Computational AI to predict and optimize the relationship between dye removal efficiency and Gibbs free energy in the adsorption process utilizing TiO2/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/TiO2 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 TiO2/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 R2 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.

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. The regression coefficients of the RSM model for the removal percentage and Gibbs free energy changes were 0.9776 and 0.9864, respectively. Also, the F-values of RSM for the removal efficiency and Gibbs free energy were 81.72365 and 93.78053, respectively, show the proper accuracy of model. The best structure of the neural network with 5 hidden layers, which has 3, 3, 6, 4, 2 neurons in each layers, respectively. Also the transfer function was tansig, tansig, logsig, tansig, tansig for each layer. The initial population of the study for the purpose of optimization with NSGA-II algorithm was consist of 50 samples. The results of two methods NSGA-II and RSM show that the maximum removal efficiency (92%) and minimum ΔGo (-5 Kj/mol) are achieved at the highest temperature (55 °C) and lowest initial concentration of solution (10 ppm). The desirability degree for the RSM optimization obtained 0.981.