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 deter...
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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 m...
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This paper presents a Bayesian approach, based on infinite Gaussian mixtures, for the calculation of control limits for a multivariate statistical processcontrol scheme. Traditional approaches to calculating control ...
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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. ...
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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 custome...
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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 r...
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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 mechanist...
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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 p...
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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 ...
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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 limit...
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