Melt Index Prediction Based on Adaptive Particle Swarm Optimization Algorithm‐Optimized Radial Basis Function Neural Networks

Reliable estimation of the melt index (MI) is crucial in the quality control of practical propylene polymerization (PP) processes. In this paper, a novel predictive neural network system, combining the particle swarm optimization (PSO) algorithm and radial-basis function neural networks (RBFN), is presented to infer MI from real PP process variables, where the PSO algorithm dynamically constructs the RBFN structure and parameters and a new adaptive PSO (APSO) algorithm, which adjusts the algorithm behavior based on evolution information of swarms, further accelerates the convergence speed. Principle component analysis is applied to select the most relevant process features and to reduce the number of input variables in the model. A detailed comparison between PSO, APSO and the gradient descent algorithm is carried out using historical data from a real plant.

[1]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[2]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[3]  Jie Zhang,et al.  Inferential Estimation of Polymer Melt Index Using Sequentially Trained Bootstrap Aggregated Neural Networks , 2006 .

[4]  Yeong-Koo Yeo,et al.  A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant , 2009 .

[5]  Sudhir S. Bafna,et al.  A design of experiments study on the factors affecting variability in the melt index measurement , 1997 .

[6]  Youxian Sun,et al.  Melt index prediction by neural networks based on independent component analysis and multi-scale analysis , 2006, Neurocomputing.

[7]  S. Gupta,et al.  Dynamic simulation of propylene polymerization in continuous flow stirred tank reactors , 1993 .

[8]  Visakan Kadirkamanathan,et al.  Stability analysis of the particle dynamics in particle swarm optimizer , 2006, IEEE Transactions on Evolutionary Computation.

[9]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[11]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[12]  Hsuan-Ming Feng,et al.  Self-generation RBFNs using evolutional PSO learning , 2006, Neurocomputing.

[13]  Yeong-Koo Yeo,et al.  Prediction and quality control of the melt index during production of high-density polyethylene , 2008 .

[14]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[15]  Marc Toussaint,et al.  On Classes of Functions for which No Free Lunch Results Hold , 2001, Inf. Process. Lett..

[16]  Young-Kiu Choi,et al.  An adaptive neurocontroller using RBFN for robot manipulators , 2004, IEEE Trans. Ind. Electron..

[17]  Nam Mai-Duy,et al.  Approximation of function and its derivatives using radial basis function networks , 2003 .

[18]  J. Macgregor,et al.  On‐line inference of polymer properties in an industrial polyethylene reactor , 1991 .

[19]  Timothy F. L. McKenna,et al.  Single particle modelling for olefin polymerization on supported catalysts: A review and proposals for future developments , 2001 .

[20]  Chonghun Han,et al.  Melt index modeling with support vector machines, partial least squares, and artificial neural networks , 2005 .

[21]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[22]  Jun Zhang,et al.  Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms , 2007, IEEE Transactions on Evolutionary Computation.

[23]  Xinggao Liu,et al.  Melt index prediction by weighted least squares support vector machines , 2006 .