# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
###### Abstract
[14] developed a new prediction model based on gene expression programming (GEP) to predict SAG mill power and selected feed moisture, mass flowrate, mill load cell weight, SAG mill solid percentage, inlet and outlet water to the SAG mill and work index as the input of GEP. The experimental results show that the GEP model is superior to nonlinear multiple regression (NLMR) in predicting mill power. [PERSON] et al. [15] established a random forest (RF) model using an industrial ball mill database to explore the relationship between power consumption and monitoring operating parameters, and select the ball charge and work index that are most relevant to the ball mill power consumption as the input of RF. Results show that RF significantly improves power consumption prediction accuracy compared to typical regression methods. As prediction tasks become more and more complex, a single model is no longer sufficient to meet the requirements of high accuracy, so hybrid models have been widely used in the prediction field. Based on federated learning (FL), [PERSON] et al. [16] proposed a personalized model using model-independent meta-learning (MAML), which greatly enhanced the personalization and compatibility of federated learning. In order to solve the client drift (CD) problem, the stochastic controlled average (SCA) algorithm is integrated as a federated aggregation method. Numerical results show that this method can provide high accuracy and robust performance even in the case of data heterogeneity and packet loss. In another study, [PERSON] et al. [17] explored the coupling characteristics of node networksand designed a multi-peak feature module based on an initialization strategy and gated temporal convolutional network (GTCN) to reveal the implicit coupling characteristics in node load data. Then, they proposed a new multi-objective neural network model that combines multi-modal feature modules with gated recurrent units (GRU) to simultaneously predict the load of multiple nodes. Simulation results show that this method can effectively capture the spatiotemporal coupling characteristics of multi-node load data and achieve an accurate prediction of node load. Due to the complexity of data, CNN-LSTM models with strong feature extraction capabilities and temporal dependency capture capabilities are increasingly used in prediction tasks. [PERSON] et al. [18] proposed a multi-level tandem learning (METL) model that combines variational mode decomposition (VMD), multi-head convolutional neural network (MCNN), stacked long short-term memory network (SLSTM) and attention mechanism (AT). Experimental results show that the METL model outperforms other single models and classification models and has higher accuracy in iron ore price prediction. [PERSON] et al. [19] proposed a new long short-term memory attention model for accurate prediction of drilling pump energy consumption. By combining the long short-term memory (LSTM) network with the attention mechanism, the model can effectively capture the complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and convolutional neural network (CNN) models show that the LSTM-Attention model outperforms these models in multiple evaluation indicators, significantly reduces the prediction error, and enhances robustness and adaptability. In summary, previous studies have mainly considered parameters with strong correlation with SAG mill power consumption, such as mass flow rate of SAG mill, weight of mill weighing sensor, solid content of SAG mill, inlet and outlet of SAG mill, etc., when predicting SAG mill power consumption, and mostly ignored parameters with weak correlation. These parameters contain key information that affects the power consumption prediction performance and are also of great significance for accurately predicting SAG mill power consumption.