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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

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