NOx Emission Prediction Based on SSA-DELM via CFD and DCS Data Fusion

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.

A Novel ISSA–DELM Model for Predicting Rock Mass Permeability

“…However, the initial weighting of the DELM depends on random selection, which can affect the training process in terms of accuracy and time consumption [16]. Researchers typically use swarm intelligence optimization algorithms to optimize objective parameters to avoid this limitation [17][18][19]. The sparrow search algorithm (SSA) has been shown to have excellent search capabilities [20,21].…”