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.
Issue Section:
Research Papers
Topics:
Air flow,
Biomass,
Coal,
Co-firing,
Combustion,
Emissions,
Flames,
Nitrogen oxides,
Sensors,
Fuels,
Boilers,
Artificial neural networks
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