Batch Process Monitoring and Quality Prediction Based on Multi-way Kernel Entropy PLS

Aiming at the nonlinear,unequal length of the data in the batch processes,a multi-way entropy partial least squares(MKEPLS) process monitoring and quality prediction method was proposed combining with kernel partial least squares and kernel entropy analysis. The method solved the unequal length and missing data problem by expanding the original three-dimensional data in a new unfolding way,and the nonlinear character of the data could also be solved through the kernel mapping process,which mapped the data from low dimensional input space into a high dimensional feature space to achieve the nonlinear relationship between variables linear transformation. Then, the data dimensionality reduction was conducted according to the kernel entropy eigenvalues and eigenvectors,which made up the shortcomings in MKPLS method. Moreover,the kernel feature extraction algorithm was introduced to reduce the computational kernel space to enable the online applications of MKEPLS. The numerical examples and practical industrial process data performance show that MKEPLS method can monitor the fault effectively,improve the fault alarm rate,and predict the quality of the final product at the same time.