NOx Emission Prediction of Coal Fired Utility Boiler Based on FAR-HK-ELM

Abstract

A prediction method for NOx emission of coal-fired utility boiler based on FAR-HK-ELM was proposed by combining the Fast Attribute Reduction (FAR) and Hybrid Kernel Extreme Learning Machine (HK-ELM) algorithms. First, the main influencing attributes of NOx emission are selected by FAR algorithm, and the redundant information of high-dimensional characteristics is eliminated; Then, HK-ELM based on global polynomial kernel and local gaussian radial basis function is constructed to model NOx emission, and the optimal parameters of the model are obtained through the constrained weight linear decreasing particle swarm optimization algorithm and cross validation. By taking a coal-fired utility boiler operation system as an example, the model was applied to the real operation data for prediction analysis and verification. Compared with BP, SVM, PK-ELM, GK-ELM and HK-ELM models, the proposed method further improves the generalization ability of the model. This study lays a foundation for the combustion optimization of coal-fired utility boiler system.

Keywords

• nitrogen oxide emission
• attribute reduction
• hybrid kernel extreme learning machine (hk-elm)
• prediction model