Soft Sensor

 This study presents a transformative approach for real-time monitoring of continuous slug-flow tubular crystallizers in the pharmaceutical and fine chemical industries, marking a shift from traditional batch processing to continuous manufacturing. Leveraging advanced computer vision techniques within inline imaging systems, including single, binocular, and trinocular stereo-vision, we offer a novel solution for the multispatial monitoring and analysis of the crystallization process. This methodology facilitates the automatic detection of solution slugs and bulk crystal regions, enabling the estimation of dynamic bulk crystal density and slug volumes as well as porosity in real-time—a capability previously unattained. The deployment of ResNet18 and Mask R-CNN models underpins the method’s efficacy, demonstrating remarkable performance metrics: ResNet18 ensures precise image detection, while Mask R-CNN achieves an average precision (AP) of 96.4%, with 100% at both AP50 and AP70 thresholds for bulk crystals and solution slugs segmentation. These results validate the models’ accuracy and reliability in estimating quality variables essential for continuous slug flow crystallization. This advancement not only addresses the limitations of existing monitoring methods but also signifies a leap forward in applying computer vision for process monitoring, offering significant implications for enhancing decision-making, optimization, and control in continuous manufacturing operations. 

Associated members: Adams Derick (KAIST), Seunguk Baek