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科技导报
| 研究论文 2015, 33(15): 27-31
超大能力超细全尾砂长距离自流输送临界流速ELM预测
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王新民, 张国庆, 张钦礼, 李帅
作者信息
ELM prediction of critical flow velocity in large-capacity long self-flowing transportation of super fine tailings slurry
Affiliations
出版时间: 2015-08-13
doi: 10.3981/j.issn.1000-7857.2015.15.003
文章导航
为准确预测司家营铁矿超大能力超细全尾砂浆体长距离管道自流输送的临界流速,对比传统的BP 神经网络、支持向量机(SVM),建立了以管道直径、物料平均粒径、浆体体重和体积浓度为输入因子,临界流速为输出因子的极限学习机(ELM)预测新模型。研究结果表明,ELM 模型与SVM 模型的相对误差均控制在5%以内,远低于BP 神经网络模型的9.56%。由于隐层节点参数均随机选取且无需调节,使得ELM 算法在隐层节点数为110 和200 时,训练时间仅为0.02 s 和0.05 s,远少于同节点状态SVM 模型的0.04 s 和0.095 s,且隐含节点数越多,训练时间差距越大,运算效率越高。
超大能力
/
临界流速
/
极限学习机
/
预测精度
/
运算效率
To accurately predict the critical flow velocity of Sijiaying's large-capacity super fine tailings slurry in long self-flowing transportation, a new ELM prediction model is developed. The ELM model takes pipe diameter, grain diameter, slurry density and volume concentration as input factors, and critical flow velocity as output factor. By comparing it with traditional BP neural networks and support vector machines (SVMs), the superiority of ELM in improving precision and efficiency is demonstrated. It is revealed that ELM model's relative error is blow 5%, which is lower than BP model's 9.56%. With the hidden node number being 110 and 200, the training times of ELM are 0.02 s and 0.05 s, respectively, which both are far below the corresponding SVM's 0.04 s and 0.095 s. The random choice and good adaptability of hidden node number makes the new ELM model superior in improving precision and efficiency.
large-capacity
/
critical flow velocity
/
extreme learning machine
/
prediction accuracy
/
computational efficiency
王新民, 张国庆, 张钦礼, 李帅.
超大能力超细全尾砂长距离自流输送临界流速ELM预测.
科技导报,
2015
, 33
(15)
: 27
-31
.
DOI: 10.3981/j.issn.1000-7857.2015.15.003
WANG Xinmin, ZHANG Guoqing, ZHANG Qinli, LI Shuai.
ELM prediction of critical flow velocity in large-capacity long self-flowing transportation of super fine tailings slurry[J].
Science & Technology Review ,
2015
, 33
(15)
: 27
-31
.
DOI: 10.3981/j.issn.1000-7857.2015.15.003
2015年第33卷第15期
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文章信息
doi: 10.3981/j.issn.1000-7857.2015.15.003
接收时间：2014-11-17
首发时间：2015-08-28
出版时间：2015-08-13
收稿日期：2014-11-17
修回日期：2015-04-20
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2015.15.003
引用本文
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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