LAUSR

Abstract As a key stage in polyester production, polymerization process is difficult to model due to its complex reaction mechanism. As a result, online viscosity prediction in industrial polyester polymerization processes is not an easy task. An efficient data-driven prediction model is considered in this work. In order to solve the problem of low accuracy of the online viscosity measuring instrument and considerably time-consuming laboratory analysis, variables that are easily monitored during the polymerization process, i.e. temperature and pressure in the main reactor as well as the viscometer values, are selected to establish an Extreme Learning Machine (ELM) viscosity prediction model. A Just-in-time-based ELM model was established to predict the viscosity values under multi-mode operating and multi-standard production conditions. Consequently, without relying on the time-consuming laboratory analysis process, the PET viscosity can be predicted online. The industrial PET viscosity prediction results show the improved prediction performance of the proposed modeling approach in comparison with ELM and JPCR (Just-in-time principal component regression) approaches.