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IEEE Conference on control technology and Applications (CCTA)

作者： Zhihua-Yiong Jie Zhang University of Newcastle Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne U. K.

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On-line re-optimisation control of a batch polymerisation reactor based on a hybrid recurrent neural network model

On-line re-optimisation control of a batch polymerisation re...

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American control Conference (ACC)

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

A hybrid recurrent neural network model based on-line re-optimisation control strategy is developed for batch polymerisation reactors. The hybrid model contains a simplified mechanistic model covering material balance and simplified reaction kinetics only and recurrent neural networks. Based on this hybrid neural network model, optimal control policy can be calculated. A difficulty in the optimal control of batch polymerisation reactors is that optimisation effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. A technique for on-line estimation of reactive impurity and reactor fouling has been developed by Zhang et al. (1999). In this contribution, on-line reactive impurity estimation is combined with batch reactor optimal control to form a novel re-optimisation control strategy. When there exists an unknown amount of reactive impurities, the off-line calculated optimal control profile will be no longer optimal. On-line impurity estimation is applied to estimate the amount of reactive impurities during the early stage of the batch. Based on the estimated amount of reactive impurities, on-line re-optimisation is applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimisation control of a simulated batch MMA polymerisation process.

关键词： Polymers Inductors Optimal control Impurities Neural networks Recurrent neural networks Chemical analysis process control Chemical technology Kinetic theory

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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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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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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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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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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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Latent variable selection in partial least squares modelling

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IFAC Proceedings Volumes 2001年第25期34卷 463-468页

作者： Baibing Li Elaine Martin Julian Morris Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne NE1 7RU United Kingdom

Selection of the number of latent variables in partial least squares (PLS) is an important issue in process modelling. In this paper, the Bayesian Information Criterion (BIC) is used to establish a rule for the determination of the number of latent variables. Unlike Wold's R criterion, where the number of latent variables is determined by the prediction error sum of squares, the philosophy of the BIC rule is based on model accuracy and model parsimony. A simulation study and a practical application are used to demonstrate that BIC is a competitive alternative to Wold's R criterion for latent variable selection in PLS.

关键词： Identification Modelling Parameterisation process control Subspace

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The Monitoring of an Industrial Fed-Batch Fermentation process

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IFAC Proceedings Volumes 2001年第5期34卷 143-148页

作者： N.M. Fletcher E.B. Martin A.J. Morris H. Quinn B. Hinge Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne NE1 7RU UK Pfizer Ltd. Sandwich Kent

The monitoring of the evolution of batch and fed-batch processes based on the multivariate statistical projection technique of principal component analysis is investigated. Prior to developing a process representation, a sensitivity analysis is carried out to identify the key variables that influence process behaviour. Graphical representations are then used to demonstrate the suitability of techniques for process monitoring where the objective is to identify, at any early stage, the onset of changes in process operation and deviations in product quality. The strategy is illustrated on an industrial biological fed-batch fermentation

关键词： Fermentation processes Statistical Analysis Biotechnology

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Actuator fault diagnosis in a continuous stirred tank reactor using identification techniques

Actuator fault diagnosis in a continuous stirred tank reacto...

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European control Conference (ECC)

作者： Ron J. Patton C. J. Lopez Toribio Silvio Simani Julian Morris Elaine Martin Jie Zhang School of Engineering University of Hull UK School of Engineering University of Hull Hull Kingston upon Hull GB University of Ferrara Ferrara Italy Centre for Process Analytics and Control Technology (CPACT) University of Newcastle UK

This paper presents a model-based procedure for the detection and isolation of actuator faults in a chemical process. The diagnosis system is based on the estimation of process outputs. A dynamic Multi-input, multiple output (MIMO) process of the process under investigation is obtained by identification procedures, exploiting both Auto Regressive exogenous and Takagi-Sugeno (T-S) fuzzy input-output systems. Fuzzy systems are exploited to cope with different process working conditions. Residual analysis and geometrical tests are then used for fault detection and isolation, respectively. The proposed designs were evaluated using a benchmark simulation of a Continuous Stirred Tank Reactor.

关键词： Mathematical model Actuators Chemical reactors Valves Predictive models Fault detection Inductors

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