Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications

Abstract

In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal types, the actual application of coal types inconsistent with expectations may lead to low combustion efficiency of boilers, cause disturbances to the normal operation of thermal power units, increased energy waste and carbon emissions, and even lead to serious explosion accidents. Therefore, the online identification of coal types for thermal power units is of great significance. To obtain the precise type of coal online, in the present work, a data-driven coal identification method is proposed based on convolutional networks that do not necessitate additional hardware detection equipment and are easy to implement. Experimental results indicate that the proposed method exhibits superior performance in comparison to traditional methods, thus ultimately improving the performance of thermal power plant.