Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies.
BACKGROUND Enzymatic reactive distillation (ERD) is a biocatalyzed process, in which enzymes are immobilized in catalytic packing. The combination of enzymatic reaction and thermal separation helps to overcome chemical reaction and phase equilibrium limitations. Processing of chiral molecules, in particular, can benefit from ERD application, which might lead to more eco‐efficient processing of these valuable chemicals. Therefore an integrated approach to evaluate this technology is followed. RESULTS To evaluate ERD the transesterification of racemic (R/S)‐1‐phenylethanol (RPE/SPE) to (R)‐phenylethyl acetate (PEA) catalyzed by Candida antarctica lipase B (EC: 3.1.1.3) is investigated with regard to kinetics and physical property data, which provide a basis for the modeling of an ERD process. Furthermore, ERD experiments show a selective conversion of RPE to PEA, which is predicted by the established ERD model with high accuracy. CONCLUSION The ERD experiments demonstrate the feasibility of chiral processing for the transesterification by means of ERD and a validated ERD model is developed, allowing for a conceptual evaluation of ERD. This provides the basis for a future comparison of ERD with benchmark processes that will reveal the economic potential of ERD processes. © 2017 Society of Chemical Industry
In solution crystallization processes of organic compounds, agglomeration is often an undesired phenomenon because it influences important particle properties such as size, shape, and purity. However, until now, it is still challenging to measure and quantify agglomeration in the process. Kinetics are often determined by fitting the agglomeration kernel together with other kinetic parameters to a measured particle size distribution (PSD). The approach presented decouples agglomeration from particle sizes and crystal growth. By sophisticated image analysis using a deep learning approach, process monitoring of agglomerates is enabled. Extended by modeling and experimental investigation, this allows for a systematic investigation of the agglomeration behavior of the system depending on operation conditions. Here, L-alanine/water is used as a model system. Based on the developed inline image acquisition and instance segmentation, including detection and classification of single crystals and agglomerates, the agglomeration during crystallization is tracked, and the agglomeration kernel is estimated. Further, the agglomeration degree of the final product can be predicted within the limits of the investigated parameters. It is found that the system only tends to agglomerate moderately and agglomeration is reduced when the crystallization time is short, which is defined by the saturation temperature or the cooling rate.
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