摘要
利用探索性数据分析法中的箱线图法对选矿数据进行分析,找到最优的精矿品位、回收率组合。并将其作为输入因素输入到主成分分析-神经网络模型进行验证预测,找到在此组合下的更合理的药剂用量。用此药剂量指导生产,可以得到更好的精矿品位和回收率。
The best compounding of grade and recovery of concentrate was found through analysis of mineral processing data using box line diagram of exploratory data analyzing method, which were inputted to the ANN BP model based on principal component analysis method to find the more reasonable dosage of reagents at the condition of best compounding, then the dosage can be validated in practical producing and used to gain higher grade and recovery of concentrate.
出处
《中国矿业》
北大核心
2009年第11期107-109,共3页
China Mining Magazine
关键词
箱线图
主成分分析
神经网络模型
box line diagram
principal component analysis method
ANN BP model
