Lactose crystallization: Integrating machine learning with process analytical technologies

Lactose is recovered from whey through crystallization process, where a concentrated supersaturated solution is cooled to crystallize the lactose, leaving the impurities in the mother liquor. Designing this process requires considerations over various parameters, particularly the concentration of the feed solution and the cooling profile. To optimize the parameters, most developers depend on trial-and-error methods, a manageable task for the experienced but challenging for novices. This study presents a novel system that leverages machine learning (ML) and process analytical technologies (PAT) to streamline lactose crystallization process development, going beyond manual trial and error interpretations. The automated system initiated with Direct Chord Length (DCL) feedback control run, which provided the foundational data for the ML model, which was then employed in subsequent AN1 and AN2 iterative runs. These iterative runs have smoother concentration and temperature curves, and it generates larger crystal with enhanced productivity and yield. The results indicate that the ML-driven approach can significantly outperform conventional methods, enabling the precise control of nucleation and growth phases to produce larger lactose crystals.