Selectivity Modeling of Fischer-Tropsch Synthesis over Fe-Mn Catalyst Using Artificial Neural Networks

Hossein. Atashi; Neda. Poudineh; Amin. Ein Beigi

Abstract

The Fischer-Tropsch synthesis is the collection of several reactions which are used to produce hydrocarbon products from synthesis gas. This method is able to produce more than 70 types of products. The selectivity models of the products such as: diesel, gasoline, methane and C21+ in the FischerTropsch synthesis over Fe-Mn catalyst were obtained while have been discussed less about the selectivity models in the references. Neural networks and response surface method were used to determine the effect of operating parameters such as: temperature, pressure and H2/CO ratio and space velocity on the selectivity of products. Operating parameters were varied as follow: reaction temperature 523-573 K, reaction pressure 1.5-3 Mpa and H2/CO ratio 0.67-2. The highest influence related to temperature and H2/CO ratio parameters and the lowest was pressure on the selectivity of productssuch that has been removed from some of the models. In addition, the interaction between temperature and H2/CO ratio was interaction parameter. The results showed that the values predicted by the neural network could satisfy the experimental data, so use of neural network to predict the selectivity values in Fischer-Tropsch synthesisis an effective method.

Keywords

Artificial neural networks, Fischer-Tropsch synthesis, Fe-Mn catalyst, selectivity, Response surface model

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Cites this article as

H. Atashi, N. Poudineh, A. E. Beigi, "Selectivity Modeling of Fischer-Tropsch Synthesis over Fe-Mn Catalyst Using Artificial Neural Networks", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-3, Issue-3, Page No-229-240, 2016. Available from: