A Brief Review of the Combustion Diagnosing Techniques for Coal-Fired Boilers of Power Plants in China

Abstract

China has a large number of coal-fired power plants which are commonly suffered with many problems like uncertain coal source, frequent variation of coal quality, blended coal combustion and great fluctuation of the load. It is dire need of a system that can conduct combustion diagnosis, which can enables new horizon in optimization and to ensure the safety and efficiency of a unit. The contents of the combustion diagnosis include flame stability evaluation and temperature measurement. The latter provides quantitative information of the furnace, which can be used for unburnt carbon and pollutant emissions prediction. Diagnosing techniques are generally based on flame radiation intensity detect, flame image analysis, acoustic pyrometry and artificial intelligence analysis. The performance and engineering application of these techniques are reviewed and compared in this study. Artificial intelligence analysis based on the flame radiation intensity signal is recommended for flame stability evaluation as it is easily implemented in practical boilers with good accuracy and low cost. The challenge of the method based on flame image is thought to be that the reliability of the hardware system for flame image collection remains to be improved as they are easily affected by the high temperatures and fouling in engineering applications. The particles produced in the furnace is the hinder for application of the acoustic pyrometry in coal-fired boilers.

Keywords

• Combustion diagnosis
• coal-fired power plants
• stability evaluation
• features extraction
• artificial intelligence