SCI和EI收录∣中国化工学会会刊

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (8): 1653-1661.DOI: 10.1016/j.cjche.2017.10.027

• Selected Papers from the Chinese Process Systems Engineering Annual Meeting 2017 • Previous Articles     Next Articles

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

Wende Tian, Guixin Zhang, Xiang Zhang, Yuxi Dong   

  1. College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
  • Received:2017-08-24 Online:2018-09-21 Published:2018-08-28
  • Supported by:

    Supported by the National Natural Science Foundation of China (21576143).

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

Wende Tian, Guixin Zhang, Xiang Zhang, Yuxi Dong   

  1. College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
  • 作者简介:Wende Tian,E-mail address:tianwd@qust.edu.cn
  • 基金资助:

    Supported by the National Natural Science Foundation of China (21576143).

Abstract: To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis (PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level (normally one alarm per minute). Finally, variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

Key words: Alarm threshold, Chemical process, PCA, Johnson transformation, Variable weight

摘要: To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis (PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level (normally one alarm per minute). Finally, variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

关键词: Alarm threshold, Chemical process, PCA, Johnson transformation, Variable weight