Enhancing burner diagnostics and control with chaos-based signal analysis techniques

Driven by economic pressures and government emissions regulations, the electric power industry is moving toward tighter control of boilers to improve plant efficiency and reduce emissions. Tighter control depends on better diagnostic tools that discriminate short-time-scale flame patterns and correlations between those flame patterns and overall performance. Research indicates that improved discrimination of short-time-scale patterns in boilers can be achieved by combining traditional data analysis techniques and chaotic time series analysis. Suggested analysis tools and data acquisition procedures are described, along with example results for measurements from a low-NO{sub x} pulverized coal combustor.