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A Novel ABRM Model for Predicting Coal Moisture Content

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Abstract

Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

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Data Availability

The data used to support the finding of this study are available from the corresponding author upon request.

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Funding

This research was supported by the National Natural Science Foundation of China (grant number 51874300), Fundamental Research Funds for the Central Universities (grant number 2021YJSJD02), the Graduate Program of ideological and political construction, China University of Mining and Technology of Beijing (grant number YKC-SZ202100404S), and the Open Research Fund of Key Laboratory of Intelligent Mining and Robotics, China University of Mining and Technology of Beijing (grant number U03462).

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Authors and Affiliations

  1. School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing, China

    Fan Zhang, Hao Li, ZhiChao Xu & Wei Chen

  2. Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing, China

    Fan Zhang & Wei Chen

  3. Institute of Intelligent Mining and Robotics, China University of Mining and Technology (Beijing), Beijing, China

    Fan Zhang & ZhiChao Xu

Authors
  1. Fan Zhang
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  2. Hao Li
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  3. ZhiChao Xu
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  4. Wei Chen
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Contributions

Professor Fan Zhang made primary contributions to the conceptualization or design of the work, review and editing. Master Hao Li completed original draft, data curation and algorithm development, and Dr. Zhichao Xu completed the validation and visualization. Professor Wei Chen made optimization of the software architecture, review.

Corresponding authors

Correspondence to Fan Zhang or Hao Li.

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Approval was obtained from the ethics committee of the China University of Mining and Technology of Beijing.

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Zhang, F., Li, H., Xu, Z. et al. A Novel ABRM Model for Predicting Coal Moisture Content. J Intell Robot Syst 104, 30 (2022). https://doi.org/10.1007/s10846-021-01552-6

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