|本期目录/Table of Contents|

[1]张钦礼,陈秋松,王新民,等.全尾砂絮凝沉降参数GA-SVM优化预测模型研究[J].中国安全生产科学技术,2014,10(5):24-30.[doi:10.11731/j.issn.1673-193x.2014.05.004]
 ZHANG Qinli,CHEN Qiusong,WANG Xinming,et al.Study on GA_SVM optimal prediction model on flocculating sedimentation parameter of unclassified tailings[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(5):24-30.[doi:10.11731/j.issn.1673-193x.2014.05.004]
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全尾砂絮凝沉降参数GA-SVM优化预测模型研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年5期
页码:
24-30
栏目:
学术论著
出版日期:
2014-05-31

文章信息/Info

Title:
Study on GA_SVM optimal prediction model on flocculating sedimentation parameter of unclassified tailings
文章编号:
20140504
作者:
张钦礼 陈秋松 王新民 肖崇春
(中南大学 资源与安全工程学院,湖南 长沙 410083)
Author(s):
ZHANG QinliCHEN QiusongWANG XinmingXIAO Chongchun
(School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China)
关键词:
充填全尾砂絮凝沉降支持向量机遗传算法
Keywords:
filling unclassified tailings flocculating sedimentation support vector machine genetic algorithm
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2014.05.004
文献标志码:
A
摘要:
为了得到经济、高效的絮凝沉降参数,建立GA_SVM预测模型进行优化选择。在优选过程中,以供砂浓度、絮凝剂单耗和絮凝剂添加浓度作为输入因子,以沉降速度作为综合输出因子,通过室内试验,建立训练、验证样本集;建立支持向量机(SVM)回归预测模型,用训练集对模型进行训练,进而以验证集预测值的均方误差作为适应度函数,通过遗传算法(GA)对SVM模型参数进行优化选择,应用优化得到的SVM模型对絮凝沉降参数进行预测、优化。以湖南某铅锌银矿为例,通过建立的GA_SVM模型对全尾砂絮凝沉降参数进行预测,优选出该矿最佳絮凝沉降参数为:供砂浓度20%-25%,絮凝剂单耗8g/t,添加浓度009%。经实验对比,该模型对絮凝沉降参数预测结果的相对误差能控制在5%左右,精确度较高,可以作为絮凝沉降参数优选的一种新思路
Abstract:
A GA_SVM model was established to optimize the flocculating sedimentation parameters. The tailings concentration, flocculant consumption and flocculant concentration were used as the input parameters and the sedimentation speed was confirmed to be the synthesized output parameter. Some training and validating samples were established through indoor experiment. Then, for predicting flocculating sedimentation parameters, a support vector machine (SVM) regression model was established. The mean square error of the value was made as a fitness function. Then, the model parameters were optimized through the genetic algorithm (GA). GA_SVM model was used in some mine, and the results showed that the best tailings concentration, flocculant consumption and flocculant concentration are 20%~25%, 10g/t and 0.09%. Comparing with the experiment results, the relative error of prediction result can be controlled at about 5%. The application indicates that this mode makes good effect, and it provides a new method to optimize the flocculating sedimentation parameters.

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备注/Memo

备注/Memo:
国家“十二五”科技支撑计划项目(2012BAC09B02)
更新日期/Last Update: 2014-05-31