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Steady-State NOx Emission Model for Gas-Fired Heating and Hot Water Combi-Boilers with Factor Analysis and Artificial Neural Network

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Abstract

In light of partial state of NOx of gas-fired heating and hot water combi-boilers cannot be monitored or the NOx measurement is inaccurate, an online NOx emission model with high accuracy is essential. Seven parameters of NOx have been selected in this study, and an effective NOx emission model based on the factor analysis and radial basis function was developed. The prediction model is also compared to other five artificial neural network and regression methods. Twenty combi-boilers are measured, and the results are utilized for factor analysis and radial basis function model prediction, artificial neural network, and regression prediction. The results indicate that factor analysis and radial basis function model have significantly higher prediction accuracy than artificial neural network and regression models. The proposed factor analysis and radial basis function model may be a preferable solution for the development of accurate NOx emission online monitoring system of gas-fired combi-boilers.

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Funding

This research was funded by the Key Research and Development Program of Tianjin under grant number 19YFZCCG00550 and Innovation Platform Special Project of Tianjin under grant number 19PTSYJC00090.

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, People’s Republic of China

    Weiye Zhou & Hongting Ma

  2. Urban Gas & Heat Research Institute, North China Municipal Engineering Design & Research Institute Co., Ltd, Tianjin, 300384, People’s Republic of China

    Weiye Zhou, Wenxue Gao, Wenbo Liu, Lijie Yang & Lin Yang

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  1. Weiye Zhou
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  2. Wenxue Gao
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  4. Wenbo Liu
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Correspondence to Weiye Zhou.

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Zhou, W., Gao, W., Ma, H. et al. Steady-State NOx Emission Model for Gas-Fired Heating and Hot Water Combi-Boilers with Factor Analysis and Artificial Neural Network. Emiss. Control Sci. Technol. 8, 182–191 (2022). https://doi.org/10.1007/s40825-022-00216-7

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