A verified open-access AI-based chemical microparticle image database for in-situ particle visualization and quantification in multi-phase flow

Deep learning-based image segmentation and classification had been previously proposed for the in-situ measurement of microcrystal size and shape, which showed great potential to expand as a process analytical technology (PAT) to chemical multi-phase flow processes. In this study, an open-access chemical microparticle image database (open-CMD) was established containing particle agglomerations (A), bubbles (B), crystals (C), and droplets (D) in various chemical multi-phase flow scenarios. The advanced neural network, Mask R-CNN, coupled with 2,500 labeled images containing more than 50,000 labeled particles in open-CMD, was trained to build the ability of target particle segmentation and classification. The training results indicated that a data augmentation strategy could significantly improve the accuracy (<3.8 % average precision) of the trained models, which were named MicropNet(+) and MicropNet according to whether the augmented data was used for training or not. Based on the superior capability of MicropNet(+), multidimensional particle descriptors were extracted, and further, the degree of agglomeration and agglomeration distribution were defined and quantified. Then, two classical multi-phase flow processes, crystallization and emulsification, were analyzed using the MicropNet(+) model, in which the agglomeration degree and distribution (C-in A) of succinic acid crystals and the relative number and diameter (D-eq) of droplets were analyzed quantitatively under different operations conditions. It was concluded that the well-trained MicropNet(+) model has high accuracy and efficiency in microparticle segmentation and classification. At last, the open-CMD database and the MicropNet(+) model were released to inspire potential applications in chemical multi-phase flow areas.