A multi-scale prediction model based on empirical mode decomposition and chaos theory for industrial melt index prediction

Abstract

Melt index (MI) is one of the most important variables determining the product quality in the industrial propylene polymerization process. In this paper, a multi-scale prediction model is proposed for MI prediction by combining the empirical mode decomposition (EMD), chaos theory and optimized relevance vector machine (RVM) model. First, the EMD method is used to decompose the MI time series into intrinsic mode functions (IMFs) and the residual. Then the chaotic characteristics of each component are identified with chaos theory. For the components with chaotic characteristics, relevance vector machine (RVM) chaotic prediction model is developed as the predictive model. For the components without chaotic characteristics, least squares support vector machine (LSSVM) is used as the predictive model. At the same time, an improved ant colony optimization (IACO) algorithm is used to optimize the parameters of RVM and LSSVM. In the end, the final prediction results of MI are obtained by summing the predicted results of all components. Research on the proposed multi-scale model is carried out on a real propylene polymerization plant and the results are compared among the RVM-chaos, IACO-RVM-chaos and multi-scale models. The research results show that the model developed achieves a good performance in the industrial MI prediction process.