Neuro-evolutionary optimization methodology applied to the synthesis process of ash based adsorbents

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.