The ability to obtain the coal quality of each coal mill in real time is of great significance to optimization and adjustment of boiler combustion. The conventional coal quality detection procedure is cumbersome and the detection data lag behind. Therefore

a coal quality identification method based on electrostatic method combined with long short term memory(LSTM) neural network is proposed. In this method

according to the characteristics of electrostatic changes in the transmission process of different pulverized coal

electro-static sensors are installed to detect the electrostatic signal strength of different pulverized coal passing through the pipeline

the correlation and delay of several parameters such as air-coal ratio

pulverized coal flow rate and coall mill outlet temperature is analyzed by combining with the site data

and an LSTM neural network model for coal quality identification is established. Taking the measured data of a 600 MW unit boiler as an example

after parameter optimization

the coal quality identification accuracy of the LSTM neural network model reaches 86.84%

and all the AUC values of the four coal quality classification results are above 0.9. Compared with other machine learning models

the LSTM neural network model has higher identification accuracy

which is feasible and accurate.