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

Improved operation of a batch polymerisation reactor through...

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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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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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Gaussian process regression for batch process modelling 7th

Gaussian process regression for batch process modelling

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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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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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Monitoring performance in flexible process manufacturing 7th

Monitoring performance in flexible process manufacturing

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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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Computation of the performance of shewhart control charts 7th

Computation of the performance of shewhart control charts

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

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

The performance of a control chart in statistical process control is often quantified in terms of the Average Run Length (ARL). The ARL enables a comparison to be undertaken between various monitoring strategies. These are often determined through Monte Carlo simulation studies. Monte Carlo simulations are time consuming and if too few runs are performed then the results will be inaccurate. An alternative approach is proposed based on analytical computation. The analytical results are compared with those of the Monte Carlo simulations for three case studies. Copyright © IFAC 2003

关键词： Control charts

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Multi-site performance monitoring in batch pharmaceutical production 7th

Multi-site performance monitoring in batch pharmaceutical pr...

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

作者： Wong, C.W.L. Escott, R.E.A. 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 GlaxoSmithKline Chemical Development Tonbridge United Kingdom

A challenge facing the pharmaceutical and chemical industries is how to understand and identify differences in process behaviour where a product is manufactured at two different sites. Three approaches based on multi-group principal component analysis are investigated and benchmarked against single site models. The multi-group approach is shown to remove differences between sites such as operational scale thereby enabling the analysis to focus on identifying differences in variation between the two sites that are not a consequence of process configurations. From the analysis it is observed that the multi-group approach can assist in the understanding of manufacturing performance. Copyright © IFAC 2003

关键词： Chemical industry

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Investigation of Calibration-Free Resolution Techniques and Independent Component Analysis

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IFAC Proceedings Volumes 2004年第1期37卷 433-438页

作者： S. Triadaphillou A.J. Morris E.B. Martin I. Wells Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NEl 7RU UK Avecia Process Technology Department Grangemouth FK3 8XG UK

Calibration-free resolution techniques provide an alternative approach to the development of a calibration model. These combine spectroscopic measurement coupled with mathematical and statistical assumptions and give spectral profiles and non-quantitative concentration profiles for the unknown mixture. In this paper, a number of calibration free techniques including VARIMAX. ITTFA, EFA, FSWEFA, SIMPLISMA are described and applied to a synthetic spectral data set and the results are compared with the complementary technique of Independent Component Analysis (ICA) in particular FastICA and JADE. The results were comparable in all cases with ICA separating the signal from the constituent components successfully.

关键词： Reaction Monitoring and Control Spectral Conjunction Spectral Deconvolution Multivariate Calibration Iterative Resolution Methods

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