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

Sadat, Tarik

doi:10.1016/j.cplett.2023.141022

Cited by 1 publication

(1 citation statement)

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“…Scikit-learn provides various preprocessing methods including data scaling, normalisation, categorical variable coding, dealing with missing values, and feature extraction. These methods play an important role before using the data in machine learning models [32].…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features

Qin,

Wang,

Tang

et al. 2024

Applied Sciences

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In recent years, machine learning has made significant progress in the field of micro-fluids, and viscosity prediction has become one of the hotspots of research. Due to the specificity of the application direction, the input datasets required for machine learning models are diverse, which limits the generalisation ability of the models. This paper starts by analysing the most obvious kinetic feature induced by viscosity during flow—the variation in droplet neck contraction with time (hmin/R∼τ). The kinetic processes of aqueous glycerol solutions of different viscosities when dropped in air were investigated by high-speed camera experiments, and the kinetic characteristics of the contraction of the liquid neck during droplet falling were extracted, using the Ohnesorge number (Oh=μ/(ρRσ)1/2) to represent the change in viscosity. Subsequently, the liquid neck contraction data were used as the original dataset, and three models, namely, random forest, multiple linear regression, and neural network, were used for training. The final results showed superior results for all three models, with the multivariate linear regression model having the best predictive ability with a correlation coefficient R2 of 0.98.

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Section: Methodsmentioning

confidence: 99%