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Boiler furnace temperature and oxygen content prediction based on hybrid CNN, biLSTM, and SE-Net models

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Abstract

Furnace temperature and oxygen content are important parameters reflecting the combustion inside a circulating fluidized bed (CFB) boiler. Accurately predicting boiler output is a complex task due to the high noise and nonsmoothness of actual boiler input and output data. In this paper, a new hybrid convolutional neural network (CNN), bidirectional long short-term memory (biLSTM) network, and squeezing and excitation (SE) network prediction model is proposed to significantly improve the prediction accuracy of oxygen content and furnace temperature by combining the advantages of multiple deep learning networks. This network can extract spatiotemporal characteristics of input parameters such as coal feed to effectively predict boiler furnace temperature and oxygen content. CNNs can extract complex features such as dynamic and static nonlinearities between multiple variables affecting the furnace temperature and oxygen content, as well as high noise. The biLSTM network layer can efficiently handle the temporal information of irregular trends in modeling time series components; SE can extract the important information between channels through the feature relationships between channels for better overfeature extraction. The CNN-biLSTM-SE model can effectively solve the problem of nonlinear mapping complexity between inputs and outputs. Experiments show that the proposed CNN-biLSTM-SE model outperforms existing methods. The experimental results showed that the average MAPE errors for oxygen content prediction were CNN-biLSTM-SE (0.038), CNN-biLSTM with attention mechanism (AM) (0.043), CNN-biLSTM (0.051), CNN-LSTM (0.051), biLSTM (0.051), RNN (0.051), LSTM(0.0052), and CNN(0.0054). Extensive experiments in CFB boilers with oxygen content and furnace temperature show that the proposed CNN-biLSTM-SE model achieves better results in terms of goodness-of-fit, generalization ability and accuracy.

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Data Availability

The data that has been used is confidential.

Abbreviations

CNN:

Convolutional Neural Networks

LSTM:

Long short-term memory

biLSTM:

bidirectional Long Short-Term Memory

RNN:

Recurrent Neural Network

ANN:

Artifical Neural Networks

SE:

Sequeeze and Excitation Net

SVM:

Support Vector Machines

CFB:

Circulating Fluidized Bed

WA:

Wavelet Analysis

AE:

Auto-Encoder

MM-PSMC:

Multimodel Predictive Sliding Mode Control

MAE:

Mean Absolute Error

RMSE:

Root Mean Square Error

MSE:

Mean Square Error

AM:

Attention Mechanism

ELM:

Extreme Learning Machine

PCA:

Principal Component Analysis

AI:

Artificial Intelligence

DNN:

Deep Neural Networks

CAM:

Channel Attention Mechanism

TAM:

Temporal Attention Mechanism

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Acknowledgements

This research was supported by the basic Science Research Project of Education Department of Liaoning Province, No. LJKMZ20220731; Natural Science Foundation of Liaoning Province (No.2020-MS-283)

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Authors and Affiliations

  1. School of Information and Control Engineering, Liaoning Petrochemical University, No.1, Dandong Road West Section, Fushun, 113001, Liaoning, China

    Zhaoyu Ji, Wenhua Tao & Jiaming Ren

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  1. Zhaoyu Ji
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  2. Wenhua Tao
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  3. Jiaming Ren
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Contributions

Zhaoyu Ji: Conceptualization, Methodology, Writing-Original draft preparation. Wen hua Tao: Data curation. Jiaming Ren:Software.

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Correspondence to Wenhua Tao.

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Ji, Z., Tao, W. & Ren, J. Boiler furnace temperature and oxygen content prediction based on hybrid CNN, biLSTM, and SE-Net models. Appl Intell 54, 8241–8261 (2024). https://doi.org/10.1007/s10489-024-05609-5

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