# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 5 Case Study
### Model Performance Analysis  
A total of 288,000 data points were collected from TPPs and classified into five categories. The resulting 47-dimensional matrix was obtained by processing the data based on the number of features (47) and sampling frequency (50 s/time). Given the presence of redundant variables, which can interfere with model accuracy and increase model operation time, feature selection was performed using MIC. Each of the 47 features was individually computed to derive the corresponding MIC value against the remaining features (\(mic,mic\in[0,1]\)), where a value close to 1 indicates a stronger correlation among the features, indicating the need for feature elimination. The top k features with the strongest correlation were removed by sorting the MIC values in descending order, and 30 features were retained with a k value of 17, which was determined through trial and error to yield the best results. In order to establish robustness and optimal performance of the proposed approach, a hyperparameter tuning process was conducted using a trial and error method. The hyperparameters, including batch size, dropout rate, and learning rate, were systematically adjusted to identify the configuration that yields the best results. Batch size, a critical hyperparameter influencing the convergence and generalization of the model, was fine-tuned through a series of experiments. Starting with a conservative value, we gradually increased the batch size while monitoring the training dynamics performance. This process allowed us to determine an optimal batch size that strikes a balance between computational efficiency and model convergence. The value of dropout is typically in the range of 0.2 to 0.5. If the dropout rate is too small (e.g., 0.1 or lower), it might not have a strong regularizing effect. Conversely, if the dropout rate is too large (e.g., 0.7 or higher), it may hinder the network's ability to learn and generalize effectively. After conducting multiple tests, the appropriate value for Dropout can be determined. Furthermore, the parameter of learning rate, which impacts convergence speed and model stability, was rigorously tested. We employed a learning rate schedule, gradually reducing the learning rate during the training epochs and evaluating its impact on training dynamics, ensuring stable convergence without the risk of divergence. Three comparative algorithms are utilized to compare and validate the effectiveness of the proposed method in this study. 1D-CNN model: 1D-CNN encompasses a convolutional layer, pooling layer, fully connected layer, and classification output layer. In this experiment, three convolutional layers are used, and each convolutional layer executes two convolutional calculations. The pooling layer adopts maximum pooling after each convolutional layer. Dropout is implemented before the fully connected layer, with a value of \(Dropout=0.3\), \(Epochs=2,Batchsize=400\). The optimizer used in this study is Adam, with a learning rate set at \(1\times 10^{-4}\). The original power plant data are preprocessed and split into training and test sets in a 7:3 ratio. Following this step, the features are non-linearly combined using the fully connected layer, and the coal categories are obtained through the classification output layer. The results of model training and testing are recorded. 2. MIC-CNN model: During the establishment of the MIC-CNN coal identification model, the data are initially subjected to feature selection using MIC, followed by data pre-processing and division into a training set and a test set at a ratio of 7:3. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Number of Samples** & **Coal Type** & **Label** \\
**Training Set/Test Set** & & \\ \hline
40,320/17,280 & Coal type1 & 0 \\
40,320/17,280 & Coal type2 & 1 \\
40,320/17,280 & Coal type3 & 2 \\
40,320/17,280 & Coal type4 & 3 \\
40,320/17,280 & Coal type5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coal type dataset.