Application of Phase Division Based on Dissimilarity Index in Batch Process Monitoring

2012 ◽  
Vol 566 ◽  
pp. 134-139 ◽  
Author(s):  
Li Ying Jiang ◽  
Bao Jian Xu ◽  
Jian Hui Xi ◽  
Guo Xiu Fu
Keyword(s):  
Fermentation Process ◽  
Principal Component ◽  
Batch Process ◽  
Batch Processes ◽  
Time Slice ◽  
Monitoring Methods ◽  
Dissimilarity Index ◽  
Process Data ◽  
Batch Process Monitoring ◽  
Simulation Results

An important feature of batch process data is that many batch processes have multiple phases. Many different phased-based monitoring methods had been proposed. The key question of those methods is how to divide the phases of batch process. However, PCA-based methods of phase division that identify phases by extracting the first principal component of each time slice lead easily to high misclassification. In order to overcome the shortcoming of PCA-based methods, a novel phase-division method based on dissimilarity index is proposed. In proposed division method, integral information of each time slice is used to divide phases. The phase-based PCA is built in each phase to monitoring Penicillin fermentation process in order to verify performance of proposed method. The simulation results show that the proposed method is able to detect process faults more prompt and accurate than single MPCA model.

Fault Detection for Batch Processes Based on Segmentation MPCA

2014 ◽  
Vol 1030-1032 ◽  
pp. 1701-1708
Author(s):  
Ning Lv ◽  
Guang Yuan Bai ◽  
Lei Lei Jiang
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Detection Method ◽  
Piecewise Linear ◽  
Nonlinear Modeling ◽  
Principal Component ◽  
Batch Processes ◽  
Piecewise Linear Approximation ◽  
Process Data ◽  
Speed And Accuracy

Aiming at the dynamic characteristic of batch production process changes fast and the accurate modeling of it is difficult, so this paper proposes an intermittent fault detection method of the principal component analysis based on process segment. According to the different dynamic characteristics of process data, the process is divided into multiple stages, with the method of piecewise linear approximation of nonlinear modeling to model different stages of the process, in order to make up the deficiency of traditional MPCA fault diagnosis methods. Through the fault detection of the beer fermentation process experiments to verify that the method can detect process faults promptly and improve the speed and accuracy of process monitoring.

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part II—Assessing Detection Speed

2018 ◽  
Vol 57 (15) ◽  
pp. 5338-5350 ◽  
Author(s):  
Tiago J. Rato ◽  
Ricardo Rendall ◽  
Veronique Gomes ◽  
Pedro M. Saraiva ◽  
Marco S. Reis
Keyword(s):  
Process Monitoring ◽  
Batch Process ◽  
Monitoring Methods ◽  
Batch Process Monitoring ◽  
Systematic Methodology ◽  
Detection Speed

MONITORING OF BATCH PROCESSES WITH VARYING DURATIONS BASED ON THE HAUSDORFF DISTANCE

2006 ◽  
Vol 13 (03) ◽  
pp. 213-236 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA ◽  
ARIANE FERREIRA PORTO ROSA
Keyword(s):  
Hausdorff Distance ◽  
Batch Process ◽  
Batch Processes ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Innovative Methods ◽  
Batch Process Monitoring ◽  
On Line ◽  
Case Data

In some industrial situations, the classical assumption used in the batch process monitoring that all batches have equal durations and are synchronized does not hold. A batch process is carried out in sequential phases and a significant variability generally occurs in the duration of the phases such that events signifying the beginning or the end of a phase are generally misaligned in time within the various batches. The consequence is that the variable trajectories, in the different runs of the same batch process, are unsynchronized. In this case, data analysis from process for performing the multivariate statistical process control can be difficult. In this paper, we propose several innovative methods for the off-line and on-line monitoring of batch processes with varying durations, all based on the Hausdorff distance. These methods have been successfully tested on a simulated example and on an industrial case example. The conclusion is that these methods are able to efficiently discriminate between nominal and non-nominal batches.

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