Abstract
AbstractIn this research, deactivation model of an industrial γ‐alumina‐supported iron catalyst was investigated. Experiments were carried out in a fixed‐bed micro reactor over one‐time and four‐time impregnated catalysts at a reaction temperature of 250 °C, H2/CO ratio of 1, gas hourly space velocity (GHSV) of 3000 h−1, and reaction pressure of 2 bar. Neural networks strategy was used to predict the steady‐state catalytic activity for the ultimate purpose of determining the best deactivation model. Non‐linear regression and statistical analyses showed that the second‐order generalized power‐law equation (GPLE) could adequately described the deactivation behaviors of the considered catalysts. A comparison on the model parameters indicated higher deactivation rate of the four‐time impregnated catalyst, as compared to the one‐time one.
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