On-line batch process monitoring using multiway kernel independent component analysis

For on-line batch process monitoring, multiway principal component analysis (MPCA) is a useful tool. But the MPCA-based methods suffer two disadvantages: (i) it restricts itself to a linear setting, where high-order statistical information is discarded; (ii) all the measurement variables must follow Gaussian distribution and the objective of MPCA is only to decorrelate variables, but not to make them independent. To improve the ability of batch process monitoring, this paper proposes a monitoring method named multiway kernel independent component analysis (MKICA). By using kernel trick, the new monitoring indices are investigated, which have been mapped into high-dimensional feature space. On the benchmark simulator of fed-batch penicillin production, the presented method has been validated.