Neurocomputing approach for the prediction of NOx emissions from CFBC in air-fired and oxygen-enriched atmospheres
Abstract
This paper presents a way of predicting NOx emissions from circulating fluidized bed combustors (CFBC) in air-fired and oxyfuelconditions, using the Artificial Neural Network (ANN) Approach. The Original Neural Networks Model was successfullyapplied to calculate the NOx (i.e. NO + NO2) emissions from coal combustion under air-fired and oxygen-enriched conditionsin several CFB boilers. The ANN model was shown to give quick and accurate results in response to the input pattern. TheNOx emissions, evaluated using the developed ANN model are in good agreement with the experimental results.References
[1] National Center for Environmental Assessment-RTP Division, Office of
Research and Development, U.S. Environmental Protection Agency,
Research Triangle Park, EPA/600/R-08/082F. Integrated Science Assessment
for Oxides of Nitrogen - Health Criteria (2008).
[2] L. Amand, B. Leckner, K. Dam-Johansen, Influence of SO2 on the
NO/N2O chemistry in fluidized bed combustion: 1. full-scale experiments,
Fuel 72 (1993) 557–564.
[3] L.-E. Åmand, B. Leckner, Influence of fuel on the emission of nitrogen
oxides (NO and N2O) from an 8-MW fluidized bed boiler, Combustion
and Flame 84 (1-2) (1991) 181–196.
[4] L. Amand, B. Leckner, The role of fuel volatiles for the emission of
nitrogen oxides from fluidized bed boilers - a comparison between designs,
in: Proc. of the 23rd International Symposium on Combustion,
1990, pp. 927–933.
[5] P. Basu, Combustion of coal in circulating fluidized-bed boilers: a review,
Chemical Engineering Science 54 (22) (1999) 5547–5557.
[6] E. Bool, S. Laux, E. Eddings, Oxy-coal combustion in small-scale CFB,
in: Proc. Of th 35th International Technical Conference on Clean Coal
& Fuel Systems, Florida, USA, 2010, pp. 190–196.
[7] T. Czakiert, W. Muskala, S. Jankowska, G. Krawczyk, P. Borecki, L. Jesionowski,
W. Nowak, The effect of oxygen concentration on nitrogen
conversion in oxy-fuel CFB environment, in: 21 International Conference
on fluidized bed combustion, Naples, Italy, 2012, pp. 495–502.
[8] F. Luis, C. A. Londono, X. S. Wang, B. M. Gibbs, Influence of operating
parameters on nox and n2o axial profiles in a circulating fluidized bed
combustor, Fuel 75 (8) (1996) 971–978.
[9] T. Eriksson, O. Sippu, A. Hotta, M. K. Zhen Fan, T. Hyppänen,
T. Pikkarainen, Oxyfuel CFB boiler as a route to near zero CO2 emission
coal firing, in: Power-GEN Europe, 2007, pp. 26–28.
[10] B. Feng, H. Liu, J.-W. Yuan, Z.-J. Lin, D.-C. Liu, B. Leckner, Nitrogen
oxides emission from a circulating fluidized bed combustor, International
Journal of Energy Research 20 (11) (1996) 1015–1025.
[11] A. Gungor, Prediction of so 2 and no x emissions for low-grade turkish
lignites in cfb combustors, Chemical Engineering Journal 146 (3)
(2009) 388–400.
[12] A. Gungor, N. Eskin, Two-dimensional coal combustion modeling of
cfb, International Journal of Thermal Sciences 47 (2) (2008) 157–174.
[13] A. Hayhurst, A. Lawrence, The amounts of nox and n2o formed in a
fluidized bed combustor during the burning of coal volatiles and also of
char, Combustion and flame 105 (3) (1996) 341–357.
[14] L. Jia, Y. Tan, D. McCalden, Y. Wu, I. He, R. Symonds, E. Anthony,
Commissioning of a 0.8 mw th cfbc for oxy-fuel combustion, International
Journal of Greenhouse Gas Control 7 (2012) 240–243.
[15] L. Jia, Y. Tan, Y. Wu, E. Anthony, Oxy-fuel combustion tests using a 0.8
mwth pilot-scale circulating fluidized bed, in: Proceedings of the 21st
International Conference on Fluidized Bed Combustion, Naples, Italy,
2012, pp. 381–388.
[16] L. Jia, Y. Tan, E. Anthony, Emissions of so2 and no x during oxy- fuel
cfb combustion tests in a mini-circulating fluidized bed combustion reactor,
Energy & Fuels 24 (2) (2009) 910–915.
[17] L. Jia, Y. Tan, C. Wang, E. Anthony, Experimental study of oxy-fuel
combustion and sulfur capture in a mini-cfbc, Energy & Fuels 21 (6)
(2007) 3160–3164.
[18] T. Knöbig, J. Werther, L.-E. Åmand, B. Leckner, Comparison of largeand
small-scale circulating fluidized bed combustors with respect to
pollutant formation and reduction for different fuels, Fuel 77 (14) (1998)
1635–1642.
[19] B. Leckner, Fluidized bed combustion: mixing and pollutant limitation,
Progress in Energy and Combustion Science 24 (1) (1998) 31–61.
[20] B. Leckner, L.-E. Åmand, K. Lücke, J. Werther, Gaseous emissions
from co-combustion of sewage sludge and coal/wood in a fluidized
bed, Fuel 83 (4) (2004) 477–486.
[21] B. Leckner, A. Lyngfelt, Optimization of emissions from fluidized bed
combustion of coal, biofuel and waste, International journal of energy
research 26 (13) (2002) 1191–1202.
[22] H. Liu, B. M. Gibbs, The influence of limestone addition at different
positions on gaseous emissions from a coal-fired circulating fluidized
bed combustor, Fuel 77 (14) (1998) 1569–1577.
[23] M. Liukkonen, M. Heikkinen, T. Hiltunen, E. Hälikkä, R. Kuivalainen,
Y. Hiltunen, Artificial neural networks for analysis of process states in
fluidized bed combustion, Energy 36 (1) (2011) 339–347.
[24] M. Liukkonen, T. Hiltunen, E. Hälikkä, Y. Hiltunen, Modeling of the
fluidized bed combustion process and no x emissions using selforganizing
maps: an application to the diagnosis of process states,
Environmental Modelling & Software 26 (5) (2011) 605–614.
[25] D. Mao, J. R. Edwards, A. V. Kuznetsov, R. K. Srivastava, Threedimensional
numerical simulation of a circulating fluidized bed reactor
for multi-pollutant control, Chemical engineering science 59 (20) (2004) 4279–4289.
[26] N. Nsakala, G. Liljedahl, D. Turek, Greenhouse gas emissions control
by oxygen firing in circulating fluidized bed boilers: Phase ii – pilot
scale testing and updated performance and economics for oxygen
fired CFB, Tech. rep., PPL Report No. PPL-04-CT-25 under cooperative
agreement No. DE-FC26-01NT41146 (2004).
[27] G. Scheffknecht, L. Al-Makhadmeh, U. Schnell, J. Maier, Oxy-fuel
coal combustion—a review of the current state-of-the-art, International
Journal of Greenhouse Gas Control 5 (2011) S16–S35.
[28] Y. Tan, L. Jia, Y. Wu, E. Anthony, Experiences and results on a 0.8
mwth oxy-fuel operation pilot-scale circulating fluidized bed, Applied
Energy 92 (2012) 343–347.
[29] M. B. Toftegaard, J. Brix, P. A. Jensen, P. Glarborg, A. D. Jensen, Oxyfuel
combustion of solid fuels, Progress in energy and combustion science
36 (5) (2010) 581–625.
[30] T. Klajny, J. Krzywanski, W. Nowak, Mechanism and kinetics of coal
combustion in oxygen enhanced conditions, in: 6th International Symposium
on Coal Combustion, Wuhan, China, 2007, pp. 148–153.
[31] J. Zhao, C. Brereton, J. R. Grace, C. J. Lim, R. Legros, Gas concentration
profiles and NOx formation in circulating fluidized bed combustion,
Fuel 76 (9) (1997) 853–860.
[32] L. Duan, C. Zhao, W. Zhou, C. Qu, X. Chen, O 2/co 2 coal combustion
characteristics in a 50kw th circulating fluidized bed, International
Journal of Greenhouse Gas Control 5 (4) (2011) 770–776.
[33] T. Eriksson, K. Nuortimo, A. Hotta, K. Myöhänen, T. Hyppänen,
T. Pikkarainen, Near zero co2 emissions in coal firing with oxyfuel cfb
boiler, in: Proc. of the 9th International Conference on Circulating Fluidized
Beds, Hamburg, Germany, May, 2008, pp. 13–16.
[34] A. Blaszczuk, W. Nowak, S. Jagodzik, Effects of operating conditions
on denox system efficiency in supercritical circulating fluidized bed
boiler, Journal of Power Technologies 93 (1) (2013) 1.
[35] A. Blaszczuk, M. Komorowski, W. Nowak, Distribution of solids concentration
and temperature within combustion chamber of sc-otu cfb
boiler, Journal of Power Technologies 92 (1) (2012) 27–33.
[36] B. Harris, J. Davidson, Modelling options for circulating fluidized beds:
a core/annulus deposition model, Circulating fluidized bed technology
IV (1994) 32.
[37] J. Krzywa´ nski, T. Czakiert, W. Muskała, W. Nowak, Modelling of CO2,
CO, SO2, O2 and NOx emissions from the oxy-fuel combustion in a
circulating fluidized bed, Fuel Processing Technology 92 (3) (2011)
590–596.
[38] J. Krzywanski, T. Czakiert,W. Muskala, R. Sekret,W. Nowak, Modeling
of solid fuels combustion in oxygen-enriched atmosphere in circulating
fluidized bed boiler: Part 1. The mathematical model of fuel combustion
in oxygen-enriched CFB environment, Fuel Processing Technology
91 (3) (2010) 290–295.
[39] J. Krzywanski, T. Czakiert, W. Muskala, R. Sekret, W. Nowak, Modeling
of solid fuel combustion in oxygen-enriched atmosphere in circulating
fluidized bed boiler: Part 2. Numerical simulations of heat transfer
and gaseous pollutant emissions associated with coal combustion in
O2/CO2 and O2/N2 atmospheres enriched with oxygen under circulating
fluidized bed conditions, Fuel Processing Technology 91 (3) (2010)
364–368.
[40] W. Muskała, J. Krzywi´ nski, R. Sekret, W. Nowak, Model research of
coal combustion in circulating fluidized bed boilers, Chemical and Process
Engineering 29 (2008) 473–492.
[41] K. Myöhänen, T. Hyppänen, M. Loschkin, Converting measurement
data to process knowledge by using three-dimensional cfb furnace
model, in: The 8th International Conference on CFB, 2005.
[42] D. Pallares, F. Johnsson, M. Palonen, A comprehensive model of cfb
combustion, in: 9th Int. Conf. on Circulating Fluidized Beds, 2008.
[43] M. Palonen, D. Pallarès, A. Larsson, F. Johnsson, V. Ylä-Outinen,
J. Laine, Circulating fluidized bed combustion-build-up and validation
of a three-dimensional model.
[44] T. Pikkarainen, A. Tourunen, H. Nevalainen, T. Leino, J. Saastamoinen,
T. Eriksson, Small scale fluidized bed experiments under oxygen combustion
conditions, in: International Conference on Coal Science &
Techology, University of Nottingham UK, 2007.
[45] J. Saastamoinen, A. Tourunen, T. Pikkarainen, H. Häsä, J. Miettinen,
T. Hyppänen, K. Myöhänen, Fluidized bed combustion in high concentrations
of o2 and co2, in: 19th FBC Conference, 2006.
[46] J. Werther, Fluid dynamics, temperature and concentration fields in
large-scale cfb combustors, in: 8-th International Conference on Circulating
Fluidized Beds, 2005.
[47] N. Zhang, B. Lu, W. Wang, J. Li, 3D CFD simulation of combustion in
a 150 MWe circulating fluidized bed boiler, in: Proc. of the 10-th International
Conference on Circulating Fluidized Beds and Fluidization
Technology - CFB-10, Sunriver, Oregon, USA, 2011, pp. 537–544.
[48] C. Zhao, W. Zhou, L. Duan, X. Chan, 2d Euler-Euler simulation of oxycoal
combustion in a circulating fluidized bed., in: Proc. of the 21 International
Conference on fluidized bed combustion, Naples, Italy, 2012,
pp. 495–502.
[49] W. Zhou, C. Zhao, L. Duan, X. Chen, D. Liu, A simulation study of
coal combustion under o 2/co 2 and o 2/rfg atmospheres in circulating
fluidized bed, Chemical engineering journal 223 (2013) 816–823.
[50] W. Zhou, C. Zhao, L. Duan, X. Chen, C. Liang, Two-dimensional computational
fluid dynamics simulation of nitrogen and sulfur oxides emissions
in a circulating fluidized bed combustor, Chemical engineering
journal 173 (2) (2011) 564–573.
[51] W. Zhou, C. Zhao, L. Duan, D. Liu, X. Chen, Cfd modeling of oxycoal
combustion in circulating fluidized bed, International Journal of
Greenhouse Gas Control 5 (6) (2011) 1489–1497.
[52] W. Zhou, C. Zhao, L. Duan, D. Liu, X. Chen, Simulation study of oxyfuel
combustion in a circulating fluidized bed, in: The 35th International
Technical Conference on Clean Coal & Fuel Systems, Clearwater,
Florida, USA, 2010.
[53] K. K. Win, W. Nowak, H. Matsuda, M. Hasatani, Z. Bis, J. Krzywanski,
W. Gajewski, Transport velocity of coarse particles in multi-solid
fluidized bed, Journal of Chemical Engineering of Japan 28 (5) (1995)
535–540.
[54] J. Kotowicz, A. Balicki, Analysis of the thermodynamic and economic
efficiency of supercritical power unit with cfb boiler fed with lignite and
air separation unit based on high-temperature membrane technology,
Journal of Power Technologies 93 (5) (2013) 308–313.
[55] D. Pallares, F. Johnsson, Macroscopic modelling of fluid dynamics in
large-scale circulating fluidized beds, Progress in energy and combustion
science 32 (5) (2006) 539–569.
[56] S. Kalogirou, Applications of artificial neural networks in energy systems,
Energy Conversion and Management 40 (10) (1999) 1073–
1087.
[57] C. Zhao, L. Dun, W. Zhou, X. Chen, D. Zeng, T. Flynn, D. Kraft, Coal
combustion characteristics on an oxy-cfb combustor with warm flue
gas recycle, in: Proceedings of the 21st International Conference on
Fluidized Bed Combustion, 2012, pp. 3–6.
[58] R. R. Jensen, S. Karki, H. Salehfar, Artificial neural network-based
estimation of mercury speciation in combustion flue gases, Fuel Processing
Technology 85 (6) (2004) 451–462.
[59] U. Kesgin, Genetic algorithm and artificial neural network for engine
optimisation of efficiency and nox emission, Fuel 83 (7) (2004) 885–
895.
[60] F. Christo, A. Masri, E. Nebot, T. Turanyi, Utilising artificial neural network
and repro-modelling in turbulent combustion, in: Neural Networks,
1995. Proceedings., IEEE International Conference on, Vol. 2,
IEEE, 1995, pp. 911–916.
[61] S. Milanic, R. Karba, Neural network models for predictive control of a
thermal plant, in: Proc. of the Int. Conf. EANN ’96, London, UK, 1996,
pp. 151–154.
[62] B. Muller, H. Keller, Neural networks for combustion process modeling.,
in: Proc. of the Int. Conf. EANN ’96, London, UK, 1996, pp. 87–
90.
[63] M. W. Gardner, S. Dorling, Artificial neural networks (the multilayer
perceptron)—a review of applications in the atmospheric sciences, Atmospheric
environment 32 (14) (1998) 2627–2636.
[64] S. Arumugam, G. Sriram, P. S. Subramanian, Application of artificial
intelligence to predict the performance and exhaust emissions of
diesel engine using rapeseed oil methyl ester, Procedia Engineering
38 (2012) 853–860.
[65] P. Boniecki, J. Dach, K. Pilarski, H. Piekarska-Boniecka, Artificial neural
networks for modeling ammonia emissions released from sewage
sludge composting, Atmospheric Environment 57 (2012) 49–54.
[66] C. Carnevale, G. Finzi, E. Pisoni, M. Volta, Neuro-fuzzy and neural network
systems for air quality control, Atmospheric Environment 43 (31) (2009) 4811–4821.
[67] A. de Lucas, A. Durán, M. Carmona, M. Lapuerta, Modeling diesel
particulate emissions with neural networks, Fuel 80 (4) (2001) 539–
548.
[68] Z. Hao, C. Kefa, M. Jianbo, Combining neural network and genetic algorithms
to optimize low no x pulverized coal combustion, Fuel 80 (15)
(2001) 2163–2169.
[69] R. Karada˘ g, Ö. Akgöbek, The prediction of convective heat transfer in
floor-heating systems by artificial neural networks, International Communications
in Heat and Mass Transfer 35 (3) (2008) 312–325.
[70] T. Kwater, Z. Kedzior, B. Twarog, Estimation by artificial neural network
in ecological problems., in: Proc. AMSE-Conference MS’2001, Lviv,
Ukraine, 2001, pp. 212–215.
[71] J. Krzywanski, W. Muskała, W. Nowak, Application of neural networks
for determining of the convective heat transfer coefficient in a circulating
fluidized bed, Power Engineering 11 (2008) 761–764.
[72] J. Krzywanski, W. Nowak, Modeling of heat transfer coefficient in the
furnace of cfb boilers by artificial neural network approach, International
Journal of Heat and Mass Transfer 55 (15) (2012) 4246–4253.
[73] Y. Miyamoto, Y. Kurosaki, H. Fujiyama, E. Nanbu, Dynamic characteristic
analysis and combustion control for a fluidized bed incinerator,
Control Engineering Practice 6 (9) (1998) 1159–1168.
[74] E. Molga, Neural network approach to support modelling of chemical
reactors: problems, resolutions, criteria of application, Chemical Engineering
and Processing: Process Intensification 42 (8) (2003) 675–
695.
[75] S. S. Nagendra, M. Khare, Artificial neural network approach for modelling
nitrogen dioxide dispersion from vehicular exhaust emissions,
Ecological Modelling 190 (1) (2006) 99–115.
[76] L. E. Olcese, B. M. Toselli, A method to estimate emission rates
from industrial stacks based on neural networks, Chemosphere 57 (7)
(2004) 691–696.
[77] S. L. Reich, D. Gomez, L. Dawidowski, Artificial neural network for the
identification of unknown air pollution sources, Atmospheric Environment
33 (18) (1999) 3045–3052.
[78] S. S. Sablani, A neural network approach for non-iterative calculation
of heat transfer coefficient in fluid–particle systems, Chemical Engineering
and Processing: Process Intensification 40 (4) (2001) 363–
369.
[79] Ü. A. S¸ ahin, C. Bayat, O. N. Uçan, Application of cellular neural network
(cnn) to the prediction of missing air pollutant data, Atmospheric
Research 101 (1) (2011) 314–326.
[80] S. Tasdemir, I. Saritas, M. Ciniviz, N. Allahverdi, Artificial neural network
and fuzzy expert system comparison for prediction of performance
and emission parameters on a gasoline engine, Expert Systems
with Applications 38 (11) (2011) 13912–13923.
[81] A. Tuma, H.-D. Haasis, O. Rentz, Development of emission orientated
production control strategies using fuzzy expert systems, neural networks
and neuro-fuzzy approaches, Fuzzy sets and systems 77 (3)
(1996) 255–264.
[82] Q. Wang, G. Xie, M. Zeng, L. Luo, Prediction of heat transfer rates for
shell-and-tube heat exchangers by artificial neural networks approach,
Journal of Thermal Science 15 (3) (2006) 257–262.
[83] M. Xiaomin, Recognition of toxic gases emission in power plant based
on artificial neural network, Energy Procedia 17 (2012) 1578–1584.
[84] J. Zhang, F. Haghighat, Development of artificial neural network based
heat convection algorithm for thermal simulation of large rectangular
cross-sectional area earth-to-air heat exchangers, Energy and Buildings
42 (4) (2010) 435–440.
[85] Y. Zhang, Y. Ding, Z. Wu, L. Kong, T. Chou, Modeling and coordinative
optimization of no x emission and efficiency of utility boilers with neural
network, Korean Journal of Chemical Engineering 24 (6) (2007) 1118–
1123.
[86] H. Zhou, K. Cen, J. Fan, Modeling and optimization of the nox emission
characteristics of a tangentially fired boiler with artificial neural
networks, Energy 29 (1) (2004) 167–183.
[87] B. P. Grandjean, et al., Special issue on application of neural networks
to multiphase reactors (2003).
[88] C. Faur-Brasquet, P. Le Cloirec, Modelling of the flow behavior of activated
carbon cloths using a neural network approach, Chemical Engineering
and Processing: Process Intensification 42 (8) (2003) 645–
652.
[89] H. Lin, W. Chen, A. Tsutsumi, Long-term prediction of nonlinear hydrodynamics
in bubble columns by using artificial neural networks, Chemical
Engineering and Processing: Process Intensification 42 (8) (2003)
611–620.
[90] A. Shaikh, M. Al-Dahhan, Development of an artificial neural network
correlation for prediction of overall gas holdup in bubble column reactors,
Chemical Engineering and Processing: Process Intensification
42 (8) (2003) 599–610.
[91] G. Bollas, S. Papadokonstadakis, J. Michalopoulos, G. Arampatzis,
A. Lappas, I. Vasalos, A. Lygeros, Using hybrid neural networks in
scaling up an fcc model from a pilot plant to an industrial unit, Chemical
Engineering and Processing: Process Intensification 42 (8) (2003)
697–713.
[92] T. Abbas, M. Awais, F. Lockwood, An artificial intelligence treatment
of devolatilization for pulverized coal and biomass in co-fired flames,
Combustion and flame 132 (3) (2003) 305–318.
[93] J. Kukkonen, L. Partanen, A. Karppinen, J. Ruuskanen, H. Junninen,
M. Kolehmainen, H. Niska, S. Dorling, T. Chatterton, R. Foxall, et al.,
Extensive evaluation of neural network models for the prediction of NO
2 and PM 10 concentrations, compared with a deterministic modelling
system and measurements in central Helsinki, Atmospheric Environment
37 (32) (2003) 4539–4550.
[94] H. Ye, R. Nicolai, L. Reh, A bayesian–gaussian neural network and its
applications in process engineering, Chemical Engineering and Processing:
Process Intensification 37 (5) (1998) 439–449.
[95] W. Muskała, J. Krzywa´ nski, T. Czakiert, W. Nowak, The research of
CFB boiler operation for oxygen-enhanced dried lignite combustion,
Rynek Energii 1 (92) (2011) 172–176.
[96] J. Krzywa´ nski, R. Rajczyk, W. Nowak, Model research of gas emissions
from lignite and biomass co-combustion in a large scale cfb
boiler, Chemical and Process Engineering 35 (2) (2014) 217–231.
Research and Development, U.S. Environmental Protection Agency,
Research Triangle Park, EPA/600/R-08/082F. Integrated Science Assessment
for Oxides of Nitrogen - Health Criteria (2008).
[2] L. Amand, B. Leckner, K. Dam-Johansen, Influence of SO2 on the
NO/N2O chemistry in fluidized bed combustion: 1. full-scale experiments,
Fuel 72 (1993) 557–564.
[3] L.-E. Åmand, B. Leckner, Influence of fuel on the emission of nitrogen
oxides (NO and N2O) from an 8-MW fluidized bed boiler, Combustion
and Flame 84 (1-2) (1991) 181–196.
[4] L. Amand, B. Leckner, The role of fuel volatiles for the emission of
nitrogen oxides from fluidized bed boilers - a comparison between designs,
in: Proc. of the 23rd International Symposium on Combustion,
1990, pp. 927–933.
[5] P. Basu, Combustion of coal in circulating fluidized-bed boilers: a review,
Chemical Engineering Science 54 (22) (1999) 5547–5557.
[6] E. Bool, S. Laux, E. Eddings, Oxy-coal combustion in small-scale CFB,
in: Proc. Of th 35th International Technical Conference on Clean Coal
& Fuel Systems, Florida, USA, 2010, pp. 190–196.
[7] T. Czakiert, W. Muskala, S. Jankowska, G. Krawczyk, P. Borecki, L. Jesionowski,
W. Nowak, The effect of oxygen concentration on nitrogen
conversion in oxy-fuel CFB environment, in: 21 International Conference
on fluidized bed combustion, Naples, Italy, 2012, pp. 495–502.
[8] F. Luis, C. A. Londono, X. S. Wang, B. M. Gibbs, Influence of operating
parameters on nox and n2o axial profiles in a circulating fluidized bed
combustor, Fuel 75 (8) (1996) 971–978.
[9] T. Eriksson, O. Sippu, A. Hotta, M. K. Zhen Fan, T. Hyppänen,
T. Pikkarainen, Oxyfuel CFB boiler as a route to near zero CO2 emission
coal firing, in: Power-GEN Europe, 2007, pp. 26–28.
[10] B. Feng, H. Liu, J.-W. Yuan, Z.-J. Lin, D.-C. Liu, B. Leckner, Nitrogen
oxides emission from a circulating fluidized bed combustor, International
Journal of Energy Research 20 (11) (1996) 1015–1025.
[11] A. Gungor, Prediction of so 2 and no x emissions for low-grade turkish
lignites in cfb combustors, Chemical Engineering Journal 146 (3)
(2009) 388–400.
[12] A. Gungor, N. Eskin, Two-dimensional coal combustion modeling of
cfb, International Journal of Thermal Sciences 47 (2) (2008) 157–174.
[13] A. Hayhurst, A. Lawrence, The amounts of nox and n2o formed in a
fluidized bed combustor during the burning of coal volatiles and also of
char, Combustion and flame 105 (3) (1996) 341–357.
[14] L. Jia, Y. Tan, D. McCalden, Y. Wu, I. He, R. Symonds, E. Anthony,
Commissioning of a 0.8 mw th cfbc for oxy-fuel combustion, International
Journal of Greenhouse Gas Control 7 (2012) 240–243.
[15] L. Jia, Y. Tan, Y. Wu, E. Anthony, Oxy-fuel combustion tests using a 0.8
mwth pilot-scale circulating fluidized bed, in: Proceedings of the 21st
International Conference on Fluidized Bed Combustion, Naples, Italy,
2012, pp. 381–388.
[16] L. Jia, Y. Tan, E. Anthony, Emissions of so2 and no x during oxy- fuel
cfb combustion tests in a mini-circulating fluidized bed combustion reactor,
Energy & Fuels 24 (2) (2009) 910–915.
[17] L. Jia, Y. Tan, C. Wang, E. Anthony, Experimental study of oxy-fuel
combustion and sulfur capture in a mini-cfbc, Energy & Fuels 21 (6)
(2007) 3160–3164.
[18] T. Knöbig, J. Werther, L.-E. Åmand, B. Leckner, Comparison of largeand
small-scale circulating fluidized bed combustors with respect to
pollutant formation and reduction for different fuels, Fuel 77 (14) (1998)
1635–1642.
[19] B. Leckner, Fluidized bed combustion: mixing and pollutant limitation,
Progress in Energy and Combustion Science 24 (1) (1998) 31–61.
[20] B. Leckner, L.-E. Åmand, K. Lücke, J. Werther, Gaseous emissions
from co-combustion of sewage sludge and coal/wood in a fluidized
bed, Fuel 83 (4) (2004) 477–486.
[21] B. Leckner, A. Lyngfelt, Optimization of emissions from fluidized bed
combustion of coal, biofuel and waste, International journal of energy
research 26 (13) (2002) 1191–1202.
[22] H. Liu, B. M. Gibbs, The influence of limestone addition at different
positions on gaseous emissions from a coal-fired circulating fluidized
bed combustor, Fuel 77 (14) (1998) 1569–1577.
[23] M. Liukkonen, M. Heikkinen, T. Hiltunen, E. Hälikkä, R. Kuivalainen,
Y. Hiltunen, Artificial neural networks for analysis of process states in
fluidized bed combustion, Energy 36 (1) (2011) 339–347.
[24] M. Liukkonen, T. Hiltunen, E. Hälikkä, Y. Hiltunen, Modeling of the
fluidized bed combustion process and no x emissions using selforganizing
maps: an application to the diagnosis of process states,
Environmental Modelling & Software 26 (5) (2011) 605–614.
[25] D. Mao, J. R. Edwards, A. V. Kuznetsov, R. K. Srivastava, Threedimensional
numerical simulation of a circulating fluidized bed reactor
for multi-pollutant control, Chemical engineering science 59 (20) (2004) 4279–4289.
[26] N. Nsakala, G. Liljedahl, D. Turek, Greenhouse gas emissions control
by oxygen firing in circulating fluidized bed boilers: Phase ii – pilot
scale testing and updated performance and economics for oxygen
fired CFB, Tech. rep., PPL Report No. PPL-04-CT-25 under cooperative
agreement No. DE-FC26-01NT41146 (2004).
[27] G. Scheffknecht, L. Al-Makhadmeh, U. Schnell, J. Maier, Oxy-fuel
coal combustion—a review of the current state-of-the-art, International
Journal of Greenhouse Gas Control 5 (2011) S16–S35.
[28] Y. Tan, L. Jia, Y. Wu, E. Anthony, Experiences and results on a 0.8
mwth oxy-fuel operation pilot-scale circulating fluidized bed, Applied
Energy 92 (2012) 343–347.
[29] M. B. Toftegaard, J. Brix, P. A. Jensen, P. Glarborg, A. D. Jensen, Oxyfuel
combustion of solid fuels, Progress in energy and combustion science
36 (5) (2010) 581–625.
[30] T. Klajny, J. Krzywanski, W. Nowak, Mechanism and kinetics of coal
combustion in oxygen enhanced conditions, in: 6th International Symposium
on Coal Combustion, Wuhan, China, 2007, pp. 148–153.
[31] J. Zhao, C. Brereton, J. R. Grace, C. J. Lim, R. Legros, Gas concentration
profiles and NOx formation in circulating fluidized bed combustion,
Fuel 76 (9) (1997) 853–860.
[32] L. Duan, C. Zhao, W. Zhou, C. Qu, X. Chen, O 2/co 2 coal combustion
characteristics in a 50kw th circulating fluidized bed, International
Journal of Greenhouse Gas Control 5 (4) (2011) 770–776.
[33] T. Eriksson, K. Nuortimo, A. Hotta, K. Myöhänen, T. Hyppänen,
T. Pikkarainen, Near zero co2 emissions in coal firing with oxyfuel cfb
boiler, in: Proc. of the 9th International Conference on Circulating Fluidized
Beds, Hamburg, Germany, May, 2008, pp. 13–16.
[34] A. Blaszczuk, W. Nowak, S. Jagodzik, Effects of operating conditions
on denox system efficiency in supercritical circulating fluidized bed
boiler, Journal of Power Technologies 93 (1) (2013) 1.
[35] A. Blaszczuk, M. Komorowski, W. Nowak, Distribution of solids concentration
and temperature within combustion chamber of sc-otu cfb
boiler, Journal of Power Technologies 92 (1) (2012) 27–33.
[36] B. Harris, J. Davidson, Modelling options for circulating fluidized beds:
a core/annulus deposition model, Circulating fluidized bed technology
IV (1994) 32.
[37] J. Krzywa´ nski, T. Czakiert, W. Muskała, W. Nowak, Modelling of CO2,
CO, SO2, O2 and NOx emissions from the oxy-fuel combustion in a
circulating fluidized bed, Fuel Processing Technology 92 (3) (2011)
590–596.
[38] J. Krzywanski, T. Czakiert,W. Muskala, R. Sekret,W. Nowak, Modeling
of solid fuels combustion in oxygen-enriched atmosphere in circulating
fluidized bed boiler: Part 1. The mathematical model of fuel combustion
in oxygen-enriched CFB environment, Fuel Processing Technology
91 (3) (2010) 290–295.
[39] J. Krzywanski, T. Czakiert, W. Muskala, R. Sekret, W. Nowak, Modeling
of solid fuel combustion in oxygen-enriched atmosphere in circulating
fluidized bed boiler: Part 2. Numerical simulations of heat transfer
and gaseous pollutant emissions associated with coal combustion in
O2/CO2 and O2/N2 atmospheres enriched with oxygen under circulating
fluidized bed conditions, Fuel Processing Technology 91 (3) (2010)
364–368.
[40] W. Muskała, J. Krzywi´ nski, R. Sekret, W. Nowak, Model research of
coal combustion in circulating fluidized bed boilers, Chemical and Process
Engineering 29 (2008) 473–492.
[41] K. Myöhänen, T. Hyppänen, M. Loschkin, Converting measurement
data to process knowledge by using three-dimensional cfb furnace
model, in: The 8th International Conference on CFB, 2005.
[42] D. Pallares, F. Johnsson, M. Palonen, A comprehensive model of cfb
combustion, in: 9th Int. Conf. on Circulating Fluidized Beds, 2008.
[43] M. Palonen, D. Pallarès, A. Larsson, F. Johnsson, V. Ylä-Outinen,
J. Laine, Circulating fluidized bed combustion-build-up and validation
of a three-dimensional model.
[44] T. Pikkarainen, A. Tourunen, H. Nevalainen, T. Leino, J. Saastamoinen,
T. Eriksson, Small scale fluidized bed experiments under oxygen combustion
conditions, in: International Conference on Coal Science &
Techology, University of Nottingham UK, 2007.
[45] J. Saastamoinen, A. Tourunen, T. Pikkarainen, H. Häsä, J. Miettinen,
T. Hyppänen, K. Myöhänen, Fluidized bed combustion in high concentrations
of o2 and co2, in: 19th FBC Conference, 2006.
[46] J. Werther, Fluid dynamics, temperature and concentration fields in
large-scale cfb combustors, in: 8-th International Conference on Circulating
Fluidized Beds, 2005.
[47] N. Zhang, B. Lu, W. Wang, J. Li, 3D CFD simulation of combustion in
a 150 MWe circulating fluidized bed boiler, in: Proc. of the 10-th International
Conference on Circulating Fluidized Beds and Fluidization
Technology - CFB-10, Sunriver, Oregon, USA, 2011, pp. 537–544.
[48] C. Zhao, W. Zhou, L. Duan, X. Chan, 2d Euler-Euler simulation of oxycoal
combustion in a circulating fluidized bed., in: Proc. of the 21 International
Conference on fluidized bed combustion, Naples, Italy, 2012,
pp. 495–502.
[49] W. Zhou, C. Zhao, L. Duan, X. Chen, D. Liu, A simulation study of
coal combustion under o 2/co 2 and o 2/rfg atmospheres in circulating
fluidized bed, Chemical engineering journal 223 (2013) 816–823.
[50] W. Zhou, C. Zhao, L. Duan, X. Chen, C. Liang, Two-dimensional computational
fluid dynamics simulation of nitrogen and sulfur oxides emissions
in a circulating fluidized bed combustor, Chemical engineering
journal 173 (2) (2011) 564–573.
[51] W. Zhou, C. Zhao, L. Duan, D. Liu, X. Chen, Cfd modeling of oxycoal
combustion in circulating fluidized bed, International Journal of
Greenhouse Gas Control 5 (6) (2011) 1489–1497.
[52] W. Zhou, C. Zhao, L. Duan, D. Liu, X. Chen, Simulation study of oxyfuel
combustion in a circulating fluidized bed, in: The 35th International
Technical Conference on Clean Coal & Fuel Systems, Clearwater,
Florida, USA, 2010.
[53] K. K. Win, W. Nowak, H. Matsuda, M. Hasatani, Z. Bis, J. Krzywanski,
W. Gajewski, Transport velocity of coarse particles in multi-solid
fluidized bed, Journal of Chemical Engineering of Japan 28 (5) (1995)
535–540.
[54] J. Kotowicz, A. Balicki, Analysis of the thermodynamic and economic
efficiency of supercritical power unit with cfb boiler fed with lignite and
air separation unit based on high-temperature membrane technology,
Journal of Power Technologies 93 (5) (2013) 308–313.
[55] D. Pallares, F. Johnsson, Macroscopic modelling of fluid dynamics in
large-scale circulating fluidized beds, Progress in energy and combustion
science 32 (5) (2006) 539–569.
[56] S. Kalogirou, Applications of artificial neural networks in energy systems,
Energy Conversion and Management 40 (10) (1999) 1073–
1087.
[57] C. Zhao, L. Dun, W. Zhou, X. Chen, D. Zeng, T. Flynn, D. Kraft, Coal
combustion characteristics on an oxy-cfb combustor with warm flue
gas recycle, in: Proceedings of the 21st International Conference on
Fluidized Bed Combustion, 2012, pp. 3–6.
[58] R. R. Jensen, S. Karki, H. Salehfar, Artificial neural network-based
estimation of mercury speciation in combustion flue gases, Fuel Processing
Technology 85 (6) (2004) 451–462.
[59] U. Kesgin, Genetic algorithm and artificial neural network for engine
optimisation of efficiency and nox emission, Fuel 83 (7) (2004) 885–
895.
[60] F. Christo, A. Masri, E. Nebot, T. Turanyi, Utilising artificial neural network
and repro-modelling in turbulent combustion, in: Neural Networks,
1995. Proceedings., IEEE International Conference on, Vol. 2,
IEEE, 1995, pp. 911–916.
[61] S. Milanic, R. Karba, Neural network models for predictive control of a
thermal plant, in: Proc. of the Int. Conf. EANN ’96, London, UK, 1996,
pp. 151–154.
[62] B. Muller, H. Keller, Neural networks for combustion process modeling.,
in: Proc. of the Int. Conf. EANN ’96, London, UK, 1996, pp. 87–
90.
[63] M. W. Gardner, S. Dorling, Artificial neural networks (the multilayer
perceptron)—a review of applications in the atmospheric sciences, Atmospheric
environment 32 (14) (1998) 2627–2636.
[64] S. Arumugam, G. Sriram, P. S. Subramanian, Application of artificial
intelligence to predict the performance and exhaust emissions of
diesel engine using rapeseed oil methyl ester, Procedia Engineering
38 (2012) 853–860.
[65] P. Boniecki, J. Dach, K. Pilarski, H. Piekarska-Boniecka, Artificial neural
networks for modeling ammonia emissions released from sewage
sludge composting, Atmospheric Environment 57 (2012) 49–54.
[66] C. Carnevale, G. Finzi, E. Pisoni, M. Volta, Neuro-fuzzy and neural network
systems for air quality control, Atmospheric Environment 43 (31) (2009) 4811–4821.
[67] A. de Lucas, A. Durán, M. Carmona, M. Lapuerta, Modeling diesel
particulate emissions with neural networks, Fuel 80 (4) (2001) 539–
548.
[68] Z. Hao, C. Kefa, M. Jianbo, Combining neural network and genetic algorithms
to optimize low no x pulverized coal combustion, Fuel 80 (15)
(2001) 2163–2169.
[69] R. Karada˘ g, Ö. Akgöbek, The prediction of convective heat transfer in
floor-heating systems by artificial neural networks, International Communications
in Heat and Mass Transfer 35 (3) (2008) 312–325.
[70] T. Kwater, Z. Kedzior, B. Twarog, Estimation by artificial neural network
in ecological problems., in: Proc. AMSE-Conference MS’2001, Lviv,
Ukraine, 2001, pp. 212–215.
[71] J. Krzywanski, W. Muskała, W. Nowak, Application of neural networks
for determining of the convective heat transfer coefficient in a circulating
fluidized bed, Power Engineering 11 (2008) 761–764.
[72] J. Krzywanski, W. Nowak, Modeling of heat transfer coefficient in the
furnace of cfb boilers by artificial neural network approach, International
Journal of Heat and Mass Transfer 55 (15) (2012) 4246–4253.
[73] Y. Miyamoto, Y. Kurosaki, H. Fujiyama, E. Nanbu, Dynamic characteristic
analysis and combustion control for a fluidized bed incinerator,
Control Engineering Practice 6 (9) (1998) 1159–1168.
[74] E. Molga, Neural network approach to support modelling of chemical
reactors: problems, resolutions, criteria of application, Chemical Engineering
and Processing: Process Intensification 42 (8) (2003) 675–
695.
[75] S. S. Nagendra, M. Khare, Artificial neural network approach for modelling
nitrogen dioxide dispersion from vehicular exhaust emissions,
Ecological Modelling 190 (1) (2006) 99–115.
[76] L. E. Olcese, B. M. Toselli, A method to estimate emission rates
from industrial stacks based on neural networks, Chemosphere 57 (7)
(2004) 691–696.
[77] S. L. Reich, D. Gomez, L. Dawidowski, Artificial neural network for the
identification of unknown air pollution sources, Atmospheric Environment
33 (18) (1999) 3045–3052.
[78] S. S. Sablani, A neural network approach for non-iterative calculation
of heat transfer coefficient in fluid–particle systems, Chemical Engineering
and Processing: Process Intensification 40 (4) (2001) 363–
369.
[79] Ü. A. S¸ ahin, C. Bayat, O. N. Uçan, Application of cellular neural network
(cnn) to the prediction of missing air pollutant data, Atmospheric
Research 101 (1) (2011) 314–326.
[80] S. Tasdemir, I. Saritas, M. Ciniviz, N. Allahverdi, Artificial neural network
and fuzzy expert system comparison for prediction of performance
and emission parameters on a gasoline engine, Expert Systems
with Applications 38 (11) (2011) 13912–13923.
[81] A. Tuma, H.-D. Haasis, O. Rentz, Development of emission orientated
production control strategies using fuzzy expert systems, neural networks
and neuro-fuzzy approaches, Fuzzy sets and systems 77 (3)
(1996) 255–264.
[82] Q. Wang, G. Xie, M. Zeng, L. Luo, Prediction of heat transfer rates for
shell-and-tube heat exchangers by artificial neural networks approach,
Journal of Thermal Science 15 (3) (2006) 257–262.
[83] M. Xiaomin, Recognition of toxic gases emission in power plant based
on artificial neural network, Energy Procedia 17 (2012) 1578–1584.
[84] J. Zhang, F. Haghighat, Development of artificial neural network based
heat convection algorithm for thermal simulation of large rectangular
cross-sectional area earth-to-air heat exchangers, Energy and Buildings
42 (4) (2010) 435–440.
[85] Y. Zhang, Y. Ding, Z. Wu, L. Kong, T. Chou, Modeling and coordinative
optimization of no x emission and efficiency of utility boilers with neural
network, Korean Journal of Chemical Engineering 24 (6) (2007) 1118–
1123.
[86] H. Zhou, K. Cen, J. Fan, Modeling and optimization of the nox emission
characteristics of a tangentially fired boiler with artificial neural
networks, Energy 29 (1) (2004) 167–183.
[87] B. P. Grandjean, et al., Special issue on application of neural networks
to multiphase reactors (2003).
[88] C. Faur-Brasquet, P. Le Cloirec, Modelling of the flow behavior of activated
carbon cloths using a neural network approach, Chemical Engineering
and Processing: Process Intensification 42 (8) (2003) 645–
652.
[89] H. Lin, W. Chen, A. Tsutsumi, Long-term prediction of nonlinear hydrodynamics
in bubble columns by using artificial neural networks, Chemical
Engineering and Processing: Process Intensification 42 (8) (2003)
611–620.
[90] A. Shaikh, M. Al-Dahhan, Development of an artificial neural network
correlation for prediction of overall gas holdup in bubble column reactors,
Chemical Engineering and Processing: Process Intensification
42 (8) (2003) 599–610.
[91] G. Bollas, S. Papadokonstadakis, J. Michalopoulos, G. Arampatzis,
A. Lappas, I. Vasalos, A. Lygeros, Using hybrid neural networks in
scaling up an fcc model from a pilot plant to an industrial unit, Chemical
Engineering and Processing: Process Intensification 42 (8) (2003)
697–713.
[92] T. Abbas, M. Awais, F. Lockwood, An artificial intelligence treatment
of devolatilization for pulverized coal and biomass in co-fired flames,
Combustion and flame 132 (3) (2003) 305–318.
[93] J. Kukkonen, L. Partanen, A. Karppinen, J. Ruuskanen, H. Junninen,
M. Kolehmainen, H. Niska, S. Dorling, T. Chatterton, R. Foxall, et al.,
Extensive evaluation of neural network models for the prediction of NO
2 and PM 10 concentrations, compared with a deterministic modelling
system and measurements in central Helsinki, Atmospheric Environment
37 (32) (2003) 4539–4550.
[94] H. Ye, R. Nicolai, L. Reh, A bayesian–gaussian neural network and its
applications in process engineering, Chemical Engineering and Processing:
Process Intensification 37 (5) (1998) 439–449.
[95] W. Muskała, J. Krzywa´ nski, T. Czakiert, W. Nowak, The research of
CFB boiler operation for oxygen-enhanced dried lignite combustion,
Rynek Energii 1 (92) (2011) 172–176.
[96] J. Krzywa´ nski, R. Rajczyk, W. Nowak, Model research of gas emissions
from lignite and biomass co-combustion in a large scale cfb
boiler, Chemical and Process Engineering 35 (2) (2014) 217–231.
Published
2017-07-21
How to Cite
KRZYWAŃSKI, Jarosław; NOWAK, Wojciech.
Neurocomputing approach for the prediction of NOx emissions from CFBC in air-fired and oxygen-enriched atmospheres.
Journal of Power Technologies, [S.l.], v. 97, n. 2, p. 75--84, july 2017.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/646>. Date accessed: 30 dec. 2024.
Issue
Section
Combustion and Fuel Processing
Keywords
Nitrogen oxides; Circulating fluidized bed; Oxy combustion; Artificial neural networks
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