Classification of Droplets of Water-PVP Solutions with Different Viscosity Values Using Artificial Neural Networks

Abstract

When a liquid flows, it has an internal resistance to flow. Viscosity is the property that measures this resistance, which is a fundamental characteristic parameter of liquids. The monitoring of viscosity is essential for quality control in many industrial areas, such as the pharmaceutical, chemical, and energy-related industries. Several instruments measure the viscosity of a liquid, the most used being the capillary viscometers. These instruments are complex, associated with high cost and expensive prices. This represents a challenge in several industries, where accurate viscosity knowledge is essential in designing various industrial equipment and processes. Using image processing and machine learning algorithms is a promising alternative to the current measurement methods. This work aims to extract characteristic information from videos of droplets of different samples using image processing algorithms. An Artificial Neural Network model utilizes the extracted characteristics to classify the droplets in the correct category, which is correlated with the viscosity of the sample. Different solutions samples were created using different ratios of Water and PVP (Polyvinylpyrrolidone) and videos of their droplets were taken and processed. It was found that for water-PVP solutions, the proposed ANN model was able to successfully classify the droplets using the data extracted from the videos with high accuracy. The results imply that the ANN model can recognize the features that affect the viscosity values.