# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
###### Abstract  
The semi-autogenous (SAG) mill is crucial equipment in the beneficiation process, and power consumption is a key indicator of its operational status. Due to the complex and variable operating environment, the power consumption of the SAG mill has the characteristics of strong coupling of multiple factors, nonlinearity and uncertainty. In order to effectively extract the features that affect the mill power consumption prediction performance and dynamically adjust the weights of each feature, we propose a hybrid prediction model based on channel attention convolutional network (CACN) and long short-term memory (LSTM). The CACN-based network extracts high-dimensional features of input parameters and dynamically assigns weights to them to better capture the key features that characterize the power consumption of the SAG mill, and the LSTM captures long-term dependencies to enable accurate prediction of SAG mill power consumption. To validate the superiority of the proposed method, actual hourly power consumption data from a SAG mill in the beneficiation plant in Yunnan Province is utilized, and experiments are conducted comparing it with models such as GRU, ARIMA, SVM, LSTM, TCN, CNN-GRU, and CNN-LSTM. Experimental results confirm that the proposed model has better prediction performance than other models, and indicators such as R\({}^{2}\) have increased by at least 5%. S 202  
developing an accurate and practical SAG mill power consumption prediction model is crucial to guide the production of SAG mills. Different from the time series prediction based on a multi-input parameter problem, the grinding process of the SAG mill is a dynamic process, which is nonlinear and strongly coupled between input parameters. Fluctuations between various parameters will affect each other, which leads to different degrees of influence on power consumption [6]. In addition, due to the harsh working conditions, the collected SAG mill operating data is uncertain [7]. These factors hinder the accuracy of the power consumption for the SAG mill. In recent years, prediction methods based on deep learning have been widely used in the mining industry [8]. These methods predict mill power consumption through automatic feature extraction and nonlinear mapping. Compared with traditional mechanism models or artificial experience models, deep learning, as a universal approximator, can map complex functions and is therefore highly complex in processing. It shows significant advantages in non-linear prediction tasks [9]. [PERSON] et al. [10] proposed a method to predict SAG mill power consumption using real-time operating variables to significantly improve the power consumption prediction accuracy of SAG mills in mining operations. [PERSON] et al. [11] introduced a novel data-driven modeling methodology employing recurrent neural networks (RNNs) to attain auxiliary nonlinear feedback control of mills, and it shows great prediction results of energy consumption. A similar study proposed a new data-driven method called the multi-state approach (MRA) based on the deep neural network (DNN) to better predict the energy consumption of grinding mills [12]. However, due to the existence of numerous input parameters, many vital information affecting power consumption prediction cannot be captured in time. It leads to a negative impact on power consumption prediction for the SAG mill. In order to screen out key parameters, scholars have also carried out many studies. [PERSON] et al. [13] developed artificial neural network (ANN) and genetic algorithm neural network (GANN) models using a dataset of 185 full-scale SAG mills. Feed moisture, mass flow rate, mill load cell mass, SAG mill solids percentage, SAG mill water inlet and outlet, and operating index were selected as model inputs. Experiments have shown that GANN is more effective than ANN in predicting SAG power. [PERSON] et al.