Analysis and prediction of denitration performance of Mn1Co0.5Cr0.5Ox catalyst based on CFD and BP-GA method

Zhihui Li; Haixia Li; Zhiheng Song; Yun Zhang; Yuxuan Jing; Anchao Zhang; Zhijun Sun

doi:10.1016/j.cjche.2024.09.024

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Analysis and prediction of denitration performance of Mn₁Co_0.5Cr_0.5O_x catalyst based on CFD and BP-GA method

Updated：2026-01-06

- Analysis and prediction of denitration performance of Mn₁Co_0.5Cr_0.5O_x catalyst based on CFD and BP-GA method
- Analysis and prediction of denitration performance of Mn₁Co_0.5Cr_0.5O_x catalyst based on CFD and BP-GA method
- 中国化学工程学报（英文版） 2025年77卷第1期页码：225-235
- Affiliations：
  
  School of Mechanical and Power Engineering, Henan Polytechnic University,Jiaozuo,China,454000
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2024.09.024
  中图分类号：
- 纸质出版：2025
- Accepted：
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Zhihui Li, Haixia Li, Zhiheng Song, 等. Analysis and prediction of denitration performance of Mn₁Co_0.5Cr_0.5O_x catalyst based on CFD and BP-GA method[J]. 中国化学工程学报（英文版）, 2025,77(1):225-235. DOI: 10.1016/j.cjche.2024.09.024.

Co
Zhihui Li, Haixia Li, Zhiheng Song, 等. Analysis and prediction of denitration performance of Mn₁Co_0.5Cr_0.5O_x catalyst based on CFD and BP-GA method[J]. 中国化学工程学报（英文版）, 2025,77(1):225-235. DOI: 10.1016/j.cjche.2024.09.024. DOI：

Co DOI： 10.1016/j.cjche.2024.09.024.

摘要

Abstract

Based on the experimental data of Mn

0.5

catalysts and the component transport model in computational fluid dynamics (CFD)

a kinetic model for the standard NH

-SCR (NH

selective catalytic reduction) process was effectively established. The objective of the model development was to predict the denitrification reaction rate of the catalyst

which incorporates various factors such as the Arrhenius parameters (pre-exponential factor and activation energy)

inertial resistance

viscous resistance

and surface-to-volume ratio. To verify the practicability of the model

simulation results were compared with actual experimental data. The effects of NH

concentrations

and gas hourly space velocity (GHSV) on NO conversion were simulated and analyzed. Subsequently

the NO conversion prediction model was trained and established using a combination of numerical simulation results

back-propagation neural network

and genetic algorithm (BP-GA). Furthermore

the significance of the impact that various factors had on the denitrification activity of the catalyst was determined.

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