Forecast of in-line blade-screen high shear mixer's performance based on machine learning

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Abstract As a novel type of process intensification equipment, high-shear mixers were increasingly widely used in industry, but their design still relies on experimental scaling-up. Data on power, liquid-liquid mass transfer coefficient and emulsification of in-line high-shear mixers with different stator and rotor configurations were collected. And regression fitting analysis on the collected data was performed using machine learning algorithms such as back propagation neural network, recurrent neural network, decision tree, etc., which provided information for the design and optimization of high-shear mixers. The results show that back propagation neural network and recurrent neural network algorithms can predict high-shear mixer's performance precisely and the accuracy of the model can be further improved by fusing different machine learning models. The PyCaret program based on auto machine learning can accurately fit the data. However, its optimization performance is poor when the amount of data is small.

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	Articles by authors
	WANG Lingjie
	GUO Junheng
	LI Wenpeng
	CHENG Qin
	ZHANG Jinli

Keywords： high shear mixers； machine learning； neural networks； power； liquid-liquid mass transfer； emulsification

Received 2021-03-30;

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WANG Lingjie, GUO Junheng, LI Wenpeng, CHENG Qin, ZHANG Jinli.Forecast of in-line blade-screen high shear mixer's performance based on machine learning[J] Chemcial Industry and Engineering, 2022,V39(2): 1-8

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