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Prediction of two-metal biosorption
equilibria using a neural network A
feedforward neural network model with a single hidden layer was used to correlate and
predict biosorption equilibrium data in a binary metal system. Experimental data on the
biosorption of Fe(III) and Cr(VI) by the microalga Chlorella vulgaris reported in
the literature was used to assess the performance of the neural network. It was
demonstrated that the neural network approach was significantly more accurate than the
traditional modeling approach based on Langmuir-type models. To assess the predictive
capability of the neural network model, the network was trained using a subset of
available data. The suitably trained neural network was found to be capable of predicting
fresh data not belonging to the training set. However, training data should be selected
carefully if the best results are to be achieved. |
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