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

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.