Computational AI to predict and optimize the relationship between dye removal efficiency and Gibbs free energy in the adsorption process utilizing TiO/chitosan-polyacrylamide composite.

Building a model that can accurately anticipate and optimize the dynamics of dye removal and Gibbs free energy within the framework of an adsorption process is the main goal of this research. Furthermore, it has been determined that a correlation exists between the efficacy of dye removal and the behavior of Gibbs free energy throughout the process of adsorption. The study utilized a composite material consisting of chitosan-polyacrylamide/TiO as an adsorbent to remove anionic dye from a mainly aqueous solution.

Machine learning modeling of polycarbonate ultrafiltration membranes at different temperatures, AlO nanoparticle volumes, and water ratios.

The efficacy of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (AlO-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio were evaluated to determine the thermophysical properties of the membrane. 5%-10% of AlO-NPs have been added to the PC. A machine learning approach was used to compare the volume fraction of AlO-NPs, the temperature, and the water-to-ethylene glycol (EG) ratio.

Optimization of the removal Pb (II) and its Gibbs free energy by thiosemicarbazide modified chitosan using RSM and ANN modeling.

In this research, the removal of Pb (II) by thiosemicarbazide modified chitosan (TSFCS) using RSM and ANN modeling was studied. Also, Gibbs free energy changes of adsorption process based on changes in initial concentration and temperature of solution was investigated. Optimization of these two objectives was performed using NSGA-II and RSM.