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 R2 have increased by at least 5%.

Keywords

• SAG mill
• long and short-term memory
• power consumption prediction
• channel attention