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Li T, Wang Y, Zeng J, Pan Z, Yu G. Prediction of Bubble Size Distribution above the Bubble-Breaking Plate. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Tingting Li
- Shanghai Engineering Research Center of Coal Gasification, Institute of Clean Coal Technology, East China University of Science and Technology, Shanghai200237, China
| | - Yifei Wang
- Shanghai Engineering Research Center of Coal Gasification, Institute of Clean Coal Technology, East China University of Science and Technology, Shanghai200237, China
| | - Jie Zeng
- Shanghai Engineering Research Center of Coal Gasification, Institute of Clean Coal Technology, East China University of Science and Technology, Shanghai200237, China
| | - Zongren Pan
- Shanghai Engineering Research Center of Coal Gasification, Institute of Clean Coal Technology, East China University of Science and Technology, Shanghai200237, China
| | - Guangsuo Yu
- Shanghai Engineering Research Center of Coal Gasification, Institute of Clean Coal Technology, East China University of Science and Technology, Shanghai200237, China
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Angikath F, Pezzella G, Sarathy SM. Bubble-Size Distribution and Hydrogen Evolution from Pyrolysis of Hydrocarbon Fuels in a Simulated Ni 0.27Bi 0.73 Column Reactor. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fabiyan Angikath
- Clean Combustion Research Center, Physical Science and Engineering Division (PSE), King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Giuseppe Pezzella
- Clean Combustion Research Center, Physical Science and Engineering Division (PSE), King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - S. Mani Sarathy
- Clean Combustion Research Center, Physical Science and Engineering Division (PSE), King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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A Non-Invasive Method for Measuring Bubble Column Hydrodynamics Based on an Image Analysis Technique. Processes (Basel) 2022. [DOI: 10.3390/pr10081660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Bubble size and its distribution are the important parameters which have a direct impact on mass transfer in bubble column reactors. For this, a new robust image processing technique was presented for investigating hydrodynamic aspects and bubble behavior in real chemical or biochemical processes. The experiments were performed in a small-scale bubble column. The study was conducted for the wide range of clear liquid heights and superficial gas velocities. However, a major challenge in image analysis techniques is identification of overlapping or cluster bubbles. This problem can be overcome with the help of the proposed algorithm. In this respect, large numbers of videos were recorded using a high-speed camera. Based on detailed experiments, the gas–liquid dispersion area was divided into different zones. A foam region width was found as inversely proportional to the clear liquid height. An entry region width was found as directly proportional to the clear liquid height. Hydrodynamic parameters, including gas holdup, bubble size distribution, and Sauter mean bubble diameter were evaluated and compared for different operating conditions. The gas holdup was calculated from both height measurement and pixel intensity methods, and it was found to be indirectly proportional to clear liquid height. Bubble sizes affect the bubble column performance; therefore, bubbles are tracked to calculate the bubble size distribution. Experimental results proved that the proposed scheme is robust.
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Yadav A, Gaurav TK, Pant HJ, Roy S. Machine learning based position‐rendering algorithms for radioactive particle tracking experimentation. AIChE J 2020. [DOI: 10.1002/aic.16954] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Ashutosh Yadav
- Department of Chemical EngineeringIndian Institute of Technology Delhi New Delhi India
| | - Tuntun Kumar Gaurav
- Department of Chemical EngineeringIndian Institute of Technology Delhi New Delhi India
| | - Harish J. Pant
- Isotope and Radiation Application DivisionBhabha Atomic Research Centre Mumbai India
| | - Shantanu Roy
- Department of Chemical EngineeringIndian Institute of Technology Delhi New Delhi India
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