SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2018, Vol. 26 ›› Issue (8): 1736-1749.DOI: 10.1016/j.cjche.2018.06.009

• Selected Papers from the 28th Chinese Process Control Conference • 上一篇    下一篇

Total plant performance evaluation based on big data: Visualization analysis of TE process

Mengyao Li, Wenli Du, Feng Qian, Weiming Zhong   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • 收稿日期:2017-10-04 修回日期:2018-04-29 出版日期:2018-08-28 发布日期:2018-09-21
  • 通讯作者: Wenli Du,E-mail addresses:wldu@ecust.edu.cn;Feng Qian,E-mail addresses:fqian@ecust.edu.cn
  • 基金资助:

    Supported by the National Natural Science Foundation of China (61590923, 61422303, 21376077).

Total plant performance evaluation based on big data: Visualization analysis of TE process

Mengyao Li, Wenli Du, Feng Qian, Weiming Zhong   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2017-10-04 Revised:2018-04-29 Online:2018-08-28 Published:2018-09-21
  • Contact: Wenli Du,E-mail addresses:wldu@ecust.edu.cn;Feng Qian,E-mail addresses:fqian@ecust.edu.cn
  • Supported by:

    Supported by the National Natural Science Foundation of China (61590923, 61422303, 21376077).

摘要: The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

关键词: Performance evaluation, Structured logic rules, Hierarchical framework, Multidimensional visualization, KPCA-SOM

Abstract: The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

Key words: Performance evaluation, Structured logic rules, Hierarchical framework, Multidimensional visualization, KPCA-SOM