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. The parameters have been analyzed using response surface methodology (RSM), artificial neural networks (ANN), and machine learning (ML) techniques in this particular context. The obtained F-value of 814.62 for the RSM model, which assesses dye removal efficiency, suggests that the model under examination is statistically significant. Furthermore, based on the RSM data, the proposed model demonstrates a significant level of accuracy in predicting the performance of the TiO/chitosan-polyacrylamide composite as an adsorbent during the dye removal adsorption process. The ANN model achieved a high level of accuracy, as evidenced by its R value of 0.999455. Through the utilization of neural networks and machine learning, the intended objective of forecasting dye removal efficiency and Gibbs free energy behavior in the adsorption process was effectively accomplished.