We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
NOx Emission Prediction Based on SSA-DELM via CFD and DCS Data Fusion.
- Authors
Manli Lv; Jianping Zhao; Shengxian Cao; Zhenhao Tang
- Abstract
The objective of this study was to obtain NOx emission prediction model at the inlet selective catalytic reduction (SCR) reactors, which was the basis of combustion optimization and denitrification treatment. A deep extreme learning machine (DELM) optimized by the sparrow optimization algorithm (SSA) was adopted to establish the NOx model based on data fusion of Computational Fluid Dynamics (CFD) simulation and Distributed Control System (DCD). The mechanism analysis and XGBoost algorithm was used to select input variables. The results show that the XGBoost-SSA-DELM-based prediction model has high prediction accuracy with mean absolute error of 2.54 mg/m3. The results of this study have important implications for research on improving combustion efficiency and reducing pollutant emissions.
- Subjects
MULTISENSOR data fusion; MACHINE learning; COMBUSTION efficiency; COMPUTATIONAL fluid dynamics; DEEP learning; DATA fusion (Statistics); NITROGEN oxides emission control
- Publication
International Journal of Heat & Technology, 2022, Vol 40, Issue 6, p1514
- ISSN
0392-8764
- Publication type
Article
- DOI
10.18280/ijht.400621