[1]仲兆平,王春华,李睿,等.喷动流化床射流穿透度智能拟合[J].东南大学学报(自然科学版),2010,40(1):139-143.[doi:10.3969/j.issn.1001-0505.2010.01.026]
　Zhong Zhaoping,Wang Chunhua,Li Rui,et al.Intelligent fitting of jet penetration depth in spout-fluid bed[J].Journal of Southeast University (Natural Science Edition),2010,40(1):139-143.[doi:10.3969/j.issn.1001-0505.2010.01.026]
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喷动流化床射流穿透度智能拟合()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
40
期数:
2010年第1期
页码:
139-143
栏目:
化学化工
出版日期:
2010-01-20

文章信息/Info

Title:
Intelligent fitting of jet penetration depth in spout-fluid bed
作者:
仲兆平1;  王春华1;  李睿1;  鄂加强2
1 东南大学能源与环境学院,南京 210096; 2 湖南大学机械与运载工程学院,长沙 410082
Author(s):
Zhong Zhaoping1;  Wang Chunhua1;  Li Rui1;  E Jiaqiang2
1 School of Energy and Environment, Southeast University, Nanjing 210096, China
2 College of Mechanical and Automotive Engineering, Hunan University, Changsha 410082, China
关键词:
喷动流化床;  射流穿透度;  最小二乘支持向量机;  自适应遗传算法
Keywords:
spout-fluid bed;  jet penetration depth;  least square support vector machine;  adaptive genetic algorithm
分类号:
TQ051
DOI:
10.3969/j.issn.1001-0505.2010.01.026
摘要:
在三维冷态试验台架上对喷动流化床射流穿透度进行了实验研究,得出了射流穿透度随喷口尺寸、载气密度、喷动气速的增大而增大,随静止床高、颗粒尺寸、颗粒密度、流化气率、载气黏度的增大而减小的结论.在实验研究的基础上利用最小二乘支持向量机对射流穿透度与喷动流化床主要设计参数之间的数值关系进行了智能拟合,并利用自适应遗传算法优化了最小二乘支持向量机的初始参数. 通过15个预测样本的检验,最小二乘支持向量机模型的预测平均相对误差减小至4.0%,其性能大大优于常用的经验公式以及神经网络.
Abstract:
Experiments for jet penetration depth measurement was carried out in 3-dimensional cold-testing bench of spout-fluid bed. It is concluded that jet penetration depth increases with the increase of nozzle diameter, gas density and spouted flow velocity and decreases with the increase of static bed height, particle size, particle density and gas flow rate. On the basis of experiments, relation between jet penetration depth and the main designing parameters is intelligently fitted by least square support vector machine. Adaptive genetic algorithm was applied into the optimization of initial parameters of least square support vector machine. Through examination of 15 forecasting samples, the average relative error is reduced to 4.0% by the forecasting model of least square support vector machine, which is more superior to commonly used empirical formula and neural network.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 仲兆平(1965—),男,博士,教授,博士生导师,zzhong@seu.edu.cn.
基金项目: 国家重点基础研究发展计划(973计划)资助项目(2007CB210208)、国家自然科学基金资助项目(50776019).
引文格式: 仲兆平,王春华,李睿,等. 喷动流化床射流穿透度智能拟合[J]. 东南大学学报:自然科学版,2010,40(1):139-143.[doi:10.3969/j.issn. 1001-0505.2010.01.026]
更新日期/Last Update: 2010-01-20