Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2002.8.10.827

SOx Process Simulation, Monitoring, and Pattern Classification in a Power Plant  

최상욱 (포항공과대학교)
유창규 (포항공과대학교)
이인범 (포항공과대학교)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.8, no.10, 2002 , pp. 827-832 More about this Journal
Abstract
We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.
Keywords
ARX modeling; radial basis function; recursive least squares; power spectrum; process simulation; SOx;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Boznar, M. Lesjak and P. Mlakar, 'A neural network based method for short term predictions of ambient $SO_2$ concentrations in highly polluted industrial areas of complex terrain,' Atmospheric Environment, 27B(2), pp. 221-230, 1993
2 B. Bakal, T. Adali, M. K. Sonmez and R. Fakory, 'Time delay neural networks for $NO_x$ and CO prediction in fossil fuel plants,' Proc. of World Congress on Neural Networks(WCNN), Washington, DC, 3, pp. 111-115, 1995
3 S. H. Sohn, S. C. Oh and Y. K. Yeo, 'Prediction of Air pollutants by using an artificial neural network,' Korean J. Chem. Eng., 16(3), pp. 382-387, 1999   DOI
4 A. C. Comrie, 'Comparing neural network and regression models for ozone forecasting,' Journal of the Air and Waste Management Association, 47, pp. 653-663, 1997   DOI
5 L. Ljung. 'System Identification,' P T R Prentice Hall, Englewood Cliffs, New Jersey, 1987
6 J. S. R. Jang, C.T. Sun and E. Mizutani, 'Neuro-fuzzy and soft computing,' Prentice-Hall, Inc., New Jersey,1997
7 N. De Nevers, 'Air pollution control engineering,' McGraw-Hill, Inc., Singapore, 1995
8 S. Haykin, 'Neural networks,' Prentice Hall International, Inc., New Jersey, 1999
9 C. - T. Lin and C. S. G. Lee, 'A Neuro-Fuzzy Systems,' Prentice-Hall, Inc., New Jersey, 1996
10 D. W. Cho and K. F. Eman, 'Pattern recognition for on-line chatter detection,' Mechanical Systems and Signal Processing, 2(3), pp. 279-290, 1988   DOI   ScienceOn
11 T. J. Moody and C. J. Darken, 'Fast learning in networks of local-ly tuned processing units,' Neural Computing, 1, pp. 281-294, 1989   DOI
12 D. M. Himmelblau, 'Application of artificial neural networks in chemical engineering,' Korean J. Chem. Eng., 17, pp. 373-392, 2000   과학기술학회마을   DOI
13 G. E. P. Box and G. M. jenkins, 'Time series analysis : forecasting and control,' san francisco, CA : Holden-Day, 1970