ENHANCED REMOVAL OF Cr (VI) FROM WASTEWATER WITH GREEN AND LOW-COST NANOMATERIAL USING FUZZY INFERENCE SYSTEMS (FIS) AND ARTIFICIAL NEURAL NETWORKS (ANN)

In this study, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used to predict the adsorption potential of the adsorbent for the removal of chromium (VI) from aqueous solution. Four operational variables were studied to assess their impact on the adsorption study in the ANFIS model, including 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 (R²) for the ANFIS and ANN models were 0.99 and 0.98, respectively. The results show a good match between model-predicted and experimental data, indicating that the models are appropriate and compatible. The RMSE between 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 between predicted and experimental removal percentage values for the ANFIS and ANN models was determined to be 0.009 and 0.045, respectively. The MSE between predicted and experimental removal percentages for the ANFIS and ANN models was found to be 0.002 and 0.035, respectively. the optimum conditions were pH 6, initial concentration of 275 mg/L, contact time of 60 min, and a dosage of 12.5 mg/L, the absorption was 91.00%.