Özet:

: In this study, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used to predict the adsorption potential of an adsorbent for the removal of chromium (VI) from an aqueous solution. Four operational variables were studied to assess their impact on the adsorption study in the ANFIS model: initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L). To build the ANN model, 70% of the data was used for training and 15% for testing and validation. The network was trained using feedforward propagation and the Levenberg–Marquardt algorithm. The regression coefficients (R 2) for the ANFIS and ANN models were 0.99 and 0.98, respectively. The results show good agreement between the model-predicted and experimental data, indicating that the models are appropriate and compatible. The RMSE between the predicted and observed removal percentage values for the ANFIS model was 0.008, whereas the RMSE for the ANN model was 0.06. The AARE values between the predicted and experimental removal percentage values for the ANFIS and ANN models were determined to be 0.009 and 0.045, respectively. The MSE vales between the predicted and experimental removal percentages for the ANFIS and ANN models were found to be 0.002 and 0.035, respectively. The optimum conditions were as follows: pH 6, an initial concentration of 275 mg/L, a contact time of 60 min, and a dosage of 12.5 mg/L; the absorption was 91.00%.

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