This paper proposes a novel variable selection method for the improvement of the quality
estimation performance and knowledge extraction in a batch process. The quality estimation
method is an effective alternative to the costly and time-consuming quality measurement.
However, degradation of the prediction performance of the quality estimation model caused by
inclusion of insignificant variables is a serious problem. The preprocessing of variable selection
is thus important to improve the prediction accuracy by removing the variables uncorrelated
with quality variables. The variable selection technique also can be used as a knowledge-extraction tool. The technique allows us to identify the process characteristics related to product
quality. The problem of inaccurate variable selection results caused by a large number of variables
and a limited number of samples of batch process data is solved by the bootstrapping technique.
Despite increased computational load, combination of bootstrapping with variable selection
enhances the reliability of the variable selection result. An industrial poly(vinyl chloride)
polymerization process is used as a case study to show the improved performance of the proposed
method compared with multiway partial least squares (MPLS). The proposed method shows
better accuracy than MPLS in both detecting the quality-related variables and estimating the
real values of quality variables.
A novel method is proposed for fault detection and operation mode identification in processes with multimode operations. The proposed method employs the support vector machine as a classification tool together with an entropy-based variable selection method to deal with normal data clusters corresponding to multiple operational modes and abnormal data corresponding to faults. The use of the classification method in fault detection and operation mode identification allows us to build decision boundaries among the data clusters without the assumption of normal distribution. In addition, selection of variables by minimizing the total entropy of training data ensures superior generalization performance in classification. The performance of the proposed method is compared with that of the traditional PCA-based fault detection method using test data where class information is already known. While the existing method has produced considerable error rate (1.9% by T 2 chart and 24.2% by Q chart) in detecting faults, the proposed method has shown no error in this example in either fault detection or operation mode identification. Despite these outstanding results, it should be noted that the performance of the proposed method depends critically on the quality of the training data.
We propose a two-stage variable-selection strategy performed in the wavelet domain in order to extract quality-related information from batch trajectories of process variables and to build parsimonious quality-estimation
models. The proposed variable-selection method proceeds in two stages and uses the discrete wavelet transform
of the batch trajectories. This approach greatly reduces the computation time required for finding those wavelet
coefficients related to product quality. A quality-estimation model built with the selected wavelet coefficient
subset is shown for the case study we discuss to exhibit satisfactory estimation accuracy. The application of
the method to an industrial PVC polymerization process was found to achieve the same prediction accuracy
as a multiway partial least-squares (MPLS) based model that uses all variables in the time domain.
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