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International Joint Conference on Neural Networks (IJCNN)

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

A single neural network model developed from a limited amount of data usually lacks robustness. Thus combining multiple neural networks can enhance the neural network model performance. In this paper, a Bayesian combination method is developed for nonlinear dynamic process modelling and compared with simple averaging. Instead of using fixed combination weights, the estimated probability of a particular network being the true model is used as the combination weight for combining that network. A nearest neighbour method is used in estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. The prior probability is estimated using the SSE of individual networks on a sliding window covering the most recent sampling times. It is shown that Bayesian combination generally outperforms simple averaging.

关键词： Bayesian methods Neural networks Robustness Training data process control Robust control Artificial neural networks Industrial training Chemical analysis Chemical technology

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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

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.

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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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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IEEE International Symposium on Intelligent control (ISIC)

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

ISBN: (纸本)0780386353

This work 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.

关键词： process control Neural networks Optimal control Polymers Manufacturing processes Inductors Chemical analysis Chemical technology Chemical engineering Electronic mail

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Multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks

Multi-objective optimal control of batch processes using rec...

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International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (纸本)0780378989

A recurrent neuro-fuzzy network based strategy for batch process modelling and multi-objective optimal control is presented. In this recurrent neuro-fuzzy network a "global" nonlinear long-range prediction model is constructed from the fuzzy conjunction of a number of "local" linear dynamic models. 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 this paper, a recurrent neuro-fuzzy network is used to model a fed-batch reactor and to calculate the optimal feeding policy.

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

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Batch-to-batch model-based iterative optimisation control for a batch polymerisation reactor

Batch-to-batch model-based iterative optimisation control fo...

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American control Conference (ACC)

作者： Zhihua Xiong Jie Zhang Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780378962

A batch-to-batch model-based iterative optimisation control strategy for a batch polymerisation reactor is proposed. Because the reference sequences for product quality variables during a batch are usually difficult to be set reasonably and the interest usually lies in the end-of-batch product quality, a modified quadratic objective function is introduced to track the desired qualities at the end-point of a batch. Predictions of recurrent neural network models are modified using the prediction errors in the previous runs. Because model errors are gradually reduced from batch to batch, the control trajectory gradually approaches to the optimal control policy. The proposed scheme is illustrated on a simulated batch polymerisation reactor.

关键词： Polymers Inductors Recurrent neural networks Predictive models Error correction Optimal control Temperature control Chemical analysis process control Chemical technology

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Computation of the Performance of Shewhart control Charts

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IFAC Proceedings Volumes 2004年第1期37卷 275-280页

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

The perfonnance 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 detennined through Monte Carlo simulation studies. Monte Carlo simulations are time consuming and if too few runs are perfonned 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.

关键词： Statistical process control control charts Serial correlation Monte Carlo simulations

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SENSOR FAULT IDENTIFICATION USING WEIGHTED COMBINED CONTRIBUTION PLOTS

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IFAC Proceedings Volumes 2006年第13期39卷 908-913页

作者： Ahmed Alawi Sang Wook Choi Elaine Martin Julian Morris Centre for Process Analytics and Control Technology School of chemical Engineering and Advanced materials University of Newcastle Newcastle Upon Tyne NE1 7RU UK

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 T 2 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.

关键词： process monitoring Fault diagnosis Fault detection

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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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Gaussian process Regression for Batch process Modelling

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IFAC Proceedings Volumes 2004年第9期37卷 817-822页

作者： Xiaoling Ou 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 UK

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.

关键词： Batch process dynamic modelling Gaussian process Monte Carlo methods

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Parameter Estimation for Batch processes Using a Bayesian Approach

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IFAC Proceedings Volumes 2004年第9期37卷 703-708页

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

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.

关键词： Bayesian Analysis Parameter estimation Polymerization

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