Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost
Fully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts’ experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based o...
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MDPI AG
2025-01-01
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| author | Yue Wu Wenlong Sang Xiangang Cao Longlong He |
| author_facet | Yue Wu Wenlong Sang Xiangang Cao Longlong He |
| author_sort | Yue Wu |
| collection | DOAJ |
| description | Fully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts’ experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based on Extreme Gradient Boosting Regression Trees (XGBoost), which is developed based on the mapping relationships among geological parameters, fully mechanized mining face conditions, and the parameters of fully mechanized mining equipment. Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. Different optimization algorithms are used to optimize the hyperparameters of XGBoost, and the results show that the Whale Optimization Algorithm (WOA) outperforms other algorithms in terms of convergence speed and optimization effectiveness. By comparing different prediction algorithms, it is found that the WOA-XGBoost model achieves higher prediction accuracy on the test set, with an average absolute error of 0.0458, root mean square error of 0.1610, and a coefficient of determination (R<sup>2</sup>) of 0.9451. Finally, a RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment is established, which is suitable for lightly inclined mining faces. This model reduces input complexity, improves the selection speed, minimizes reliance on experts, and ensures prediction accuracy, providing an effective reference for the parameter selection of fully mechanized mining equipment. |
| format | Article |
| id | doaj-art-90a8da4e25584f548f34f0824afcfad5 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-90a8da4e25584f548f34f0824afcfad52025-01-24T13:20:37ZengMDPI AGApplied Sciences2076-34172025-01-0115273210.3390/app15020732Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoostYue Wu0Wenlong Sang1Xiangang Cao2Longlong He3School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaFully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts’ experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based on Extreme Gradient Boosting Regression Trees (XGBoost), which is developed based on the mapping relationships among geological parameters, fully mechanized mining face conditions, and the parameters of fully mechanized mining equipment. Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. Different optimization algorithms are used to optimize the hyperparameters of XGBoost, and the results show that the Whale Optimization Algorithm (WOA) outperforms other algorithms in terms of convergence speed and optimization effectiveness. By comparing different prediction algorithms, it is found that the WOA-XGBoost model achieves higher prediction accuracy on the test set, with an average absolute error of 0.0458, root mean square error of 0.1610, and a coefficient of determination (R<sup>2</sup>) of 0.9451. Finally, a RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment is established, which is suitable for lightly inclined mining faces. This model reduces input complexity, improves the selection speed, minimizes reliance on experts, and ensures prediction accuracy, providing an effective reference for the parameter selection of fully mechanized mining equipment.https://www.mdpi.com/2076-3417/15/2/732fully mechanized mining facethree machines selectionmachine learningfeature selectionparameter prediction model |
| spellingShingle | Yue Wu Wenlong Sang Xiangang Cao Longlong He Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost Applied Sciences fully mechanized mining face three machines selection machine learning feature selection parameter prediction model |
| title | Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost |
| title_full | Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost |
| title_fullStr | Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost |
| title_full_unstemmed | Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost |
| title_short | Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost |
| title_sort | research on the parameter prediction model for fully mechanized mining equipment selection based on rf woa xgboost |
| topic | fully mechanized mining face three machines selection machine learning feature selection parameter prediction model |
| url | https://www.mdpi.com/2076-3417/15/2/732 |
| work_keys_str_mv | AT yuewu researchontheparameterpredictionmodelforfullymechanizedminingequipmentselectionbasedonrfwoaxgboost AT wenlongsang researchontheparameterpredictionmodelforfullymechanizedminingequipmentselectionbasedonrfwoaxgboost AT xiangangcao researchontheparameterpredictionmodelforfullymechanizedminingequipmentselectionbasedonrfwoaxgboost AT longlonghe researchontheparameterpredictionmodelforfullymechanizedminingequipmentselectionbasedonrfwoaxgboost |