Oxy-Fuel Burner Characterization: From Laboratory to Industry

Oxygen-fuel firing of furnaces provides numerous benefits to industry such as, reduction of capital investment, fuel savings, and reduction of NOx emission. These benefits are evident in the glass industry where an estimated 15% of the U.S. production has already been converted to oxy-fuel. However, the conversion from conventional air-fuel to oxy-fuel is complicated by the drastic differences between the combustion characteristics such as, flame temperature, momentum, flame chemistry, and heat transfer properties. Successful operation of industrial oxy-fuel furnaces often involves 3-D numerical modeling of the process, but insuring accurate prediction of critical parameters from modeling requires experimental characterization of the burner. For these reasons AIR LIQUIDE has implemented a variety of diagnostics to study industrial-scale oxy-fuel flames. Advanced techniques such as CARS temperature mapping, 2-D Mie scattering for flowfield analysis, emission spectroscopy, and UV imaging are routinely used in the development of new combustion systems. This paper will present recent experimental and modeling results and discuss their implications on the design of oxy-fuel burners

Use of Optical Sensors on Industrial Oxy-Fuel Burners for Process Control

With stricter environmental regulations, the need for manufacturers to optimize their combustion processes for emission reduction and higher fuel efficiency has become ever increasing. To achieve better optimization of the combustion process the need for improved and alternative methods for monitoring and controlling combustion parameters is required. Here we present a novel method for monitoring and controlling oxy-fuel burners by strategic placement of optical sensors. The sensors are integrated into an industrial oxy-fuel burner capable of withstanding harsh environments. Radiation from the flame at selected wavelengths that cover the OH, CH, and C2 bands are collected from the burner and transported to a PC spectrometer by fiber optics. Using neural network models the signals from these species provide real-time measure of the stoichiometry and firing rate. The processed information can then be used in a control-loop for adjusting and optimizing combustion parameters. Results using the sensor on a commercial glass furnace will be presented