PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

A tool for on-line control of high-temperature corrosion hazard in steam boilers

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
High temperature (low oxygen) corrosion processes in coal-fired boilers are intensified primarily by the use of low-emission (NOx) combustion techniques, that are associated with production of a reducing gas atmosphere near evaporator walls. For over 10 years a diagnostic system for corrosion risks determination was developed, based on continuous measurement of the composition of the flue gas in the boundary layer and artificial intelligence techniques. This paper presents the experience with the implementation of such a diagnostic system on the OP-230 hard coal fired boiler to identify the corrosion hazard of one of the evaporator walls. The obtained results indicate that it is possible to significantly simplify the measuring system by reducing the number of measurement points on the boiler wall which reduces costs of installation and exploitation. This is possible through the use of neural networks to visualize the measurement results. Comparisons of the O2 and CO concentration maps in the boundary layer obtained from measurements at 26 measurement points and results from the Rachel software based on neural network performance and 5-point measurement showed a high compatibility of obtained results. At present, on-line electrical resistance corrosion probes are included in the monitoring system to allow an assessment of the actual corrosion rate of entire evaporator tubes.
Rocznik
Strony
107--118
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
autor
  • Politechnika Wrocławska, Wyb.Wyspiańskiego 27, 50-370 Wrocław, Polska; tel.: +4871-320-2049; fax: +4871-320-3942
autor
  • Instytut Energetyki, Augustówka 36, 02-981 Warszawa, Polska
autor
  • EDF Polska, Ciepłownicza 1, 31-587 Kraków, Polska
Bibliografia
  • [1] Pronobis M. Modernizacja kotłów energetycznych. WNT Warszawa; 2002.
  • [2] Kordylewski W, editor. Niskoemisyjne techniki spalania w energetyce. Oficyna Wydawnicza Politechniki Wrocławskiej. Wrocław; 2000.
  • [3] Bryers RW. Fireside slagging, fouling and high-temperature corrosion of heat-transfer surface due to impurities in steam-raising fuels. Prog. Energy Combust. Sci. 1996; 22: 29–120.
  • [4] Strzelczyk F, Wawszczak A. High temperature corrosion of boilers (in Polish). 12th International Conference on Boiler Technology; Szczyrk; 2014.
  • [5] Bakker WT. The effect of deposits on waterwall corrosion in fossil fueled boilers. Mater High Temp 2003; 20(2):161–8.
  • [6] Hardy T, Mościcki K. Modernization of the monitoring system of oxygen concentration in the boundary layer of K1 boiler in EC Wroclaw. Politechnika Wrocławska; SPR 81/2015.
  • [7] Bukowski P, Hardy T, Kordylewski W. Evaluation of corrosion hazard in PF boilers applying the oxygen content in flue gases. Archivum Combustionis, 2009; 29 (1/2).
  • [8] Wejkowski R, Kalisz S, Hardy T, Sarapata B, Kubiczek H, Janda T. The control of high temperature corrosion phenomenon in boundary layer using different measurement methods. Modern Energy Technologies, Systems and Units (ed. J. Taller), Wydawnictwo Politechniki Krakowskiej; Kraków, 2013.
  • [9] Kakietek S, Andryjowicz Cz, Sokolik K, Maciejewski J, Hardy T, Golec T. The on-line system for monitoring of low emission corrosion of water walls in the case of boiler No 5 in Bełchatów Power Plant. 12th International Conference on Boiler Technology; Szczyrk, 2014.
  • [10] Ostrowski P, Kalisz S, Wejkowski R. Investigations of low NOx corrosion hazards in boiler OP650 of the Rybnik Power Plant using a mobile monitoring system. Rynek Energii 2011; 3(94).
  • [11] Hardy T, Kordylewski W. Monitoring zagrożenia korozją niskoemisyjną w kotłach pyłowych. V Konferencja naukowo-techniczna: Eksploatacja maszyn i urządzeń energetycznych; Szczyrk 5-7 grudnia 2007.
  • [12] Kakietek S, Hardy T, Golec T, Kordylewski W, Pietrzyk M. Doświadczenia eksploatacyjne systemu monitoringu zagrożenia korozją niskotlenową na przykładzie kotła BP-1150 w PGE E. Opole. XI International Conference on Boiler Technology; Szczyrk 2010.
  • [13] Kakietek S, Hardy T, Golec T, Kordylewski W. Neural networks and genetic algorithms as a tool for monitoring of low emission corrosion and optimization of utility boiler performance. Tenth International Conference on Energy for a Clean Environment, Clean Air 2009, Lisbona 7–9 July 2009.
  • [14] Jankowski J. Wpływ korozji wżerowej na ocenę skuteczności ochrony katodowej metodą korozymetrii rezystancyjnej. XI Konferencja Naukowo-Techniczna PSK Nowoczesne Technologie przeciwkorozyjne. Rawa Mazowiecka; 10-12 maja 2017 r.
  • [15] Hardy T, Kakietek S, Jankowski J. System monitoringu on-line zagrożenia korozyjnego rur parowników wykorzystujący sondy korozyjne, XIII Konferencja Elektrownie Cieplne. Eksploatacja - Modernizacje – Remonty. Słok, 2017.
  • [16] Langen J, Muller A, Strohle J, Epple B. Online measurements of fireside high temperature corrosion in power plants with membrane wall sensors. VGB PowerTech 2016; 12: 66–70.
  • [17] Wolf C, Spliethoff H. The OnCord project – investigation of online corrosion monitoring for the combined combustion of coal and biomass. VGB Workshop High Temperature Corrosion in Biomass Power Plants, Oberhausen, 5 December 2015.
  • [18] Kalogirou SA. Artificial intelligence for the modeling and control of combustion processes: A review. Progress in Energy and Combustion Science, 2003; 29 (6): 515–566.
  • [19] Abbas T, Awais MM, Lockwood F.C. An artificial intelligence treatment of devolatilisation for pulverised coal and biomass in co-fired flames. Combustion and Flame, 2003; 132(3): 305–318.
  • [20] Zhu Q, Jones JM, Williams A, Thomas KM. The predictions of coal/char combustion rate using ANN approach. Fuel 1999; 78: 1755–1762.
  • [21] Chungen Y., Zhongyang L., Junhu Z., Kefa C., A Novel Non-Linear Programming-Based Coal Blending Technology for Power Plants. Chemical Engineering Research and Design 2000; 78(A): 118–124,
  • [22] Bunsan S, Chen WY, Chen HW, Chuang YH, Grisdanurak N. Modeling the dioxins emission of a municipal solid waste incinerator using neural networks. Chemosphere 2013; 92: 258–264.
  • [23] Tan CK, Wilcox SJ, Ward J, Lewitt M. Monitoring near burner slag deposition with a hybrid neural network system. Measuring Science and Technology 2003; 14: 1–9.
  • [24] Pena B, Teruel E, Diez LI. Soft-computing models for soot-blowing optimization in coal-fired utility boilers. Applied Soft Computing 2011; 11: 1657–1668.
  • [25] Kamrunnahar M, Urquidi-Macdonald M. Prediction of corrosion behaviour using neural network as a data mining tool. Corrosion Science 2010; 52: 669–677.
  • [26] Haque ME, Sudhakar KV. Prediction of corrosion-fatigue behaviour of DP steel through artificial neural network. International Journal of Fatigue 2001; 23: 1–4.
  • [27] Cai J, Cottis RA, Lyon SB. Phenomenological modelling of atmospheric corrosion using artificial neural network. Corrosion Science 1999; 41: 2001–2030.
  • [28] Xiaomin M. Recognition of Toxic Gases Emissions in Power Plant Based on Artificial Neural Network. Energy Procedia 2012; 17: 1578–1584.
  • [29] Ozmen A, Tekce F, Ebeoglu MA, Tasaltin C, Ozturk ZZ. Finding the composition of gas mixtures by a phthalocyanine-coated QCM sensor array and an artificial neural network. Sensors and Actuators B 2006; 115: 450–454.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9f1281b1-b5da-4358-baf6-09db7106fb82
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.