Research Papers

Optimization of Co-Firing Burners

[+] Author and Article Information
Palaniappan Valliappan

School of Engineering,
Faculty of Computing, Engineering and Science,
University of South Wales,
Wales CF37 1DL, UK
e-mail: palaniappan.valliappan@southwales.ac.uk

Steve Wilcox

School of Engineering,
Faculty of Computing, Engineering and Science,
University of South Wales,
Wales CF37 1DL, UK
e-mail: steve.wilcox@southwales.ac.uk

Krzystof Jagiełło

Thermal Processes Department,
Instytut Energetyki,
Mory 8, Warszawa 01-330, Poland
e-mail: krzysztof.jagiello@ien.com.pl

Yimin Shao

State Key Laboratory of Mechanical Transmission,
Chongqing University,
Chongqing 400044, China
e-mail: ymshao@cqu.edu.cn

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received July 14, 2015; final manuscript received May 3, 2016; published online June 1, 2016. Assoc. Editor: Alexander L. Brown.

J. Thermal Sci. Eng. Appl 8(4), 041001 (Jun 01, 2016) (7 pages) Paper No: TSEA-15-1190; doi: 10.1115/1.4033582 History: Received July 14, 2015; Revised May 03, 2016

At present, unless a boiler is especially designed to burn biomass, the levels of co-firing are generally limited to around 5% by mass. Higher levels of substitution sometimes lead to burner instability and other issues. In order to co-fire higher concentrations of biomass, a technique is required which can monitor flame stability at the burner level and optimize the combustion to ensure that local NOx is maintained below set limits. This paper presents an investigation of a system that monitored the combustion flame using photodiodes with responses in the ultraviolet (UV), infrared (IR), and visible (VIS) bands. The collected data were then processed using the Wigner–Ville joint time–frequency method and subsequently classified using a self-organizing map (SOM). It was found that it was possible to relate the classification of the sensor data to operational parameters, such as the burner airflow rate and NOx emissions. The developed system was successfully tested at pilot scale (500 kWt), where the ability of the system to optimize the combustion for a variety of unseen coal/biomass blends was demonstrated.

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Grahic Jump Location
Fig. 6

Comparison of actual (square) versus predicted (asterisk) NOx in relation to total airflow for coal A

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Fig. 4

Predicted NOx using a back propagation ANN with 12 hidden neurons—training set data

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Fig. 5

Schematic of the overall burner MCS

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Fig. 8

Comparison of actual (square) versus predicted (asterisk) NOx in relation to total airflow for coal B

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Fig. 9

Comparison of actual (square) versus predicted (asterisk) CO in relation to total airflow for coal B

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Fig. 7

Comparison of actual (square) versus predicted (asterisk) CO in relation to total airflow for coal A

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Fig. 2

WVD of the IR signal with decreasing secondary air (100% coal)

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Fig. 3

NOx and CO emissions with decreasing secondary air (100% coal)




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