This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10–20 mg/L). According to these findings, cadmium (II) breakthrough occurred more quickly at lower bed heights, higher flow rates, and higher cadmium (II) concentrations. The Thomas model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (backpropagation and least squares estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solutions via modified cellulose nanocrystals. To compare the model’s predicted results with experimental data, statistical approaches were employed, including calculating the coefficient of determination (R2) and mean square error (MSE). The ANFIS model used to predict cadmium (II) adsorption via modified cellulose nanocrystals had a strong correlation value of 0.997 for least squares estimation (LSE) and 0.999 for the gradient descent (backpropagation) method, indicating the effectiveness of the trained model in predicting the cadmium (II) adsorption process.
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