Advertisement

Science China Technological Sciences

, Volume 61, Issue 7, pp 982–993 | Cite as

Numerical modeling and multi-objective optimization of a novel cross-flow heat exchanger with rotated aligned tube bank

Article
  • 14 Downloads

Abstract

A novel cross-flow heat exchanger with a rotated aligned tube bank is designed and utilized in a cement plant. The heat exchanger is numerically modeled with various tube pitches in order to obtain correlations of the shell-side average Nusselt number and friction factor. Then, a multi-objective optimization approach is performed based on the genetic algorithm. The goal of this study is to maximize the heat transfer rate and minimize the pressure drop. Pareto optimal solutions are obtained, which indicate that the increase in the heat transfer rate leads to an increase in pressure drop, and vice versa. In addition, heat exchanger effectiveness, total cost, and the ratio of the heat transfer rate to the fan/pumping power demonstrate different variations with the two objective functions. Several selection criteria are discussed to determine the optimal design and to help designers select an appropriate solution based on actual requirements. Two multi-objective optimization design schemes are compared to the original design under the same heat transfer rate. Results show that pressure drop decreases by 67.9% and 69.7%, respectively, and total annual cost decreases by 2.4% and 16.3%, respectively.

Keywords

tube bank numerical modeling multi-objective optimization genetic algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cengel Y A. Heat Transfer: A Practical Approach. New York: McGraw-Hill, 2002Google Scholar
  2. 2.
    Bergman T L, Lavine A S, Incropera F P, et al. Fundamentals of Heat and Mass Transfer. New York: John Wiley & Sons, 2011Google Scholar
  3. 3.
    Kreith F, Manglik R M, Bohn M S. Principle of Heat Transfer. Boston:Cengage Learning, 2011Google Scholar
  4. 4.
    Kays W M, London A L. Compact Heat Exchangers. New York: McGraw-Hill, 1984Google Scholar
  5. 5.
    Selbaş R, Kızılkan Ö, Reppich M. A new design approach for shelland- tube heat exchangers using genetic algorithms from economic point of view. Chem Eng Proc-Process Int, 2006, 45: 268–275CrossRefGoogle Scholar
  6. 6.
    Wildi-Tremblay P, Gosselin L. Minimizing shell-and-tube heat exchanger cost with genetic algorithms and considering maintenance. Int J Energy Res, 2007, 31: 867–885CrossRefGoogle Scholar
  7. 7.
    Caputo A C, Pelagagge P M, Salini P. Heat exchanger design based on economic optimisation. Appl Thermal Eng, 2008, 28: 1151–1159CrossRefGoogle Scholar
  8. 8.
    Guo J, Xu M, Cheng L. The application of field synergy number in shell-and-tube heat exchanger optimization design. Appl Energy, 2009, 86: 2079–2087CrossRefGoogle Scholar
  9. 9.
    Guo J, Huai X, Li X, et al. Multi-objective optimization of heat exchanger based on entransy dissipation theory in an irreversible Brayton cycle system. Energy, 2013, 63: 95–102CrossRefGoogle Scholar
  10. 10.
    Yin Q, Du W, Ji X, et al. Optimization design and economic analyses of heat recovery exchangers on rotary kilns. Appl Energy, 2016, 180: 743–756CrossRefGoogle Scholar
  11. 11.
    Özçelik Y. Exergetic optimization of shell and tube heat exchangers using a genetic based algorithm. Appl Thermal Eng, 2007, 27: 1849–1856CrossRefGoogle Scholar
  12. 12.
    Amanifard N, Nariman-Zadeh N, Borji M, et al. Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms. Energy Convers Manage, 2008, 49: 311–325CrossRefGoogle Scholar
  13. 13.
    Gholap A K, Khan J A. Design and multi-objective optimization of heat exchangers for refrigerators. Appl Energy, 2007, 84: 1226–1239CrossRefGoogle Scholar
  14. 14.
    Agarwal A, Gupta S K. Jumping gene adaptations of NSGA-II and their use in the multi-objective optimal design of shell and tube heat exchangers. Chem Eng Res Des, 2008, 86: 123–139CrossRefGoogle Scholar
  15. 15.
    Wong J Y Q, Sharma S, Rangaiah G P. Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria. Appl Thermal Eng, 2016, 93: 888–899CrossRefGoogle Scholar
  16. 16.
    Damavandi M D, Forouzanmehr M, Safikhani H. Modeling and Pareto based multi-objective optimization of wavy fin-and-elliptical tube heat exchangers using CFD and NSGA-II algorithm. Appl Thermal Eng, 2017, 111: 325–339CrossRefGoogle Scholar
  17. 17.
    Husain A, Kim K Y. Enhanced multi-objective optimization of a microchannel heat sink through evolutionary algorithm coupled with multiple surrogate models. Appl Thermal Eng, 2010, 30: 1683–1691CrossRefGoogle Scholar
  18. 18.
    Sanaye S, Hajabdollahi H. Multi-objective optimization of rotary regenerator using genetic algorithm. Int J Thermal Sci, 2009, 48: 1967–1977CrossRefGoogle Scholar
  19. 19.
    Sanaye S, Hajabdollahi H. Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm. Appl Energy, 2010, 87: 1893–1902CrossRefGoogle Scholar
  20. 20.
    Sanaye S, Hajabdollahi H. Multi-objective optimization of shell and tube heat exchangers. Appl Thermal Eng, 2010, 30: 1937–1945CrossRefGoogle Scholar
  21. 21.
    Hajabdollahi H, Ahmadi P, Dincer I. Thermoeconomic optimization of a shell and tube condenser using both genetic algorithm and particle swarm. Int J Refrig, 2011, 34: 1066–1076CrossRefGoogle Scholar
  22. 22.
    Guo J, Cheng L, Xu M. The entropy generation minimisation based on the revised entropy generation number. Internat J Exergy, 2010, 7: 607–626CrossRefGoogle Scholar
  23. 23.
    Guo J, Cheng L, Xu M. Optimization design of shell-and-tube heat exchanger by entropy generation minimization and genetic algorithm. Appl Thermal Eng, 2009, 29: 2954–2960CrossRefGoogle Scholar
  24. 24.
    Guo J, Cheng L, Xu M. Multi-objective optimization of heat exchanger design by entropy generation minimization. J Heat Transfer, 2010, 132: 081801CrossRefGoogle Scholar
  25. 25.
    Shao W, Cui Z, Cheng L. Multi-objective optimization design of air distribution of grate cooler by entropy generation minimization and genetic algorithm. Appl Thermal Eng, 2016, 108: 76–83CrossRefGoogle Scholar
  26. 26.
    Shao W, Cui Z, Cheng L. Multi-objective optimization of cooling air distribution of grate cooler with different inlet temperatures by using genetic algorithm. Sci China Tech Sci, 2017, 60: 345–354CrossRefGoogle Scholar
  27. 27.
    Chen Q, Wang Y F. Differences and relations of objectives, constraints, and decision parameters in the optimization of individual heat exchangers and thermal systems. Sci China Tech Sci, 2016, 59: 1071–1079CrossRefGoogle Scholar
  28. 28.
    Li J, Du W, Cheng L. Numerical simulation and experiment of gassolid two phase flow and ash deposition on a novel heat transfer surface. Appl Thermal Eng, 2017, 113: 1033–1046CrossRefGoogle Scholar
  29. 29.
    Wang K, Li J, Wang P, Cheng L. Experimental and numerical studies on the air-side flow and heat transfer characteristics of a novel heat exchanger. Appl Thermal Eng, 2017, 123: 830–844CrossRefGoogle Scholar
  30. 30.
    Li J, Wang P, Cheng L. Characteristics of ash deposition on a novel heat transfer surface. CIESC J, 2016, 67: 3598–3606Google Scholar
  31. 31.
    Du W, Wang P, Cheng L. Numerical simulation and experimental research on novel heat transfer surface. CIESC J, 2015, 66: 2070–2075Google Scholar
  32. 32.
    Shao W, Cui Z, Wang N H, et al. Numerical simulation of heat transfer process in cement grate cooler based on dynamic mesh technique. Sci China Tech Sci, 2016, 59: 1065–1070CrossRefGoogle Scholar
  33. 33.
    Du W, Wang H, Yuan X, et al. Evaluation of shell side performance and analysis on continuous helical baffled heat exchangers with elliptical tubes. CIESC J, 2013, 64: 1145–1150Google Scholar
  34. 34.
    Kim T. Effect of longitudinal pitch on convective heat transfer in crossflow over in-line tube banks. Ann Nucl Energy, 2013, 57: 209–215CrossRefGoogle Scholar
  35. 35.
    Fluent Inc. FLUENT 6.3 User’s Guide, 2014Google Scholar
  36. 36.
    Wilcox D C. Turbulence Modeling for CFD. California: DCW Industries, 2006Google Scholar
  37. 37.
    Han H, He Y L, Tao W Q, et al. A parameter study of tube bundle heat exchangers for fouling rate reduction. Int J Heat Mass Transfer, 2014, 72: 210–221CrossRefGoogle Scholar
  38. 38.
    Holman J P. Heat Transfer. New York: McGraw-Hill, 2010Google Scholar
  39. 39.
    Žkauskas A. Heat transfer from tubes in crossflow. Adv Heat Transfer, 1972, 8: 93–160CrossRefGoogle Scholar
  40. 40.
    Li W, Wang X. Heat transfer and pressure drop correlations for compact heat exchangers with multi-region louver fins. Int J Heat Mass Transfer, 2010, 53: 2955–2962CrossRefMATHGoogle Scholar
  41. 41.
    Shah R K, Sekulic D P. Fundamentals of Heat Exchanger Design. New Jersey: John Wiley & Sons, 2003CrossRefGoogle Scholar
  42. 42.
    Gnielinski V. New equations for heat and mass transfer in turbulent pipe and channel flow. Int Chem Eng, 1976, 75: 359–368Google Scholar
  43. 43.
    White F M. Fluid Mechanics. New York: McGraw-Hill, 2011Google Scholar
  44. 44.
    Hesselgreaves J E. Compact Heat Exchangers: Selection, Design and Operation. Pergamon: Elsevier Science, 2001Google Scholar
  45. 45.
    Taal M, Bulatov I, Klemeš J, et al. Cost estimation and energy price forecasts for economic evaluation of retrofit projects. Appl Thermal Eng, 2003, 23: 1819–1835CrossRefGoogle Scholar
  46. 46.
    Hall R S, Matley J, McNaughton K J. Current costs of process equipment. Chem Eng-New York, 1982, 4: 80–116Google Scholar
  47. 47.
    Li J, Wang K, Cheng L. Experiment and optimization of a new kind once-through heat recovery steam generator (HRSG) based on analysis of exergy and economy. Appl Thermal Eng, 2017, 120: 402–415CrossRefGoogle Scholar
  48. 48.
    Deb K. Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction. New York: John Wiley & Sons, 2001MATHGoogle Scholar
  49. 49.
    Heat exchangers: GB/T 151–2014. Beijing: Standards Press of China, 2014Google Scholar
  50. 50.
    Lamont G B, Veldhuizen D A V. Evolutionary Algorithms for Solving Multi-Objective Problems. New York: Springer, 2007MATHGoogle Scholar
  51. 51.
    Sayyaadi H, Amlashi E H, Amidpour M. Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm. Energy Convers Manage, 2009, 50: 2035–2046CrossRefGoogle Scholar
  52. 52.
    Sanaye S, Dehghandokht M. Modeling and multi-objective optimization of parallel flow condenser using evolutionary algorithm. Appl Energy, 2011, 88: 1568–1577CrossRefGoogle Scholar
  53. 53.
    Huang S, Ma Z, Wang F. A multi-objective design optimization strategy for vertical ground heat exchangers. Energy Buildings, 2015, 87: 233–242CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Thermal Science and TechnologyShandong UniversityJi’nanChina

Personalised recommendations