期刊
CATALYSIS LETTERS
卷 149, 期 9, 页码 2444-2452出版社
SPRINGER
DOI: 10.1007/s10562-019-02860-1
关键词
Fischer-Tropsch; Deactivation model; Ni; Al2O3 catalyst; Artificial neural network; Nickel loadings
资金
- University of Sistan and Baluchestan
Direct determination of the process deactivation model relates to catalyst stability and plays a crucial role in the process control. The present study aims at investigating the influence of nickel loading (10-20 wt%) on the deactivation model parameters of Ni/Al2O3 catalyst prepared by incipient wetness impregnation. Artificial neural network (ANN) predicts a steady-state activity of the catalyst for the ultimate purpose of a deactivation model selection. The results obtained from an ANN demonstrated that the first-order general power law expressions (GPLE1 model) could adequately predict the catalytic activity during long reaction time. Considering various loadings of nickel on an alumina support, better stability of 20Ni/Al2O3 catalyst was confirmed. Model parameters affirmed that a decrease in the loading of the nickel-made active phase increases the deactivation rate of the catalyst. [GRAPHICS] .
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