Noninvasive inline imaging and computer vision-based quality variable estimation for continuous slug-flow crystallizers

This study presents a transformative approach for the real-time monitoring of continuous slug-flow crystallizers in the pharmaceutical and fine chemical industries, marking a shift from traditional batch processing to continuous manufacturing. By leveraging advanced computer vision techniques within inline imaging systems, including single, binocular, and trinocular stereo visions, 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, slug volumes, and porosity in real time. 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 AP75 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.