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American control Conference (ACC)

作者： Jie Zhang Centre for Process Analytics and Control Technology Department of Chemical & Process Engineering University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780372980

A sequential training method for developing bootstrap aggregated neural network models is proposed in this paper. In this method, individual networks within a bootstrap aggregated neural network model are trained sequentially. The first network is trained to minimise its prediction error on the training data. In the training of subsequent networks, the training objective is not only to minimise the individual networks' prediction errors but also to minimise the correlation among the individual networks. Training data sets for the individual networks are different and are generated through bootstrap re-sampling of the original training data set. Training is terminated when the aggregated network prediction performance cannot be further improved. An application example demonstrates the superior performance of this neural network training strategy.

关键词： Neural networks Nonlinear systems Training data Chemical analysis Chemical technology Jacobian matrices control system analysis process control Chemical processes Chemical engineering

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Inferential feedback control of distillation composition based on PCR and PLS models

Inferential feedback control of distillation composition bas...

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American control Conference (ACC)

作者： Jie Zhang Centre for Process Analytics and Control Technology Department of Chemical & Process Engineering University of Newcastle Newcastle-upon-Tyne UK

A principal component regression (PCR) and partial least squares (PLS) model based inferential feedback control strategy for distillation composition control is developed. PCR and PLS model based software sensors are developed from process operational data so that the top and bottom product compositions can be estimated from multiple tray temperature measurements. The PCR and PLS software sensors are used in the feedback control of the top and bottom product compositions. This strategy can overcome the problem of substantial time delay in composition analysers based control and the problem of substantial bias in single tray temperature control. Static estimation bias and the resulting static control offsets are eliminated through mean updating of process measurements. Applications to a simulated methanol-water separation column demonstrate the effectiveness of this control strategy.

关键词： Feedback control Temperature measurement Temperature control Distillation equipment Time measurement Chemical analysis process control Chemical technology Least squares methods Delay effects

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Improved on-line process fault diagnosis using stacked neural networks

Improved on-line process fault diagnosis using stacked neura...

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IEEE Conference on control technology and Applications (CCTA)

作者： Jie Zhang Department of Chemical & Process Engineering Centre for Process Analytics and Control Technology University of Newcastle Newcastle-upon-Tyne UK

Since it is generally difficult, if not impossible, to develop a perfect neural network, a single neural network can lack reliability. Therefore a single neural network based fault diagnosis system may not give reliable fault diagnosis. Neural network model reliability or robustness can be improved by combining several non-perfect neural networks. Each individual network is trained on a bootstrap re-sample of the original training data. The outputs from the individual networks are averaged to give the final diagnosis results. Applications of the proposed method to a continuous stirred tank reactor demonstrate that a stacked neural network can give more reliable diagnosis than a single neural network.

关键词： Fault diagnosis Neural networks Fault detection Data analysis Chemical technology Training data Parameter estimation State estimation Predictive models Data mining

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Improving long range prediction for nonlinear process modelling through combining multiple neural networks

Improving long range prediction for nonlinear process modell...

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IEEE Conference on control technology and Applications (CCTA)

作者： Z. Ahmad Jie Zhang Department of Chemical and Process Engineering Centre for Process Analytics and Control Technology University of Newcastle Newcastle-upon-Tyne UK

Different methods for combining multiple neural networks in order to improve model long range prediction performance are compared in this paper. It is shown that combining multiple non-perfect neural networks can improve model predictions, especially long range predictions. Among the different approaches, the principal component regression based approaches generally give very good performance. Selective combination is also very beneficial to the improvement of model predictions.

关键词： Predictive models Neural networks Artificial neural networks Robustness Training data Chemical analysis Chemical technology process control Fitting Performance analysis

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Recurrent neuro-fuzzy networks for the modelling and optimal control of batch processes

Recurrent neuro-fuzzy networks for the modelling and optimal...

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Conference of the North American Fuzzy Information processing Society - NAFIPS

作者： J. Zhang Centre for Process Analytics and Control Technology Department of Chemical & Process Engineering University of Newcastle Newcastle-upon-Tyne UK

A recurrent neuro-fuzzy network based strategy for batch process modelling and optimal control is presented. The recurrent neuro-fuzzy network allows the construction of a "global" nonlinear long-range prediction model from the fuzzy conjunction of a number of "local" linear dynamic models. In this recurrent neuro-fuzzy network, the network output is fed back to the network input through one or more time delay units. This particular structure ensures that predictions from a recurrent neuro-fuzzy network are long-range or multi-step-ahead predictions. process knowledge is used to initially partition the process nonlinear characteristics into several local operating regions and to aid in the initialisation of the corresponding network weights. process input output data is then used to train the network. Membership functions of the local regimes are identified and local models are discovered through network training. In the paper, a recurrent neuro-fuzzy network is used to model a fed-batch reactor and to calculate the optimal feeding policy.

关键词： Fuzzy neural networks Optimal control Predictive models Inductors Neural networks process control Chemical analysis Chemical technology Chemical processes Delay effects

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Nonlinear model predictive control based on multiple local linear models

Nonlinear model predictive control based on multiple local l...

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American control Conference (ACC)

作者： Jie Zhang J. Morris Centre for Process Analytics and Control Technology Department of Chemical & Process Engineering University of Newcastle Newcastle-upon-Tyne UK

A long range nonlinear predictive control strategy using multiple local linear models is proposed. The multiple local linear models are identified through recurrent neuro-fuzzy network training. In this modelling approach, the process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to represent the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. Based upon the multiple local linear models, a nonlinear model based controller is developed by combining several local linear model based predictive controllers which usually have analytical solutions. control actions obtained based on local incremental models contain inherent integral actions eliminating static offsets in a natural way. The techniques are demonstrated by applying to pH control in a continuous stirred tank reactor.

关键词： Predictive models Predictive control process control Chemical technology Fuzzy neural networks Neural networks Chemical analysis Chemical processes Chemical engineering Gravity

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Impurity Detection in Batch Pharmaceutical Production

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IFAC Proceedings Volumes 2001年第5期34卷 37-46页

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

Data from a reaction vessel producing a drug intermediate was analysed using the multivariate statistical techniques of principal component analysis and partial least squares. Due to the limited number of batches available. data augmentation was used to increase the number of batches for model building. A second methodology investigated was that of multi-group modelling. This enabled between cluster variability to be removed, thereby allowing attention to focus on within process variability. The paper describes how the different approaches enabled a better understanding of the factors causing the onset of impurity formation to be obtained.

关键词： Biotechnology Statistical Analysis Manufacturing processes

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Multiple Sensor Disturbance Identification through Principal Component Analysis

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IFAC Proceedings Volumes 1999年第2期32卷 7653-7658页

作者： F. Jia E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne NE1 7RU England

Previous publications have focused upon the application of principal components analysis (PCA) for sensor fault identification through data reconstruction. These reconstruction based methods do not address the problem of fault propagation to other sensor measurements and as a consequence misleading fault identification can result. A novel method for the identification of multiple sensor disturbances during process monitoring is proposed based upon the T 2 -statistic. By using the T 2 -statistic in conjunction with the associated principal component score contribution plot, multiple sensor disturbances can be identified in a sequential manner. The methodology is demonstrated on two industrial plant simulations.

关键词： Performance Monitoring fault detection fault isolation statistical process control data reduction

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Tri-linear on-line monitoring of a batch process

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IFAC Proceedings Volumes 2001年第25期34卷 655-660页

作者： X. Meng E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne NE1 7RU England

For assured through batch process performance monitoring, a number of bilinear and tri-linear modelling techniques require information on the entire batch duration, to enable the on-line realisation of the nominal model. Various strategies have been proposed for the in-filling of these yet unknown values. A methodology is proposed where the unknown observations are calculated from a weighted combination of the scores from a new batch and those from a reference data-set. A modification to the existing confidence limits is then described for the on-line implementation of a tri-linear model. The methodology is demonstrated on a benchmark simulation of a semi-batch emulsion polymerisation.

关键词： Statistical analysis Batch Performance Monitoring

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LOCAL LINEAR AND NON-LINEAR MULTI-WAY PARTIAL LEAST SQUARES BATCH MODELLING

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IFAC Proceedings Volumes 2002年第1期35卷 373-378页

作者： N.M. Fletcher A.J. Morris E.B. Martin Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne England NE1 7RU UK

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.

关键词： Dynamic Modelling Local structures Batch control

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