Research on Gray Prediction of Heated Surface Combining Empirical Mode Decomposition and Long Short-term Memory Network

Aiming at ash and slag of boiler heated surface will reduce the heat transfer efficiency and security. This paper adopts clearness factor as the indicator to monitor healthy condition of the boiler heated surface, and put forward a model combining empirical mode decomposition (EMD) and long short-term memory (LSTM) model to predict boiler ash accumulation in the future. EMD can decompose the time series into a series of frequency-stable intrinsic mode functions. In addition, the special gate structure inside LSTM makes it possible to mine the long-term dependencies in the time series. The combination of the two increases the prediction accuracy of the time series. It is verified by simulation software that the model has a satisfactory accuracy in predicting healthy condition of the boiler heated surface, and the feasibility and effectiveness of the model are verified.