Machine learning-assisted prediction of cesium adsorption using metal–organic frameworks/reduced graphene oxide composite

Machine learning-based prediction and optimization of polymeric membranes for CO2 separation

Polymeric membranes are widely used for gas separation due to their energy efficiency and scalability, particularly for carbon dioxide (CO2) capture applications. However, accurately predicting gas permeability in polymeric membranes remains a challenge due to complex nonlinear structure–property relationships and the...

Machine learning-based prediction of chlorophenol removal from wastewater using reverse osmosis

The results demonstrated that the Gradient Boosting algorithm can achieve the best performance in predicting chlorophenol removal, followed by the XGBoost algorithm, but neural networks performed the worst.

Hybrid Isotherm–Machine Learning Modeling of CO₂ Adsorption in Modulator-Tuned MIL-101(Cr)

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

A database of 280 experimental records and 20 descriptors was compiled to predict adsorption capacity and interpret descriptor-performance relationships, and the framework supports interpretable prediction and hypothesis generation for gaseous arsenic adsorption by metal oxides.

Research on a machine learning-based prediction method for methane adsorption capacity in shale

Accurate prediction of shale methane adsorption capacity is crucial for reservoir evaluation. This study integrates 486 experimental datasets to develop a multivariate machine learning prediction model. Six key geological parameters, including depth, total organic carbon (TOC), moisture, porosity, vitrinite reflectan...

Tree‐Based Machine Learning Models for Predicting Phosphate Adsorption Performance of Layered Double Hydroxides: Insights Into Mechanisms and Process Optimization

Optimizing layered double hydroxides (LDHs) for phosphate removal is challenged by complex, multivariable interactions. To bridge this knowledge gap, this study introduces a novel data‐driven framework leveraging a comprehensive 2251‐record dataset and advanced machine learning to predict and optimize LDHs adsorption c...