Chinese Journal of Chemical Engineering ›› 2020, Vol. 28 ›› Issue (12): 3070-3078.doi: 10.1016/j.cjche.2020.08.021

• Process Systems Engineering and Process Safety • Previous Articles     Next Articles

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Shutian Chen, Qingchao Jiang, Xuefeng Yan   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2020-02-24 Revised:2020-07-05 Online:2020-12-28 Published:2021-01-11
  • Contact: Qingchao Jiang
  • Supported by:
    This work was supported in part by National Natural Science Foundation of China under Grants 61973119 and 61603138, in part by Shanghai Rising-Star Program under Grant 20QA1402600, in part by the Open Funding from Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems under Grant SKDN202001, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

Abstract: Reliable process monitoring is important for ensuring process safety and product quality. A production process is generally characterized by multiple operation modes, and monitoring these multimodal processes is challenging. Most multimodal monitoring methods rely on the assumption that the modes are independent of each other, which may not be appropriate for practical application. This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring. This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data. This process enables the identified modes to reflect the stability of actual working conditions, improve mode identification accuracy, and enhance monitoring reliability in cases of mode overlap. Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach in multimodal process monitoring with mode overlap.

Key words: Multimodal process monitoring, Gaussian mixture model, State transition matrix, Process control, Process systems, Systems engineering