Just-in-time local modeling for flooding velocity prediction in packed towers

doi:10.11949/j.issn.0438-1157.20151956

CIESC Journal ›› 2016, Vol. 67 ›› Issue (3): 1070-1075.DOI: 10.11949/j.issn.0438-1157.20151956

Just-in-time local modeling for flooding velocity prediction in packed towers

ZHOU Lichun¹, JIN Xin², LIU Yi², GAO Zengliang², JIN Fujiang¹

1. School of Information Science and Engineering, Huaqiao University, Xiamen 361021, Fujian, China;
2. Engineering Research Center of Process Equipment and Remanufacturing (Ministry of Education), Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China

Received:2015-12-24 Revised:2015-12-30 Online:2016-01-12 Published:2016-03-05
Contact: 67
Supported by:
supported by the Natural Science Foundation of China (61273069) and the Fundamental Research Funds for the Central Universities (JB-ZR1204).

即时局部建模在填料塔液泛气速预测的应用

周丽春¹, 靳鑫², 刘毅², 高增梁², 金福江¹

1. 华侨大学信息科学与工程学院, 福建厦门 361021;
2. 浙江工业大学过程装备及其再制造教育部工程研究中心, 浙江杭州 310014

通讯作者: 刘毅
基金资助:
国家自然科学基金项目(61273069);中央高校基本科研业务费专项(JB-ZR1204)。

Abstract

Abstract:

Packed towers have been widely used in industrial productions. It is important to accurately predict the flooding velocity of packed towers. In industrial practice, there are many kinds of packings which can show different characteristics. Only using a single global model is still difficult to achieve satisfied prediction results. To overcome the problem, a new local modeling method is proposed to predict the flooding velocity. First, a recursive algorithm of ridge extreme learning machine with nodes growing is formulated, which can update the online model in an efficient manner. Moreover, using the just-in-time learning manner, the local recursive ridge parameter extreme learning machine (LRRELM)-based online modeling method is proposed. The experimental results show that the LRRELM model can explore more related information among data and thus to obtain better and more reliable prediction performance, compared with the related global models.

Key words: nonlinear systems, dynamic modeling, neural networks, recursive algorithm, extreme learning machine, systems engineering

摘要：

填料塔在工业生产中应用广泛,准确预测填料塔的液泛气速具有重要的应用价值。实际的填料类型多种多样,获取的填料数据也存在差异,单一全局模型的预测效果受到一定的限制。首先给出了岭参数极限学习机模型及其节点增加的递推算法,以有效更新在线模型。结合即时学习方式,提出了局部递推岭参数极限学习机在线建模方法,用于填料塔液泛气速的预测。实验结果表明所提出方法能更充分挖掘数据间的相关信息,预测效果优于相应的全局模型。

关键词: 非线性系统, 动态建模, 神经网络, 递推算法, 极限学习机, 系统工程

CLC Number:

TP301.6
TQ02

ZHOU Lichun, JIN Xin, LIU Yi, GAO Zengliang, JIN Fujiang. Just-in-time local modeling for flooding velocity prediction in packed towers[J]. CIESC Journal, 2016, 67(3): 1070-1075.

周丽春, 靳鑫, 刘毅, 高增梁, 金福江. 即时局部建模在填料塔液泛气速预测的应用[J]. 化工学报, 2016, 67(3): 1070-1075.

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Just-in-time local modeling for flooding velocity prediction in packed towers

即时局部建模在填料塔液泛气速预测的应用

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