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Particle filters for dynamic data rectification and process change detection

Particle filters for dynamic data rectification and process ...

引用

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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Adaptive partial least squares with application to process monitoring 5

Adaptive partial least squares with application to process m...

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5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical processes, Safeprocess 2003

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

Partial least squares has been applied in process modelling and process performance monitoring where a large number of correlated variables are recorded. The most commonly implemented algorithm for identifying a PLS model has been the batch algorithm. In this case, data from the process is first required to be stored. In this paper an algorithm is proposed that recursively updates the parameters of PLS as and when a sample of measurements is obtained thereby reducing the need for data storage. The algorithm was tested on an artificially generated data set. The approach was then used to derive a statistic for process monitoring that was applied for the detection of faults in a continuous stirred tank reactor. © 2003 International Federation of Automatic control.

关键词： Fault detection

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Bayesian control limits for statistical process monitoring

Bayesian control limits for statistical process monitoring

引用

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

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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Improved operation of a batch polymerisation reactor through batch-to-batch iterative optimisation 7th

Improved operation of a batch polymerisation reactor through...

引用

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

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

A batch-to-batch iterative product quality optimisation control strategy for a batch polymerisation reactor is proposed. Recurrent neural networks are used to model the dynamic behaviour of product quality variables. Model-plant mismatches and unknown disturbances are reflected in the model prediction errors. The repetitive nature of batch processes enables this information being discovered from previous batches and used to improve the current batch operation. Recurrent neural network predictions for the current batch are modified using prediction errors in previous batches. Because modified model errors are gradually reduced from batch to batch, the control trajectory gradually approaches to the optimal control policy and tracking errors also converge. The proposed scheme is illustrated on a simulated batch polymerisation reactor. Copyright © IFAC 2003

关键词： Recurrent neural networks

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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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Monitoring performance in flexible process manufacturing 7th

Monitoring performance in flexible process manufacturing

引用

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

作者： 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

Partial Least Squares (PLS) is a popular method for the development of a framework for the detection and location of process deviations. A limitation of the approach is that it has generally been used to monitor one recipe, one product, for example, consequently applications may have been ignored because of the need for a large number of process models to monitor multi-product production. This paper introduces two extensions - multi-group and multi-group-multi-block PLS. These techniques enable a number of similar products, manufactured across different unit processes, to be monitored using a single model. The methodologies are demonstrated by application to a multi-recipe industrial manufacturing process. Copyright © IFAC 2003

关键词： Least squares approximations

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Integrated batch-to-batch control and within batch control of batch processes using neural network models

Integrated batch-to-batch control and within batch control o...

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Proceedings of the 2004 IEEE International Symposium on Intelligent control - 2004 ISIC

作者： Zhang, Jie Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU United Kingdom

This paper presents an integrated batch-to-batch control and within batch re-optimisation control strategy for batch processes using neural network models. To overcome the difficulties in developing detailed mechanistic models, neural network models are developed from process operation data. Due to model-plant mismatches and unknown disturbances, the optimal control policy calculated based on the neural network model may not be optimal when applied to the actual process. Utilising the repetitive nature of batch processes, neural network model based iterative learning control is used to improve the process performance from batch to batch. Batch-to-batch control can only improve the performance of the future batches. Within batch re-optimisation should be used to overcome the detrimental effect of disturbances on the current batch. The proposed technique is successfully applied to a simulated batch polymerisation process. © 2004 IEEE.

关键词： control system analysis

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Sensor fault identification using weighted combined contribution plots

Sensor fault identification using weighted combined contribu...

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作者： Alawi, Ahmed Choi, Sang Wook Martin, Elaine Morris, Julian 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

Fault detection and diagnosis are important for the safe and efficient operation of a chemical plant. This paper proposes a weighted combined contribution plot. Fault identification using either Q or T2 contribution plot have shown to be incapable of identifying faults for correlated sensors systems. This paper proposes a combined index of Q and T 2. The new index was able to uniquely identify the fault in the case of correlated sensors. The proposed approach is applied to a simulated CSTR process to show its performance. Copyright © 2006 IFAC.

关键词： process monitoring

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Gaussian process regression for batch process modelling 7th

Gaussian process regression for batch process modelling

引用

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

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

This paper proposes the application of Gaussian process regression for the empirical modelling of batch processes to provide long range predictions. Gaussian processes are flexible, non-parametric Bayesian regression techniques. In the training stage, hyper-parameters that define the covariance structure of the Gaussian process can be obtained using Markov Chain Monte Carlo sampling. Model predictions can then be achieved by taking the average of the Monte Carlo samples. The proposed technique is evaluated by application to a benchmark simulation of a fed-batch bioreactor. The results show that comparable results can be achieved with other non-parametric modelling approaches such as recurrent neural networks. © IFAC Dynamics and control of process Systems, Cambridge, Massachusetts, USA, 2004

关键词： Monte Carlo methods

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State and parameter estimation through dynamic Bayesian forecasting 7th

State and parameter estimation through dynamic Bayesian fore...

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

作者： Lu, Zhen Martin, Elaine 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

Successful application of model based control depends on having good estimates for the system dynamic states and parameters. A multivariate dynamic linear model is developed for the estimation of the states from limited measurements in a non-linear system comprising model uncertainties. Since the noise statistics are rarely available a priori, the noise covariance matrix is treated as a tuning parameter and determined through repeated simulations. For non-linear, time varying processes, the assumption of a constant process noise covariance matrix does not realise accurate estimates. In this paper Monte Carlo simulations are used to obtain the time-varying noise covariance matrix. The methodology is demonstrated on a benchmark polymerisation process. Copyright © IFAC 2003

关键词： Extended Kalman filters

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