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Prediction of the continuous cadmium removal efficiency from aqueous solution by the packed-bed column using GMDH and ANFIS models

In the present study, the performance of a packed-bed column filled with Sargassum angustifolium brown seaweed in cadmium ions removal from the aqueous solution was predicted using the group method of data handling (GMDH), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) models. During the modeling, various values of column bed height and feed flow rate to the column were considered as the model inputs while the adsorption efficiency was predicted as the output parameter of the model. Comparison between the predictions and the real experimental data indicated that both of the applied models had a great and almost similar performance (R2 > 0.99) in the prediction of cadmium uptake from the aqueous solution. Moreover, mean squared error values were 0.0006 and 0.0003 for GMDH and ANFIS, respectively. According to the results, the two proposed models showed a great potential in the prediction of cadmium adsorption from aqueous solutions using a packed-bed column filled with S. angustifolium.