• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Qi, Yongsheng (Qi, Yongsheng.) | Wang, Pu (Wang, Pu.) | Gao, Xuejin (Gao, Xuejin.) (学者:高学金)

收录:

EI

摘要:

In industrial manufacturing, most batch processes are multi-phase and uneven-length batch processes in nature, phase-based approaches are intuitively well suited for batch process monitoring and quality prediction. In this paper, a new strategy is proposed using multi-phase dynamic partial least squares (DPLS) for batch processes monitoring and quality prediction. Firstly, batch process data was automatically divided into several phases using Gaussian mixture model (GMM) clustering arithmetic. Then runtorun variations among different instances of a phase are synchronized by using dynamic time warping (DTW). Finally, multi-phase DPLS model is built between each phase and the quality variables. The proposed method easily handles the following problems: (1) static single model; (2) process and its model do not match; (3) linear method may not be efficient in compressing and extracting dynamic nonlinear process data. The idea and algorithm are illustrated with respect to the typical data collected from a benchmark simulation of fed-batch penicillin fermentation production. The simulation results demonstrate the effectiveness of the proposed method in comparison to original DPLS. © 2011 Chinese Assoc of Automati.

关键词:

Batch data processing Forecasting Gaussian distribution Least squares approximations Process control Process monitoring

作者机构:

  • [ 1 ] [Qi, Yongsheng]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Qi, Yongsheng]College of Electric Power, Inner Mongolia University of Technology, Huhhot 010051, China
  • [ 3 ] [Wang, Pu]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Gao, Xuejin]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

来源 :

年份: 2011

页码: 5258-5263

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

归属院系:

在线人数/总访问数:120/3603920
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司