湿法冶金2025，Vol.44Issue(5)：698-705,8.DOI:10.13355/j.cnki.sfyj.2025.05.016
基于集成学习算法的金浸出率智能预测模型设计研究
Design of Intelligent Prediction Model for Gold Leaching Rate Based on Ensemble Learning Algorithms
周一帆1
作者信息
- 1. 驻马店职业技术学院信息工程学院,河南驻马店 463000||鸿蒙生态开发重点实验室,河南 驻马店 463000
- 折叠
摘要
Abstract
To address the problem of low prediction accuracy and large noise data in gold leaching rate prediction,a high-efficiency intelligent prediction method was proposed.Combining the improved XGBoost and LightGBM models,a dynamic learning rate,regularization optimization,and Bayesian optimization algorithm were used to design an ensemble learning model for gold leaching rate prediction.The results show that the ensemble learning model has a significantly improved prediction accuracy compared to traditional single models,and its MSE is about 28.8％ and 22.9％ lower than XGBoost and LightGBM,respectively.The model has high stability in real production environments.The ensemble learning model can effectively improve the prediction accuracy of gold leaching rate and has strong practical application value.关键词
金浸出率/预测/集成学习/XGBoost模型/LightGBM模型/数值仿真Key words
gold leaching rate/prediction/ensemble learning/XGBoost model/LightGBM model/numerical simulation分类
通用工业技术引用本文复制引用
周一帆..基于集成学习算法的金浸出率智能预测模型设计研究[J].湿法冶金,2025,44(5):698-705,8.
