The process chemometrics approach to process monitoring and fault detection

Wise, Barry M.; Gallagher, Neal B.

doi:10.1016/0959-1524(96)00009-1

Cited by 739 publications

(216 citation statements)

References 26 publications

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“…Similar information can be derived from the weight matrices in the ANN models [18], but not as straightforward as in the PLS case. Another advantage with the projection methods (PLS and PCA) is that new samples not belonging to any of the modeled classes can be identified from unusually high residuals [19].…”

Section: Resultsmentioning

confidence: 99%

Classification of lactate dehydrogenase of different origin by liquid chromatography-mass spectrometry and multivariate analysis

Bylund

Samskog

Markides

et al. 2003

J. Am. Soc. Mass Spectrom.

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A method intended to serve as a multivariate quality control tool in the production of pharmaceutical proteins is presented. The method is based on multivariate analysis of peptide maps generated with liquid chromatography-mass spectrometry (LC-MS). Lactate dehydrogenase (LDH) from different species and tissues were used as model compounds in the study. The proteins were digested with Endoproteinase Lys-C before the LC-MS analysis. After data pretreatment of the peptide maps, successful classification of the LDHs were obtained by discriminant analysis with partial least squares regression and artificial neural networks. Further, principal component analysis was applied to visualize the relationships between the samples. (J Am Soc Mass Spectrom 2003, 14, 236 -240)

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Section: Resultsmentioning

confidence: 99%

Classification of lactate dehydrogenase of different origin by liquid chromatography-mass spectrometry and multivariate analysis

Bylund

Samskog

Markides

et al. 2003

J. Am. Soc. Mass Spectrom.

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“…In recent years, multivariate statistical process control, such as principal component analysis (PCA) and partial least squares (PLS), has become increasingly popular in many industrial fields (MacGregor and Kourti, 1995;Tates et al, 1999;Wise and Gallagher, 1996). These techniques can be used to extract the state of the system from the enormous volume of stored data via application of statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Lee

Vanrolleghem²

2003

Biotech & Bioengineering

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Abstract:Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.

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“…Different variants of PCA and PLS have been used in numerous papers for the purposes of process monitoring and fault diagnostics (see e.g. References [8][9][10][11][12][13][14][15]). …”

Section: Theorymentioning

confidence: 99%

A comparison of methods for analysing regression models with both spectral and designed variables

Jørgensen

Segtnan

Thyholt

et al. 2004

Journal of Chemometrics

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In many situations one performs designed experiments to find the relationship between a set of explanatory variables and one or more responses. Often there are other factors that influence the results in addition to the factors that are included in the design. To obtain information about these socalled nuisance factors, one can sometimes measure them using spectroscopic methods. The question then is how to analyze this kind of data, i.e. a combination of an orthogonal design matrix and a spectroscopic matrix with hundreds of highly collinear variables. In this paper we introduce a method that is an iterative combination of partial least squares (PLS) and ordinary least squares (OLS) and compare its performance with other methods such as direct PLS, OLS and a combination of principal component analysis and least squares. The methods are compared using two real data sets and using simulated data. The results show that the incorporation of external information from spectroscopic measurements gives more information from the experiment and lower variance in the parameter estimates. We also find that the introduced algorithm separates the information from the spectral and design matrices in a nice way. It also has some advantages over PLS in showing lower bias and being less influenced by the relative weighting of the design and spectroscopic variables.

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The process chemometrics approach to process monitoring and fault detection

Cited by 739 publications

References 26 publications

Classification of lactate dehydrogenase of different origin by liquid chromatography-mass spectrometry and multivariate analysis

Classification of lactate dehydrogenase of different origin by liquid chromatography-mass spectrometry and multivariate analysis

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

A comparison of methods for analysing regression models with both spectral and designed variables

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