On-line image analysis for evaporative crystallization of xylose

On-line imaging technique was investigated for monitoring industrial production of xylose crystals via evaporation, with the focus being on the fine crystals since they affect the efficacy of downstream filtration, and also lead to product loss. High solid concentration and crystal agglomeration pose great challenges to the image analysis, hindering quantitative understanding of the crystallization process and possible control actions. Three different analysis methods were examined, including deep learning-based semantic segmentation (Swin Transformer) and instance segmentation (Mask R-CNN) together with an advanced orthodox approach. Swin Transformer outperformed other two methods by a large margin, indicating that the semantic segmentation is more suitable for the present scenario of evaporation crystallization. Its precision, recall, F1 score, Jaccard index, and accuracy reach 0.903, 0.887, 0.894, 0.810, and 0.873, respectively. The fine crystals were extracted by image post-processing, and the evolution of their area fractions, equivalent diameters, and aspect ratios were analyzed in details.