Utilizing molecular simulation, ideal adsorbed solution theory and ensemble learning algorithms to investigate adsorption and separation of sulfides on amorphous nanoporous materials

Abstract

Using grand canonical Monte Carlo method, we investigated the adsorption of pure H₂S and SO₂ gases on amorphous materials, and the separation of CH₄-H₂S and CO₂-SO₂ mixtures. At 303 K, the optimal adsorbent for both gases was found to be HCP-Colina-id016, with 16 mmol/g. For CH₄-H₂S mixture, despite aCarbon-Marks-id002 exhibiting the highest selectivity (approximately 80), the H₂S adsorption was low (around 1 mmol/g), while Kerogen-Coasne-id013 demonstrated a high H₂S adsorption of 12 mmol/g with a selectivity of 20. In the case of CO₂-SO₂, HCP-Colina-id018 exhibited a SO₂ selectivity exceeding 30, with a high SO₂ adsorption of 12 mmol/g. The Ideal Adsorbed Solution Theory underestimated the adsorption and selectivity of both mixtures, particularly evident in CO₂-SO₂. Molecular simulations revealed that, for the CO₂-SO₂ system, CO₂ underwent condensation, resulting in a sudden drop in the SO₂ adsorption isotherm. However, IAST accurately predicted this abrupt change. Based on the adsorption data obtained from molecular simulations, we compared the predictive performance of four ensemble learning algorithms, namely Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and CatBoost, for H₂S and SO₂ pure gases in amorphous porous materials. The rankings were observed to be XGBoost > GBDT > RF > CatBoost.