SCI和EI收录∣中国化工学会会刊

›› 2014, Vol. 22 ›› Issue (7): 828-836.DOI: 10.1016/j.cjche.2014.05.003

• SOFT SENSOR • 上一篇    下一篇

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Weiming Shao1, Xuemin Tian1, Ping Wang1,2   

  1. 1. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao 266580, China;
    2. State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Huadong), Qingdao 266580, China
  • 收稿日期:2013-06-25 修回日期:2013-11-27 出版日期:2014-07-28 发布日期:2014-08-23
  • 通讯作者: Xuemin Tian
  • 基金资助:
    Supported by the National Natural Science Foundation of China (61273160) and the Fundamental Research Funds for the Central Universities (14CX06067A, 13CX05021A).

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Weiming Shao1, Xuemin Tian1, Ping Wang1,2   

  1. 1. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao 266580, China;
    2. State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Huadong), Qingdao 266580, China
  • Received:2013-06-25 Revised:2013-11-27 Online:2014-07-28 Published:2014-08-23
  • Supported by:
    Supported by the National Natural Science Foundation of China (61273160) and the Fundamental Research Funds for the Central Universities (14CX06067A, 13CX05021A).

摘要: Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation, which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted. Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

关键词: Local learning, Online soft sensing, Partial least squares, F-test, Multi-output process, Process state division

Abstract: Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation, which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted. Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

Key words: Local learning, Online soft sensing, Partial least squares, F-test, Multi-output process, Process state division