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Improving Combustion Performance by Online Learning

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Book cover Optimization in the Energy Industry

Part of the book series: Energy Systems ((ENERGY))

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Summary

In this chapter, combustion process is improved by computing control settings with clustering algorithms. The framework involves learning from a high-dimensional data stream generated by the combustion process. Thus the system's dynamics is captured. The concepts of virtual age of the boiler and the control settings are introduced. The confidence of applying a control setting to improve boiler performance is quantified. The framework is easy to implement and it handles a large number of process variables. The ideas introduced in this paper have been implemented at a 20 MW boiler controlled with a standard control system. That system makes run-time recommendations to the standard control system.

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References

  1. C.C. Aggarwal, J.W. Han, J.Y. Wang, and P.S. Yu. On high dimensional projected clustering of data streams. Data Mining and Knowledge Discovery, 10(3):251–273, 2005.

    Article  MathSciNet  Google Scholar 

  2. J. Beringer and E. Hűllermeier. Online clustering of parallel data streams. Data Mining and Knowledge Engineering, 58(2):180–204, 2006.

    Article  Google Scholar 

  3. C.R. Cassady, I.M. Iyoob, K. Schneider, and E.A. Pohl. A generic model of equipment availability under imperfect maintenance. IEEE Transactions on Reliability, 54(4):564–571, 2005.

    Article  Google Scholar 

  4. K. L. Chung and J.S. Lin. Faster and more robust point symmetry-based k-means algorithm. Pattern Recognition, 40(2):410–422, 2007.

    Article  MathSciNet  MATH  Google Scholar 

  5. B.R. Dai, J.W. Huang, M.Y. Yeh, and M.S. Chen. Adaptive clustering for multiple evolving streams. IEEE Transactions on Knowledge and Data Engineering, 18(9):1166–1180, 2006.

    Article  Google Scholar 

  6. C.E. Ebeling. An Introduction to Reliability and Maintainability Engineering. Waveland, Long Grove, IL, 2005.

    Google Scholar 

  7. S. Engell. Feedback control for optimal process operation. Journal of Process Control, 17(3):203–219, 2007.

    Article  Google Scholar 

  8. S. Farhad, M. Younessi-Sinaki, and M. Saffar-Avval. Energy saving in operating steam power plants based on asme performance test code. ASME Power Conference 2005, 2005.

    Google Scholar 

  9. S. Ferrer-Nadal, I. Yelamos-Ruiz, M. Graells, and L. Puigjaner. An integrated framework for on-line supervise d optimization. Computers and Chemical Engineering, 31(1):32–40, 2006.

    Article  Google Scholar 

  10. M. Guay and J.F. Forbes. Real-time dynamic optimization of controllable linear systems. Journal of Guidance, Control, and Dynamics, 29(4):929–935, 2006.

    Article  Google Scholar 

  11. P. Hansen and N. Mladenovic. J-means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34(2):405–413, 2001.

    Article  MATH  Google Scholar 

  12. V. Havlena and J. Findejs. Application of model predictive control to advanced combustion control. Control Engineering Practice, 13(6):671–680, 2005.

    Article  Google Scholar 

  13. S.A. Kalogirou. Artificial intelligence for the modeling and control of combustion processes: A review. Progress in Energy and Combustion Science, 29(6):515–566, 2003.

    Article  Google Scholar 

  14. A. Kusiak. Engineering Design: Products, Processes, and Systems. Academic, San Diego, CA, 1999.

    Google Scholar 

  15. A. Kusiak. Data mining: Manufacturing and service applications. International Journal of Production Research, 44(18/19):4175–4191, 2006.

    Article  MATH  Google Scholar 

  16. A. Kusiak and S. Shah. A data-mining-based system for prediction of water chemistry faults. IEEE Transactions on Industrial Electronics, 53(2):593–603, 2006.

    Article  Google Scholar 

  17. A. Kusiak and Z. Song. Combustion efficiency optimization and virtual testing: A data-mining approach. IEEE Transactions on Industrial Informatics, 2(3):176–184, 2006.

    Article  Google Scholar 

  18. A. Kusiak, A. Burns, and F. Milster. Optimizing combustion efficiency of a circulating fluidized boiler: A data mining approach. International Journal of Knowledge-Based and Intelligent Engineering Systems, 9(4):263–274, 2005.

    Google Scholar 

  19. J.B. MacQueen. Some Methods for Classification and Analysis of Multivariate Observations, volume 1. University of California Press, Berkeley, CA, 1967. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297.

    Google Scholar 

  20. I.P. Miletic and T.E. Marlin. On-line statistical results analysis in realtime operations optimization. Industrial and Engineering Chemistry Research, 37(9):3670–3684, 1998.

    Article  Google Scholar 

  21. D. Montgomery. Introduction to Statistical Quality Control, 5th Edition. Wiley, New York, NY, 2005.

    MATH  Google Scholar 

  22. M. Guay, N.Peters, and D. DeHaan. Real-time dynamic optimization of batch systems. Journal of Process Control, 17, 2007. Vol. 29, No. 4, pp. 929–935.

    Google Scholar 

  23. G. Poncia and S. Bittanti. Multivariable model predictive control of a thermal power plant with built-in classical regulation. International Journal of Control, 74(11):1118–1130, 2001.

    Article  MATH  Google Scholar 

  24. C.E. Romero, Y. Li, H. Bilirgen, N. Sarunac, and E.K. Levy. Modification of boiler operating conditions in coal-fired utility boilers. Fuel, 85(2):204–212, 2006.

    Article  Google Scholar 

  25. S.E. Sequeira, M. Herrera, M. Graells, and L. Puigjaner. On-line process optimization: Parameter tuning for the real time evolution (rte) approach. Computers and Chemical Engineering, 28(5):661–672, 2004.

    Article  Google Scholar 

  26. P.N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Pearson, Readings, MA, 2006.

    Google Scholar 

  27. M.H. Wright. Direct search methods: once scorned, now respectable, in D.F. Grioths and G.A. Watson (Eds.), Numerical Analysis 1995 (Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis). Addison-Wesley, UK, 1995.

    Google Scholar 

  28. Q. Xiong and A. Jutan. Continuous optimization using a dynamic simplex method. Chemical Engineering Science, 58(16):3817–3828, 2003.

    Article  Google Scholar 

  29. W.S. Yip and T.E. Marlin. Multiple data sets for model updating in real-time operations optimization. Computers and Chemical Engineering, 26(10):1345– 1362, 2002.

    Article  Google Scholar 

  30. H. Zhao, J. Guiver, R. Neelakantan, and L.T. Biegler. A non-linear industrial model predictive controller using integrated pls and neural net state-space model. Control Engineering Practice, 9(2):125–133, 2001.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Iowa, Department of Mechanical and Industrial Engineering, 3131 Seamans Center, Iowa City, IA 52242-1527, USA

    Andrew Kusiak & Zhe Song

Authors
  1. Andrew Kusiak
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  2. Zhe Song
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Editor information

Editors and Affiliations

  1. Am Mahlstein 8, 67273, Weisenheim, Germany

    Josef Kallrath

  2. Department of Industrial & Systems Engineering, University of Florida, 303 Weil Hall, 116595, Gainesville, FL, 32611-6595, USA

    Panos M. Pardalos  & Steffen Rebennack  & 

  3. ProCom GmbH, Luisenstraße 41, 52070, Aachen, Germany

    Max Scheidt

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© 2009 Springer-Verlag Berlin Heidelberg

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Kusiak, A., Song, Z. (2009). Improving Combustion Performance by Online Learning. In: Kallrath, J., Pardalos, P.M., Rebennack, S., Scheidt, M. (eds) Optimization in the Energy Industry. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88965-6_6

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  • DOI: https://doi.org/10.1007/978-3-540-88965-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88964-9

  • Online ISBN: 978-3-540-88965-6

  • eBook Packages: EngineeringEngineering (R0)

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