# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 5 Case Study
### Model Performance Analysis
Subsequently, the 1D-CNN model is trained to accomplish the coal identification task and evaluated for accuracy and generalization ability on the test set. The resulting training and test results of the model are documented. 3. BP neural network model: In building a multilayer perceptron for coal classification, an input layer with 47 nodes, an output layer with five nodes, 32 hidden layers, Relu as the activation function, and Softmax classifier as the output layer are used to output the classification results. The accuracy and training test results of the model are recorded. This paper compares the experimental results of the three aforementioned methods. Table 2 presents a comparison of the classification performance of MIC-CNN, CNN, and BP. When utilizing the BP neural network for feature extraction and classification of coal types (Figures 4 and 5), the network can extract coal type features despite the heterogeneity of the data volume and the presence of redundant and highly correlated features. However, the classification effect is not ideal, as the recognition accuracy of coal type 5 is poor. The model can correctly identify five types of coal species, with an overall test set accuracy of 68.65%. In terms of memory usage and training time, the BP neural network occupies 2237.6 MB of memory during training and takes 202.02 s to complete the training process. When passing the data through 1D-CNN for coal species recognition (Figures 6 and 7), features are extracted through four convolutional layers. Despite some high-dimensional invalid features, coal species recognition can still be achieved on the basis of the feature extraction ability of the model and sharing of convolutional kernels. During the recognition of five types of coal, the recognition accuracy of coal types 1-4 is significantly higher, and the recognition accuracy of coal type 5 is greatly improved, with an accuracy of 78.38% in its test set, which is nearly 10% higher than the accuracy achieved by the traditional BP neural network. In terms of memory usage and training time, the 1D-CNN occupies 2413.7 MB of memory during training and takes 188.58 s to complete the training process. Figure 4: Confusion matrix of the BP neural network. Figure 5: Clustering diagram of BP neural network classification. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Accuracy** & **BP** & **1D-CNN** & **MIC-CNN** \\ \hline Training set & 78.17\% & 88.54\% & 98.23\% \\ Test set & 68.65\% & 78.38\% & 94.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performance. Figure 6: Confusion matrix of 1D-CNN. In enhancing the accuracy of the model, considering the strong inter-correlation among features in actual industrial data, the MIC-CNN method has been adopted. Initially, the features with strong correlations are removed by using the MIC method, while retaining the independent features. The streamlined data, consisting of 30 features after MIC screening, are then fed into the 1D-CNN network. This process significantly reduces the interference of redundant features (Figures 8 and 9). The recognition accuracy of coal types in the test set processed by MIC-CNN reaches 94.02%, which is nearly 16% higher than the classification accuracy of 1D-CNN, indicating the efficacy of the proposed approach in achieving better coal species recognition. In terms of memory usage and training time, the MIC-CNN occupies 2217.5 MB of memory during training and takes 179.95 s to complete the training process. Figure 8: Confusion matrix of MIC-CNN. Figure 7: Clustering diagram of 1D-CNN classification.