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Artificial Neural Network (ANN) Approach To Copper Biosorption Process

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This paper demonstrates use of artificial neural network (ANN) model for the evaluation of copper biosorption process using black cumin. The experimental variables (temperature, biosorbent mass, initial copper concentration, initial pH) were used as the input to the constructed neural network to predict the adsorbed amounts of copper at any time as the output. The high R2-values, 0.89 and 0.93 for training and testing data sets, respectively; between the network prediction and the corresponding experimental data prove that modeling the biosorption process using artificial neuron network is a satisfactory method. Thermodynamic parameters such as Gibbs free energy (ΔG°), the enthalpy (ΔH°) and the entropy change of sorption (ΔS°) were also evaluated. It was found that the biosorption process was spontaneous, favorable and exothermic in nature. The equilibrium sorption of copper ions was determined from the Langmuir equation and found to be 16.13 mg/g at 293 K. A comparison between the model results and experimental data showed that the ANN model is able to predict the removal of copper using black cumin.

Yayınlandığı Kaynak : Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi