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For: Xie T, Ghiaasiaan SM, Karrila S. Flow Regime Identification in Gas/Liquid/Pulp Fiber Slurry Flows Based on Pressure Fluctuations Using Artificial Neural Networks. Ind Eng Chem Res 2003. [DOI: 10.1021/ie0304199] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Number Cited by Other Article(s)
1
Two-phase flow pattern online monitoring system based on convolutional neural network and transfer learning. NUCLEAR ENGINEERING AND TECHNOLOGY 2022. [DOI: 10.1016/j.net.2022.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
2
Simulation on the effect of particle on flow hydrodynamics in a slurry bed. POWDER TECHNOL 2020. [DOI: 10.1016/j.powtec.2019.10.096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
3
da Silva Veloso YM, de Almeida MM, de Alsina OLS, Leite MS. Artificial neural network model for the flow regime recognition in the drying of guava pieces in the spouted bed. CHEM ENG COMMUN 2019. [DOI: 10.1080/00986445.2019.1608192] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
4
Tyagi P, Buwa VV. Dense gas–liquid–solid flow in a slurry bubble column: Measurements of dynamic characteristics, gas volume fraction and bubble size distribution. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.07.042] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
5
Li CD, Chen ZF. Effect of beating revolution on dispersion of flame attenuated glass wool suspension and tensile strength of associated glass fiber wet-laid mat. POWDER TECHNOL 2015. [DOI: 10.1016/j.powtec.2015.04.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
6
Rooki R. Estimation of Pressure Loss of Herschel–Bulkley Drilling Fluids During Horizontal Annulus Using Artificial Neural Network. J DISPER SCI TECHNOL 2014. [DOI: 10.1080/01932691.2014.904793] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
7
Zhou Q. Soft measurement of pulp suspension flow velocity based on wavelet transform. CAN J CHEM ENG 2010. [DOI: 10.1002/cjce.20248] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
8
Jade A, Jayaraman V, Kulkarni B, Khopkar A, Ranade V, Ashutosh Sharma. A novel local singularity distribution based method for flow regime identification: Gas–liquid stirred vessel with Rushton turbine. Chem Eng Sci 2006. [DOI: 10.1016/j.ces.2005.08.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
9
Xie T, Ghiaasiaan S, Karrila S. Artificial neural network approach for flow regime classification in gas–liquid–fiber flows based on frequency domain analysis of pressure signals. Chem Eng Sci 2004. [DOI: 10.1016/j.ces.2004.02.017] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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