An online application of dynamic PLS to a dearomatization process

Komulainen, Tiina M.; Sourander, M.; Jämsä‐Jounela, Sirkka‐Liisa

doi:10.1016/j.compchemeng.2004.07.014

Cited by 72 publications

(41 citation statements)

References 22 publications

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“…Nevertheless, PLS based soft sensors are still very common in practical use [22][23][24][25], due to its simplicity and clear mathematical background. In this paper, PLS method is the basis for adaptive model building.…”

Section: Methods For Model Adaptationmentioning

confidence: 99%

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Adaptive Estimation of Difficult-to-Measure Process Variables

2013

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Original scientific paperThere exist many problems regarding process control in the process industry since some of the important variables cannot be measured online. This problem can be significantly solved by estimating these difficult-tomeasure process variables. In doing so, the estimator is in fact an appropriate mathematical model of the process which, based on information about easy-to-measure process variables, estimates the current value of the difficultto-measure variable. Since processes are usually time-varying, the precision of the estimation based on the process model which is built on old data is decreasing over time. To avoid estimator accuracy degradation, model parameters should be continuously updated in order to track process behavior. There are a couple of methods available for updating model parameters depending on the type of process model. In this paper, PLSR process model is chosen as the basis of the difficult-to-measure process variable estimator while its parameters are updated in several waysby the moving window method, recursive NIPALS algorithm, recursive kernel algorithm and Just-in-Time learning algorithm. Properties of these adaptive methods are explored on a simulated example. Additionally, the methods are analyzed in terms of computational load and memory requirements.Key words: Process variable estimation, Adaptive estimator, Moving window, Recursive algorithms, JITL algorithm Adaptivna estimacija teško-mjerljivih procesnih veličina. Problemi s upravljanjem mnogih procesa u industriji vezani su s nemogućnošću on-line mjerenja nekih važnih procesnih veličina. Ovaj se problem može u značajnoj mjeri riješiti estimacijom ovih teško-mjerljivih procesnih veličina. Estimator je pri tome odgovarajući matematički model procesa koji na temelju informacije o ostalim (lako-mjerljivim) procesnim veličinama procjenjuje trenutni iznos teško-mjerljive veličine. Budući da su procesi po prirodi promjenjivi, točnost estimacije zasnovane na modelu procesa izgra enog na starim podacima u pravilu opada s vremenom. Kako bi se ovo izbjeglo, parametre modela procesa je potrebno kontinuirano prepodešavati kako bi model što bolje opisivao (trenutno) vladanje procesa. Ovisno o tipu matematičkog modela, za prepodešavanje njegovih parametara na raspolaganju je više metoda. Kao osnova estimatora teško-mjerljive veličine u radu se koristi PLSR model procesa, dok se njegovi parametri prepodešavaju na više načina -metodom pomičnog prozora, rekurzivnim NIPALS algoritmom, rekurzivnim kernel algoritmom te Just-in-Time Learning metodom. Svojstva navedenih metoda adaptacije PLSR modela procesa ispitana su na odabranom primjeru. Nadalje, metode adaptacije su analizirane i s obzirom na računalnu i memorijsku zahtjevnost.Ključne riječi: estimacija procesne veličine, adaptivni estimator, pomični prozor, rekurzivni algoritmi, JITL algoritam

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Section: Methods For Model Adaptationmentioning

confidence: 99%

“…Recursive NIPALS algorithm was implemented according to (22). DTM variable estimation and absolute error of the estimation are not showed since they are very similar to Fig.…”

Section: Recursive Nipals Algorithmmentioning

confidence: 99%

Adaptive Estimation of Difficult-to-Measure Process Variables

2013

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“…LS models had an input variable set of six variables, listed in Table 1. Partial least squares (PLS) was chosen as an FDI method due to the excellent results reported by Komulainen [13]). The PLS model for IBP was trained with six variables.…”

Section: Fdi Modelsmentioning

confidence: 99%

“…A large number of successful industrial applications of these data-based monitoring methods and ANNs have been reported (e.g. Komulainen et al [13] and Jamsa-Jounela et al [10] and Kampjarvi et al [12]) and reviewed, e.g. by Isermann and Ball [8] and Meireles et al [19].…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant control for a dearomatisation process

Sourander¹,

Vermasvuori²,

Sauter³

et al. 2009

Journal of Process Control

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Author 's accepted manuscript, published in Journal of Process Control 19 (2009) [1091][1092][1093][1094][1095][1096][1097][1098][1099][1100][1101][1102] Fault tolerant control for a dearomatisation process AbstractIn this paper, a fault tolerant control (FTC) for a dearomatisation process in the presence of faults in online product quality analysers is presented. The FTC consists of a fault detection system (FDI) and a logic for triggering predefined FTC actions. FDI is achieved by combining several process data driven approaches for detecting faults in online quality analysers. The FTC exploits the diagnostic information in adapting a quality controller (MPC) to the faulty situation by manipulating tuning parameters of the MPC to produce both proactive and reactive strategies. The proposed FTC was implemented, tested offline and validated onsite at the Naantali oil refinery. The successful testing and plant validation results are presented and discussed.

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“…A fault in the analysed signal is detected when λ ≥ − n n m U (6) where λ is the threshold for fault detection. The minimum size of the detected fault (ν/2) and the detection threshold (λ) are tuning parameters, whose values were determined based on knowledge about the analysers' characteristics.…”

Section: A Dearomatisation Process Modelsmentioning

confidence: 99%

FTC based on data driven FDI for a dearomatisation process

Vermasvuori¹,

Sourander

Liikala

et al. 2008

2008 16th Mediterranean Conference on Control and Automation

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Abstract-In this paper, a fault tolerant control (FTC) system based on data driven fault detection (FDI) is presented. The behaviour of the system with proactive and reactive FTC strategies is studied in the presence of faults in an online product quality analyser with a simulated dearomatisation process operated under model predictive control (MPC). The performance of the system is validated onsite at the Neste Oil Oyj Naantali refinery. It is shown, that the inherent accommodation properties and model information in the studied MPC provide means to realise the proposed types of FTC strategies as confirmed both by simulation and the real process results. It is also shown that similar results are achieved within a simulated and the real process environments.

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An online application of dynamic PLS to a dearomatization process

Cited by 72 publications

References 22 publications

Adaptive Estimation of Difficult-to-Measure Process Variables

Adaptive Estimation of Difficult-to-Measure Process Variables

Fault tolerant control for a dearomatisation process

FTC based on data driven FDI for a dearomatisation process

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