Zhang Fan, Li Hao, Xu ZhiChao, Chen Wei
School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing, China.
Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing, China.
J Intell Robot Syst. 2022;104(2):30. doi: 10.1007/s10846-021-01552-6. Epub 2022 Feb 3.
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.
煤炭水分含量监测在煤炭运输储存环节的碳减排和清洁能源决策中发挥着重要作用。传统的煤炭水分含量检测机制严重依赖检测设备，这些设备可能成本高昂，或者在现场条件下难以部署。为了实现煤炭水分含量的快速预测，本文提出了一种基于注意力机制和双向ResNet-LSTM结构的新型神经网络模型（ABRM）。通过训练该模型来学习煤炭水分含量变化与气象条件之间的关系，从而实现对煤炭水分含量的预测。实验结果表明，与其他现有先进方法相比，该方法在水分含量预测准确性方面具有优越性能，并且ABRM模型在面对气象因素时，似乎具有预测煤炭水分含量变化的最大潜力。
