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

[Image: see text] 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 s...

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Autores principales: Tong, Zheming, Chen, Xin, Tong, Shuiguang, Yang, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992261/
https://www.ncbi.nlm.nih.gov/pubmed/35415332
http://dx.doi.org/10.1021/acsomega.2c00615
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author Tong, Zheming
Chen, Xin
Tong, Shuiguang
Yang, Qi
author_facet Tong, Zheming
Chen, Xin
Tong, Shuiguang
Yang, Qi
author_sort Tong, Zheming
collection PubMed
description [Image: see text] 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.
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spelling pubmed-89922612022-04-11 Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis Tong, Zheming Chen, Xin Tong, Shuiguang Yang, Qi ACS Omega [Image: see text] 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. American Chemical Society 2022-03-22 /pmc/articles/PMC8992261/ /pubmed/35415332 http://dx.doi.org/10.1021/acsomega.2c00615 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Tong, Zheming
Chen, Xin
Tong, Shuiguang
Yang, Qi
Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis
title Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis
title_full Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis
title_fullStr Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis
title_full_unstemmed Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis
title_short Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis
title_sort dense residual lstm-attention network for boiler steam temperature prediction with uncertainty analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992261/
https://www.ncbi.nlm.nih.gov/pubmed/35415332
http://dx.doi.org/10.1021/acsomega.2c00615
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AT chenxin denseresiduallstmattentionnetworkforboilersteamtemperaturepredictionwithuncertaintyanalysis
AT tongshuiguang denseresiduallstmattentionnetworkforboilersteamtemperaturepredictionwithuncertaintyanalysis
AT yangqi denseresiduallstmattentionnetworkforboilersteamtemperaturepredictionwithuncertaintyanalysis