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Closed-loop identification with routine operating data: Effect of time delay and sampling time

JOURNAL OF PROCESS CONTROL 2011年第7期21卷 997-1010页

作者： Shardt, Yuri A. W. Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data is very often downsampled before being stored in a data historian. This downsampled or even compressed data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Closed-loop identification routine operating data Closed-loop identifiability

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data quality assessment of routine operating data for process identification

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COMPUTERS & CHEMICAL ENGINEERING 2013年 55卷 19-27页

作者： Shardt, Yuri A. W. Huang, Biao Univ Alberta Edmonton AB T6G 2M7 Canada

In many chemical engineering plants, process identification is often performed de novo each time that it is needed. However, it is quite possible that sufficiently excited data regions, including routine operating regions, have already been collected and are available for identifying particular model structures. Therefore, there is a need to develop techniques for extracting these regions from the other uninformative regions. One potential approach to solving this problem is to consider the condition number of the Fisher information matrix for the desired model structure. The sensitivity of this approach to changes in sampling time, model structure, controller type, and number of data points is also examined. It is shown, through theoretical and simulation analysis that the proposed method determines data quality based on the situation. Practically, the proposed method can be used to determine the upper bound for the process model order that may be identified from the given data. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： data quality Process identification Closed-loop routine operating data

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Closed-loop identification condition for ARMAX models using routine operating data

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AUTOMATICA 2011年第7期47卷 1534-1537页

作者： Shardt, Yuri A. W. Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada

In industrial process control engineering, using routine operating data obtained from closed-loop operation of a process for model identification would be extremely valuable in applications such as control performance monitoring, root cause diagnosis, and controller retuning. However, the conditions for closed-loop identifiability using routine operating data are still largely unknown or untried. In this paper, criteria for closed-loop identification of an autoregressive, moving average process with exogenous input (ARMAX) regulated with an arbitrary, rational, polynomial controller are derived. The theoretical criteria that are developed for the closed-loop identification of an ARMAX process are compared with Monte Carlo simulations and previous theoretical results. It is shown that the newly-proposed theoretical results are in agreement with the simulation results. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Closed-loop identification Identifiability ARMAX routine operating data

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Closed-Loop Identification using routine operating data: the Effect of Time Delay

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IFAC Proceedings Volumes 2011年第1期44卷 1646-1651页

作者： Yuri A.W. Shardt Biao Huang University of Alberta Edmonton Alberta Canada T6G 2V4 (Tel: 780-492-9016)

Abstract In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data are very often downsampled before being stored in a data historian. This downsampled or even compressed data are, then, used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that, if the ratio between the time delay and sampling time is sufficiently large, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. On the other hand, for processes, such as flows, pressures, or levels, with almost no time delay, then the sampling time must be extremely small in order to identify the process parameters. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using an experimental set-up with a heated tank, the effect of time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model.

关键词： model identification closed-loop routine operating data time delay discrete time continuous time

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Closed-loop identification of MIMO systems: An excitation-free approach

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Zhang, Zhi-Qiang Huang, Chun-Qing Xiamen Univ Dept Automat Xiamen 361005 Peoples R China

In general, the external excitation is indispensable for closed-loop identification of SISO and MIMO systems. In this paper, an excitation-free approach for closed-loop identification of multi-delay MIMO systems is proposed by using the routine operating closed-loop data. Both identifiability and consistency of the plant model estimation are achieved when the basic assumptions are met. The proposed approach provides an effective way to handle closed-loop identification of MIMO systems, while it becomes of a non-trivial task for the conventional identification methods and especially subspace identification method in lack of prior knowledge on the process. The effectiveness of the proposed approach is demonstrated by a 4 x 4 industrial example viz. the Alatiqi column.

关键词： Mimo systems Closed-loop identification Excitation-free routine operating data

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Stiction compensation in process control loops: A framework for integrating stiction measure and compensation

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2005年第24期44卷 9164-9174页

作者： Srinivasan, R Rengaswamy, R Clarkson Univ Dept Chem Engn Potsdam NY 13699 USA

In this paper, a framework that utilizes a stiction measure for effective stiction compensation in process control valves is proposed for the first time. The performance of a friction compensator termed the "knocker" proposed in the literature is studied. It is observed that the choice of knocker parameters has a significant influence on the performance of the compensator. It is shown that the choice of the knocker parameters can be automated based on the stiction severity exhibited by the loop. We propose the use of a combination of two approaches for estimating stiction severity. Experimental and simulation case studies are used to demonstrate the efficacy of the proposed approach. Results indicate that a reduction of 6 - 7 times can be obtained for the output variability.

关键词： .Process Control loops estimating compensator Ind Eng Chem presence of stiction control valves Integrating Stiction static friction stiction severity simulation case output variability stiction diagnosis routine operating data

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Gray-box identification of dynamic models for the bleaching operation in a pulp mill

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JOURNAL OF PROCESS CONTROL 2005年第4期15卷 451-468页

作者： Raghavan, H Gopaluni, RB Shah, S Pakpahan, J Patwardhan, R Robson, C Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Matrikon Inc Edmonton AB T5J 3N4 Canada Millar Western Forest Prod Whitecourt AB T7S 1N9 Canada

The development and application of gray-box identification techniques for modelling the bleaching operation in a Bleached Chemi-Thermo Mechanical Pulp (BCTMP) mill are explained. The process is characterized as a delay dominant recycle process with significant input nonlinearities. The identification was carried out using routine operating data in which the outputs were measured irregularly. The effects of these characteristics and consequent modifications of the system identification techniques are discussed. The resulting models are being used for online prediction and model-based controller design at the mill with satisfactory performance. (c) 2004 Elsevier Ltd. All rights reserved.

关键词： gray-box identification recycle processes irregular sampling input nonlinearities routine operating data bleached chemi-thermomechanical pulp mill expectation maximization constrained identification

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Isolation of plant-wide faults using causality detection methods

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IFAC-PapersOnLine 2016年第7期49卷 13-18页

作者： Garcia, Guilherme M. Munaro, Celso J. Department of Electrical Engineering Universidade Federal do Espirito Santo VitoriaES29075-910 Brazil

Isolation of plant-wide faults in large-scale complex systems is particularly challenging. A methodology to detect and isolate faults is proposed, detecting the faulty variables using univariate control charts and the causality information between them to indicate the source. The clustering of faulty variables uses univariate analysis to avoid the smearing effect brought by multivariate analysis. The variable where the fault took place is indicated, handling fault novelties in a very natural manner. The proposed method is discussed and illustrated through its application to the Tennessee Eastman Process and to routine operating data from a thermoelectric power plant. © 2016

关键词： Fault detection Large scale systems Multivariant analysis Process control Thermoelectric power plants Detection methods Fault isolation Large scale complex systems Multi variate analysis routine operating data Tennessee Eastman process Transfer entropy Univariate analysis

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