Interpretable machine learning for predicting gaseous arsenic adsorption by metal oxides and identifying influential descriptors.

Abstract

Identifying descriptors associated with gaseous arsenic adsorption by metal oxides remains challenging because literature data are heterogeneous and incomplete. A database of 280 experimental records and 20 descriptors from 17 studies was compiled to predict adsorption capacity and interpret descriptor-performance relationships. Mean, k-nearest neighbor (KNN), and inference-based imputation strategies were combined with gradient boosting decision tree (GBDT) and particle swarm optimization-tuned GBDT (PSO-GBDT) models. Among six configurations, the PSO-GBDT model trained on the inference-imputed dataset achieved the lowest five-fold cross-validation RMSE of 2.10 mg/g and test-set R2, RMSE, and MAE values of 0.98, 1.54 mg/g, and 0.71 mg/g, respectively. Permutation feature importance (PFI) and Shapley additive explanations (SHAP) showed that operating and gas-phase descriptors dominated predictions, with H2O concentration and adsorption time ranked highest, followed by adsorption temperature, As2O3 concentration, Fe content, and average pore diameter. Partial dependence plots (PDPs) associated higher predicted capacities with longer adsorption times, higher As2O3 concentrations, larger pore diameters, and lower adsorption temperatures within the compiled data range. As an exploratory application, the model prioritized Fe-Mn adsorbents containing 63%-81.5% Fe and 18.5%-37% Mn with pore diameters of 20-24 nm, corresponding to predicted capacities of 20-21.2 mg/g. Subsequent screening over broader operating ranges predicted a high-capacity region of 53-54.1 mg/g at As2O3 concentrations of 110-200 ppm, adsorption times of 48-240 min, and temperatures of 300-600 °C. Overall, the framework supports interpretable prediction and hypothesis generation for gaseous arsenic adsorption by metal oxides.