Analysis of Liquid Viscosity by Image Processing Techniques

Santhosh, K. V.; Shenoy, Vighnesh

doi:10.17485/ijst/2016/v9i30/98693

Cited by 7 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, they captured refracted laser images using a camera; processed them using thresholding, filtering, and histogram techniques; and used an artificial neural network to establish the relationship between the processed data and the viscosity. The results of their study were published in [23].…”

Section: Introductionmentioning

confidence: 99%

Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks

et al. 2023

View full text Add to dashboard Cite

The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required.

show abstract

Section: Introductionmentioning

confidence: 99%