Modified reconstruction of boiler numerical temperature field based on flame image feature extraction

ABSTRACT

Computational fluid dynamics (CFD) method is a common method to obtain the temperature field distribution within the furnace. However, in order to reduce computational complexity, the numerical simulation involve simplifications in boundary conditions and model parameters. As a result, the temperature field distribution deviates from the actual operating conditions. This paper proposes a numerical temperature field modification method based on flame images. Flame images corresponding to the operational conditions are collected using the Industrial Flame Monitoring System (IFMS). The flame images are preprocessed, and the contour of the flame’s core region is extracted using the Mask Region-based Convolutional Neural Network (Mask R-CNN) method. The geometric features of the flame are extracted, and matrix calculations are applied to modify the numerical temperature field. The modified temperature field exhibits a deviation in the combustion center, aligning with the actual operation of the boiler. A comparison between the modified temperature field and the temperature measurements from flue gas shows consistent temperature trends. The absolute error (AE) under different operating conditions is under 8.8 K, and the relative error (RE) remains below 1.3%. The analysis results demonstrate that it can enhance the accuracy of temperature field calculations by the modification from flame image features.

KEYWORDS:

• Flame images
• feature extraction
• CFD
• numerical temperature field
• modified reconstruction

Acknowledgements

The research was supported by the National Natural Science Foundation of China (No. 52376107), and Guangdong Basic and Applied Basic Research Foundation (2022A1515010709). We also acknowledge the support from the Fundamental Research Funds for the Central Universities (2022ZFJH04) and Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization (2013A061401005).

Disclosure statement

We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Nomenclature

Abbreviations	=
CFD	=	Computational Fluid Dynamics
Mask R-CNN	=	Mask Region-based Convolutional Neural Network
A	=	A layer of primary air
B	=	B layer of primary air
C	=	C layer of primary air
DD	=	DD layer of auxiliary air
DE	=	DE oil auxiliary air
EE	=	EE layer of auxiliary air
SOFA2	=	Separate over fire air 2
SOFA4	=	Separate over fire air 4
RE	=	Relative error
Symbol	=
Mar	=	Moisture as received coal
Var	=	Volatiles as received coal
Qar,net	=	Lower heating value as received coal
Har	=	Hydrogen content as received coal
Nar	=	Nitrogen content as received coal
$F_O$	=	Initial flame image dataset
$F_H$	=	Histogram equalization enhancement image dataset
DCS	=	Distributed Control System
AA	=	AA layer of auxiliary air
AB	=	AB oil auxiliary air
BC	=	BC oil auxiliary air
CC	=	CC layer of auxiliary air
D	=	D layer of primary air
E	=	E layer of primary air
SOFA1	=	Separate over fire air 1
SOFA3	=	Separate over fire air 3
AE	=	Absolute error
Aar	=	Ash as received coal
Fc,ar	=	Fixed carbon as received coal
Car	=	Carbon content as received coal
Oar	=	Oxygen content as received coal
Sar	=	Sulfur content as received coal
$F_G$	=	Gaussian filtering image dataset
$F_{\mathit{R}-\mathit{CNN}}$	=	Mask R-CNN processing image dataset

Additional information

Funding

The work was supported by the Basic and Applied Basic Research Foundation of Guangdong Province [2022A1515010709]; Fundamental Research Funds for the Central Universities [2022ZFJH04]; Guangdong Key Laboratory of Efficient and Clean Energy Utilization, South China University of Technology [2013A061401005]; National Natural Science Foundation of China [No. 52376107].