ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process

Abstract This paper presents the development and the industrial implementation of a virtual sensor (soft-sensor) in the polyethylene terephthalate (PET) production process. This soft-sensor, based on a feed-forward artificial neural network (ANN), was primarily used to provide on-line estimates of the PET viscosity, which is necessary for process control purposes. The ANN-based soft-sensor (ANN-SS) was also used for providing redundant measurements of the viscosity that could be compared to the results obtained from the process viscometer. It was shown that the proposed ANN-SS was able to adequately infer the polymer viscosity, in such a way so as this soft-sensor could be used in the real-time process control strategy. The proposed control system has successfully been applied in servo and regulatory problems, thus allowing an effective and feasible operation of the industrial plant.

[1]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[2]  Ricardo J. G. B. Campello,et al.  APPLICATION OF HIERARCHICAL NEURAL FUZZY MODELS TO MODELING AND CONTROL OF A BIOPROCESS , 2006, Appl. Artif. Intell..

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[5]  George W. Irwin,et al.  Neural modelling of chemical plant using MLP and B-spline networks , 1997 .

[6]  Masahiro Ohshima,et al.  Quality control of polymer production processes , 2000 .

[7]  S. Othman,et al.  A combined hardware/software sensing approach for on-line control of emulsion polymerisation processes , 1998 .

[8]  Jie Zhang,et al.  Prediction of polymer quality in batch polymerisation reactors using robust neural networks , 1998 .

[9]  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 .

[10]  C. Kiparissides,et al.  Inferential Estimation of Polymer Quality Using Stacked Neural Networks , 1997 .

[11]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[12]  Jie Zhang,et al.  Developing robust non-linear models through bootstrap aggregated neural networks , 1999, Neurocomputing.

[13]  A. J. Morris,et al.  Soft-sensing: a solution to the problem of measurement delays , 1989 .

[14]  G. Martin,et al.  Consider soft sensors , 1997 .

[15]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[16]  János Abonyi,et al.  Process analysis and product quality estimation by Self-Organizing Maps with an application to polyethylene production , 2003, Comput. Ind..

[17]  Rubens Maciel Filho,et al.  Soft sensors development for on-line bioreactor state estimation , 2000 .

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

[19]  Gary A. Montague,et al.  Neural networks for steady state modelling of an extruder , 1997, Artif. Intell. Eng..