Virtual Instruments Based on Stacked Neural Networks to Improve Product Quality Monitoring in a Refinery

In this paper, a virtual instrument for the estimation of octane number in the gasoline produced by refineries is introduced. The instrument was designed with the aim of replacing measuring hardware during maintenance operations. The virtual instrument is based on a nonlinear moving average model, implemented by using multilayer perceptron neural networks. Stacking approaches are adopted to improve the estimation performance of the instrument. Classical linear algorithms of model aggregation are compared in the paper with a nonlinear strategy, based on the neural combination of a set of first-level neural estimators. The validity of the proposed approach is verified by comparison with the performance of both linear and nonlinear modeling techniques. The designed virtual instrument has been implemented by a large refinery in Sicily, which supplied the data used during the design phase

[1]  L. T. Fan,et al.  Monitoring the process of curing of epoxy/graphite fiber composites with a recurrent neural network as a soft sensor , 1998 .

[2]  Seppo J. Ovaska,et al.  Industrial applications of soft computing: a review , 2001, Proc. IEEE.

[3]  Scott C. James,et al.  Comparative study of black-box and hybrid estimation methods in fed-batch fermentation , 2002 .

[4]  Gary Montague,et al.  Soft-sensors for process estimation and inferential control , 1991 .

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

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

[7]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.

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

[9]  Luigi Fortuna,et al.  Automatic validation of the five-channel DCN interferometer in ENEA-FTU based on soft-computing techniques , 2002 .

[10]  Luigi Fortuna,et al.  Soft sensors for product quality monitoring in debutanizer distillation columns , 2005 .

[11]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[12]  Yves Grandvalet,et al.  Software sensor design based on empirical data , 1999 .

[13]  Xiangdong He,et al.  A New Method for Identifying Orders of Input-Output Models for Nonlinear Dynamic Systems , 1993, 1993 American Control Conference.

[14]  S. Graziani,et al.  Improving monitoring of NO/sub x/ emissions in refineries , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[15]  S. A. Billings,et al.  Non-Linear Systems Identification Using Neural Networks , 1989 .

[16]  Frederic D. McKenzie,et al.  An integrated model-based approach for real-time on-line diagnosis of complex systems , 1998 .

[17]  Jie Zhang.  Improved on-line process fault diagnosis using stacked neural networks , 2002, Proceedings of the International Conference on Control Applications.

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

[19]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[20]  Jie Zhang,et al.  A comparison of different methods for combining multiple neural networks models , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[21]  J. W. Ponton,et al.  Alternatives to neural networks for inferential measurement , 1993 .

[22]  Maria Gabriella Xibilia,et al.  An innovative intelligent system for sensor validation in tokamak machines , 2002, IEEE Trans. Control. Syst. Technol..

[23]  Eric B. Bartlett,et al.  An information theoretic approach for combining neural network process models , 1999, Neural Networks.

[24]  Girijesh Prasad,et al.  Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion , 2004, Eng. Appl. Artif. Intell..