Prediction of Blasting Fragment Large Block Percentage Ratio Based on Ensemble Learning CatBoost Model

doi:10.12068/j.issn.1005-3026.2023.12.010

Abstract

Abstract: The problem of large block percentage ratio of blasting has always affected the production benefit of mine. The implementation of large block percentage ratio of blasting predication based on ensemble learning in machine learning is studied. Firstly， 36 groups of measured data collected from Wushan Copper Mine in Manzhouli are utilized as an example， and 10 kinds of characteristic data are sorted out. Then， the model is trained and tuned by cyclic training of the given parameters， and then the method of cross-validation grid search is used to perform secondary tuning， and the effect of the model after tuning is compared with random forest method， XGBoost model， LightGBM model， and CatBoost model. The results show that the prediction effect of CatBoost model， after two rounds of tuning， is significantly higher than that of other models. The precision rate 98.83% reaches， which proves that CatBoost model has a high prediction level after two rounds of tuning. At the same time， the feasibility of this method in the study of large block percentage ratio of blasting prediction is proved， which provides a reliable reference for blasting parameter design and large block percentage ratio of blasting optimization analysis.

Key words: large block percentage ratio prediction; machine learning; ensemble learning; blasting; CatBoost model

CLC Number:

TD235.3

JIN Chang-yu， YU Jia-qiang， WANG Qiang， CHEN Li-jun. Prediction of Blasting Fragment Large Block Percentage Ratio Based on Ensemble Learning CatBoost Model[J]. Journal of Northeastern University(Natural Science), 2023, 44(12): 1743-1750.

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