Inference of multi-variable trends in unmeasured process quantities

Flehmig, Folko; Marquardt, Wolfgang

doi:10.1016/j.jprocont.2007.08.004

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…1/N (16) in which N is the number of variables. Furthermore, these indexes, R T and R i , can take values between zero and one.…”

Section: Wavelet-based Methodsmentioning

confidence: 99%

“…The information of the concentration was not used for the present study, because measurements of this state are not normally available in chemical processes. The SSI for estimated states was the subject of investigation in [16]. …”

Section: Cstr Systemmentioning

confidence: 99%

“…Kelly and Hedengren [15] developed a method for SSI, which depends on the tuning of the window size and the critical value of its index, to detect non-stationary drifts in chemical processes. Flehmig and Marquardt [16] reported a technique that can infer trends in unmeasured states by using a linear process model that relates the unmeasured and measured states. Even when the model is necessary in the above method, the authors discarded the use of nonlinear state estimation due to its computational cost and mention the benefits of this technique for monitoring and control.…”

Section: Introductionmentioning

confidence: 99%

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A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

2015

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This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is used as an index for steady-state determination. In this contribution, the novel SSI method is compared to other available techniques, namely the F-like test, wavelet transform and a polynomial-based approach. These methods are implemented for SSI of three different case studies. In the first case, a simulated dataset is used for calibrating the output-based SSI methods. The second case corresponds to a literature nonlinear continuous stirred-tank reactor (CSTR) example running at different steady states in which the ARX-based approach is tuned with the available input-output data. Finally, an industrial case with real data of a depropanizer column from PETROBRAS S.A. considering different pieces of equipment is analyzed. The results for a reflux drum case indicate that the wavelet and the F-like test can satisfactorily detect the steady-state periods after careful tuning and when respecting their hypothesis, i.e., smooth data for the wavelet method and the presence of variance in the data for the F-like test. Through a heat exchanger case with different measurement frequencies, we demonstrate the advantages of using the ARX-based method over the other techniques, which include the aspect of online implementation.

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“…1/N (16) in which N is the number of variables. Furthermore, these indexes, R T and R i , can take values between zero and one.…”

Section: Wavelet-based Methodsmentioning

confidence: 99%

Section: Cstr Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

2015

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Robust and nonlinear control literature survey (No. 7)

2008

Intl J Robust & Nonlinear

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at leonardo.giovanini@eee.strath.ac.uk.

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Input estimation as a qualitative trend analysis problem

Thürlimann

Villez

2017

Computers & Chemical Engineering

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Inference of multi-variable trends in unmeasured process quantities

Cited by 4 publications

References 24 publications

A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

Robust and nonlinear control literature survey (No. 7)

Input estimation as a qualitative trend analysis problem

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