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A Case Study Applying Mesoscience to Deep Learning

Engineering 2024年第8期39卷 84-93页

作者： Li Guo Fanyong Meng Pengfei Qin Zhaojie Xia Qi Chang Jianhua Chen Jinghai Li State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China

In this paper,we propose mesoscience-guided deep learning(MGDL),a deep learning modeling approach guided by mesoscience,to study complex *** establishing sample dataset based on the same system evolution data,different from the operation of conventional deep learning method,MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition(CIC)in *** constraints are then integrated into the loss function to guide the deep learning *** methods are proposed for the addition of mesoscience *** physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are *** was evaluated using a bubbling bed modeling case and compared with traditional *** a much smaller training dataset,the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy,and it can be widely applied to various neural network *** MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model *** exploration of MGDL will be continued in the future.

关键词： Mesoscience Deep learning Complex system Gas-solid system bubbling bed

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Solids motion in bubbling gas fluidised beds

Solids motion in bubbling gas fluidised beds

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CCAST“颗粒物理”研讨会

作者： M.Stein Y.L.Ding J.P.K.Seville D.J.Parker School of Chemical Engineering. University of Birmingham. Edgbaston. Birmingham B15 2TT. UK School of Chemical Engineering. University of Birmingham. Edgbaston Birmingham B15 2TT. UK Positron Imaging Centre University of Birmingham. Edgbaston Birmingham B15 2TT. UK School of Chemical Engineering. University of Birmingham. Edgbaston Birmingham B15 2TT. UK Positron Imaging Centre University of Birmingham. Edgbaston Birmingham B15 2TT. UK Positron Imaging Centre University of Birmingham. Edgbaston. Birmingham B15 2TT. UK School of Physics. University of Birmingham Edgbaston. Birmingham B15 2TT. UK

＜正＞ Gas fluidised beds are widely employed in the chemical, petrochemical, metallurgical, and pharmaceutical industries. Solids mixing in such devices plays a central role in controlling product quality and productivity. This paper presents some observations of particle motion in 3-D gas fluidised beds operated in the bubbling mode and at atmospheric pressure. The non-invasive positron emission particle tracking（PEPT） technique was used to observe and quantify particle trajectory, solids How pattern,solids velocity, and solids circulation frequency. It is shown that for relatively-deep beds （cylindrical columns, group B particles）, particles move upward in the central region, and downward near the wall. The average upward particle velocity is - 50% of the bubble velocity under the conditions of this study. Theoretical models are proposed to correlate the overall particle upward velocity and average particle velocity in the drift to the bubble velocity, and to estimate the duration of particle residence in the drift. Model predictions show reasonable agreement with experiments. It is suggested that solids motion in gas fluidised beds can be characterised by ’jump’, ’idle’ and ’relaxation’ times. These times may also be linked to particle kinetic energy transfer in lluidised beds. 2000 Elsevier Science Ltd. All rights reserved.

关键词： Gas fluidised bed bubbling bed Solids motion Particle velocity Mixing PEPT

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