Continuous industrial crystallization image processing system based on deep learning

Abstract

The invention discloses a continuous industrial crystallization image processing system based on deep learning, the system comprises a data acquisition module, a crystal identification module and a result analysis and display module, the data acquisition module comprises an imaging probe and an imaging controller, the imaging probe is inserted into a solution for a certain depth from the top of an industrial crystallizer, and the imaging controller is connected with the industrial crystallizer; a high-resolution camera is arranged at the bottom of the probe; the camera is used for monitoring the crystallization process in real time in a manner of shooting pictures at a certain frequency; the crystal identification module receives the image sent by the data acquisition module and predicts the position of a crystal in the image by adopting a neural network algorithm based on deep learning; and the result analysis and display module displays the crystal picture and the corresponding statistical result in real time based on the crystal prediction result of the crystal identification module. The system is suitable for on-line monitoring and accurate analysis of crystal particle size and shape distribution conditions in various continuous industrial crystallization processes, so that assistance is provided for optimization and control of a continuous crystallization operation process.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a continuous industrial crystallization image processing system based on deep learning. Background technique [0002] At present, crystallization is an important separation operation widely used in the purification of solid products in chemical, pharmaceutical and food processing industries. In the process of industrial crystallization, it is of great significance to monitor and analyze the size and shape distribution of crystals online. For the batch crystallization operation, the solid concentration is usually small in the relatively early stage and becomes large only in the relatively unimportant later stage; while for the continuous crystallization operation on an industrial scale, the solid concentration will always be maintained At high levels, this often results in a large number of crystal agglomerates or overlapping phenomena. [0003] Existing technologies mai...

AI Technical Summary

Problems solved by technology

Although these methods perform well in some simple cases, their accuracy is affected by many factors, such as image quality, solid concentration, agglomeration and overlap between crystals, etc., so traditional image recognition methods are not suitable for crystals in continuous industrial crystallization processes. image analysis

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