Larimore's state space model derivation and stochastic estimation algorithm, first published in 1983, have been the adopted standard for deriving the state variables and parameters of the five (5) matrices state s...
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Optimisation of fed-batch processes can be described as a constrained nonlinear end-point dynamic optimisation problem. Although iterative dynamic programming (IDP) is feasible, it is usually very time-consuming and v...
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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 bui...
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The detection of process changes using a partial least squares (PLS) based monitoring scheme can be achieved through the interrogation of two metrics, Hotelling's T and the Q-statistic. The Q-statistic has been sh...
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A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants alway...
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A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants always contain some peak noise, but ‘denoise’ algorithms such as ‘SureShrink’ can have difficulty in handling sudden large excursions in the corrupting noise. In the new algorithm the peak noise is reduced prior to filtering using the SureShrink algorithm. The pre screened data can be used to build a number of projections to latent structures regression models. Data from an industrial fluidized bed reactor is used to evaluate the new algorithm, which demonstrates improved performance in terms of improved modeling capability through use of the new data pre filtering algorithm.
This study contributes to the comparison of partial least squares (PLS) and canonical variate analysis (CVA) for the identification of dynamic systems. Two model forms, autoregressive with exogenous inputs and state s...
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This study contributes to the comparison of partial least squares (PLS) and canonical variate analysis (CVA) for the identification of dynamic systems. Two model forms, autoregressive with exogenous inputs and state space representations, are developed with PLS and CVA being used to calculate the model parameters. The different models are compared using two case studies: a benchmark simulation of a binary distillation column and an industrial fluidised bed reactor.
An issue often raised in multivariate statistical processcontrol, when using statistical projection-based techniques to define nominal process behaviour, is that of the assured identification of the variables causing...
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An issue often raised in multivariate statistical processcontrol, when using statistical projection-based techniques to define nominal process behaviour, is that of the assured identification of the variables causing an out-of-statistical-control signal. One approach which has been adopted is that once a change in process operating conditions has been detected, the contribution of the individual variables to the principal component scores or squared prediction error, the Q-statistic, are examined. Adopting this approach, it is important that those variables responsible for, or contributing to, the process change are clearly identifiable. In process modelling and estimation studies, confidence bounds are typically placed around the model predictions. Currently confidence bounds are not used to identify the limits of normal behaviour for the individual multivariate statistical contributions, resulting in the interpretation of the contribution plot being left to the user. This paper presents a potential solution to the definition of confidence bounds for contribution plots. The methodology is based on bootstrap estimates of the standard deviations of the loading matrix. The proposed approach is evaluated using data from a benchmark simulation of a continuous stirred tank reactor system. The preliminary results are encouraging. Copyright (C) 2000 John Wiley and Sons, Ltd.
With the increasing take-up of PAT1 by the pharma- and bio-industries there is a critical need for robust spectral calibrations for processes which are subject to the variations in physical properties such as sample c...
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ISBN:
(纸本)9783902661616
With the increasing take-up of PAT1 by the pharma- and bio-industries there is a critical need for robust spectral calibrations for processes which are subject to the variations in physical properties such as sample compactness, surface topology, etc. The variation in the optical path-length materializing from the physical differences between samples may result in multiplicative light scattering influencing spectra in a nonlinear manner leading to the poor calibration performance. A new approach "Optical Path Length Estimation and Correction" overcomes the limitations of existing light scattering correction methods. Copyright 2007 IFAC.
With a view to ensuring the validity of multivariate calibration models in the presence of temperature variations, a new methodology, individual contribution standardization (ICS), is proposed to correct for temperatu...
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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-prod...
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