scholarly journals Two-Dimensional, Two-Layer Quality Regression Model Based Batch Process Monitoring

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 43
Author(s):  
Luping Zhao ◽  
Xin Huang
Keyword(s):  
Regression Model ◽  
Layer Structure ◽  
Batch Process ◽  
Batch Processes ◽  
Quality Prediction ◽  
Two Dimensional ◽  
Batch Process Monitoring ◽  
Multi Phase ◽  
The Relationship ◽  
Multi Mode

In this paper, a two-dimensional, two-layer quality regression model is established to monitor multi-phase, multi-mode batch processes. Firstly, aiming at the multi-phase problem and the multi-mode problem simultaneously, the relations among modes and phases are captured through the analysis between process variables and quality variables by establishing a two-dimensional, two-layer regression partial least squares (PLS) model. The two-dimensional regression traces the intra-batch and inter-batch characteristics, while the two-layer structure establishes the relationship between the target process and historical modes and phases. Consequently, online monitoring is carried out for multi-phase, multi-mode batch processes based on quality prediction. In addition, the online quality prediction and monitoring results based on the proposed method and those based on the traditional phase mean PLS method are compared to prove the effectiveness of the proposed method.

scholarly journals Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1321
Author(s):  
Luping Zhao ◽  
Xin Huang ◽  
Hao Yu
Keyword(s):  
Process Monitoring ◽  
Single Mode ◽  
Batch Processes ◽  
Quality Analysis ◽  
Quality Prediction ◽  
Monitoring Strategy ◽  
Related Information ◽  
Multi Phase ◽  
Different Characteristics ◽  
Multi Mode

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.

Batch process modeling by using temporal feature and Gaussian mixture model

2019 ◽  
Vol 42 (6) ◽  
pp. 1204-1214
Author(s):  
Wei Guo ◽  
Tianhong Pan ◽  
Zhengming Li ◽  
Shan Chen
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Batch Process ◽  
Gaussian Mixture ◽  
Batch Processes ◽  
Separable Space ◽  
Temporal Features ◽  
Learning Techniques ◽  
Multi Phase ◽  
Work First

Multi-model/multi-phase modeling algorithm has been widely used to monitor the product quality in complicated batch processes. Most multi-model/ multi-phase modeling methods hinge on the structure of a linearly separable space or a combination of different sub-spaces. However, it is impossible to accurately separate the overlapping region samples into different operating sub-spaces using unsupervised learning techniques. A Gaussian mixture model (GMM) using temporal features is proposed in the work. First, the number of sub-model is estimated by using the maximum interval process trend analysis algorithm. Then, the GMM parameters constrained with the temporal value are identified by using the expectation maximization (EM) algorithm, which minimizes confusion in overlapping regions of different Gaussian processes. A numerical example and a penicillin fermentation process demonstrate the effectiveness of the proposed algorithm.

Improved Batch Process Monitoring and Quality Prediction Based on Multiphase Statistical Analysis

2008 ◽  
Vol 47 (3) ◽  
pp. 835-849 ◽  
Author(s):  
Chunhui Zhao ◽  
Fuli Wang ◽  
Zhizhong Mao ◽  
Ningyun Lu ◽  
Mingxing Jia
Keyword(s):  
Statistical Analysis ◽  
Process Monitoring ◽  
Batch Process ◽  
Quality Prediction ◽  
Batch Process Monitoring

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.

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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