Synthetic Image Analysis for Accurate Agglomerate Characterization and Control in Liquid–Liquid Phase Separation Systems

Agglomeration control is important for both the crystallization process and product quality optimization. Fast and precise description of the agglomerate is crucial for agglomeration control. This work proposed a synthetic image analysis method to accurately count primary particles within agglomerates by recognizing and clustering the salient features. In addition, a new definition of the agglomeration degree distribution was introduced to underscore the proportion of heavy agglomerates within the particle population. Further, the image analysis method and agglomeration degree distribution were tested in raspberry ketone-1-propanol-water system, which is a typical antisolvent oiling-out system accompanied by liquid–liquid phase separation (LLPS) and agglomeration. A seeding strategy was built up by investigating the effects of seeding conditions, including seed size and addition time, on agglomeration. The results demonstrated the effectiveness of the proposed method, which correctly counts more than 78% of the particles even for heavy agglomerates. The agglomeration was effectively inhibited via introducing small seeds after LLPS. The image analysis method and the concept of agglomeration degree distribution proposed in this work could be used to provide valuable insights into the agglomeration behavior and agglomeration control of similar systems.