PULVERIZED COAL COMBUSTION ADVANCED CONTROL TECHNIQUES
Konrad Gromaszek
k.gromaszek@pollub.plLublin 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 networksReferences
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
Authors
Konrad Gromaszekk.gromaszek@pollub.pl
Lublin University of Technology, Institute of Electronics and Information Technology Poland
http://orcid.org/0000-0002-3265-3714
Statistics
Abstract views: 199PDF downloads: 137
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Victor Mashkov, Andrzej Smolarz, Volodymyr Lytvynenko, Konrad Gromaszek, THE PROBLEM OF SYSTEM FAULT-TOLERANCE , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 4 No. 4 (2014)
- Andrzej Smolarz, Volodymyr Lytvynenko, Olga Kozhukhovskaya, Konrad Gromaszek, COMBINED CLONAL NEGATIVE SELECTION ALGORITHM FOR DIAGNOSTICS OF COMBUSTION IN INDIVIDUAL PC BURNER , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 3 No. 4 (2013)