Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method

In this paper, a quality-related monitoring scheme of batch process using multi-phase dynamic non-Gaussian model is presented. Product quality of a batch process is difficult to be effectively guaranteed because of its frequent start-stop operation, variable operating conditions, strong dynamic and non-Gaussian character of process data. A direct dynamic PLS (DDPLS), in which weighted time-lagged matrix is used to extract dynamic components, is introduced to the dynamic problem. Meanwhile, independent component analysis (ICA) is proposed to deal with non-Gaussianity of dynamic components in DDPLS. Considering most batch processes are multi-phase in nature, in order to well describe the characteristics of every phase and set up sub-models, GMM algorithm is adopted for phase division and fuzzy membership method for transition identification. TE benchmark is used to verify the validity and superiority of our new method over traditional PLS, DPLS. Then the new method is applied to a real hot strip mill production plant.