Journal of Industrial and Engineering Chemistry, Vol.20, No.2, 597-604, March, 2014
Neuro-evolutionary optimization methodology applied to the synthesis process of ash based adsorbents
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Ash and modified ash were investigated as alternative adsorbents for copper ions. Our aim was to establish optimal working conditions for obtaining the new adsorbents, using a neuro-evolutionary optimization methodology. The materials were characterized by SEM, FT-IR, EDAX, XRD, and by the removal percentage. Three multilayer perceptron neural networks were developed and aggregated into a stack to form the model of the process. The neural model was integrated into an optimization procedure solved with a genetic algorithm to obtain the optimum values for the percentage of adsorption. The new adsorbents provide two benefits: environmental protection and energy recovery.
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