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Prediction of ball milling performance by a convolutional neural network model and transfer learning. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2022.117409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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ANN prediction of particle flow characteristics in a drum based on synthetic acoustic signals from DEM simulations. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.117012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Li Y, Bao J, Yang R. Acoustic signals of rotating drums generated based on DEM simulations. EPJ WEB OF CONFERENCES 2021. [DOI: 10.1051/epjconf/202124914019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Acoustic emission (AE) or vibration signal has been applied in detecting operations of grinding mills in many industries. This paper proposes an approach to generate AE signals based on the particle-wall impacts. Through a combination of multi-mode vibrations and the calibration of the key parameters, the model was able to reproduce experimental data. The AE model was then implemented into a discrete element method (DEM) modelling of particle flow in a rotating mill. The AE signals of the mill under different filling levels and rotation speeds were generated and analysed, mainly focusing on the frequency and magnitude of each vibration mode. The link between the AE signals and the particle-wall impact energy was explored.
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Li Y, Bao J, Yu A, Yang R. A combined data-driven and discrete modelling approach to predict particle flow in rotating drums. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2020.116251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ratnayake P, Chandratilleke R, Bao J, Shen Y. A soft-sensor approach to mixing rate determination in powder mixers. POWDER TECHNOL 2018. [DOI: 10.1016/j.powtec.2018.06.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Modeling collective dynamics of particulate systems under time-varying operating conditions based on Markov chains. ADV POWDER TECHNOL 2013. [DOI: 10.1016/j.apt.2012.10.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Tjakra JD, Bao J, Hudon N, Yang R. Analysis of collective dynamics of particulate systems modeled by Markov chains. POWDER TECHNOL 2013. [DOI: 10.1016/j.powtec.2012.10.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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McElroy L, Bao J, Jayasundara C, Yang R, Yu A. A soft-sensor approach to impact intensity prediction in stirred mills guided by DEM models. POWDER TECHNOL 2012. [DOI: 10.1016/j.powtec.2011.12.031] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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