Discharge of heavy metals into aquatic ecosystems has become a matter of concern over the last few decades. The search for new technologies involving the removal of toxic metals from wastewaters has directed the attention to biosorption, based on metal binding capacities of various biological materials. Degree of sorbent affinity for the sorbate determines its distribution between the solid and liquid phases and this behavior can be described by adsorption isotherm models (Freundlich and

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... r isotherm models) representing the classical approach. In this study, an artificial neural network (ANN) was proposed to predict the sorption efficiency in single and binary component solutions of Cd 2+ , Zn 2+ and Co 2+ ions by biosorbent prepared from biomass of moss Rhytidiadelphus squarrosus. Calculated non-linear ANN models presented in this paper are advantageous for its capability of successful prediction, which can be problematic in the case of classical isotherm approach. Quality of prediction was proved by strong agreement between calculated and measured data, expressed by the coefficient of determination in both, single and binary metal systems (R 2 = 0.996 and R 2 = 0.987, respectively). Another important benefit of these models is necessity of significantly smaller amount of data (about 50%) for the model calculation. Also, it is possible to calculate Qeq for all studied metals by one combined ANN model, which totally overcomes a classical isotherm approach.