Development of PP Melt Index Inferential Model
The melt index (MI) is an important quality specification in polypropylene (PP) production industries. Accurate online measurement of MI becomes vital in PP quality control. A new MI inferential model is proposed by using macro heat of reaction (MHR) as a key factor in MI correlation. MHR is calculated from a dynamic propylene polymerization model. The proposed MI model is validated by plant data and the prediction error is less than 5%. Simulations are performed by three different MI inferential models. The results show that the proposed model has least errors in various operation conditions. The MI model could be used as online soft sensor model or MI control
Melt index prediction by RBF neural network with an ICO-VSA hybrid optimization algorithm
Melt index (MI) is considered as one of the most significant parameter to determine the quality and the grade of the practical polypropylene polymerization products. A novel ICO-VSA-RNN (RBF neural network with ICO-VSA algorithm) MI prediction model is proposed based on radial basis function (RBF) neural network and improved chaos optimization (ICO), and variable-scale analysis (VSA), where the ICO is first added and then combined with the VSA to overcome the defects of ICO and VSA, then the parameters of the RBF neural network are optimized with them. At last, the RBF neural network model for MI prediction model is developed. Further researches on the optimal RBF neural network model of MI prediction are carried out with the data from a real industrial plant, and the prediction results show that the performance of this prediction model is much better than the RBF neural network model without optimization. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
The simultaneous solution and sensitivity analysis of systems described by ordinary differential equations
The methodology for the simultaneous solution of ordinary differential equations and the associated first-order parametric sensitivity equations is presented, and a detailed description of its implementation as a modification of a widely disseminated implicit ODE solver is given. The error control strategy ensures that local error criteria are independently satisfied by both the model and sensitivity solutions. The internal logic effectuated by this implementation is detailed. Numerical testing of the algorithm is reported; results indicate that greater reliability and improved efficiency is offered over other sensitivity analysis methods.