Optimization of Fischer-Tropsch synthesis using neural networks

Fischer-Tropsch synthesis is an important chemical process for the production of liquid fuels and olefins. Optimization of hydrocarbon products such as diesel and gasoline produced by Fischer-Tropsch synthesis usually requires the knowledge of the complex polymerization mechanism and the kinetic parameters associated with it in order to optimize production. The Fischer-Tropsch reaction mechanism is still not fully understood, making optimization a hard task. In this work, a neural network was used in substitution to the reaction mechanism to optimize diesel and gasoline production based on few experimental data for the reaction. The neural network has yielded satisfactory predictions of the product distribution (with prediction errors lower than 5%) and the optimum operating conditions for gasoline and diesel production were found for a commercial iron based catalyst.