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

吉布斯自由能 吸附 聚丙烯酰胺 复合数 壳聚糖 过程(计算) 化学工程 化学 材料科学 工艺工程 热力学 计算机科学 复合材料 有机化学 工程类 物理 操作系统
作者
Seyed Peiman Ghorbanzade Zaferani,Mahmoud Kiannejad Amiri,Ali Akbar Amooey
出处
期刊:International Journal of Biological Macromolecules [Elsevier]
卷期号:264: 130738-130738
标识
DOI:10.1016/j.ijbiomac.2024.130738
摘要

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.
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