# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 4 Experiments
### Parameter Settings and Hyperparameters Experiment  
Due to the inherent randomness of deep learning algorithms, we conduct 10 trials for each of the eight models to enhance the reliability of the results, taking the average of the 10 trials as the final outcome. Based on the experiments conducted, this paper analyzes the performance of different models on the SAG mill grinding process dataset and evaluates the accuracy of the prediction results. The parameter settings for the CACN-LSTM model are shown in Table 3, which are obtained after multiple trials. The Adam optimization algorithm is employed to optimize the other parameters of the prediction model, with a learning rate of 0.001.  
After the general parameter setting, we conducted hyperparameter selection experiments to optimize the model. A grid search was performed for multiple hyperparameters. Table 4 is a summary of the parameters considered and the value ranges explored.  
As shown in Figure 7a, the model shows the best overall performance on the dataset when the number of units is 64. The performance decreases when the number of units is 128, indicating overfitting. Although the overall results are relatively good when the number is 32, 128 units provide the best overall accuracy.  
Similarly, in Figure 7b,c, due to non-convergence, the performance is not as good when the number of hidden layers is 1 as when the number of hidden layers is 2. When the number of hidden layers exceeds 2, the prediction accuracy decreases beyond 2 layers due to overfitting. A batch size of 64 provides the best prediction results, with smoother fluctuations and avoids overfitting.  
Our CACN-LSTM model achieved a high accuracy starting from 50 epochs. To ensure the accuracy of the prediction and keep it consistent with other models, we set the epoch to 100. Comparing the effect analysis of TCN, LSTM, GRU, CNN-LSTM, and CNN-GRU, the parameter settings are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Possible Values** & **Evaluation Metric** \\ \hline LSTM Units & 8, 16, 32, 64, 128 & \\ LSTM Layers & 1, 2, 3, 4, 5 & R\({}^{2}\) \\ Batch Size & 8, 16, 32, 64, 128 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter grid search set for LSTM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module** & **Hyper-Parameter** & **Value** \\ \hline \multirow{5}{*}{CACN} & kernel size & \(3\times 1\) \\  & number of convolutional layers & 2 \\  & number of convolutional kernels & [32; 64] \\  & stride & 1 \\  & fully connected layer1 & 16 \\  & fully connected layer2 & 64 \\  & Activation Function & ReLU \\ \hline \multirow{5}{*}{LSTM} & units & [64; 32] \\  & Batch\_size & 64 \\ \cline{1-1}  & time window & 22 \\ \cline{1-1}  & Activation Function & Tanh \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyper-parameter set for CACN-LSTM.  
It should be noted that in the above models, the CNN settings in CNN-LSTM and CNN-GRU are the same as those of CACN-LSTM. Since SVM and ARIMA have different parameters from deep learning algorithms, they are explained separately here. In SVM, the RBF kernel is selected as c = 10, epsilon = 0.1, and gamma ='scale'. In ARIMA, \(p\) = 2, d = 1, q = 2.