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A revised state space modelling approach and improved fault detection using combined index monitoring for dynamic processes

A revised state space modelling approach and improved fault ...

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作者： Stubbs, S. Zhang, J. Morris, J. Centre for Process Analytics and Control Technology Newcastle University Newcastle NE1 7RU United Kingdom

ISBN: (纸本)9783902661692

Larimore's state space model derivation and stochastic estimation algorithm, first published in 1983, have been the adopted standard for deriving the state variables and parameters of the five (5) matrices state space model representation which continues to be applied extensively in the literature for applications ranging from controls, system identification and process monitoring. This paper presents an alternate derivation and stochastic estimation algorithm. The paper also discusses how strategic classification of the process inputs may, for some applications, facilitate the use of a simplified stochastic estimation algorithm. The alternative state space modeling approaches demonstrated better fault monitoring statistic performance for specific types of faults simulated. The canonical variate based state space modeling approaches were evaluated on a simulate CSTR process - with recycle through a heat exchanger. The results demonstrates the potential benefits to be derived from using a combined monitoring index based upon monitoring statistics derived from independent state space models for improved overall fault detecting capabilities and reliability of the fault monitoring scheme. © 2009 IFAC.

关键词： Fault detection

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Neural network based on-line re-optimisation of fed-batch processes using iterative dynamic programming for discrete-time systems 15th

Neural network based on-line re-optimisation of fed-batch pr...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Xiong, Zhihua Zhang, Jie Centre for Process Analytics and Control Technology Department of Chemical and Process Engineering University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661746

Optimisation of fed-batch processes can be described as a constrained nonlinear end-point dynamic optimisation problem. Although iterative dynamic programming (IDP) is feasible, it is usually very time-consuming and very difficult to apply to on-line optimisation because of solving the non-linear differential-algebraic equations of the process model in each iteration. The replacement of a rigorous mechanistic model by an equivalent neural network (NN) model takes the advantage of high speed processing, since simulation with a NN model involves only a few non-iterative algebraic calculations. To use IDP algorithm for NN model based on-line re-optimisation, a modified algorithm is proposed and is called as iterative dynamic programming for discrete-time system (IDP/DTS). The novel IDP/DTS algorithm can obtain a reduction of many times in computational time compared to the conventional IDP algorithm. In this paper, an effective optimisation and control scheme for on-line re-optimisation of fed-batch processes is proposed based on NN models and the novel IDP/DTS algorithm. The proposed scheme is illustrated using simulation studies of an ethanol fermentation process. Copyright © 2002 IFAC.

关键词： Dynamic programming

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Local linear and non-linear multi-way partial least squares batch modelling 15th

Local linear and non-linear multi-way partial least squares ...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Fletcher, N.M. Morris, A.J. Martin, E.B. Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661746

An industrial fed-batch fermentation process forms the basis of a study that illustrates the development and use of local models for the construction of a performance monitoring scheme. Linear models are initially built for different operating regions within the batch using Dynamic Partial Least Squares (DPLS) to capture the underlying process dynamics. The local models are then pieced together and a global model developed based on the latent variable scores. A comparison with a global non-linear quadratic DPLS model is then made prior to using the latent variable scores from the local models to build a nominal process representation. Copyright © 2002 IFAC.

关键词： Least squares approximations

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Detection of process model change in PLS based performance monitoring 15th

Detection of process model change in PLS based performance m...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Kumar, Sukhbinder Martin, Elaine B. Morris, Julian Centre for Process Analytics and Control Technology Merz Court University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661746

The detection of process changes using a partial least squares (PLS) based monitoring scheme can be achieved through the interrogation of two metrics, Hotelling's T and the Q-statistic. The Q-statistic has been shown to be insensitive to small changes in the process model parameters. In this paper, a modified statistic based on the local approach is proposed to detect changes in the model parameters in a PLS based monitoring scheme. The performance of the Q-statistic is compared with the modified statistic through their application to fault detection in a continuous stirred tank reactor. Copyright © 2002 IFAC.

关键词： Fault detection

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A comparison of canonical variate analysis and partial least squares for the identification of dynamic processes

A comparison of canonical variate analysis and partial least...

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American control Conference (ACC)

作者： A. Simoglou E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology University of Newcastle UK

This study contributes to the comparison of partial least squares (PLS) and canonical variate analysis (CVA) for the identification of dynamic systems. Two model forms, autoregressive with exogenous inputs and state space representations, are developed with PLS and CVA being used to calculate the model parameters. The different models are compared using two case studies: a benchmark simulation of a binary distillation column and an industrial fluidised bed reactor.

关键词： Least squares methods Autoregressive processes State-space methods Fluid dynamics Distillation equipment Fluidization Inductors process control Space technology Steady-state

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Confidence limits for contribution plots

Confidence limits for contribution plots

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作者： Conlin, A.K. Martin, E.B. Morris, A.J. Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne NE1 7RU United Kingdom

An issue often raised in multivariate statistical process control, when using statistical projection-based techniques to define nominal process behaviour, is that of the assured identification of the variables causing an out-of-statistical-control signal. One approach which has been adopted is that once a change in process operating conditions has been detected, the contribution of the individual variables to the principal component scores or squared prediction error, the Q-statistic, are examined. Adopting this approach, it is important that those variables responsible for, or contributing to, the process change are clearly identifiable. In process modelling and estimation studies, confidence bounds are typically placed around the model predictions. Currently confidence bounds are not used to identify the limits of normal behaviour for the individual multivariate statistical contributions, resulting in the interpretation of the contribution plot being left to the user. This paper presents a potential solution to the definition of confidence bounds for contribution plots. The methodology is based on bootstrap estimates of the standard deviations of the loading matrix. The proposed approach is evaluated using data from a benchmark simulation of a continuous stirred tank reactor system. The preliminary results are encouraging. Copyright (C) 2000 John Wiley and Sons, Ltd.

关键词： Principal component analysis

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PAT: The extraction of maximum information from messy spectral data 10th

PAT: The extraction of maximum information from messy spectr...

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10th IFAC Symposium on Computer Applications in Biotechnology, 2007

作者： Chen, Zeng-Ping Morris, Julian Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661616

With the increasing take-up of PAT1 by the pharma- and bio-industries there is a critical need for robust spectral calibrations for processes which are subject to the variations in physical properties such as sample compactness, surface topology, etc. The variation in the optical path-length materializing from the physical differences between samples may result in multiplicative light scattering influencing spectra in a nonlinear manner leading to the poor calibration performance. A new approach "Optical Path Length Estimation and Correction" overcomes the limitations of existing light scattering correction methods. Copyright 2007 IFAC.

关键词： Light scattering

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Modelling temperature4nduced spectral variations in chemical process monitoring 7th

Modelling temperature4nduced spectral variations in chemical...

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7th IFAC Symposium on Dynamics and control of process Systems, DYCOPS 2004

作者： Chen, Zeng-Ping Morris, Julian Martin, Elaine Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle upon Tyne NE1 7RU United Kingdom

With a view to ensuring the validity of multivariate calibration models in the presence of temperature variations, a new methodology, individual contribution standardization (ICS), is proposed to correct for temperature-induced spectral variations. The methodology was applied to shortwave NIR spectral data sets recorded at different temperatures. The results showed that ICS can almost eliminate temperature effects resulting in calibration models that exhibit good predictive performance. Compared with other methods, such as continuous piecewise direct standardization, ICS has the advantages of easy implementation, simple selection of the calibration model and less restrictions on the training samples. © IFAC Dynamics and control of process Systems, Cambridge, Massachusetts, USA, 2004

关键词： Standardization

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Batch monitoring through common subspace models 7th

Batch monitoring through common subspace models

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7th International Symposium on Advanced control of Chemical processes, ADCHEM 2003

作者： Lane, S. Martin, E.B. Morris, A.J. Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

Multi-way statistical projection techniques have typically been applied in the development of monitoring models for single recipe or single grade production As defined, implementation of these techniques in multi-product applications necessitates the development of a large number of process models. This issue can be overcome through the use of common sub-space models constructed by pooling the individual vananee-co variance matrices. A second issue with multi-way approaches is the difficulty of interpreting multiway contribution plots. An alternative approach is the U2 statistic. In this paper an extension is proposed, the V2 statistic, based on the cumulative contribution of variables at each sample point. The methodologies are demonstrated on two industrial applications. Copyright © IFAC 2003

关键词： Manufacturing processes Modelling Statistical analysis

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Particle filters for dynamic data rectification and process change detection

Particle filters for dynamic data rectification and process ...

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作者： Chen, Tao Morris, Julian Martin, Elaine Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU United Kingdom

ISBN: (纸本)9783902661142

The objectives of dynamic data rectification are wide-ranging and include the estimation of the process states, process signal de-noising, and outlier detection and removal. One approach reported in the literature for dynamic data rectification is the conjunction of the extended Kalman filter (EKF) and the expectation-maximization algorithm. However, this approach is limited in terms of its applicability due to the EKF being less appropriate where the state and measurement functions are highly non-linear or where the posterior distribution of the states is non-Gaussian. This paper proposes an alternative approach whereby particle filters are utilized for dynamic data rectification. By formulating the rectification problem within a probabilistic framework, the particle filters generate Monte Carlo samples from the posterior distribution of the system states, and thus provide the basis for rectifying the process measurements. Furthermore, the proposed technique is capable of detecting changes in process operation and thus complements the task of process fault diagnosis. The appropriateness of particle filters for dynamic data rectification is demonstrated through its application to a benchmark pH neutralization process. Copyright © 2006 IFAC.

关键词： Extended Kalman filters

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