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Original Articles

Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers

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Pages 869-890 | Received 22 Nov 2007, Published online: 19 Aug 2008

References

  • Basu , P. 2006 . Combustion and gasification in fluidized beds , Boca Raton, FL : Taylor & Francis .
  • Belegundu , A. D. and Chandrupatla , T. R. 1999 . Optimization concepts and applications in engineering , New York : Prentice-Hall .
  • Brink , A. , Kilpinen , P. and Hupa , M. 2001 . A simplified kinetic rate expression for describing the oxidation of volatile fuel-N in biomass combustion . Energy & Fuels , 15 ( 5 ) : 1094 – 1099 .
  • Bryden , K. M. 2003 . Optimization of heat transfer utilizing graph based evolutionary algorithms . International Journal of Heat and Fluid Flow , 24 ( 2 ) : 267 – 277 .
  • Catalano , L. A. 2006 . Efficient design optimization of duct-burners for combined-cycle and cogenerative plants . Engineering Optimization , 38 ( 7 ) : 801 – 820 .
  • Chandrupatla , T. R. 1998 . An efficient quadratic fit-sectioning algorithm for minimization without derivatives . Computer Methods in Applied Mechanics and Engineering , 152 ( 1–2 ) : 211 – 217 .
  • Chen , J.-Y. 2003 . Optimization of homogeneous charge compression ignition with genetic algorithms . Combustion Science and Technology , 175 ( 2 ) : 373 – 392 .
  • Chu , J.-Z. 2003 . Constrained optimization of combustion in a simulated coal-fired boiler using artificial neural network model and information analysis . Fuel , 82 ( 6 ) : 693 – 703 .
  • Cohon , J. L. 1978 . Multiobjective programming and planning , New York : Academic Press .
  • Das , I. and Dennis , J. E. 1997 . A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems . Structural and Multidisciplinary Optimization , 14 ( 1 ) : 63 – 69 .
  • Deb , K. 2001 . Multi-objective optimization using genetic algorithms , Chichester, , UK : Wiley .
  • Duo , W. , Dam-Johansen , K. and Østergaard , K. 1992 . Kinetics of the gas-phase reaction between nitric oxide, ammonia and oxygen . Canadian Journal of Chemical Engineering , 70 : 1014 – 1020 .
  • Elliot , L. 2004 . Genetic algorithms for optimisation of chemical kinetics reaction mechanisms . Progress in Energy and Combustion Science , 30 ( 3 ) : 297 – 328 .
  • Ertesvåg , I. S. and Magnussen , B. F. 2000 . The eddy dissipation turbulence energy cascade model . Combustion Science and Technology , 159 ( 1 ) : 213 – 235 .
  • Fluent Inc . 2005 . FLUENT 6.2 User's Guide , Available from: http://www.fluent.com/
  • Fonseca , C. M. and Fleming , P. J. 2000 . “ Multiobjective optimization ” . In Evolutionary computation 2—advanced algorithms and operators , Edited by: Bäck , T. , Fogel , D. B. and Michalewicz , Z. 25 – 37 . Bristol, , UK : Institute of Physics .
  • Giannakoglou , K. C. 2002 . Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence . Progress in Aerospace Sciences , 38 ( 1 ) : 43 – 76 .
  • Giannakoglou , K. C. and Giotis , A. P. 2007 . The evolutionary algorithm system EASY version 1.5 , Greece : National Technical University of Athens . Available from: http://147.102.55.162/EASY/
  • Hämäläinen , J. P. , Malkamäki , T. and Toivanen , J. 1999 . “ Genetic algorithms in shape optimization of a paper machine headbox ” . In Evolutionary algorithms in engineering and computer science , Edited by: Miettinen , K. 435 – 443 . Chichester, , UK : Wiley .
  • Homma , R. and Chen , J.-Y. Combustion process optimization by genetic algorithms: reduction of NO2 emission via optimal postflame process . Proceedings of the Combustion Institute . Vol. 28 , pp. 2483 – 2489 .
  • Johnson , R. W. , Landon , M. D. and Perry , E. C. 2001 . “ Design optimization ” . In Computational fluid dynamics in industrial combustion , Edited by: Baukal , C. E. Jr. , Gershtein , V. Y. and Li , X. 557 – 584 . Boca Raton, FL : CRC Press .
  • Jones , W. P. and Lindstedt , R. P. 1988 . Global reaction schemes for hydrocarbon combustion . Combustion and Flame , 73 ( 3 ) : 233 – 249 .
  • Kalogirou , S. A. 2003 . Artificial intelligence for the modeling and control of combustion processes: a review . Progress in Energy and Combustion Science , 29 ( 6 ) : 515 – 566 .
  • Koski , J. 1985 . Defectiveness of weighting method in multicriterion optimization of structures . Communications in Applied Numerical Methods , 1 : 333 – 337 .
  • Magnussen , B. F. and Hjertager , B. H. On mathematical modeling of turbulent combustion with special emphasis on soot formation and combustion . Proceedings of the Combustion Institute , Vol. 16 , pp. 719 – 729 .
  • Marler , R. T. and Arora , J. S. 2004 . Survey of multi-objective optimization methods for engineering . Structural and Multidisciplinary Optimization , 26 ( 6 ) : 369 – 395 .
  • Miettinen , K. 1999 . Nonlinear multiobjective optimization , Boston, MA : Kluwer Academic .
  • Miller , J. A. and Bowman , C. T. 1989 . Mechanism and modeling of nitrogen chemistry in combustion . Progress in Energy and Combustion Science , 15 ( 4 ) : 287 – 338 .
  • Montgomery , C. J. 2006 . Selecting the optimum quasi-steady-state species for reduced chemical kinetic mechanisms using a genetic algorithm . Combustion and Flame , 144 ( 1–2 ) : 37 – 52 .
  • Osyczka , A. 2002 . Evolutionary algorithms for single and multicriteria design optimization , Heidelberg : Physica-Verlag .
  • Poinsot , T. and Veynante , D. 2005 . Theoretical and numerical combustion , 2 , Philadelphia, PA : R.T. Edwards .
  • Polifke , W. , Geng , W. and Döbbeling , K. 1998 . Optimization of rate constants for simplified reaction mechanisms with genetic algorithms . Combustion and Flame , 113 ( 1–2 ) : 119 – 135 .
  • Poloni , C. 2000 . Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics . Computer Methods in Applied Mechanics and Engineering , 186 ( 2–4 ) : 403 – 420 .
  • Radojevic , M. 1998 . Reduction of nitrogen oxides in flue gases . Environmental Pollution , 102 ( S1 ) : 685 – 689 .
  • Raithby , G. D. and Chui , E. H. 1990 . A finite-volume method for predicting a radiant heat transfer in enclosures with participating media . Journal of Heat Transfer—Transactions of the ASME , 112 : 415 – 423 .
  • Risio , B. 2005 . Towards an innovative virtual optimisation machine for the power industry . Progress in Computational Fluid Dynamics , 5 ( 7 ) : 398 – 405 .
  • Saario , A. and Oksanen , A. 2008a . Comparison of global ammonia chemistry mechanisms in biomass combustion and selective noncatalytic reduction process conditions . Energy & Fuels , 22 ( 1 ) : 297 – 305 .
  • Saario , A. and Oksanen , A. 2008b . Effect of computational grid in industrial-scale boiler modeling . International Journal of Numerical Methods for Heat & Fluid Flow , accepted for publication
  • Saario , A. , Oksanen , A. and Ylitalo , M. 2006 . Combination of genetic algorithm and computational fluid dynamics in combustion process emission minimisation . Combustion Theory and Modelling , 10 ( 6 ) : 1037 – 1047 .
  • Shih , T.-H. 1995 . A new k–ε eddy viscosity model for high Reynolds number turbulent flows . Computers & Fluids , 24 ( 3 ) : 227 – 238 .
  • Smith , T. F. , Shen , Z. F. and Friedman , J. N. 1982 . Evaluation of coefficients for the weighted sum of gray gases model . Journal of Heat Transfer—Transactions of the ASME , 104 : 602 – 608 .
  • Tan , C. K. , Wilcox , S. J. and Ward , J. 2006 . Use of artificial intelligence techniques for optimisation of co-combustion of coal with biomass . Journal of the Institute of Energy , 79 ( 1 ) : 19 – 25 .
  • Ward , J. The application of artificial intelligence in combustion engineering . 7th European conference on industrial furnaces and boilers . April 18–21 2006 , Oporto, Portugal.
  • Wierzbicki , A. P. 1982 . A mathematical basis for satisficing decision making . Mathematical Modelling , 3 ( 25 ) : 391 – 405 .
  • Zhou , H. , Cen , K. and Fan , J. 2005 . Multi-objective optimization of coal combustion performance with artificial neural networks and genetic algorithms . International Journal of Energy Research , 29 ( 6 ) : 499 – 510 .
  • Zhou , H. , Cen , K. and Mao , J. 2001 . Combining neural network and genetic algorithms to optimize low NO x pulverized coal combustion . Fuel , 80 ( 15 ) : 2163 – 2169 .

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