On-line quality prediction of batch processes using a new kernel multiway partial least squares method

The conventional data-driven soft sensor methods such as multiway partial least squares have been encountering nonlinear problems in predictions of batch processes, and kernel methods have been used to deal with these problems. In this work, a new data-driven soft sensor method is proposed by developing a Reduced Dual Kernel multiway partial least squares algorithm. First, the number of kernel vectors is reduced by the feature vector selection method. Then, by projecting both input data and the output data into two reduced kernel spaces, dual kernel matrices are established. These two matrices can be used to build PLS models. Finally, the predicted data in the kernel space can be reversely projected onto its original space during online prediction. Comparisons were made among the proposed method and some pervious algorithms through a numerical example and an Escherichia coli fermentation batch process.