Development of a Digital Twin for the Prediction and Control of Supersaturation during Batch Cooling Crystallization

Finechemicals produced via batch crystallization with propertiesdependent on the crystal size distribution require precise controlof supersaturation, which drives the evolution of crystal size overtime. Model predictive control (MPC) of supersaturation using a mechanisticmodel to represent the behavior of a crystallization process requiresless experimental time and resources compared with fully empiricalmodel-based control methods. Experimental characterization of thehexamine-ethanol crystallization system was performed in orderto collect the parameters required to build a one-dimensional (1D)population balance model (PBM) in gPROMS FormulatedProducts software(Siemens-PSE Ltd.). Analysis of the metastable zone width (MSZW) anda series of seeded batch cooling crystallizations informed the suitableprocess conditions selected for supersaturation control experiments.The gPROMS model was integrated with the control software PharmaMV(Perceptive Engineering Ltd.) to create a digital twin of the crystallizer.Simulated batch crystallizations were used to train two statisticalMPC blocks, allowing for in silico supersaturation control simulationsto develop an effective control strategy. In the supersaturation set-pointrange of 0.012-0.036, the digital twin displayed excellentperformance that would require minimal controller tuning to steadyout any instabilities. The MPC strategy was implemented on a physical500 mL crystallizer, with the simulated solution concentration replacedby in situ measurements from calibrated attenuated total reflection-Fouriertransform infrared (ATR-FTIR) spectroscopy. Physical supersaturationcontrol performance was slightly more unstable than the in silicotests, which is consistent with expected disturbances to the heattransfer, which were not specifically modeled in simulations. Overall,the level of supersaturation control in a real crystallizer was foundto be accurate and precise enough to consider future adaptations tothe MPC strategy for more advanced control objectives, such as thecrystal size.

Automated summary

Finechemicals produced via batch crystallization require precise control of supersaturation for desired crystal size distribution. A model predictive control (MPC) strategy using a mechanistic model was developed to control supersaturation, which showed excellent performance in simulation. The MPC strategy was implemented on a physical crystallizer, with slightly more instability due to unmodeled heat transfer disturbances. Overall, the level of supersaturation control in a real crystallizer was accurate and precise enough to consider future adaptations for more advanced control objectives.