Usefulness of ANN-based model for copper removal from aqueous solutions using agro industrial waste materials

The purpose of this study was to investigate the adsorption properties of locally available lignocelluloses biomaterials as biosorbents for the removal of copper ions from aqueous solution. Materials are generated from juice production (apricot stones) and from the corn milling process (corn cob). Such solid wastes have little or no economic value and very often present a disposal problem. Using batch adsorption techniques the effects of initial Cu(II) ions concentration (Ci), amount of biomass (m) and volume of metal solution (V), on biosorption efficiency and capacity were studied for both materials, without any pre-treatments. The optimal parameters for both biosorbents were selected depending on a highest sorption capability of biosorbent, in removal of Cu(II). Experimental data were compared with second order polynomial regression models (SOPs) and artificial neural networks (ANNs). SOPs showed acceptable coefficients of determination (0.842 - 0.997), while ANNs performed high prediction accuracy (0.980-0.986) in comparison to experimental results. [Projekat Ministarstva nauke Republike Srbije, br. TR 31003, TR 31055