A comparative study of machine learning approaches for predicting viscosity in Sacran/CNF solutions

Abstract

This study explores the predictive capabilities of machine learning algorithms for Sacran/CNF solution viscosity. Leveraging data on initial CNF amount, shear rate, shear stress, speed, torque, and temperature, decision tree (DT) and random forest (RF) models were employed. Both models demonstrated strong alignment with experimental results, achieving a coefficient of determination (R2) of 0.99. The main features of those models established that 60 % of the importance is attributed to speed or shear rate, while 31 % is attributed to the ratio of CNF content in the solutions. This research underscores the potential of machine learning in effectively analyzing viscosity datasets.