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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In order to solve the problem of the uneven distribution of the flow and ammonia concentration field in the selective catalytic reduction (SCR) denitrification system of a 660 MW coal-fired power plant, a three-dimensional computational fluid dynamics (CFD) model was established at a scale of 1:1. The existing flow guide and ammonia fume mixing device were then calibrated and optimized. The relative standard deviation of the velocity field distribution upstream of the ammonia injection grid (AIG) was optimized from 15.4% to 9.9%, with a reasonable radius of the deflector at the inlet flue elbows, and the relative standard deviation of the velocity field distribution above the inlet surface of the first catalyst layer in the reactor was optimized from 25.4% to 10.2% by adjusting the angle between the deflector and the wall plate of the inlet hood. Additionally, with the use of a double-layer spoiler ammonia fume mixing device, the relative standard deviation of the ammonia mass concentration distribution above the inlet surface of the first catalyst layer in the reactor was optimized from 12.9% to 5.3%. This paper can provide a valuable reference with practical implications for subsequent research.

Details

Title
Numerical Simulation and Optimization of SCR-DeNOx Systems for Coal-Fired Power Plants Based on a CFD Method
Author
Wang, Huifu 1 ; Sun, Jian 1 ; Li, Yong 2   VIAFID ORCID Logo  ; Cao, Zhen 3 

 Datang Environmental Industry Group Co., Ltd., Beijing 100097, China 
 College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China 
 Department of Energy Sciences, Lund University, SE-22100 Lund, Sweden 
First page
41
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
22279717
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2767266767
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.