A hybrid prediction approach for enhancing heat transfer efficiency of coal-fired power plant boiler

Abstract

Predicting future fouling status is a crucial but tough topic in the applications of energy conservation and pollution reduction at coal-fired power plants because of the significant influence that ash slag has on the heat transfer efficiency of boilers in coal-fired power plants. For the prediction of gray areas in heated areas, a hybrid system based on complementary ensemble empirical modal decomposition, gray models, and long short-term memory networks is presented. This is because the time series of ash pollution degrees is not linear and not smooth. Initially, using a complementary ensemble empirical modal decomposition, the original sequence after wavelet threshold denoising is divided into a number of subseries components. The projected values for the cleanliness factor were then generated by superimposing the predictions from the IMF and residual components. The experimental findings support the model’s precision and dependability and demonstrate that the CEEMD-GM-LSTM model does, in fact, perform very well in forecasting the ash situation in the heated zone.

Keywords

• Coal-fired power plant boiler
• Heat transfer efficiency
• Ash fouling prediction
• Cleanliness factor
• Long short-term memory