Kinetic modeling of GTL product distribution over a promoted cobalt catalyst
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abstract
Qatar is the world leader in fuel production from gas-to-liquid (GTL) technology and home of the largest GTL plant in the world (Pearl GTL, a joint development by Qatar Petroleum and Shell). In the GTL process natural gas is converted into liquid fuels and waxes. Fischer-Tropsch synthesis (FTS) is the key part of that process. FTS is a heterogeneously catalyzed reaction in which a mixture of CO and H is converted into a wide range of hydrocarbon products. Advanced design and optimization of large scale FTS reactors requires a detailed knowledge of reaction chemistry. Kinetic models used for this application need to be robust, physically reasonable and fundamental. This study will present one such a model. Experiments were conducted in a 1-L slurry reactor over 25% Co/0.48% Re/AlO catalyst. A broad range of operating conditions was achieved (i.e., temperatures of 478, 493 and 503 K, pressures 1.5 and 2.5 MPa, H2/CO feed ratio 1.4 and 2.1 and gas space velocities of 1.0-22.5 NL/g-cat/h). Rate laws for the kinetic model have been derived using the CO-insertion mechanism and chain-length-dependent 1-olefin desorption concept. The model accounts for the formation of n-paraffins and 1-olefins. CO hydrogenation and insertion of CO into the growing chain are considered to be rate determining, as well as the chain termination steps. Non-isothermal model parameters are estimated by minimization of a multi-response objective function. A global minimum is obtained with the hybrid genetic algorithm and a total of 696 experimental responses used in the estimation. Estimated model parameters are meaningful, considering physicochemical tests and statistical tests. They are also in a good agreement with previously reported values for activation energies. The model fit is in good agreement with experimental data and the mean absolute relative residual (MARR) was 24%. The model also provides a good prediction of CO and H rates of consumption, with a MARR of 17.7 and 16.1%, respectively. The main advantage of the proposed model is its ability to explain and predict the main features of GTL product distribution in a physically meaningful and fundamental way over a wide range of industrially relevant process conditions.
