Improving steady-state identification

Roux, G.; Santoro, Bruno F.; Sotelo, Francisco Falla; Teissier, Mathieu; Joulia, Xavier

doi:10.1016/s1570-7946(08)80081-8

Cited by 21 publications

(12 citation statements)

References 4 publications

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“…The existing online methods typically utilize a moving data window, based on which some test statistics are developed to decide if the signal has entered the steady state. Examples of such methods include polynomial interpolation test (PIT) which is proposed by Savitzky and Golay, Roux et al ., variance ratio test (VRT) which is proposed by Crow et al ., Cao and Rhinehart, slope detection method (SDM) which is proposed by Holly et al ., Bethea and Rhinehart, Wu et al ., and t ‐test which is proposed by Narasimhan et al . However, the performance of all these methods is highly dependent on the selection of the data window size.…”

Section: Introductionmentioning

confidence: 99%

Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo

Hou

Chen

2016

Quality & Reliability Eng

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Online detection of whether a data stream has reached the steady state is known to be an important problem in many applications such as process control, data reconciliation and fault detection. This paper introduces a novel online steady state detection algorithm under the Bayesian framework based on a multiple change-point state space formulation and the sequential Monte Carlo methods. A Rao-Blackwellization technique is proposed to substantially reduce the variance of Monte Carlo estimation and greatly enhance the computational efficiency. In addition, a resampling scheme called the Optimal Resampling is used for eliminating duplicate samples and the robustness of steady state detection is significantly improved by using the information of the particles more efficiently. Numerical studies based on simulated signals and application to a real data set are used to evaluate the performance of the proposed method and compare with other existing methods from the literature. The proposed method is shown to establish a more robust performance than other methods. And it is much more computationally efficient than the standard sequential Monte Carlo method.where μ τt ;tÀ1 ; Σ τt ;tÀ1 ; a τt ;tÀ1 ; b τt ;tÀ1 are parameters associated with the line segment y τt :tÀ1 that can be obtained using equations in (11), t 1 (Á) the univariate non-standardized student's t-distribution.

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Section: Introductionmentioning

confidence: 99%

Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo

Hou

Chen

2016

Quality & Reliability Eng

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“…Yao and coworkers [20] implemented an SSI methodology based on principal component analysis specific for batch processes, in which the Mahalanobis distance, the eigenvalue information, among others steps, were used in the proposed method. Finally, Le Roux et al [21] and Tao et al [22] proposed a technique based on a polynomial equation assuming a specified degree and a moving window of data. Such techniques were employed for SSI by using the polynomial slope as the index or critical value.…”

Section: Introductionmentioning

confidence: 99%

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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“…For each of the filters a parameter is chosen between 0 and 1. Values of these parameters are set based on relevance of actual values in comparison to the past ones, and could be interpreted as forgetting factors and express something analog to a window size [51]. The authors proposed the parameters to be tuned empirically and present some guidelines for that.…”

Section: Steady-state Detectionmentioning

confidence: 99%

“…Authors of [53] developed an algorithm for a filter to treat noisy processes data. LeRoux proposed an original usage of this filter [51]. Choosing a data widows containing an odd number n of elements the measurements series is filtered.…”

Section: Steady-state Detectionmentioning

confidence: 99%

A multi-agent software system for real-time optimization of chemical plants.

Martínez¹

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Improving steady-state identification

Cited by 21 publications

References 4 publications

Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo

Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo

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

A multi-agent software system for real-time optimization of chemical plants.

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