# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 7 Conclusions  
Online coal identification forms the basis of combustion optimization control and plays an important role in improving boiler efficiency. In the present work, in order to monitor the changes of coal types effectively, an online coal identification method is proposed based on 1D-CNN. First, MIC is used to extract important features from the original process variables. The selected features are then used to construct a 1D-CNN model. Based on MIC, the proposed method compensates for the influence of feature redundancy and high feature dimension on CNN model construction, improves the modeling efficiency, and reduces the influence of noise on CNN's performance by eliminating redundant features. Industrial data experiments show that the accuracy of coal identification can reach over 94.02%. The purpose of online coal identification is to detect changes in coal quality over time so as to adjust the control parameters of combustion optimization. In the future work, we will focus on approaches to combine the results of coal identification with combustion optimization control, as well as utilize intelligent algorithms for automatic parameter tuning to further enhance boiler combustion efficiency. in order to further improve the boiler combustion efficiency. S.M. : Software, data curation, Visualization, Investigation; K.H. : Conceptualization, methodology, Writing--Original draft preparation; X.P. : Writing--Reviewing and Editing. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the National Natural Science Foundation of China (NSFC) under Grant (62273214, 62233012), partly by the Research Fund for Postdoctoral Innovation Talent Support Plan of Shandong Province of China (SDBX202015), and partly by the Research Fund for the Taishan Scholar Project of Shandong Province of China (Corresponding author: [PERSON]). The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study and are not authorized for publication. The authors would like to thank the editors and anonymous referees for their invaluable comments and suggestions. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} TPP & Thermal power plants \\ CNN & Convolutional neural network \\
1D-CNN & One-dimensional convolution network \\ SVM & Support vector machine \\ VGG & Visual geometry group \\ ADARTS & Attention for differentiable neural architecture search \\ MOEA-PS & Multi-objective evolutionary algorithm with a probability stack \\ EPC-DARTS & Efficient partial channel connection for differentiable architecture search \\ MIC & Maximum information coefficient \\ SWUP & Spiral Wound Universal Pressure Boiler \\ DCS & Distributed Control System \\ \end{tabular}   
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