Design of Simulation System for Batch Process

2011 ◽  
Vol 55-57 ◽  
pp. 1693-1698
Author(s):  
Zhong Hu Yuan ◽  
Xiao Yu Qi ◽  
Xiao Wei Han
Keyword(s):  
Fault Diagnosis ◽  
Production Process ◽  
Batch Process ◽  
Simulation System ◽  
Experimental Simulation ◽  
Research Focus ◽  
Industrial Control ◽  
Simulation Data ◽  
Process Fault Diagnosis ◽  
Penicillin Fermentation

Process monitoring and fault diagnosis of batch process is a research focus in the industrial control field. In this paper, penicillin fermentation is taken as the research background, a visual batch process simulation system is designed based on mathematical models of an actual production process. By introducing different fault signals to the penicillin fermentation simulation process, the designed system can be used to simulate the real penicillin fermentation production process clearly. In the end, an ideal experimental simulation data for batch process fault diagnosis is provided.

Phase partition and identification based on kernel entropy component analysis and multi-class support vector machines-fireworks algorithm for multi-phase batch process fault diagnosis

2020 ◽  
Vol 42 (12) ◽  
pp. 2324-2337
Author(s):  
Min Zhang ◽  
Ruiqi Wang ◽  
Zhenyu Cai ◽  
Wenming Cheng
Keyword(s):  
Fault Diagnosis ◽  
Batch Process ◽  
Support Vector ◽  
Fireworks Algorithm ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Structure Similarity ◽  
Multi Phase ◽  
Process Fault Diagnosis ◽  
Entropy Component

For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.

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