Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

Zheming Tong; Xin Chen; Shuiguang Tong; Qi Yang

doi:10.1021/acsomega.2c00615

Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

ACS Omega. 2022 Mar 22;7(13):11422-11429. doi: 10.1021/acsomega.2c00615. eCollection 2022 Apr 5.

Authors

Zheming Tong¹, Xin Chen¹, Shuiguang Tong¹, Qi Yang¹

Affiliation

¹ State Key Laboratory of Fluid Power and Mechatronic Systems and School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.

Abstract

Flexible operation of large-scale boilers for electricity generation is essential in modern power systems. An accurate prediction of boiler steam temperature is of great importance to the operational efficiency of boiler units to prevent the occurrence of overtemperature. In this study, a dense, residual long short-term memory network (LSTM)-attention model is proposed for steam temperature prediction. In particular, the residual elements in the proposed model have a great advantage in improving the accuracy by adding short skip connections between layers. To provide overall information for the steam temperature prediction, uncertainty analysis based on the proposed model is performed to quantify the uncertainties in steam temperature variations. Our results demonstrate that the proposed method exhibits great performance in steam temperature prediction with a mean absolute error (MAE) of less than 0.6 °C. Compared to algorithms such as support-vector regression (SVR), ridge regression (RIDGE), the recurrent neural network (RNN), the gated recurrent unit (GRU), and LSTM, the prediction accuracy of the proposed model outperforms by 32, 16, 12, 10, and 11% in terms of MAE, respectively. According to our analysis, the dense residual LSTM-attention model is shown to provide an accurate early warning of overtemperature, enabling the development of real-time steam temperature control.