Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers

Abstract:

This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.

• Periodical: Advanced Materials Research (Volume 699)
• Pages: 893-899
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.699.893
• Online since: May 2013
• Authors: K. Sujatha, N. Pappa, U. Siddharth Nambi, C.R. Raja Dinakaran, K. Senthil Kumar
• Keywords: Back Propagation Network, Combustion Quality, Fisher’s Linear Discriminant, Flame Image, Monitoring Flue Gas Emissions, Radial Basis Function Network, Temperature Monitoring

References

[1] Fisher R.A., "The Use of Multiple Measurements in Taxonomic Problems," Ann. of Eugenics, vol. 7, p.178 – 188, 1936.

[2] Hong Z.Q.and Yang Y.J., "Optimal Discriminate Plane for a Small Number of Samples and Design Method of Classifier on the Plane," pattern recognition, vol. 24, p.317 – 324, 1991.

DOI: 10.1016/0031-3203(91)90074-f

[3] Foley D.H., "Consideration of Sample and Feature Size," IEEE Transactions on Information Theory, vol.18, no. 5, pp.626-681, 1972.[4] Lippmann R.P., "An introduction to computing with neural nets", IEEE Transactions on Acoustics, Speech and Signal Processing Magazine, v35, no 4), p.4.-22, 1987.

DOI: 10.1109/tit.1972.1054863

[4] Purushothaman S. and Srinivasa Y.G., "A procedure for training artificial neural network with the application of tool wear monitoring", Int. J. of Production Research, vol.36, no.3, pp.635-651, 1998.

DOI: 10.1080/002075498193615

[5] Meng Joo Er,.Shiqian Wu, Juwei Lu and Hock Lye Toh, "Face Recognition with Radial Basis Function (RBF) Neural Networks", IEEE Transactions on Neural Networks, vol. 13, no.3, p.697 – 910, 2002.

DOI: 10.1109/tnn.2002.1000134

[6] Pu Han, Xin Zhang, Chenggang Zhen and Bing Wang, "Boiler Flame Image Classification Based on Hidden Markov Model", IEEE ISIE, pp.9-12, 2006.

DOI: 10.1109/isie.2006.295522

[7] Lu G, Gilabert G and Yan Y, "Vision based monitoring and characterization of combustion flames", Journal of Physics, Conference Series 15, 194–200, 2005.

DOI: 10.1088/1742-6596/15/1/033

[8] Wojcik.W, "Application of Fibre-optic flame monitoring systems to diagnostics of combustion process in power boilers", Bulletin of the Polish Academy Of Sciences, Technical Sciences, Vol. 56, No. 2, 2008.