Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

Applied Energy

Volumn 130, Issue , 2014, Pages 658-669

  Liu, Xingrang ^a,b   Bansal, R C ^a,b  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b UNIVERSITY OF PRETORIA   (South Africa)

Author keywords

ANSYS Fluent; Carbon emission; Coal fired power plant combustion optimization; CORBA; Multi objective optimization; Slagging and fouling

Indexed keywords

AIR; BOILER CONTROL; C++ (PROGRAMMING LANGUAGE); COAL FIRED BOILERS; COMBUSTION; COMMON OBJECT REQUEST BROKER ARCHITECTURE (CORBA); COMPUTATIONAL FLUID DYNAMICS; COMPUTER PROGRAMMING; COMPUTER SOFTWARE; EFFICIENCY; GENETIC ALGORITHMS; HEAT TRANSFER; SLAGS; TEMPERATURE;

ANSYS FLUENT; CARBON EMISSIONS; COAL-FIRED POWER PLANT; COMBUSTION OPTIMIZATION; HEAT TRANSFER EFFICIENCY; HEAT TRANSFER EQUIPMENT; NON DOMINATED SORTING GENETIC ALGORITHM (NSGA II); SLAGGING AND FOULING;

MULTIOBJECTIVE OPTIMIZATION;

ARTIFICIAL NEURAL NETWORK; BIOFOULING; CARBON EMISSION; COAL-FIRED POWER PLANT; COMBUSTION; COMPUTATIONAL FLUID DYNAMICS; ELECTRICITY GENERATION; EQUIPMENT; MEASUREMENT METHOD; OPTIMIZATION;

EID: 84904801531     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2014.02.069     Document Type: Article

References (36)

1. Chattopadhyay P. Boiler operation engineering questions and answers 2001, McGraw Hill, New York, p. 941-98. 2nd ed.
2. Wang X., Wang M., Wei Y. Simulation study on data of coal fired power plant boiler experiment with cold state. Energy Res Util 2007, 4(1):5-8.
3. Palmé T., Fast M., Thern M. Gas turbine sensor validation through classification with artificial neural networks. Appl Energy 2011, 88:3898-3904.
4. Colorado D., Hernández J.A., Rivera W., Martínez H., Juárez D. Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse. Appl Energy 2011, 88:1281-1290.
5. Liu X, Bansal RC. Integrating multi-objective optimization with computational fluid dynamics to optimize coal fired power plant boiler combustion process. In: 5th International conference on applied energy (ICAE), Pretoria; 2013.
6. Yang W., Lee K.Y., Junker S.T., Ayagh H.G. Fuzzy fault diagnosis and accommodation system for hybrid fuel-cell/gas-turbine power plant. IEEE Trans Energy Convers 2010, 25(4):1187-1194.
7. Zarenezhad B., Aminian A. Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model. Energy Convers Manage 2011, 52(2):911-916.
8. Chandok J.S., Kar I.N., Tuli S. Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks. Energy Convers Manage 2008, 49(8):1989-1998.
9. Liu X., Bansal R.C. Optimizing combustion process by adaptive tuning technology based on integrated genetic algorithm and computational fluid dynamics. Energy Convers Manage 2012, 56(4):53-62.
10. Liu X., Bansal R.C. Improving fossil fuel boiler combustion efficiency based on integrating real time simulation with online learning technology. Int J Amb Energy 2012, 3(2):130-141.
11. US Department of Energy. Power plant optimization demonstration projects, clean coal technology. Department of Energy, The United States of America, September 2007. p. 6-7. <>. http://www.netl.doe.gov/technologies/coalpower/cctc/topicalreports/pdfs/topical25.pdf.
12. Murugan P., Kannan S., Baskar S. Application of NSGA-II algorithm to single-objective transmission constrained generation expansion. IEEE Trans Power Syst 2009, 24(4):1790-1796.
13. Meng X, Ni J. Research of active vibration control optimal disposition based on MIGA and NSGA-II. In: 6th International conference on natural computation, Yantai; 2010.
14. Baghel V, Panda G, Srihari P, Rajarajeswari K, Majhi B. An efficient multi-objective pulse radar compression technique using RBF and NSGA-II. In: Congress on nature & biologically inspired computing, Bhubaneswar; 2009.
15. Fallanhpour MB, Hemmati KD, Poumohammand A, Golmakani A. Multi objective optimization of a LNA using genetic algorithm based on NSGA-II. In: International conference electrical engineering and informatics; 2011.
16. Álvarez L., Gharebaghi M., Jones J.M., Pourkashanian M., Williams A., Riaza J., et al. CFD modeling of oxy-coal combustion: prediction of burnout, volatile and NO precursors release. Appl Energy 2013, 104:653-665.
17. Galindo J., Fajardo P., Navarro R.L., García-Cuevas M. Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modelling. Appl Energy 2013, 103:116-127.
18. Vuthaluru H.B., Vuthaluru R. Control of ash related problems in a large scale tangentially fired boiler using CFD modelling. Appl Energy 2013, 87:1418-1426.
19. Rohit M.V., Agarwa G.D., Mathu J. Derating factor new concept for evaluating thermal performance of earth air tunnel heat exchanger: a transient CFD analysis. Appl Energy 2013, 102:418-426.
20. 2 absorption in a riser reactor. Appl Energy 2013, 112:224-234.
21. Xu Y, Jin Q, Yuang J. CFD simulations of gate leaves design in the SCR-DeNOx facility for coal-fired power plant. In: 30th Chinese control conference, Yantai; 2011.
22. Zhou J, Han W, Liu X. Design and experiment of silencer for discharging waste water of high temperature and pressure in nuclear power plant. In: Asia-Pacific power and energy engineering conference, Chengdu; 2010.
23. Shen D, Zhao P. Computational fluid dynamics-based simulation of optimal design in power plant flue gas denitrification. In: International conference on communication and control, Zhejiang; 2011.
24. Fan Y, Si P, Bai B. Analysis and numerical simulation on an improved oxygen-enriched burner. In: Asia-Pacific power and energy engineering conference, Chengdu; 2010.
25. Dang X, Hu H. Study of numerical simulation and experimental of gas flow distribution of electric composite filter bag in 300MW power unit in coal-fired power plant. In: 4th International conference on bioinformatics and biomedical engineering, Chengdu; 2010.
26. Juniper L. Thermal coal technology. Department of Mines and Energy, Queensland, Government, Australia; 2000. p. 71-128.
27. Versteeg H.K. An introduction to computational fluid dynamics - the finite volume method 2007, Pearson Education, London, p. 343-416. 2nd ed.
28. ANSYS Inc. ANSYS Fluent theory guide, ANSYS Inc., United States; 2012. p. 121-36.
29. Baukal C.E., Gershtein J.V., Li X. Computational fluid dynamics in industrial combustion 2001, CRC Press, New York, p. 139-43.
30. Dukelow SG. The control of boilers. 2nd ed. The United States of America: The Instrumentation, Systems, and Automation Society; 1991. p. 253-6.
31. Cengel Y.A., Boles M.A. Thermal dynamics - an engineering approach 2011, High Education, London, p. 689-833. 7th edition in SI units.
32. Lohner R. Applied CFD techniques - an introduction based on finite element methods 2001, John Wiley & Sons Ltd., Chichester, p. 1-3.
33. An E., Zhou H. Digital simulation for coal fired power plant combustion. J Tongji Univ (Nat Sci) 2004, 32(8):1042-1045.
34. Tian Z, Witt PJ, Schwarz MP, Yang W. Numerical modelling of brown coal combustion in a tangentially fired furnace. In: 7th International Conference on CFD in the Mineral and Process Industry, Melbourne; December 2009. p. 1-8.
35. Tu J., Yeoh G.H., Liu C. Computational fluid dynamics - a practical approach 2008, Elsevier, New York, p. 180-221.
36. Beyer H.G., Deb H. On self - adaptive features in real parameter evolutionary algorithms. IEEE Trans Evol Comput 2001, 5(3):250-268.