Modeling and optimization of the NO_X generation characteristics of the coal-fired boiler based on interpretable machine learning algorithm

ABSTRACT

The present work focused on modeling the nitrogen oxides (NO_X) generation characteristics based on the interpretable machine learning algorithm for an in-service coal-fired power plant. Computational Fluid Dynamics is available to obtain the NO_X generation data, which coupled with the historical operation data collected from Distributed Control System were used to improve the model’s prediction ability. The results showed that the depth and integrity of the dataset could be improved by adding simulation data. Compared with the Artificial Neural Network (ANN) and Support Vector Regression (SVR), the Gradient Boost Regression Tree (GBRT) model had higher accuracy than that of ANN and SVR model, and the GBRT model with more vital nonlinear transformation expression and time sequence is more suitable for the dataset, where the mean absolute error and coefficient of determination of the GBRT model were 3.85 and 0.98, respectively. Moreover, the Shapley additive interpretation analysis approach was presented for the GBRT model of NO_X generation prediction, which is helpful to the field operators to realize the efficient and low pollution operation of boiler equipment.

KEYWORDS:

• NO_X
• cfd
• interpretable machine learning
• gbrt
• shap

Nomenclature

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Acknowledgments

This work was supported by the National Key R&D Program of China (2016YFB0600704). We also acknowledge the support from the Key Laboratory of Efficient, Clean Energy Utilization of Guangdong Higher Education Institutes (KLB10004) and the Fundamental Research Funds for the Central Universities (2020ZYGXZR027) and Guangdong Basic and Applied Basic Research Foundation (2020A1515110297).

Conflict of interest

The authors declare no competing financial interest.

Additional information

Funding

This work was supported by the National Key R&D Program of China [2016YFB0600704]; Key Laboratory of Efficient and Clean Energy Utilization of Guangdong Higher Education Institutes [KLB10004]; Fundamental Research Funds for the Central Universities [2020ZYGXZR027]; Guangdong Basic and Applied Basic Research Foundation [2020A1515110297].