Nearest Group Standardization KPCA Based Monitoring Method for Nonlinear Multi-Operation Batch Process

A nearest group standardization kernel principal component analysis (NGS-KPCA) based monitoring method specifically designed for nonlinear multi-operation batch process is proposed in this paper. The key feature of the proposed method can deal with nonlinearity and non-Gaussian characteristics in the process. To start, phase partition is implemented according to operation. Then, k-means clustering algorithm produces an optimal ensemble clustering solution at each phase. Meanwhile, to deal with non-Gaussian characteristic produced by randomness and uncertainty of the operation, nearest group standardization (NGS) method is exploited to make the data belonging to the same cluster obey Gaussian distribution. To deal with nonlinear characteristic, KPCA is chosen to build monitoring model for each clustering. The proposed method is illustrated through the 120t ladle furnace (LF) steelmaking process. Simulation results have shown better performance than multiway kernel principal component analysis (MKPCA).