Intelligent Support Vector Machine Approach And Its Application On Melt Index Prediction In Propylene Polymerization

Melt index (MI) is considered as one of the important variables of the quality in the propylene (PP) polymerization process, and it is crucial to propose a reliable estimation model of MI.. PP polymerization process usually involves complex kinetic mechanism, and various plants, which makes it a challenge to study the process though mechanism modeling approaches. Based on the dada, the statistical method doesn't need much knowledge of the studied process, so it is used to predict MI popularly.Statistical Learning Theory (SLT) is a theatrical framework of machine learning for small samples, and Support Vector Machine (SVM) comes out from this theory. According to the structural risk minimization rule, it can get the global optimal linear decision function in a higher dimensional feature space according to a kernel function. It avoids the curse of dimensionality and it is of good generalization ability. Its good performance makes it become a hot topic in machine learning. However, its good performance depends on the chosen of parameters, and this paper makes the use of intelligent optimization algorithms to optimize the parameters, so many kinds of intelligent SVM models are proposed. The main contributions of the present work are as follows:1. In order to improve the training speed,generalization ability and space ability of the traditional SVM, this paper does some research on Least Square SVM (LSSVM),Weighted LSSVM (WLSSVM),Relevance Vector Machine (RVM), and these methods are used to predict MI. The results confirm the models'validity.2. The kernel parameter and the regularization factor determine the performance of SVM, so obtaining the best parameters can improve the prediction ability of models directly. In order to choose better parameter for SVM,this paper applies improved Particle Swarm Optimization (PSO) algorithm, which has an inertia weight factor, to optimize the parameters of LSSVM. WLSSVM and RVM, so the pure intelligent SVM models (PSO-LSSVM. PSO-WLSSVM and PSO-RVM) are proposed. The good search capability and fast convergence make PSO algorithm applicable to parameter optimization. The results show that the optimal models have better prediction ability.3. Addressing the deficiency of the Particle Swarm Optimization (PSO) algorithm whose particles are easy to sink into premature convergence and run into local optimization in the iterative process, with the clone selection strategy of immune system, the Immune Clone PSO (ICPSO) algorithm based on immune strategy was proposed to make the particles of ICPSO maintain the diversity during the iterative process so as to overcome the defect of premature convergence of PSO; in order to reduce the blindness of the research of PSO and avoid premature, Ant Colony Optimization (ACO) algorithm is used to find an optimal path for ICPSO algorithm, thus the AC-ICPSO algorithm is proposed. Then the two algorithms are used to optimize the LSSVM and WLSSVM's parameters, so the hybrid intelligent SVM models (ICPSO-LSSVM,AC-ICPSO-LSSVM,ICPSO-WLSSVM. AC-ICPSO-WLSSVM) are found. Researches on the optimized model were illustrated with the real plant of propylene polymerization, and the results showed that the proposed approach had great prediction accuracy and validity.4. In order to avoid the characteristics of high complexity, uncertainty, multi level nature and so on in PP polymerization process, Online Correction Strategy (OCS) is presented in this paper. With the update of the data, the forecast models need adjustment constantly to adapt the latest conditions. The experiments show that the corrected models have better prediction ability.