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

Abstract

Water scarcity is a global problem affecting millions of people. It can lead to severe economic, social, and environmental consequences. It can also have several impacts on agriculture, industry, and households, leading to a decrease in human quality of life. To address water scarcity, governments, communities, and individuals must work in synergy for the sake of water resources conservation and the implementation of sustainable water management practices. Following this urge, the enhancement of water treatment processes and the development of novel ones is a must. Here, we have investigated the potential of the applicability of “Green Aerogels” in water treatment’s ion removal section. Three families of aerogels originating from nanocellulose (NC), chitosan (CS), and graphene (G) are investigated. In order to reveal the difference between aerogel samples in-hand, a “Principal Component Analysis” (PCA) has been performed on the physical/chemical properties of aerogels, from one side, and the adsorption features, from another side. Several approaches and data pre-treatments have been considered to overcome any bias of the statistical method. Following the different followed approaches, the aerogel samples were located in the center of the biplot and were surrounded by different physical/chemical and adsorption properties. This would probably indicate a similar efficiency in the ion removal of the aerogels in-hand, whether they were nanocellulose-based, chitosan-based, or even graphene-based. In brief, PCA has shown a similar efficiency of all the investigated aerogels towards ion removal. The advantage of this method is its capacity to engage and seek similarities/dissimilarities between multiple factors, with the elimination of the shortcomings for the tedious and time-consuming bidimensional data visualization.