Monitoring of Distillation Column Based on Indiscernibility Dynamic Kernel PCA
Aiming at complicated faults detection of distillation column industrial process, it has faced a grave challenge. In this paper, a new indiscernibility dynamic kernel principal component analysis (I DKPCA) method is presented and applied to distillation column. Compared with traditional statistical techniques, I DKPCA not only can capture nonlinear property and dynamic characteristic of processes but also can extract relevant variables from all the variables. Applying this new method to distillation column process (a hardware in the loop simulation system) the results prove the proposed method has great advantages, that is, lower missing rate and higher detection rate for the faults, compared with KPCA and DPCA.