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.

Keywords

• Amorphous materials
• Adsorption separation
• Sulfides
• Molecular simulation
• Machine learning