Multivariate statistical monitoring of multiphase batch processes with between‐phase transitions and uneven operation durations

Yao, Yuan; Dong, Wen‐Kui; Zhao, Luping; Gao, Furong

doi:10.1002/cjce.21617

Cited by 15 publications

(13 citation statements)

References 46 publications

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“…Each unfolding method has its own advantages, so the method to choose depends on the characteristics of the batch processes. The papers published in the last decade can be divided into three groups: (i) papers that applied the existing continuous process approaches to batch process monitoring to handle the non‐Gaussian problem, nonlinear problem, dynamic problem, and/or multimode problem; (ii) papers that focused on handling the uneven duration problem in batch processes; and (iii) papers that introduce industry applications of these algorithms …”

Section: Issues In Process Monitoringmentioning

confidence: 99%

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

et al. 2018

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Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over the past decade. In this paper, several issues related to MSPM methods are reviewed and studied. First, the annual publication numbers of journal articles concerning MSPM are provided to show the active development of this important research field and to point out several promising directions in the future. Second, the annual numbers of patents are also shown to demonstrate the practicality of different MSPM methods. Particularly, this paper also lists and analyzes the number of MSPM‐related publications in China. The statistics indicate that Chinese researchers and engineers may have different viewpoints from those of other countries, which results in different development trends of MSPM in China.

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Section: Issues In Process Monitoringmentioning

confidence: 99%

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

et al. 2018

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“…Undoubtedly, these data may contain a great deal of valuable information for online process monitoring. Multivariate statistical process monitoring (MSPM) is widely used to extract the most useful modelling information from these historical data . Its data processing is generally executed in a subspace by dimensionality reduction due to the large amount of samples and the highly correlated process variables.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate statistical process monitoring (MSPM) is widely used to extract the most useful modelling information from these historical data. [1][2][3][4][5] Its data processing is generally executed in a subspace by dimensionality reduction due to the large amount of samples and the highly correlated process variables. Among all MSPM methods, the principal component analysis (PCA) is the most practical and is, therefore, widely used in chemical process monitoring.…”

Section: Introductionmentioning

confidence: 99%

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Huang

Yan

2019

Can J Chem Eng

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In chemical process monitoring based on principal component analysis (PCA), sampling data with outliers and optimally select principal components are two challenging problems that have a main effect on monitoring performance. Given this situation, firstly, a novel outlier detection method, i.e., a robust CDC‐MVT‐PCA method (CMP), which integrates CDC‐MVT (the closest distance to centre and multivariate trimming) with PCA to identify and eliminate the outliers, is proposed to clean sample data. Secondly, based on the cleaning sample data, PCA is employed to obtain PCs. The cumulative frequency representing the variability of each PC is defined to find the optimal PCs, which are able to represent the current variability information. Finally, selecting optimal PCs online based on the cumulative frequency of each PC (CF‐PCA) is proposed to keep the most responsive components and, thus, to improve the monitoring performance. The effectiveness of the proposed robust fault monitoring algorithm is verified through a simple numerical simulation and the Tennessee Eastman process.

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“…A small number of process monitoring methods have been devoted to tackle the problem, which may be divided into two types: batch length synchronization based monitoring methods [30][31][32] and irregular phase partition based monitoring methods 33,34 . Besides, mixture model methods were proposed to cope with the uneven-length problem for monitoring multiphase batch processes 35,36 .…”

Section: Introductionmentioning

confidence: 99%

Window-Based Stepwise Sequential Phase Partition for Nonlinear Batch Process Monitoring

Liu

Zhang

2016

Ind. Eng. Chem. Res.

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In this paper a window-based step-wise sequential phase partition method is proposed to improve monitoring performance for nonlinear batch processes with multiphase operations.The three-dimensional information matrix of batch operation is unfolded and normalized in the batch-wise direction to facilitate establishing the kernel principle component analysis (KPCA) models for phase partition. A moving window is introduced to improve the partition performance with respect to the process dynamics and time sequence for operation. Consequently, phase partition algorithms are developed for even-and uneven-length batch processes, respectively.Moreover, a traversal algorithm is given to determine the optimal choice of the KPCA parameters and the window size for phase partition. A numerical case and two industrial multiphase processes of injection molding and penicillin fermentation are used to demonstrate the effectiveness and merit of the proposed phase partition method.

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Multivariate statistical monitoring of multiphase batch processes with between‐phase transitions and uneven operation durations

Cited by 15 publications

References 46 publications

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Window-Based Stepwise Sequential Phase Partition for Nonlinear Batch Process Monitoring

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