Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches

Filipi França dos Santos; Kelly Cristina Da Silveira; Daniela Herdy Carrielo; Gesiane Mendonça Ferreira; Guilherme de Melo Baptista Domingues; Monica Calixto Andrade

doi:10.14295/vetor.v33i1.15167

Authors

Filipi França dos Santos Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, Brasil
Kelly Cristina Da Silveira Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, Brasil http://orcid.org/0000-0002-6055-6778
Daniela Herdy Carrielo Universidade do Estado do Rio de Janeiro, Instituto Politécnico – Nova Friburgo, RJ, Brasil https://orcid.org/0000-0002-0869-4911
Gesiane Mendonça Ferreira Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, Brasil https://orcid.org/0000-0001-9338-1217
Guilherme de Melo Baptista Domingues Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, Brasil
Monica Calixto Andrade Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, Brasil https://orcid.org/0000-0001-6530-0651

DOI:

https://doi.org/10.14295/vetor.v33i1.15167

Keywords:

cellulose acetate membranes, machine learning, Thermogravimetric analysis (TG)

Abstract

Thermogravimetric analysis (TGA) is a characterization technique routinely used in materials science. In this particular case, TGA determines the variation of weight with temperature. The thermogravimetric analysis of cellulose acetate (CA) hybrid membranes can provide similar results, despite their different chemical composition. The present study uses machine learning algorithms to correlate data from thermogravimetric analyses with variations in chemical composition. Experimental points relating to temperature and weight from these analyses were treated in different ways and used to estimate the composition of the membranes. The Extra-Trees Classifier, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) algorithms were applied to this data and then evaluated using a confusion and accuracy matrix. The decision tree-based algorithms demonstrated a superior capacity for estimating the composition, albeit with negligible disparities in the thermogravimetric profile. The Extra-Trees Classifier algorithm, in particular, stood out for its ability to estimate composition in all tests, achieving 90% accuracy.

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