe, G., Tayakout-Fayolle, M. (2014a).  Characterization of a
continuous micro-scale pilot unit for petroleum residue hydroconversion with
dispersed catalysts: Hydrodynamics and performances in once-through and
recycling mode. Chemical Engineering Journal., 253, 493-501.

Jansen,
T., Dimitri, G., Ropars, M., Lacroix, M., Christophe, G., Tayakout-Fayolle, M.
(2014b). Simulation of Petroleum Residue Hydroconversion in a Continuous Pilot
Unit Using Batch Reactor Experiments and a Cold Mock-Up. Ind. Eng. Chem. Res,
53 (41), 15852-15861.

Nguyen,
TS, Tayakout-Fayolle, M., Ropars, M., Christophe, G. (2013). Hydroconversion of
an atmospheric residue with a dispersed catalyst in a batch reactor: Kinetic
modeling including vapor-liquid equilibrium. Chem. Eng. Sci., 94, 214-223.