Partial least squares (PLS) based monitoring and control of batch digesters

Kesavan, Parthasarathy; Lee, Jay H.; Saucedo, Victor M.; Krishnagopalan, Gopal A.

doi:10.1016/s0959-1524(99)00028-1

Cited by 29 publications

(7 citation statements)

References 9 publications

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“…Figure 4 shows a schematic diagram of a typical industrial batch pulp digester configuration 9,14 . Wood chips and white liquor are fed to the batch pulp digester.…”

Section: Preliminariesmentioning

confidence: 99%

“…Amirthalingam and Lee 13 designed a linear MPC system for a pulp digester based on a model from a subspace identification and a multirate Kalman filter. Kesavan et al 14 designed an MPC system for a pulp digester by combining a partial least square regression model and a semi‐empirical model developed by Chari 15 . Padhiyar et al 16 applied an NMPC scheme to a pulp digester with a multirate EKF to estimate the Kappa number profile.…”

Section: Introductionmentioning

confidence: 99%

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Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

2021

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This work presents the application of a Koopman operator approach to a batch pulp digester. To manufacture paper products with desired properties, it is essential to consider both macroscopic and microscopic attributes of pulp. However, the complexity of multiscale dynamics of pulping processes hinders proper control system design. Therefore, we utilize extended dynamic mode decomposition (EDMD), which is based on Koopman operator theory, to derive a global linear representation of a pulp digester. Then, we design an offset‐free Koopman‐based model predictive control (KMPC) system to regulate the Kappa number and cell wall thickness (CWT) of fibers at a batch pulp digester while compensating for the influence of plant‐model mismatch and disturbance during operation. The numerical experiments demonstrate that the linear state‐space model, obtained via EDMD, properly predicts the behavior of a batch pulp digester, and the designed offset‐free KMPC system successfully drives the Kappa number and CWT to set‐point values.

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“…Figure 4 shows a schematic diagram of a typical industrial batch pulp digester configuration 9,14 . Wood chips and white liquor are fed to the batch pulp digester.…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

2021

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“…Online multi sensor monitoring of yogurt and filmj [111] PLS Customer satisfaction index estimation by PLS for monitoring [112]. PLS Complex industrial-multivariate process monitoring [41,43,113] PLS, simPLS, icPLS, and rPLS Computation of optimal control moves for quality control [114,115] PLS and qPLS Quality characteristics of crude oil [116] PLS Nonlinear multivariate quality estimation [50] kPLS Classification…”

Section: Pls Application Genomics Application Detail Pls Variantmentioning

confidence: 99%

The diversity in the applications of partial least squares: an overview

Mehmood

Ahmed

2015

Journal of Chemometrics

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Advances in technology make it happen to have massive amount of information in the form of multiple variables per object. The use of multivariate approaches for modeling the real-life phenomena is natural in such situation. There are numerous multivariate approaches in the literature, and its a challenge to stay updated on the possibilities. Partial least squares (PLSs) are one of the many modeling approaches for high-throughput data, and its use in different fields to address the variety of problems has been increased in recent years. We therefore present an overview of PLS's applications. The objective of this paper is to give a comprehensive overview on the advances in PLS algorithm together with its applications for regression, classification, variable selection, and survival analysis problems covering genomics, chemometrics, neuroinformatics, process control, computer vision, econometric, environmental studies, and so on. We have mainly presented different PLS approaches and their applications, so that the reader can easily get an understanding of possibility to use PLS for their own field. For further reading, literature references together with software availability are provided. Figure 2. Illustration of partial least squares (PLS) algorithm, and how to use it for regression, classification, variable selection, and survival analysis, is presented. For regression, trained regression coefficients together with test data provide the fitted response, while variable selection is actually to find the subset of X. In classification and survival analysis, usually, the influential PLS scores are respectively used with linear discriminant analysis (LDA) or with quadratic discriminant analysis (QDA) and proportional hazard regression.

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“…Most of soft sensor modeling methods can be summarized into two different classes, namely model-driven and data-driven (Petr et al, 2009;Vapnik, 1995). Mode-driven methods usually cause severe errors of the on-line estimations because suffering from the inaccuracies of available instruments and depending on the accuracy of the process model (Kesavan et al, 2000;Gulnur and Cenk, 2002). The data-driven soft sensors gained increasing popularity in the process industry.…”

Section: Introductionmentioning

confidence: 99%

A novel soft sensor model based on artificial neural network in the fermentation process

Yu¹,

Mei²,

Ding³

2011

Afr. J. Biotechnol.

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Some crucial process variables in fermentation process could not be measured directly. Soft sensor technology provided an effective way to solve the problem. There has been considerable interest in modeling a soft sensor by using artificial neural network (ANN) in bioprocess. To generate a more efficient soft sensor model, we proposed a novel soft sensor model based on artificial neural network (SS-ANN). By analyzing a grey-box model of fermentation process, the secondary variables were selected. In modeling, on-line measurable variables could be taken as the input of ANN and the output is the derivatives of immeasurable variables. The estimated values of immeasurable variables were calculated by integrating the outputs of the well-trained ANN. The novel SS-ANN is different from the general SS-ANN. Experimental results of erythromycin fermentation process showed the novel soft sensor model could estimate mycelia concentration, sugar concentration and chemical potency with higher accuracy and generalization ability than the general soft sensor based on ANN. The novel soft sensor modeling method provides the theory basis for selecting the secondary variables. The dynamic characteristic of the process is considered, the novel model improves the estimation accuracy and generation ability. It can be concluded that the soft sensor model mentioned in this paper is reasonable and effective.

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Partial least squares (PLS) based monitoring and control of batch digesters

Cited by 29 publications

References 9 publications

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

The diversity in the applications of partial least squares: an overview

A novel soft sensor model based on artificial neural network in the fermentation process

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