Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
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 fa...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/2 |
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| author | Dingchao Zhang Xin Xiong Chongyang Shao Yao Zeng Jun Ma |
| author_facet | Dingchao Zhang Xin Xiong Chongyang Shao Yao Zeng Jun Ma |
| author_sort | Dingchao Zhang |
| collection | DOAJ |
| description | 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<sup>2</sup> have increased by at least 5%. |
| format | Article |
| id | doaj-art-13d9f319a18f41e290590a0ff3f60df8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-13d9f319a18f41e290590a0ff3f60df82025-01-10T13:14:07ZengMDPI AGApplied Sciences2076-34172024-12-01151210.3390/app15010002Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTMDingchao Zhang0Xin Xiong1Chongyang Shao2Yao Zeng3Jun Ma4Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaThe 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<sup>2</sup> have increased by at least 5%.https://www.mdpi.com/2076-3417/15/1/2SAG milllong and short-term memorypower consumption predictionchannel attention |
| spellingShingle | Dingchao Zhang Xin Xiong Chongyang Shao Yao Zeng Jun Ma Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM Applied Sciences SAG mill long and short-term memory power consumption prediction channel attention |
| title | Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM |
| title_full | Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM |
| title_fullStr | Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM |
| title_full_unstemmed | Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM |
| title_short | Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM |
| title_sort | semi autogenous mill power consumption prediction based on cacn lstm |
| topic | SAG mill long and short-term memory power consumption prediction channel attention |
| url | https://www.mdpi.com/2076-3417/15/1/2 |
| work_keys_str_mv | AT dingchaozhang semiautogenousmillpowerconsumptionpredictionbasedoncacnlstm AT xinxiong semiautogenousmillpowerconsumptionpredictionbasedoncacnlstm AT chongyangshao semiautogenousmillpowerconsumptionpredictionbasedoncacnlstm AT yaozeng semiautogenousmillpowerconsumptionpredictionbasedoncacnlstm AT junma semiautogenousmillpowerconsumptionpredictionbasedoncacnlstm |