Data-driven models for the prediction of H2 generation through chemical reaction and performance evaluation of on-board hydrogen fuelled sintering tube furnace

Biswajyoti Das, P.S. Robi and Pinakeswar Mahanta

Renewable Energy, 2024, vol. 235, issue C

Abstract: Hydrogen is emerging as a sustainable fuel for the future. In this present work, data-driven modelling tools viz. Radial Basis Function Neural Network (RBFNN) and Least Square Fit (LSF) method, are employed to determine the rate of production of H2 gas by the chemical reaction between aluminium and water in the presence of aq. NaOH. Reactions are carried out at NaOH concentrations of 1M–5M, water temperatures 303K–333K. Hydrogen gas obtained at 333K/4M is found to have a yield of ⁓88 % of the theoretical yield. The activation energy of the reaction is found to be 57.62 kJ mol−1. The fitted models are validated with experimental results for two unknown conditions. The correlation coefficient obtained for the RBFNN model is 0.999, which indicates the high reliability of the model. On-board production of H2 gas by the chemical reaction was used for heating a sintering tube furnace. The hot products of the combustion of hydrogen and air at various fuel-air ratios were used to heat the sintering tube furnace. The maximum thermal efficiency obtained for the furnace is 76.22 % at a fuel-air ratio of 1:60 corresponds to a fuel-air equivalent ratio (λ) of 0.57.

Keywords: Aluminium; Chemical reaction; Furnace thermal efficiency; Hydrogen; Radial basis function neural network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:235:y:2024:i:c:s0960148124013922

DOI: 10.1016/j.renene.2024.121324

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