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Lake sediment based catalyst for hydrogen generation via methanolysis of sodium borohydride: an optimization study with artificial neural network modelling

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Abstract

In the current study, lake sediment, a heterogeneous and complex organic matter, utilized as a catalyst upon acid treatment for efficient hydrogen generation from sodium borohydride. In order to synthesise the catalyst that bears the best catalytic activity, ANOVA, cubic stepwise linear regression and artificial neural network optimization techniques were applied to determine the optimal level of treatment parameters. The results suggest that only Taguchi orthogonal arrays method was able to accurately reflect the overall surface of objective variable. Among the 16 catalyst samples Exp(15) showed the superior catalytic activity followed by Exp(13), Exp(12), Exp(14) and Exp(7). The minimum reaction completion time for Exp(15) corresponding to maximum hydrogen production rate of 3247.15 mL/min/gcat was 2.25 min. A detailed characterization of the final product was carried out by using a Fourier transform infrared spectra (FTIR—Perkin Elmer), an X-ray diffractometer (Bruker D8 Advance XRD), a scanning electron microscopy and energy dispersive X-ray spectroscopy.

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Engineering, Siirt University, 56100, Siirt, Turkey

    Mesut Bekirogullari & Tulin Avci Hansu

  2. Department of Computer Engineering, Faculty of Engineering, Siirt University, 56100, Siirt, Turkey

    Serdar Abut

  3. Department of Biology, Faculty of Science, Erciyes University, 38039, Kayseri, Turkey

    Fatih Duman

Authors
  1. Mesut Bekirogullari
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  2. Serdar Abut
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  3. Fatih Duman
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  4. Tulin Avci Hansu
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Correspondence to Mesut Bekirogullari.

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Bekirogullari, M., Abut, S., Duman, F. et al. Lake sediment based catalyst for hydrogen generation via methanolysis of sodium borohydride: an optimization study with artificial neural network modelling. Reac Kinet Mech Cat 134, 57–74 (2021). https://doi.org/10.1007/s11144-021-02057-x

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