Copula-based JITL for polyester fiber polymerization process modeling

The polymerization process is the critical stage of polyester production, and the reaction mechanism of the polymerization process is complex and involves a large number of variables. The melt intrinsic viscosity is an essential indicator of the polymer, control and prediction of melt intrinsic viscosity is particularly crucial for the synthetic fibers industry. This paper introduces the Copula feature selection method, and nonlinear features of the polymerization process are selected to establish the least squares support vector machine (LSSVM) model for the viscosity prediction. Since the Just-in-time learning (JITL) can account for the dynamic behavior of the process, and the Copula function can determine the nonparametric measure of the dependence between variables, a Copula-based JITL is proposed for viscosity prediction of polyester fiber polymerization process. The effectiveness of the proposed approach is verified by the actual industrial data.