Dynamic inferential estimation using principal components regression (PCR)

Hartnett, Margaret; Lightbody, Gordon; Irwin, G.W.

doi:10.1016/s0169-7439(98)00021-5

Cited by 34 publications

(22 citation statements)

References 12 publications

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“…But the measured spectral data on the modern spectroscopic instrument, such as ultraviolet or near infrared instruments, are usually of high colinearity, which is the commonplace faced by analytical chemists. To address this problem, a variety of techniques based on latent variables (LVs) have been proposed, such as principal component regression (PCR) [1,2] and partial least squares (PLS) [3,4]. Typically, the establishment of a calibration model usually includes all the measured wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

Liang

et al. 2009

Analytica Chimica Acta

1,209

554

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Section: Introductionmentioning

confidence: 99%

Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

Liang

et al. 2009

Analytica Chimica Acta

1,209

554

View full text Add to dashboard Cite

“…44 and a natural frequency cvn = 112T2 = 0.0943 s [24]. The corresponding result for the optimal number of components a= 4 when the estimator was identified by use of the high-sampling-rate y i data was The RMSEP values for the PCA+OE estimator were then 16% lower than the optimal value in the present multirate sampling case [13].…”

Section: Resultsmentioning

confidence: 84%

“…Wikström et al [12] used PLSR for time series modeling related to an electrolysis process (they had no known inputs and were thus identifying an AR model). Harnett et al [13] extended the work of Wise [8] in order to facilitate the development of a predictive model of the overheads condenser and reflux drum system for a distillation column.…”

Section: Arx Modelsmentioning

confidence: 99%

Dynamic system multivariate calibration with low-sampling-rate y data

Ergon

Halstensen

2000

J. Chemometrics

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When the data in principal component regression (PCR) or partial least squares regression (PLSR) form time series, it may be possible to improve the prediction/estimation results by utilizing the correlation between neighboring observations. The estimators may then be identified from experimental data using system identification methods. This is possible also in cases where the response variables in the experimental data are sampled at a low and possibly irregular rate, while the regressor variables are sampled at a higher rate. After a discussion of the options available, the paper shows how the autocorrelation of the regressor variables in such multirate sampling cases may be utilized by identification of parsimonious output error (OE) estimators. An example using acoustic power spectrum regressor data is finally presented.

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“…Hartnett and coworkers published two different papers in the end of the 1990s. In the first of the two papers, genetic algorithms are used in combination with principal components regression (PCR), to do dynamic inferential estimation of process variables . The measurement equation of an underlying state space model is used, but the focus is not on the state space model itself.…”

Section: State Space Models In Chemometricsmentioning

confidence: 99%

Subspace methods for dynamic model estimation in PAT applications

Thygesen

Berg

2012

Journal of Chemometrics

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One primary goal in the application of process analytical technology tools is improved process monitoring and control. A second is to obtain a better understanding of how a normal process behaves (i.e. the normal dynamics). In order to perform feed-forward control, time series models of the process data are required. Such models could be developed on the basis of known physical/chemical knowledge of the system (i.e. first principal or mechanistic modeling). However, very often, this is not possible because of the lack of sufficient information. This leads to the need of system identification (SI). One class of models within SI is the state space models, linear models that relate the input of the system at time k to the output at time k via estimation of the so-called system states. State space models may be fitted using what is known as the subspace methods. Subspace methods are based on the projection of data on subspaces identified by, for example, the singular value decomposition of time-shifted data during a training phase. This paper introduces state space models, illustrates how subspace methods are closely related to known chemometric tools, and how they can be applied in, for example, model-based feed-forward process monitoring and control. The concepts are illustrated using a data set from an intrinsically nonlinear milk coagulation process that can be approximated well by a linear dynamic model using a small set of virtual (or principal) states. We present an alternative process-monitoring strategy where the dynamic components and boundary conditions of a developing milk coagulation batch are estimated in real-time and compared to normal operating conditions.

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Dynamic inferential estimation using principal components regression (PCR)

Cited by 34 publications

References 12 publications

Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

Dynamic system multivariate calibration with low-sampling-rate y data

Subspace methods for dynamic model estimation in PAT applications

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