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Dynamic model-based batch process monitoring
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DOI：10.1016/j.ces.2007.09.046.png)
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Abstract
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An integrated framework consisting of a multivariate autoregressive (AR) model and multi-way principal component analysis (MPCA) is described for the monitoring of the performance of a batch process. After pre-processing the data, i.e., batch data unfolding, mean-centring and scaling. the data are then filtered using an AR model to remove the auto- and cross-correlation inherent within the pre-processed batch data. Model order is determined using Akaike information criterion and the model parameters are estimated through the application of partial least squares to attain a stable solution. MPCA is then applied to the residuals from the AR model. Three monitoring statistics are considered for the detection of the onset of process abnormalities in the batch process. The main advantage of the proposed approach is that it can monitor batch dynamics along the mean trajectory without the requirement to estimate future observed values. The proposed AR model-based approach is illustrated through its application to two polymerization processes. The case studies indicate that it gives better monitoring results in terms of sensitivity and time to fault detection than the approaches proposed by Nomikos and MacGregor [1994. Monitoring batch processes using multi-way principal components. A.I.Ch.E. Journal 40(8), 1361-1375] and Wold et al. [1998. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems 44, 331-340]. Crown Copyright (C) 2007 Published by Elsevier Ltd. All rights reserved.
Keywords:
batch process monitoring
multivariate autoregressive model
partial least squares
principal component analysis
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