Online monitoring of batch processes combining subspace design of latent variables with support vector data description

Abstract

The correlation relations of batch process variables are quite complex. For local abnormalities, there is a problem that the variant features are overwhelmed. In addition, batch process variables have obvious non-Gaussian distributions. In response to the above two problems, a new multiple subspace monitoring method called principal component analysis - multiple subspace support vector data description (PCA-MSSVDD) is proposed, which combines the subspace design of latent variables with the SVDD modeling method. Firstly, PCA is introduced to obtain latent variables for removing redundant information. Secondly, the subspace design result is obtained through K-means clustering. Finally, SVDD is introduced to build the monitoring model. Numerical simulation and penicillin fermentation process prove that the proposed PCA-MSSVDD method has better monitoring performance than traditional methods.

Keywords

Batch process monitoring / principal components analysis / support vector data description

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