Convolutional Neural Network (CNN)-Based Measurement of Properties in Liquid–Liquid Systems

Abstract

The rise of artificial intelligence (AI)-based image analysis has led to novel application possibilities in the field of solvent analytics. Using convolutional neural networks (CNNs), better and more automated analysis of optically visible phenomena becomes feasible, broadening the spectrum of non-invasive measurements. These so-called smart sensors have attracted increasing attention in pharmaceutical and chemical process engineering; their additional sensor data enables more precise process control as additional process parameters can be monitored. This contribution presents an approach to analyzing single rising droplets to determine their physical properties; for example, geometrical parameters such as diameter, projection area and volume. Additionally, the rising velocity is determined, as well as the density and interfacial tension of the rising liquid droplet, determined from the force balance. Thus, a method was developed for analyzing liquid–liquid properties suitable for real-time applications. Here, the size range of the investigated droplet diameters lies between 0.68 mm and 7 mm with an accuracy for AI detecting droplets of ±4 µm. The obtained densities lie between 0.822 kg·m−3 for rising n-butanol droplets and 0.894 kg·m−3 for toluene droplets. For the derived parameters, such as the interfacial tension estimation, all of the data points lie in a range from 12.75 mN·m−1 to 15.25 mN·m−1. The trueness of the investigated system thus is in a range from −1 to +0.4 mN·m−1, with a precision of ±0.3 to ±0.6 mN·m−1. For density estimation using our system, a standard deviation of 1.4 kg m−3 from the literature was determined. Using camera images in conjunction with image analysis improved by artificial intelligence algorithms, combined with using empirical mathematical formulas, this article contributes to the development of easily accessible, cheap sensors.