Critical values for a steady-state identifier

Cao, Songling; Rhinehart, R. Russell

doi:10.1016/s0959-1524(96)00026-1

Cited by 40 publications

(23 citation statements)

References 3 publications

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“…The method is based on the R-statistic, using a ratio of two variances measured on the same set of data by two methods and employs three first-order filter operations, providing computational efficiency and robustness to process noise and non-noise patterns. Critical values and statistical evidence of the method to reject the steady-state hypothesis, as well as to reject the transient state hypothesis have already been discussed in the literature [42,43]. This method was also modified in [44] by optimizing the filter constants so as to minimize Type I and II errors and simultaneously reduce the delay in state detection.…”

Section: Tool Amentioning

confidence: 99%

Performance Evaluation of Real Industrial RTO Systems

2016

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Abstract:The proper design of RTO systems' structure and critical diagnosis tools is neglected in commercial RTO software and poorly discussed in the literature. In a previous article, Quelhas et al. (Can J Chem Eng., 2013, 91, 652-668) have reviewed the concepts behind the two-step RTO approach and discussed the vulnerabilities of intuitive, experience-based RTO design choices. This work evaluates and analyzes the performance of industrial RTO implementations in the face of real settings regarding the choice of steady-state detection methods and parameters, the choice of adjustable model parameters and selected variables in the model adaptation problem, the convergence determination of optimization techniques, among other aspects, in the presence of real noisy data. Results clearly show the importance of a robust and careful consideration of all aspects of a two-step RTO structure, as well as of the performance evaluation, in order to have a real and undoubted improvement of process operation.

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Section: Tool Amentioning

confidence: 99%

Performance Evaluation of Real Industrial RTO Systems

2016

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“…To determine if the process is at steady-state, the calculated R-statistic is compared to a threshold or critical value (R-crit). Critical values for the R-statistic assuming process at steady-state with white noise (independent identically normal distributed data) were obtained by Cao and Rhinehart [10] for different type I risk (a) and filter factors (l 1 , l 2 , l 3 ).…”

Section: Three-way Data and Statistical Approachesmentioning

confidence: 99%

“…This selection can be done by trying different filter factors and R-crit values, and choosing the combination of these values that yields low type I and II risks together with a fast tracking of the process. Cao and Rhinehart [10] assuming white noise suggested that the values for the filter factors l 1 ¼ 0.2 and l 2 ¼ l 3 ¼ 0.1 lead to the best balance between type I and type II risks. These suggested values were used in our study.…”

Section: Three-way Data and Statistical Approachesmentioning

confidence: 99%

“…For each one of the uncorrelated latent variables as well as the distance to the model (DmodX) from the PCA, the corresponding R-statistics are worked out using Equations (1)-(4). These R-statistics are then compared to the corresponding threshold (R-crit), obtained from Cao and Rhinehart [10] for a given type I risk (a) (after Bonferroni correction) and filter factors (l 1 , l 2 , l 3 ). In this way, each batch can be classified as being from the start-up period or from a steady-state period.…”

Section: Three-way Data and Statistical Approachesmentioning

confidence: 99%

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Using Unfold‐PCA for batch‐to‐batch start‐up process understanding and steady‐state identification in a sequencing batch reactor

Aguado

Ferrer

Seco

et al. 2007

Journal of Chemometrics

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In chemical and biochemical processes, steady-state models are widely used for process assessment, control and optimisation. In these models, parameter adjustment requires data collected under nearly steady-state conditions. Several approaches have been developed for steady-state identification (SSID) in continuous processes, but no attempt has been made to adapt them to the singularities of batch processes. The main aim of this paper is to propose an automated method based on batch-wise unfolding of the three-way batch process data followed by a principal component analysis (Unfold-PCA) in combination with the methodology of Brown and Rhinehart [2] for SSID. A second goal of this paper is to illustrate how by using Unfold-PCA, process understanding can be gained from the batch-to-batch start-ups and transitions data analysis. The potential of the proposed methodology is illustrated using historical data from a laboratory-scale sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal (EBPR). The results demonstrate that the proposed approach can be efficiently used to detect when the batches reach the steady-state condition, to interpret the overall batch-to-batch process evolution and also to isolate the causes of changes between batches using contribution plots.

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“…Cao and Rhinehart [4] proposed a methodology, denoted as the F-like test, based on the comparison of two variances for the determination of steady states with efficient computational time. Cao and Rhinehart [5] and Shrowti et al [6] showed that tuning the critical values of the F-like test can improve the performance of the method. In addition, this technique has been tested for validation on different chemical processes, such as a pilot-scale two-phase flow experiment [7,8] and a fractional distillation unit [9].…”

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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Critical values for a steady-state identifier

Cited by 40 publications

References 3 publications

Performance Evaluation of Real Industrial RTO Systems

Performance Evaluation of Real Industrial RTO Systems

Using Unfold‐PCA for batch‐to‐batch start‐up process understanding and steady‐state identification in a sequencing batch reactor

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

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