Integrated RSM and machine learning modelling of Pb(II) adsorption using bio-engineered walnut shells

Machine learning–assisted sustainable removal of cationic dyes using Butea monosperm a leaf powder as an eco-friendly biosorbent

Machine Learning-Based Prediction and Optimization of Membrane Fouling in a Hybrid Biochar-Membrane System for Treating Maize Flour Mill Wastewater

Agro-processing mill wastewater has a high organic load, which negatively impacts the environment. In this study, a hybrid coconut-shell-derived biochar (CSDB) membrane system was developed, and machine learning models were used to predict and optimize the removal of pollutants and membrane fouling. CSDB with a porosit...

Sustainable sugarcane bagasse-derived activated carbon for comparative Cr(VI) and Co(II) removal: experimental and ANN modeling integration

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

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.

Interpretable Machine Learning for Elucidating Component Synergy in a Solid Waste-Derived Multicomponent Adsorbent for Heavy Metal Removal.

Waste magnesia-carbon bricks and drinking water treatment residues are used by mechanochemical method to prepare a multi-component adsorbent (MC-WT) composed of LDHs, graphite, and siliceous particles to remove As(V), Cr(VI), Pb(II), and Cu(II) from soil. Adsorption equilibrated within 180 min and followed pseudo-secon...