Machine Learning-Based Prediction of Adsorption Capacity of Metal-Doped and Undoped Activated Carbon: Assessing the Role of Metal Doping.

ABSTRACT

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.66 and 148.28 mg/g, respectively. Feature analysis using SHAP values revealed that adsorbent characteristics accounted for 53.5 % of the adsorption capacity in pristine AC, while experimental conditions were crucial for metal-doped AC (61.3%), with surface area and initial concentration being the most significant features, showing mean SHAP values of 0.317 and 0.117, respectively. Statistical comparisons of adsorbent characteristics between pristine and metal-doped AC showed that metal doping significantly altered surface area (p-value = 0.0014), pore volume (p-value = 0.0029), and elemental composition (C% (p-value = 3.9513*10ˆ-7) and O% (p-value = 0.0007)) of AC. Despite the reduction in surface area and consistent pore volume after metal doping, the enhanced adsorption capacity of metal-doped AC was attributed to increased oxygen content from 10.89% to 17.28 % as mean values. This suggests that oxygen-containing functional groups play a critical role in the improved adsorption capacity of metal-doped AC. This research lays the groundwork for optimizing AC adsorbents by identifying key factors in metal-doped AC and suggest further studies on the interaction between specific metal dopants and resulting functional groups to improve adsorption capacity and reduce repeated labor work.