A Machine Learning-Based Modeling Approach for Dye Removal Using Modified Natural Adsorbents

Abstract

This study used machine learning models to investigate the potential of biosorbents derived from natural fruit seed waste (apricot, almond, and walnut) for removing a cationic dye. Levulinic acid (LA)-modified powders of almond shell (ASh), apricot kernel shell (APKSh), and walnut shell (WSh) were used to remove methylene blue (MB) from an aqueous solution, producing 105 experimental data points under various circumstances. Attributes included pH (3-5), adsorbent dose (0.4-6.0 g/L), concentration (10-500 mg/L), time (30-600 min), and temperature (25-55 degrees C). Species information was incorporated into the data set using the One-Hot Encoding method. The data were normalized using the min-max method, and due to the non-normal distribution of the data, Spearman correlation analysis was employed to rank the importance of the attributes. Gradient Boosting (GB), Multilayer Perceptron (MLP), XGBoost (XGB), and Random Forest (RF) algorithms were applied for regression estimation. Based on 5-fold cross-validation results, the GB model achieved the highest performance, with R2 values of 0.8858 for removal percentage and 0.9532 for adsorption capacity.