Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation

Abstract

In response to escalating global wastewater issues, particularly from dye contaminants, many studies have begun using hydrochar to adsorb dye from wastewater. However, the relationship between the preparation conditions of hydrochar, the properties of hydrochar, experimental conditions, types of dyes, and equilibrium adsorption capacity (Q) has not yet been fully explored. This study conducted a comprehensive assessment using twelve distinct ML models. The Gradient Boosting Regressor (GBR) model exhibited superior performance with R² (0.9629) and RMSE (0.1166) in the test dataset, marking it as the most effective among the evaluated models. Moreover, this study also proved the feasibility of the GBR model through stability testing and residual analysis. A feature importance analysis prioritized the variables as follows: experimental conditions (41.5 %), properties of hydrochar (26.0 %), preparation conditions (18.1 %), and type of dye (14.4 %). Meanwhile, experimental conditions (C0 > 30 mmol/g, pH > 8, and higher solvent temperatures) and hydrochar properties (the BET surface area > 2000 m²/g, an (O+N)/C molar ratio < 0.6, and an H/C molar ratio of approximately 0.06) show higher Q for dyes. Experimental validation of the GBR model confirmed its practical utility with a suitable predictive accuracy (R² = 0.8704). Moreover, the study developed a Python-based GUI that has integrated the best GBR models to facilitate researchers' ongoing application and improvement of this predictive model. This study not only underscores the efficacy of ML in enhancing the understanding of dye adsorption by hydrochar but also sets a precedent for future research on sustainable contaminants removal through bio-based adsorbents.