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Neural Computing Strategy for Predicting Deactivation of Fischer–Tropsch Synthesis With Different Nickel Loadings

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Abstract

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.

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Acknowledgments

This research is financially supported by the University of Sistan and Baluchestan. The authors particularly thank the University of Sistan and Baluchestan.

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Engineering, University of Sistan and Baluchestan, P.O. Box 98164-161, Zahedan, Iran

    Maliheh Ghofran Pakdel, Hossein Zohdi-Fasaei & Hossein Atashi

  2. Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, P.O. Box 98135-674, Zahedan, Iran

    Ali Akbar Mirzaei

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  1. Maliheh Ghofran Pakdel
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  2. Hossein Zohdi-Fasaei
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  3. Ali Akbar Mirzaei
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Correspondence to Hossein Zohdi-Fasaei.

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Ghofran Pakdel, M., Zohdi-Fasaei, H., Mirzaei, A.A. et al. Neural Computing Strategy for Predicting Deactivation of Fischer–Tropsch Synthesis With Different Nickel Loadings. Catal Lett 149, 2444–2452 (2019). https://doi.org/10.1007/s10562-019-02860-1

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  • DOI: https://doi.org/10.1007/s10562-019-02860-1

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