中国科技核心期刊
美国化学文摘社（CAS）数据库
美国EBSCO学术数据库
日本科学技术振兴机构数据库(JST)
基于DE-SVM算法的淘洗机选矿过程优化研究
- 作者：
- 熊杨|董克彬
- 作者单位：
- 中国地质调查局军民融合地质调查中心|中国地质调查局军民融合地质调查中心
基金项目：
- isnull
详细信息：
- 作者简介：
- 熊杨（1982—），男，高级工程师，硕士，研究方向为机器学习及人工智能；E-mail：qcsg126@126.com
- 通讯作者：
- isnull
- PDF下载
Optimization study of mineral processing in elutriation machine using DE-SVM algorithm
- English Author：
- Civil-Military Integration Center of China Geological Survey|Civil-Military Integration Center of China Geological Survey
- Unit：
- 摘要
- 在线预览
- 参考文献
摘要:
研究了基于差分进化算法（DE）和支持向量机（SVM）的混合算法在淘洗机选矿过程中的应用。针对选矿过程中淘洗机选矿质量差、效率低等问题，提出了DE-SVM算法，并构建了相应的选矿质量预测模型。试验结果表明，DE-SVM算法的平均预测准确率和预测精准率分别为93.7 %和95.6 %，基于该算法的淘洗机选矿质量预测模型的预测精矿回收率和预测精矿品位绝对误差分别为98.4 %和0.309 %。相较于其他算法和模型，DE-SVM算法和基于该算法的淘洗机选矿质量预测模型表现出显著优势，为提高淘洗机选矿质量和效率提供了有效方法。
关键词:
DE算法；SVM；选矿；淘洗机；过程优化
Abstract:
This study explores the application of a hybrid algorithm based on Differential Evolution (DE) and Support Vector Machine (SVM) in the mineral processing of elutriation machine.To address the problems of low quality and efficiency in metal beneficiation during elutriation,the DE-SVM algorithm was proposed,and a corresponding beneficiation quality prediction model was constructed.Experimental results showed that the average prediction accuracy and precision of the DE-SVM algorithm were 93.7 % and 95.6 %,respectively.The predicted concentrate recovery rate and the absolute error of predicted concentrate grade using the model were 98.4 % and 0.309 %,respectively.Compared with other algorithms and models,the DE-SVM algorithm and its associated elutriation machine beneficiation quality prediction model demonstrated significant advantages,providing an effective method to improve the quality and efficiency of precious metal beneficiation.
Keywords:
DE algorithm;SVM;beneficiation;elutriation machine;process optimization
