AI-Based Integrated Smart Process Sensor for Emulsion Control in Industrial Application

Abstract

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples.