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Wang J, Wang Y, She X, Wang J, Xue Q. Numerical study on the distribution of flue gas residence time in the desulfurization and denitrification system by the optimization of the model. INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2022. [DOI: 10.1515/ijcre-2022-0043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
During the operation of the CSCR activated carbon desulfurization and denitrification flue gas purification system, due to the unreasonable design of flue gas pipe diameter and the uneven distribution of activated carbon porosity, the uniformity of flue gas distribution is prone to problems, which affects flue gas removal efficiency and increases operating costs. In this article, the Fluent software was used to establish a three-dimensional numerical model of the flue gas purification system, and the flow characteristics of the flue gas were studied by continuously optimizing the structure of the system model. The effects of different pipe diameters, the accumulation method of activated carbon in the desulfurization and denitration module and the distribution of porosity on the flue gas are analyzed, and the effects on the average residence time and variance of flue gas were further analyzed. The results show that the average residence time, the variance, the dead volume fraction, and well-mixed volume decreased, while the dispersed plug volume fraction increased for a module after optimization. The average residence time and the dispersed plug volume fraction decreased. While the variance, the dead volume fraction, and well-mixed volume fraction increased for the system after optimization.
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Affiliation(s)
- Junjie Wang
- State Key Laboratory of Advanced Metallurgy , University of Science and Technology of Beijing , Beijing 100083 , PR China
| | | | - Xuefeng She
- State Key Laboratory of Advanced Metallurgy , University of Science and Technology of Beijing , Beijing 100083 , PR China
| | | | - Qingguo Xue
- State Key Laboratory of Advanced Metallurgy , University of Science and Technology of Beijing , Beijing 100083 , PR China
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Zhou H, Xu K, Huang J, Kou M, Wu S, Zhang Z, Zhao B, Ma X. Numerical simulation of inner characteristics in COREX shaft furnace with center gas distribution: influence of bed structure. INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2022. [DOI: 10.1515/ijcre-2022-0004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
The packed structure, such as packing density and particle size distribution, of COREX shaft furnace, directly affects the gas flow and reaction process. A two-dimensional steady-state mathematical model was developed to study the influence of five packed structures on the gas flow, pressure distribution, species composition, and solid metallization rate in a COREX shaft furnace with center gas supply. The results show that the gas velocity is relatively uniform along the radial direction under Case-P. Under Case-InV and Case-V, the gas velocity increases and decreases gradually from the center to the wall zone respectively. The gas velocity contour in the upper part of the shaft in Case-M is ‘M’ shape, while it shows ‘W’ shape in Case-W. The order of pressure drop under five packed structures is Case-P > Case-M > Case-W > Case-InV > Case-V, and for the solid metallization rate, the order is Case-V > Case-W > Case-P > Case-M > Case- InV. As Case-V has the lowest pressure drop and largest metallization rate, the charging matrix in practical production should develop towards a ‘V’ shaped burden profile in the upper of the packed bed in the COREX shaft furnace.
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Affiliation(s)
- Heng Zhou
- State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing , Beijing 100083 , China
- School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing , Beijing 100083 , China
| | - Kun Xu
- State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing , Beijing 100083 , China
- School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing , Beijing 100083 , China
| | - Jian Huang
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing , Beijing 100083 , China
| | - Mingyin Kou
- State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing , Beijing 100083 , China
- School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing , Beijing 100083 , China
| | - Shengli Wu
- State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing , Beijing 100083 , China
- School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing , Beijing 100083 , China
| | - Zuoliang Zhang
- School of Metallurgy Engineering, Liaoning Institute of Science and Technology , Benxi 117004 , China
| | - Baojun Zhao
- The University of Queensland , 4072 , Brisbane , Australia
| | - Xiaodong Ma
- The University of Queensland , 4072 , Brisbane , Australia
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