Analysis and Characterization of Fischer-Tropsch Products through Thermodynamic Equilibrium using Global Optimization Techniques
Pacheco, K.
Guirardello, R.
Download PDF

How to Cite

Pacheco K., Guirardello R., 2017, Analysis and Characterization of Fischer-Tropsch Products through Thermodynamic Equilibrium using Global Optimization Techniques, Chemical Engineering Transactions, 57, 1669-1674.
Download PDF

Abstract

The Fischer-Tropsch (FT) based Gas-to-Liquid (GTL) technology presents as an opportunity to obtain clean fuels from coal, natural gas and more recently from biomass. The process has been attracting more attention to meet future energy demand. In the Fischer-Tropsch process, synthesis gas (a mixture of hydrogen and carbon monoxide) is converted to a wide variety of valuable chemicals, either using an iron-based or cobalt based catalyst. The composition and the productivity of products are controlled by different mechanisms and kinetic factors. The catalyst employed, the type of reactor and operating conditions (temperature, pressure and composition of the syngas) has significant effects on the composition and characteristics of the products. The condition for thermodynamic equilibrium is the minimum Gibbs energy of the system, therefore such system can be modeled as an optimization problem in order to solve the chemical and phase equilibrium and determine the most thermodynamic favorable phases and compositions of the formed FT products. The set of nonlinear equations was solved in GAMS/CONOPT using the non-stoichiometric formulation. The non-ideal behavior of the vapor phase was evaluated using the second virial correlation. The immiscibility of two liquid phases (organic and aqueous) takes into account the non-ideal behavior in the liquid phase. The effects of temperature (450-750 K), pressure (5-60 bar) and H2:CO ratio (1:1-3:1) were evaluated on the composition and phases of products, conversion and yield. The FT system is not at global chemical equilibrium, only some subsystems are. However, the thermodynamic distributions predict chain growth probability values, independent of mechanism and catalyst representing a generic distribution.
Download PDF