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, ve

SUBMITTER: Nguyen DK 

PROVIDER: S-EPMC12577751 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature