(576g) Crystallization Model Discrimination Using in Situ Chord Length Monitoring and a Size Conversion Model (CLD2PSD model).

A new framework has been recently introduced to correlate in situ chord length distribution (CLD) measurements and offline particle size distribution (PSD) data (Irizarry et al, 2017) and applied to different particle morphologies (Schoell et al, 2019). Subsequently the model was expanded to consider bimodal distributions and inferences of modality and morphology changes (Irizarry et al, 2020). This work applies the proposed approach to estimate the kinetics parameters for seeded batch crystallization processes of different APIs in combination with population balance modelling. First, a data-driven CLD2PSD model was built for the studied pharmaceutical compounds since the differences in optical properties and particle morphology require a new model for every single substance. Then, time-resolved PSDs have been extracted from in situ CLD data of seeded batch processes and the CLD2PSD model. Finally, the online PSDs have been combined with online solute concentration data to allow for the estimation of crystal growth, secondary nucleation and agglomeration kinetics, depending on the supersaturation level encountered during the process. The validity of this approach has been confirmed by comparison to kinetic data determined using a traditional approach relying on offline data. The main advantage of the proposed approach is that all data is generated online and in situ, thus avoiding sampling errors.

References

• Irizarry, R. et al., 2017. Data-driven model and model paradigm to predict 1D and 2D particle size distribution from measured chord-length distribution. Chemical Engineering Science, 164, pp. 202-218.
• Schoell, J. et al., 2019. Determining particle‐size distributions from chord length measurements for different particle morphologies. AIChE Journal, 65, e16560