Multivariate SPC Charts for Monitoring Batch Processes

Nomikos, Paul; MacGregor, John F.

doi:10.2307/1269152

Cited by 314 publications

(179 citation statements)

References 0 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…There are a number of MSPC tools that are available to help interpret process data, the most commonly applied methods being Principal Component Analysis (PCA) and Partial Least Squares (PLS). On the basis of the results obtained in previous studies by the authors and other researchers (9,11,12), PCA was believed to be a suitable algorithm to apply to the problem discussed here. A brief description of PCA is provided below, and further details of the algorithm are provided in ref 13.…”

Section: Multivariate Statistical Process Controlmentioning

confidence: 87%

See 1 more Smart Citation

Automated Production Support for the Bioprocess Industry

Lennox

Kipling

Glassey

et al. 2002

Biotechnology Progress

View full text Add to dashboard Cite

This paper describes the application of Artificial Intelligence and Multivariate Statistical Techniques to two industrial fermentation systems. In the first example, an Expert System is shown to provide tighter control of an important process parameter. This is shown to lead to improved consistency of operation. In the second application, Principal Component Analysis is applied to a final stage fermentation production facility. The results presented indicate that the algorithm can provide concise indicators of process faults that can be presented to the operators to assist them in taking suitable corrective actions.

show abstract

Section: Multivariate Statistical Process Controlmentioning

confidence: 87%

“…To overcome the problems associated with univariate SPC, multivariate SPC (MSPC) techniques have been developed (9) and subsequently applied to batch fermentation systems. Preliminary results reported in the literature indicate the suitability of this approach to industrial fermentation systems.…”

Section: Introductionmentioning

confidence: 99%

Automated Production Support for the Bioprocess Industry

Lennox

Kipling

Glassey

et al. 2002

Biotechnology Progress

View full text Add to dashboard Cite

show abstract

“…From the shape of the CGF, the optimum number of PCs is estimated. Eastment and Krzanowski [3] and Nomikos and MacGregor [4] suggested the use of cross-validation when the PCA model is going to be used for future observations, which are independent of the calibration data. This is because cross-validation allows the estimation of the prediction error expected for incoming data.…”

Section: Introductionmentioning

confidence: 99%

Cross‐validation in PCA models with the element‐wise k‐fold (ekf) algorithm: theoretical aspects

Camacho

Ferrer

2012

Journal of Chemometrics

View full text Add to dashboard Cite

Cross-validation in PCA models with the element-wise k -fold (ekf ) algorithm: Theoretical aspects. April 16, 2012 AbstractCross-validation has become one of the principal methods to adjust the meta-parameters in predictive models. Extensions of the cross-validation idea have been proposed to select the number of components in Principal Components Analysis (PCA). The element-wise k -fold (ekf ) cross-validation is among the most used algorithms for PCA cross-validation. This is the method programmed in the PLS Toolbox, and it has been stated to outperform other methods under most circumstances by Bro et al. (2008) in a numerical experiment. The ekf algorithm is based on missing data imputation and it can be programmed using any method for this purpose. In this paper, the ekf algorithm with the simplest missing data imputation method, trimmed score imputation (TRI), is analyzed. A theoretical study is driven to identify in which situations the application of ekf is adequate and, more important, in which situations it is not. The results presented show that the ekf method may be unable to assess the extent to which a model represents a test set and may lead to discard Principal Components (PCs) with important information. On a second paper of this series, other imputation methods are studied within the ekf algorithm.

show abstract

“…Multivariate statistical methods, such as principal component analysis (PCA) and partial least squares (PLS), are widely used in industry for process monitoring (Nomikos and MacGregor, 1995;Qin, 2003;Ge and Song, 2008;Garcia-Alvarez et al, 2012). Other complementary multivariate statistical process monitoring methods, including canonical variate analysis, kernel PCA, dynamic PCA, and independent component analysis, have been proposed to address the limitations of PCA-or PLSbased monitoring strategies (Russell et al, 2000;Juricek et al, 2004;Lee et al, 2004a;2006).…”

Section: Introductionmentioning

confidence: 99%

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

Tong

Yan

2013

J. Zhejiang Univ. Sci. A

View full text Add to dashboard Cite

Abstract:In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T 2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

show abstract

Multivariate SPC Charts for Monitoring Batch Processes

Cited by 314 publications

References 0 publications

Automated Production Support for the Bioprocess Industry

Automated Production Support for the Bioprocess Industry

Cross‐validation in PCA models with the element‐wise k‐fold (ekf) algorithm: theoretical aspects

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

Contact Info

Product

Resources

About