Feature space dimension-reduction based process monitoring of solvent dehydration separation process

The measurement variables of chemical process usually show the characteristic of nonlinear and non-Gaussian behaviors. A novel modeling method was proposed by integrating the improved kernel principal component analysis (KPCA) with support vector data description (SVDD). The Mexican hat wavelet function was introduced to construct the kernel function by utilizing the advantage of extracting the subtle feature of nonlinear non-stationary signal. The nonlinear mapping and anti-noise capability of kernel function was enhanced. Then the cluster analysis was used in the kernel feature space. The data that represented the characteristic center in every cluster were chosen, which can decrease the computational complexity and improve the results of real-time monitor. Furthermore, the SVDD was adopted to describe the feature space with dimension-reduction, and a new monitor index was constructed by SVDD to describe the non-Gaussian information. The method was applied to a solvent dehydration distillation process. Simulation results demonstrate that the method can detect the fault promptly and effectively.