Application of Unsupervised Learning for the Evaluation of Aerogels’ Efficiency towards Dye Removal—A Principal Component Analysis (PCA) Approach

Abstract

Water scarcity is a growing global issue, particularly in areas with limited freshwater sources, urging for sustainable water management practices to insure equitable access for all people. One way to address this problem is to implement advanced methods for treating existing contaminated water to offer more clean water. Adsorption through membranes technology is an important water treatment technique, and nanocellulose (NC)-, chitosan (CS)-, and graphene (G)- based aerogels are considered good adsorbents. To estimate the efficiency of dye removal for the mentioned aerogels, we intend to use an unsupervised machine learning approach known as “Principal Component Analysis”. PCA showed that the chitosan-based ones have the lowest regeneration efficiencies, along with a moderate number of regenerations. NC2, NC9, and G5 are preferred where there is high adsorption energy to the membrane, and high porosities could be tolerated, but this allows lower removal efficiencies of dye contaminants. NC3, NC5, NC6, and NC11 have high removal efficiencies even with low porosities and surface area. In brief, PCA presents a powerful tool to unravel the efficiency of aerogels towards dye removal. Hence, several conditions need to be considered when employing or even manufacturing the investigated aerogels.