Quality control in the polypropylene manufacturing process: An efficient, data‐driven approach

In the propylene polymerization process, the melt index (MI), as a critical quality variable in determining the product specification, cannot be measured in real time. What we already know is that MI is influenced by a large number of process variables, such as the process temperature, pressure, and level of liquid, and a large amount of their data are routinely recorded by the distributed control system. An alternative data-driven model was explored to online predict the MI, where the least squares support vector machine was responsible for establishing the complicated nonlinear relationship between the difficult-to-measure quality variable MI and those easy-to-measure process variables, whereas the independent component analysis and particle swarm optimization technique were structurally integrated into the model to tune the best values of the model parameters. Furthermore, an online correction strategy was specially devised to update the modeling data and adjust the model configuration parameters via adaptive behavior. The effectiveness of the designed data-driven approach was illustrated by the inference of the MI in a real polypropylene manufacturing plant, and we achieved a root mean square error of 0.0320 and a standard deviation of 0.0288 on the testing dataset. This proved the good prediction accuracy and validity of the proposed data-driven approach. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41312.

[1]  Lei Xie,et al.  Inferential Model for Industrial Polypropylene Melt Index Prediction with Embedded Priori Knowledge and Delay Estimation , 2012 .

[2]  Huaqin Jiang,et al.  Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm , 2013, Neurocomputing.

[3]  Zhiqiang Ge,et al.  Quality prediction for polypropylene production process based on CLGPR model , 2011 .

[4]  D J Choi,et al.  A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. , 2001, Water research.

[5]  Sirish L. Shah,et al.  Inferential sensors for estimation of polymer quality parameters: Industrial application of a PLS-based soft sensor for a LDPE plant , 2006 .

[6]  Ginés Rubio,et al.  Global and Local Modelling in Radial Basis Functions Networks , 2009, IWANN.

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

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

[9]  Xiao Dong Chen,et al.  A CFD-PBM-PMLM integrated model for the gas-solid flow fields in fluidized bed polymerization reactors , 2012 .

[10]  Johan A. K. Suykens,et al.  Optimal control by least squares support vector machines , 2001, Neural Networks.

[11]  De-Pan Shi,et al.  Steady-state and dynamic modeling of commercial bulk polypropylene process of Hypol technology , 2009 .

[12]  Che-Yen Wen,et al.  A histogram based data-reducing algorithm for the fixed-point independent component analysis , 2008, Pattern Recognit. Lett..

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

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

[15]  Yeong-Koo Yeo,et al.  Prediction of the melt flow index using partial least squares and support vector regression in high-density polyethylene (HDPE) process , 2010 .

[16]  M. Janga Reddy,et al.  Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management , 2009 .

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

[18]  Ting Wang,et al.  Melt index prediction by aggregated RBF neural networks trained with chaotic theory , 2014, Neurocomputing.

[19]  Xinggao Liu,et al.  Modeling mass transport of propylene polymerization on Ziegler–Natta catalyst , 2005 .

[20]  Kok Seng Chua,et al.  Efficient computations for large least square support vector machine classifiers , 2003, Pattern Recognit. Lett..

[21]  Alex Arenas,et al.  Neural virtual sensor for the inferential prediction of product quality from process variables , 2002 .

[22]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[23]  Zheng‐Hong Luo,et al.  Industrial Loop Reactor for Catalytic Propylene Polymerization: Dynamic Modeling of Emergency Accidents , 2010 .

[24]  J. Pinto,et al.  Correlating Polymer Parameters to the Entire Molecular Weight Distribution: Application to the Melt Index , 2006 .

[25]  P. Melo,et al.  Modeling of the Separation of Propene/Propane Mixtures by Permeation through Membranes in a Polymerization System , 2007 .

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

[27]  G. M. Sadeghi,et al.  General and more precise relationships between molecular weight, blend ratio, and melt index of binary polyethylene blends , 2008 .

[28]  H. Azizi,et al.  Controlled-peroxide degradation of polypropylene: Rheological properties and prediction of MWD from rheological data , 2008 .