Optimization and prediction of dye adsorption utilising cross-linked chitosan-activated charcoal : Response Surface Methodology and machine learning

Water pollution poses a significant environmental threat due to the discharge of organic dyes from industrial processes. In this study, we investigated a novel adsorptive composite material for removing MB dye from water and applied new models for optimizing and predicting adsorption efficiency. The cross-linked chitosan-activated charcoal composite material was synthesized using a single-step process and characterized by analytical techniques including FTIR, XPS, BET, SEM, TGA, and zeta potential, which demonstrated its remarkable physicochemical properties. Batch adsorption experiments showed a direct correlation between increased adsorbent dosage and enhanced MB dye adsorption, with an optimal dosage of 0.4 g/25 mL. Additionally, pH-dependent studies revealed excellent performance at lower pH levels, achieving up to 95 % adsorption. The composite reached equilibrium within the first 360 min of contact time and maintained a stable 99 % adsorption efficiency afterward. These results were confirmed by an applied optimization RSM model. Additionally, adsorption isotherms and kinetics were thoroughly analyzed by fitting them into various models, revealing a maximum dye adsorption capacity of 625 mg/g. ML models, such as RF, XGBoost and stacking, were used to predict adsorption efficiency, with a stacked model combining RF and XGBoost showing the highest predictive accuracy. Variable importance plots indicated that contact time was the most significant factor influencing adsorption, followed by dosage and pH, in line with RSM findings. This research highlights the eco-friendliness and cost-effectiveness of the composite material in addressing water pollution challenges. Integrating ML models and RSM enhances the predictability and optimization of the adsorption process, offering valuable insights for mitigating the impact of organic dyes on aquatic ecosystems and public health.