Research on Quality Control of Ore Processing Based on Xgboost and Decision Tree Algorithm

Xiyuan Liang

doi:10.54097/hset.v16i.2614

Authors

Xiyuan Liang

DOI:

https://doi.org/10.54097/hset.v16i.2614

Keywords:

XGBoost algorithm, decision tree algorithm, machine learning regression.

Abstract

Improving the quality of ore processing can directly or indirectly save non-renewable mineral resources and the energy required for processing, thus promoting energy saving and emission reduction and helping to achieve the goal of "double carbon". Ore processing is a complex process, in which voltage, water pressure and temperature, as important factors affecting ore processing, directly affect the quality of ore products. In order to explore the influence of temperature and other factors on ore quality and qualification rate, the corresponding model is established to solve the model of predicting four product quality indexes by system temperature, and fully consider the influence of other uncertain factors to predict the highest possible product quality index at similar temperature.

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