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Volume 9
Issue 4
10.3390/gels9040304
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Article

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

by
Khaled Younes
1,*,
Yahya Kharboutly
1,
Mayssara Antar
1,
Hamdi Chaouk
1,
Emil Obeid
1,
Omar Mouhtady
1,
Mahmoud Abu-samha
1,
Jalal Halwani
2 and
Nimer Murshid
1
1
College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
2
Water and Environment Sciences Lab, Lebanese University, Tripoli 22100, Lebanon
*
Author to whom correspondence should be addressed.
Gels 2023, 9(4), 304; https://doi.org/10.3390/gels9040304
Submission received: 16 March 2023 / Revised: 2 April 2023 / Accepted: 3 April 2023 / Published: 4 April 2023
(This article belongs to the Section Gel Applications)
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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.
Keywords: ion removal; machine learning; aerogels; principal component analysis; water treatment ion removal; machine learning; aerogels; principal component analysis; water treatment

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MDPI and ACS Style

Younes, K.; Kharboutly, Y.; Antar, M.; Chaouk, H.; Obeid, E.; Mouhtady, O.; Abu-samha, M.; Halwani, J.; Murshid, N. Application of Unsupervised Machine Learning for the Evaluation of Aerogels’ Efficiency towards Ion Removal—A Principal Component Analysis (PCA) Approach. Gels 2023, 9, 304. https://doi.org/10.3390/gels9040304

AMA Style

Younes K, Kharboutly Y, Antar M, Chaouk H, Obeid E, Mouhtady O, Abu-samha M, Halwani J, Murshid N. Application of Unsupervised Machine Learning for the Evaluation of Aerogels’ Efficiency towards Ion Removal—A Principal Component Analysis (PCA) Approach. Gels. 2023; 9(4):304. https://doi.org/10.3390/gels9040304

Chicago/Turabian Style

Younes, Khaled, Yahya Kharboutly, Mayssara Antar, Hamdi Chaouk, Emil Obeid, Omar Mouhtady, Mahmoud Abu-samha, Jalal Halwani, and Nimer Murshid. 2023. "Application of Unsupervised Machine Learning for the Evaluation of Aerogels’ Efficiency towards Ion Removal—A Principal Component Analysis (PCA) Approach" Gels 9, no. 4: 304. https://doi.org/10.3390/gels9040304

APA Style

Younes, K., Kharboutly, Y., Antar, M., Chaouk, H., Obeid, E., Mouhtady, O., Abu-samha, M., Halwani, J., & Murshid, N. (2023). Application of Unsupervised Machine Learning for the Evaluation of Aerogels’ Efficiency towards Ion Removal—A Principal Component Analysis (PCA) Approach. Gels, 9(4), 304. https://doi.org/10.3390/gels9040304

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