关键词: 多相反应器, 流体力学, 本构模型构建, 机器学习

Abstract:

It is one of the hot topics in the field of process engineering to accurately understand and predict the complex hydrodynamics, transport phenomena, and reaction characteristics in multiphase reactors. In recent years, with the rapid development of experimental detection technology and high-performance computer, researchers can obtain high-precision multidimensional transient flow field data sets. In the last decade, machine learning, as a new interdisciplinary subject, is widely applied in data mining, image recognition, intelligent control, etc. This article summarizes several common machine learning methods, including neural network model, support vector machine model, decision tree model, clustering algorithm model, etc. Afterward, a summarization of the construction process of the machine learning model, including data set establishment, feature variable selection, algorithm framework selection, model parameter optimization, model validation, and testing, etc, is also provided. Subsequently, the application progress of machine learning assisted multiphase reactors in constitutive model construction, flow field image reconstruction, flow pattern identification, prediction and optimization of key parameters in the flow field, uncertainty analysis, and digital twin technology platform are reviewed. Finally, we analyze the challenges in the field of coupling machine learning with multiphase reactors, meanwhile, the possible development direction of machine learning in multiphase reactors is prospected.

Key words: multiphase reactor, hydrodynamics, constitutive model development, machine learning