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

ZHOU Lichun; JIN Xin; LIU Yi; GAO Zengliang; JIN Fujiang

doi:10.11949/j.issn.0438-1157.20151956

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2025-12-10

- Just-in-time local modeling for flooding velocity prediction in packed towers
- CIESC Journal Issue 3, Pages: 1070-1075(2016)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151956
  CLC： P301.6TQ02
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
移动端阅览
ZHOU Lichun, JIN Xin, LIU Yi, et al. Just-in-time local modeling for flooding velocity prediction in packed towers[J]. CIESC Journal, 2016, (3): 1070-1075.
DOI：

ZHOU Lichun, JIN Xin, LIU Yi, et al. Just-in-time local modeling for flooding velocity prediction in packed towers[J]. CIESC Journal, 2016, (3): 1070-1075. DOI： 10.11949/j.issn.0438-1157.20151956.

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。

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.

关键词

Keywords

references

Views

149

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A selective recursive method for online identification of nonlinear systems

Physics-informed spatio-temporal graph convolutional network for fermentation processes

A dual-stream multirate GRU soft sensor modeling based on data chunk strategy

ESO-based action dependent heuristic dynamic programming control and its application in a four-stage CSTR process

Soft sensor development based on deep extended variational autoencoder with just-in-time learning

Related Author

ZHOU Lichun

LIU Yi

JIN Fujiang

DING Zhuo

LIU Fei

WANG Zhiguo

ZHANG Zhen

SHI Xudong

Related Institution

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University

School of Internet of Things Engineering, Jiangnan University

Key Laboratory of Advanced Process Control for Industry (Ministry of Education), Jiangnan University

College of Information Science and Technology, Beijing University of Chemical Technology

Wuxi Advance Technologies

⁰