| Summary: | In this study, activated carbon -based adsorbent was prepared from eggshells and coconut shells. The effects of contact
time, initial H2S concentration, and the calcium impregnated coconut shell activated carbon (Ca-CSAC) adsorption dosage on the
hydrogen sulphide (H2S) removal efficiency and adsorption capacity were investigated. The batch adsorption data obtained from
the experimental runs were employed to fit an artificial neural network (ANN) model. An initial optimization was performed to
obtain the most suitable number of hidden neurons for training and validation of the ANN. The optimization results show that 16
hidden neurons was the most appropriate choice. The trained ANN was adequately validated and tested with coefficients of
determination (R2
) of 0.99 and 0.95, respectively. The ANN was found to be a robust tool for modeling of H2S removal
efficiency by and adsorption capacity on Ca-CSAC under different process conditions.
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