Artificial neural networks for analysis of process states in fluidized bed combustion

Energy

Volumn 36, Issue 1, 2011, Pages 339-347

  Liukkonen, M ^a   Heikkinen, M ^a   Hiltunen, T ^b   Halikka E ^b   Kuivalainen, R ^b   Hiltunen, Y ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

^b Foster Wheeler Power Group   (Finland)

Author keywords

Fluidized bed; Multilayer perceptron; Neural network; Process state; Self organizing map

Indexed keywords

BOILERS; CONFORMAL MAPPING; ELECTRIC LOADS; FLUIDIZATION; FLUIDIZED BED PROCESS; FLUIDIZED BEDS; MULTILAYERS; NITRIC OXIDE; NITROGEN OXIDES; PATTERN RECOGNITION SYSTEMS; SELF ORGANIZING MAPS;

ARTIFICIAL NEURAL NETWORK; CIRCULATING FLUIDIZED BED BOILER; COMBUSTION PRO-CESS; ENERGY PLANT; ENERGY PRODUCTIONS; FORMATION OF NITROGEN OXIDES; HARMFUL EMISSIONS; K-MEANS CLUSTERING; MULTI-LAYER PERCEPTRONS; MULTILAYER PERCEPTRON; NOVEL METHODS; PROCESS DATA; PROCESS DIAGNOSTICS; PROCESS IMPROVEMENT; PROCESS STATE; VARIABLE SELECTION;

FLUIDIZED BED COMBUSTION;

ARTIFICIAL NEURAL NETWORK; COMBUSTION; EMISSION CONTROL; FLUIDIZATION; MONITORING SYSTEM; NITROGEN DIOXIDE; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; POWER PLANT;

EID: 78650740102     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2010.10.033     Document Type: Article

References (41)

1. Directive 2009/29/EC of the European Parliament and of the Council of 23 April 2009 amending Directive 2003/87/EC so as to improve and extend the greenhouse gas emission allowance trading scheme of the Community. European Council; April 23, 2009.
2. Nitrogen: Multiple and Regional Impacts February 2002, EPA/430/R-01/006, United States Environmental Protection Agency, Washington DC.
3. Integrated Science Assessment for oxides of nitrogen - health criteria July 2008, EPA/600/R-08/071, United States Environmental Protection Agency, Research Triangle Park, NC.
4. Harding J.A., Shahbaz M., Srinivas, Kusiak A. Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering 2006, 128:969-976.
5. Haykin S. Neural networks and learning machines 2009, Pearson Education Inc, Upper Saddle River, New Jersey. 3rd ed.
6. Kohonen T. Self-organizing maps 2001, Springer-Verlag, Berlin Heidelberg New York. 3rd ed.
7. Meireles M.R.G., Almeida P.E.M., Simões M.G. A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Industrial Electronics 2003, 50:585-601.
8. Application of neural network and other learning technologies in process engineering 2001, Imperial College Press, London, UK. I.M. Mujtaba, M.A. Hussain (Eds.).
9. Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22:4-37.
10. Alhoniemi E., Hollmén J., Simula O., Vesanto J. Process monitoring and modeling using the self-organizing map. Integrated Computer Aided Engineering 1999, 6:3-14.
11. Heikkinen M., Hiltunen T., Liukkonen M., Kettunen A., Kuivalainen R., Hiltunen Y. A modelling and optimization system for fluidized bed power plants. Expert Systems with Applications 2009, 36:10274-10279.
12. Kalteh A.M., Hjorth P., Berndtsson R. Review of the self-organizing map (SOM) approach in water resources: analysis, modeling and application. Environmental Modelling & Software 2008, 23:835-845.
13. Kasslin M., Kangas J., Simula O. Process state monitoring using self-organizing maps. Artificial neural networks 2 1992, vol. I:1532-1534. Amsterdam, North-Holland. I. Aleksander, J. Taylor (Eds.).
14. Kohonen T., Oja E., Simula O., Visa A., Kangas J. Engineering applications of the self-organizing map. Proceedings of the IEEE 1996, 84:1358-1384.
15. Liukkonen M., Havia E., Leinonen H., Hiltunen Y. Application of self-organizing maps in analysis of wave soldering process. Expert Systems with Applications 2009, 36:4604-4609.
16. Liukkonen M., Hiltunen T., Havia E., Leinonen H., Hiltunen Y. Modeling of soldering quality by using artificial neural networks. IEEE Transactions on Electronics Packaging Manufacturing 2009, 32:89-96.
17. Liukkonen M., Heikkinen M., Hiltunen T., Hälikkä E., Kuivalainen R., Hiltunen Y. Modeling of process states by using artificial neural networks in a fluidized bed energy plant. Proc. MATHMOD 09 Vienna [CD-ROM] 2009, 397-402. Argesim Publishing House, Vienna. I. Troch, F. Breitenecker (Eds.).
18. Liukkonen M., Havia E., Leinonen H., Hiltunen Y. Quality-oriented optimization of wave soldering process by using self-organizing maps. Applied Soft Computing 2011, 11:214-220.
19. Yu J.-B., Wang S. Using minimum quantization error chart for the monitoring of process states in multivariate manufacturing processes. Computers & Industrial Engineering 2009, 57:1300-1312.
20. Heikkinen M., Nurminen V., Hiltunen T., Hiltunen Y.A. Modeling and optimization tool for the expandable polystyrene batch process. Chemical Product and Process Modeling 2008, 3. article 3.
21. López G., Batlles F.J., Tovar-Pescador J. Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy 2005, 30:1675-1684.
22. Tso G.K.F., Yau K.K.W. Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks. Energy 2007, 32:1761-1768.
23. x emission characteristics of a tangentially fired boiler with artificial neural networks. Energy 2004, 29:167-183.
24. Smrekar J., Assadi M., Fast M., Kuštrin I., De S. Development of artificial neural network model for a coal-fired boiler using real plant data. Energy 2009, 34:144-152.
25. Basu P. Combustion and gasification in fluidized Beds 2006, CRC Press, United States of America.
26. Basu P. Combustion of coal in circulating fluidized-bed boilers: a review. Chemical Engineering Science 1999, 54:5547-5557.
27. Koornneef J., Junginger M., Faaij A. Development of fluidized bed combustion-an overview of trends, performance and cost. Progress in Energy and Combustion Science 2007, 33:19-55.
28. 2O emissions in pressurized fluidized bed combustion of subbituminous coals. Fuel 2004, 83:1095-1103.
29. Tourunen A., Saastamoinen J., Nevalainen H. Experimental trends of NO in circulating fluidized bed combustion. Fuel 2009, 88:1333-1341.
30. Leckner B., Lyngfelt A. Optimization of emissions from fluidized bed combustion of coal, biofuel and waste. International Journal of Energy Research 2002, 26:1191-1202.
31. MacQueen J. Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume I: Statistics 1967, 281-297. University of California Press, Berkeley and Los Angeles.
32. Jain A.K., Murty M.N., Flynn P.J. Data clustering: a review. ACM Computing Surveys 1999, 31:264-323.
33. Davies D.L., Bouldin D.W. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1979, 1:224-227.
34. Werbos P.J. The roots of backpropagation: from ordered derivatives to neural networks and political forecasting 1994, John Wiley & Sons, New York.
35. Blum A.L., Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 1997, 97:245-271.
36. Guyon I., Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 2003, 3:1157-1182.
37. Computational methods of feature selection 2008, Chapman & Hall, United States of America. H. Liu, H. Motoda (Eds.).
38. Whitney A.W. A direct method of nonparametric measurement selection. IEEE Transactions on Computers 1971, C-20:1100-1103.
39. Kiviluoto K. Topology preservation in self-organizing maps. Proceedings of IEEE International Conference on Neural Networks 1996, vol. 1:294-299.
40. Vesanto J., Himberg J., Alhoniemi E., Parhankangas J. Self-organizing map in Matlab: the som toolbox. Proceedings of the Matlab DSP Conference 1999, 35-40. The toolbox is available via:. http://www.cis.hut.fi/projects/somtoolbox/.
41. Willmott C.J. On the validation of models. Physical Geography 1981, 2:184-194.