Integrating artificial intelligence with kinetic studies for Cr(vi) removal using young durian fruit biochar: a random forest regressor approach

Abstract

This study presents a novel approach to predicting the adsorption kinetics of Cr(VI) using biochar derived from young durian fruit (YDF), integrating artificial intelligence (AI) to overcome limitations of conventional experimental methods. A Random Forest Regressor (RFR) model was developed to predict the adsorption capacity (Q_e) based on key operational parameters, including contact time, pH, biochar dosage, ionic strength, and initial Cr(VI) concentration. The RFR model demonstrated high predictive accuracy and robustness in capturing nonlinear relationships, even under untested conditions. In parallel, ten conventional kinetic models, such as pseudo-first-order (PFO) model, pseudo-second-order (PSO) model, mix-order (MO) model, intraparticle diffusion (IDF) model, vermeulen model, elovic model, Mathews and Weber (M&W) model, boyd's intraparticle diffusion model, Weber and Morris (W&M) model, pore volume and surface diffusion (PVSD) model, were evaluated. Among them, the PSO model exhibited the highest goodness of fit (R² = 0.989), indicating that the adsorption process is predominantly chemisorption-driven. The random forest regressor (RFR) achieved R² = 0.994, significantly outperforming conventional kinetic models and enabling robust forecasting under untested scenarios, thereby bridging the gap between mechanistic modeling and AI-enhanced environmental applications. The results confirm that the AI-based model not only reduces the experimental workload but also offers strong generalizability and interpretability for kinetic behavior analysis. This integration of AI and environmental chemistry provides a powerful tool for developing cost-effective and sustainable water treatment systems using bio-based materials.