<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The intelligent methods for process control and diagnostics of the mill fan system is an established field of scientific and applied investigations. In the present paper several types of process control approaches with different structures are considered. In order to choose the most efficient one, comparative analysis is carried out. The mill fans are a basic element of the dust-preparing systems of steam generators with direct breathing of the coal dust in the furnace chamber. Such generators in Bulgaria are the ones in Maritsa East 2 Thermal Power Plant, in Maritsa East 3 Thermal Power Plant and also in Bobov Dol Thermal Power Plant. The subject of this research is a device from Maritsa East 2 Thermal Power Plant. This is the largest thermal power plant on the Balkan Peninsula. Standard statistical and probabilistic (Bayesian) approaches for diagnostics are inapplicable to estimate the mill fan technical state due to non-stationarity, non-ergodicity and the significant noise level. The possibility to predict eventual damages or wearing out without switching off the device is significant for providing faultless and reliable work, avoiding the losses caused by planned maintenance.</p></abstract>

The intelligent methods for process control and diagnostics of the mill fan system is an established field of scientific and applied investigations. In the present paper several types of process control approaches with different structures are considered. In order to choose the most efficient one, comparative analysis is carried out. The mill fans are a basic element of the dust-preparing systems of steam generators with direct breathing of the coal dust in the furnace chamber. Such generators in Bulgaria are the ones in Maritsa East 2 Thermal Power Plant, in Maritsa East 3 Thermal Power Plant and also in Bobov Dol Thermal Power Plant. The subject of this research is a device from Maritsa East 2 Thermal Power Plant. This is the largest thermal power plant on the Balkan Peninsula. Standard statistical and probabilistic (Bayesian) approaches for diagnostics are inapplicable to estimate the mill fan technical state due to non-stationarity, non-ergodicity and the significant noise level. The possibility to predict eventual damages or wearing out without switching off the device is significant for providing faultless and reliable work, avoiding the losses caused by planned maintenance.

Intelligent methods for process control and diagnostics of a mill fan system

Institute of Information and Communication Technologies, 1113 Sofia

Institute of System Engineering and Robotics, 1113 Sofia

Cybernetics and Information Technologies

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"Intelligent methods for process control and diagnostics of a mill fan system"}

The intelligent methods for process control and diagnostics of the mill fan system is an established field of scientific and applied investigations. In the...

Intelligent methods for process control and diagnostics of a mill fan system

Publié en ligne: 09 avr. 2014

Pages: 151 - 160

DOI: https://doi.org/10.2478/cait-2014-0012

Mots clés
Internal model control, intelligent process control, mill fan system, intelligent methods for fault detection, maintenance

© by Lyubka A. Doukovska

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Intelligent methods for process control and diagnostics of a mill fan system

Publié en ligne: 09 avr. 2014

Pages: 151 - 160

DOI: https://doi.org/10.2478/cait-2014-0012

Mots clésInternal model control, intelligent process control, mill fan system, intelligent methods for fault detection, maintenance

© by Lyubka A. Doukovska

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mots clés
Internal model control, intelligent process control, mill fan system, intelligent methods for fault detection, maintenance