PULVERIZED COAL COMBUSTION ADVANCED CONTROL TECHNIQUES

Konrad Gromaszek

k.gromaszek@pollub.pl
Lublin University of Technology, Institute of Electronics and Information Technology (Poland)
http://orcid.org/0000-0002-3265-3714

Abstract

The paper describes the selected methods of adaptive control of the pulverized coal combustion process overview with various types of prognostic models. It was proposed to use a class of control methods that are relatively well established in industrial practice. The presented approach distinguishes the use of an additional source of information in the form of signals from an optical diagnostic system and models based on selected deep structures of recurrent networks. The research aim is to increase the efficiency of the combustion process in the power boiler, taking into account the EU emission standards, leading in consequence to sustainable energy and sustainable environmental engineering.


Keywords:

combustion control, adaptive algorithms, artificial neural networks

Bengio Y., Simard P., Frasconi P.: Learning Long-Term Dependencies with Gradient Descent is Difficult. IEEE Trans. Neural Networks 5/1994, 157–166.
  Google Scholar

Computation N.: Long Short-term Memory. Neural Comput. 9/2016, 1735–1780.
  Google Scholar

Gromaszek K., Kotyra A., et al.: Signal Process. - Algorithms, Archit. Arrange. Appl. Conf. Proceedings SPA 3/2015, 133–136.
  Google Scholar

Hopfield J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79/1982, 2554–2558.
  Google Scholar

Kauranen P., Andersson-Engels S., Svanberg S.: Spatial mapping of flame radical emission using a spectroscopic multi-colour imaging system. Appl. Phys. B Photophysics Laser Chem. 53/1991, 260–264.
  Google Scholar

Kordylewski W., Bulewicz E., Dyjakon A., Hardy T., et al.: Spalanie i Paliwa. Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2008.
  Google Scholar

Lhner R.: Applied Computational Fluid Dynamics Techniques: An Introduction Based on Finite Element Methods. J. Fluid Mech. 1/2001, 375–376.
  Google Scholar

Ordys A.W., Pike A.W., Johnson M.A., Katebi R.M., Grimble M.J.: Modelling and Simulation of Power Generation Plants, Springer–Verlag, 1994.
  Google Scholar

Sepp H., Schmidhuber J.: Long short-term memory. Neural Comput. 9/1997, 1735–1780.
  Google Scholar

Tascikaraoglu A., Uzunoglu M.: A review of combined approaches for prediction of short-term wind speed and power. Renew. Sustain. Energy Rev. 34/2014, 243–254.
  Google Scholar

Zhou H., Cen K., Fan J.: Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms. Int. J. Energy Res. 29/2005, 499–510.
  Google Scholar

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Published
2019-06-21

Cited by

Gromaszek, K. (2019). PULVERIZED COAL COMBUSTION ADVANCED CONTROL TECHNIQUES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(2), 41–45. https://doi.org/10.5604/01.3001.0013.2546

Authors

Konrad Gromaszek 
k.gromaszek@pollub.pl
Lublin University of Technology, Institute of Electronics and Information Technology Poland
http://orcid.org/0000-0002-3265-3714

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