Multimode Operating Performance Visualization and Nonoptimal Cause Identification

Processes

Wenli Du¹  Yuhui Ying¹  Minglei Yang¹  Zhi Li¹ 

[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;

关键词: multimode process;    performance assessment;    subtractive clustering;    multi-space principal component analysis;    self-organizing map;   

DOI  :  10.3390/pr8010123

来源: DOAJ

【 摘 要 】

In the traditional performance assessment method, different modes of data are classified mainly by expert knowledge. Thus, human interference is highly probable. The traditional method is also incapable of distinguishing transition data from steady-state data, which reduces the accuracy of the monitor model. To solve these problems, this paper proposes a method of multimode operating performance visualization and nonoptimal cause identification. First, multimode data identification is realized by subtractive clustering algorithm (SCA), which can reduce human influence and eliminate transition data. Then, the multi-space principal component analysis (MsPCA) is used to characterize the independent characteristics of different datasets, which enhances the robustness of the model with respect to the performance of independent variables. Furthermore, a self-organizing map (SOM) is used to train these characteristics and map them into a two-dimensional plane, by which the visualization of the process monitor is realized. For the online assessment, the operating performance of the current process is evaluated according to the projection position of the data on the visual model. Then, the cause of the nonoptimal performance is identified. Finally, the Tennessee Eastman (TE) process is used to verify the effectiveness of the proposed method.

【 授权许可】

Unknown