Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms

TL;DR

This paper proposes a hybrid data-driven dynamic prediction model for NOx emissions in SCR systems, improving accuracy and accounting for key influencing factors through advanced data processing and modeling techniques.

Contribution

It introduces a novel hybrid modeling approach combining multiple algorithms and feature selection for accurate NOx emission prediction in SCR systems.

Findings

MAPE of 2.61% in predictions

Key factors influencing NOx include ammonia injection, oxygen, and temperature

Enhanced prediction accuracy over traditional models

Abstract

Aiming at the problem that delay time is difficult to determine and prediction accuracy is low in building prediction model of SCR system, a dynamic modeling scheme based on a hybrid of multiple data-driven algorithms was proposed. First, processed abnormal values and normalized the data. To improve the relevance of the input data, used MIC to estimate delay time and reconstructed production data. Then used combined feature selection method to determine input variables. To further mine data information, VMD was used to decompose input time series. Finally, established NOx emission prediction model combining ELM and EC model. Experimental results based on actual historical operating data show that the MAPE of predicted results is 2.61%. Model sensitivity analysis shows that besides the amount of ammonia injection, the inlet oxygen concentration and the flue gas temperature have a significant impact on NOx emission, which should be considered in SCR process control and optimization.