Lowering Nitrogen Oxide Emissions in a Coal-Powered 1000-MW Boiler

Xiaojuan Chen,Haiyang Zhang,Hongwu Qin,Kelvin Wong

doi:10.1155/2021/9958972

Xiaojuan Chen, Haiyang Zhang + Show 2 more

Open Access

https://doi.org/10.1155/2021/9958972

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Abstract

Burning of coal in power plants produces excessive nitrogen oxide (NOx) emissions, which endanger people’s health. Proven and effective methods are highly needed to reduce NOx emissions. This paper constructs an echo state network (ESN) model of the interaction between NOx emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NOx emissions. The experimental results show that the ESN model of the NOx emissions is more accurate than both of the LSTM and ELM models. The simulation results show NOx emission reduction in three selected cases by 16.5%, 15.6%, and 10.2%, respectively.

Highlights

The energy statistics in China show that 59.2% of electrical energy comes from thermal electricity
The root-mean-square error (RMSE) and R2 were calculated and were found to be 10.527 and 0.86, respectively. These results enable us to conclude that the proposed echo state network (ESN) model is accurate in its nitrogen oxide (NOx) emission prediction
This indicates that the ESN model is a promising alternative for achieving the required accuracy when dealing with models of nitrogen oxide emissions

Summary

Introduction

The energy statistics in China show that 59.2% of electrical energy comes from thermal electricity. ANN and GA techniques and optimized the operational parameters for NOx emission prediction and reduction in a pulverized coal-fired boiler of a 210 MW capacity. Li et al [13] introduced ELMs as a tool for building a model for the emissions of nitrogen oxides, and they proposed an enhanced algorithm of teaching-learning-based optimization (I-TLBO), in order to fine-tune the ELM parameters and improve the modeling accuracy. The GWO method is used to optimize both the ESN model parameters and the operational parameters with the target of lowering the emissions of nitrogen oxides. We introduce a combustion optimization method to lower the emissions of nitrogen oxides for a coal boiler that has a 1000-MW capacity. For validating the proposed method, we selected three typical values of the boiler maximum continuous rating (BMCR), namely, 100%, 90%, and 80%, for optimization by the GWO algorithm

Methodology

Conclusions

Findings

Conflicts of Interest