Presentation and applied case study of a system-wide workflow which supports rapid, systematic and efficient continuous seeded cooling crystallisation process design, with the aim to deliver a robust, consistent process with tight control of particle attributes.

Efficient processing of particulate products across various manufacturing steps requires that particles possess desired attributes such as size and shape. Controlling the particle production process to obtain required attributes will be greatly facilitated using robust algorithms providing the size and shape information of the particles from in situ measurements. However, obtaining particle size and shape information in situ during manufacturing has been a big challenge. This is because the problem of estimating particle size and shape (aspect ratio) from signals provided by in-line measuring tools is often ill posed, and therefore it calls for appropriate constraints to be imposed on the problem. One way to constrain uncertainty in estimation of particle size and shape from in-line measurements is to combine data from different measurements such as chord length distribution (CLD) and imaging. This paper presents two different methods for combining imaging and CLD data obtained with in-line tools in order to get reliable estimates of particle size distribution and aspect ratio, where the imaging data is used to constrain the search space for an aspect ratio from the CLD data

This study investigates the effects of key process parameters of continuous mixing-induced supersaturation on the antisolvent crystallization of lactose using D-optimal Design of Experiments (DoE). Aqueous solutions of lactose were mixed isothermally with antisolvents using a concentric capillary mixer. Process parameters investigated were the choice of antisolvent (acetone or isopropanol), concentration of lactose solution, total mass flow rate, and the ratio of mass flow rates of lactose solution and antisolvent. Using a D-optimal DoE a statistically significant sample set was chosen to explore and quantify the effects of these parameters. The responses measured were the solid state of the lactose crystallized, induction time, solid yield and particle size. Mixtures of α-lactose monohydrate and β-lactose were crystallized under most conditions with β-lactose content increasing with increasing amount of antisolvent. Pure α-lactose monohydrate was crystallized using acetone as the antisolvent, with mass flow ratios near 1:1, and near saturated solutions of lactose. A higher resolution DoE was adopted for acetone and was processed using multivariate methods to obtain a crystallization diagram of lactose. The model was used to create an optimized process to produce α-lactose monohydrate and predicted results agreed well with those obtained experimentally, validating the model. The solid state of lactose, induction time, and solid yield were accurately predicted.

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAM^bD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.