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PARTICLE FILTERS FOR DYNAMIC DATA RECTIFICATION AND process CHANGE DETECTION

IFAC Proceedings Volumes 2006年第13期39卷 204-209页

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

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.

关键词： Dynamic data rectification particle filters process change detection sequential Monte Carlo state estimation

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NONLINEAR MULTISCALE FAULT DETECTION AND IDENTIFICATION

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IFAC Proceedings Volumes 2006年第13期39卷 120-125页

作者： Sang Wook Choi Elaine B. Martin Julian Morris In-Beum Lee Centre for Process Analytics and Control Technology School of chemical Engineering and Advanced materials University of Newcastle Newcastle Upon Tyne NE1 7RU UK Department of Chemical Engineering Pohang University of Science and Technology Pohang South Korea

A nonlinear multiscale multivariate statistical process control method is proposed to address fault detection and diagnosis issues at different scales in nonlinear processes. A kernel principal component analysis (KPCA) model is built with the reconstructed data obtained by performing wavelet transform and inverse wavelet transform sequentially on measured data. New variable contributions to monitoring statistics are also derived. A CSTR simulation study compares the proposed approach with several existing methods in terms of false alarm rate, missed alarm rate and detection delay.

关键词： process monitoring Fault diagnosis Fault detection

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P1H-2 Particle Sizing in the process Industry using Hertz-Zener Impact Theory and Acoustic Emission Spectra

P1H-2 Particle Sizing in the Process Industry using Hertz-Ze...

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IEEE Symposium (IUS) Ultrasonics

作者： A. Gachagan A. J. Mulholland G. Hayward M. Tramontana A. Nordon G. Carson Department of Mathematics University of Strathclyde Glasgow UK Department of Pure and Applied Chemistry Centre for Process Analytics and Control Technology (CPACT) University of Strathclyde Glasgow UK Department of Electronic and Electrical Engineering The Centre for Ultrasonic Engineering (CUE) University of Strathclyde Glasgow UK

The cost of implementing real-time monitoring and control of industrial processes is a significant barrier for many companies. Acoustic techniques provide complementary information to optical spectroscopic sensors and have a number of advantages: they are relatively inexpensive, can be applied non-invasively, are non-destructive, multi-point measurements are possible, opaque samples can be analysed in containers that are made from opaque materials (e.g. steel or concrete) and the analysis can be conducted in real-time. In this paper a new theoretical model is proposed which describes the transport of particles in a stirred reactor, their collision with the reactor walls, the subsequent vibrations which are then transmitted through the vessel walls, and their detection by an ultrasonic transducer. The particle-wall impact is modelled using Hertz-Zener impact theory. Experimental data is then used in conjunction with this (forward) model to form an inverse problem for the particle size distribution using a least squares cost function. Application of an integral smoothing operator to the power spectra greatly enhances the accuracy and robustness of the approach. One advantage of this new approach is that since it operates in the frequency domain, it can cope with the industrially relevant case of many particle-wall collisions. The technique will be illustrated using data from a set of controlled experiments. In the first instance a set of simplified experiments involving single particles being dropped in air onto a substrate are utilised. The second set of experiments involves particles in a carrier fluid being stirred in a reactor vessel. In each case the approach is able to successfully recover the associated particle size

关键词： Acoustic emission Inductors Optical sensors Information analysis Costs Monitoring process control Industrial control Stimulated emission Spectroscopy

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Bayesian control limits for statistical process monitoring

Bayesian control limits for statistical process monitoring

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5th International Conference on control and Automation, ICCA'05

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)0780391381

This paper presents a Bayesian approach, based on infinite Gaussian mixtures, for the calculation of control limits for a multivariate statistical process control scheme. Traditional approaches to calculating control limits have been based on the assumption that the process data follows a Gaussian distribution. However this assumption is not necessarily satisfied in complex dynamic processes. A novel probability density estimation method, the infinite Gaussian mixture model (GMM), is proposed to address the limitations of the existing approaches. The infinite GMM is introduced as an extension to the finite GMM under a Bayesian framework, and it can be efficiently implemented using the Markov chain Monte Carlo method. Based on probability density estimation, control limits can be calculated using the bootstrap algorithm. The proposed approach is demonstrated through its use for the monitoring of a simulated continuous chemical process. © 2005 IEEE.

关键词： control theory

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Batch process monitoring through the integration of spectral and process data

Batch process monitoring through the integration of spectral...

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作者： Morris, Julian Martin, Elaine Stewart, David 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)008045108X

Most applications of MSPC have tended to focus upon the manufacture of a single product with separate models being developed to monitor individual recipes. With process manufacturing trends being influenced by customer demands there has been an increase in the manufacture of a wide variety of products, there is a real need for process models which allow a range of products, grades or recipes to be monitored using a single process model. With increasing attention now being paid to the FDA process Analytical Technologies (PAT) initiative, the use of spectro-chemical information for enhanced monitoring of reactions and is now gaining impetus. an application of the performance monitoring of a multi-recipe multi-reactor industrial batch polymer manufacturing is discussed in which NIR spectroscopic data is also integrated with process data to provide enhanced batch monitoring. Copyright © 2005 IFAC.

关键词： Batch reactors

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Bayesian control limits for statistical process monitoring

Bayesian control limits for statistical process monitoring

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International Conference on control and Automation (ICCA)

作者： Tao Chen J. Morris E. Martin Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

关键词： Bayesian methods Monitoring Gaussian distribution process control Chemical processes Manufacturing processes Principal component analysis Kernel Probability density function Product safety

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BATCH process MONITORING THROUGH THE INTEGRATION OF SPECTRAL AND process DATA

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IFAC Proceedings Volumes 2005年第1期38卷 13-18页

作者： Julian Morris Elaine Martin David Stewart Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU UK

Most applications of MSPC have tended to focus upon the manufacture of a single product with separate models being developed to monitor individual recipes. With process manufacturing trends being influenced by customer demands there has been an increase in the manufacture of a wide variety of products, there is a real need for process models which allow a range of products, grades or recipes to be monitored using a single process model. With increasing attention now being paid to the FDA process Analytical Technologies (PAT) initiative, the use of spectro-chemical information for enhanced monitoring of reactions and is now gaining impetus. An application of the performance monitoring of a multi-recipe multi-reactor industrial batch polymer manufacturing is discussed in which NIR spectroscopic data is also integrated with process data to provide enhanced batch monitoring.

关键词： Multivariate Statistical process control Spectroscopic Data Multi-recipe Batch Reactors

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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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Parameter estimation for batch processes using a Bayesian approach 7th

Parameter estimation for batch processes using a Bayesian ap...

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

作者： Lu, Zhen Martin, Elaine B. Morris, Julian Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

In a changing operational environment, a major challenge that still exits is the assured state and parameter estimation of dynamic processes. The values or expressions of important parameters can be difficult to determine and initial errors may be present in some parameters as a result of changes in the initial operating conditions. Furthermore as a result of variations in the environmental and operational conditions or the dynamic characteristics of the process, parameters may be time-varying. In this paper, an on-line Bayesian parameter estimator is developed and evaluated on a simulation of a batch methyl methacrylate process. Copyright © IFAC Dynamics and control of process Systems, Cambridge, Massachusetts, USA, 2004

关键词： Parameter estimation

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