Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization

Janine Lins,Kerstin Wohlgemuth,Thomas Harweg,Frank Weichert

doi:10.3390/app12052465

Abstract

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.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Deep learning methods show high potential for application in crystallization. They do outperform classical image processing methods based on hand-crafted features in connection with machine learning methods, they enable a more detailed description of the particles
Beside a pixel-wise detection of crystals and classification into single crystals and agglomerates, Convolutional neural networks (CNNs) enable the quantification of primary crystals bound in agglomerates

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Crystallization is extensively used in the fine chemical and pharmaceutical industry and represents a key process step towards the final product [1]. To achieve product characteristics of high purity and reproducible defined particle size, the crystallization phenomena must be understood and the process parameters must be tuned during crystallization based on this knowledge. In addition to the primary crystallization phenomena of nucleation and growth, secondary phenomena such as breakage and agglomeration must be considered. Agglomeration, which is caused by collision and subsequent cementation by solid bridges [2], can have a significant and irreversible effect on the final product. E.g., spherical agglomeration, agglomeration is desired to improve processability [3]

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Conclusion