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.

Towards Understanding Aerogels’ Efficiency for Oil Removal—A Principal Component Analysis Approach

“…The first two PCs exhibited 64.02% of the total variance (42.18% for PC1 and 21.84% for PC2; Figure 1 a). Interestingly, higher variance in the case of oil removal was obtained in comparison to the dataset investigated for the case of ion and dye removal [ 12 , 13 ]. This indicates a higher scope of applicability of the adopted method for the sake of revealing the hidden patterns and the certain correlation between physico-chemical properties from one side and adsorption parameters from another side, in the case of oil removal.…”

“…However, further research is needed to explore their potential applications and optimize their performance in different water treatment scenarios. One way to seek potential uses for these types of membranes is by applying data analysis on the dataset encompassing physico-chemical properties, adsorption parameters, and even manufacturing conditions and trade-offs [ 12 , 13 ]. One of the most suited data analysis techniques is “Principal Component Analysis” (PCA).…”

“…PCA is widely used in various fields, such as geology [ 15 , 16 ], biomass characterization, and valorization [ 17 ]. For aerogels, our previous studies applied PCA for the sake of revealing hidden patterns between the physical and chemical properties and adsorption parameters [ 12 , 13 ]. The machine learning approach found its importance in evaluating several water treatment processes, including the efficiency of nanofiltration membranes and the fabrication of sustainable materials [ 18 , 19 , 20 ].…”

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

“…In brief, the data-driven approach presents a powerful tool to seek the efficiency of aerogels towards dye removal, as several conditions are to be considered when employing or even manufacturing these aerogels. In fact, the authors are currently working on another project on evaluating the efficiency of aerogels for removal of ions from wastewater [ 56 ]. Even though this method appears as an efficient one to trace similarities and dissimilarities, one should be cautious when using it, as it hides some parts of the whole image, since the total variance is rarely at its maximum.…”

Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection

“…Machine learning models have shown great potential in the estimation and prediction of water quality parameters, offering improved accuracy and efficiency compared to traditional methods. These models have the capability to analyze large volumes of data, identify complex patterns, and provide valuable insights for water quality assessment and management, making them valuable tools in the field of water resources and environmental engineering [35][36][37][38]. Despite the significant advancements in machine learning, its adoption in predicting and automating drinking water processes has been relatively limited [39][40][41][42].…”