Advertisement

Science China Technological Sciences

, Volume 61, Issue 4, pp 496–505 | Cite as

Molten steel yield optimization of a converter based on constructal theory

  • LinGen Chen
  • Xiong Liu
  • HuiJun Feng
  • YanLin Ge
  • ZhiHui Xie
Article
First Online:
Received:
Accepted:

Abstract

Constructal theory is introduced into the molten steel yield maximization of a converter in this paper. For the specific total cost of materials, generalized constructal optimization of a converter steel-making process is performed. The optimal cost distribution of materials is obtained, and is also called as “generalized optimal construct”. The effects of the hot metal composition contents, hot metal temperature, slag basicity and ratio of the waste steel price to the sinter ore price on the optimization results are analyzed. The results show that the molten steel yield after optimization is increased by 5.48% compared with that before optimization when sinter ore and waste steel are taken as the coolants, and the molten steel yield is increased by 6.84% when only the sinter ore is taken as the coolant. It means that taking sinter ore as coolant can improve the economic performance of the converter steelmaking process. Decreasing the contents of the silicon, phosphorus and manganese in the hot metal can increase the molten steel yield. The change of slag basicity affects the molten steel yield a little.

Keywords

converter molten steel yield charge composition constructal theory generalized thermodynamic optimization 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yin R Y. Metallurgical Process Engineering. New York: Springer, 2011CrossRefGoogle Scholar
  2. 2.
    Mert M S, Dilmaç Ö F, Özkan S, et al. Exergoeconomic analysis of a cogeneration plant in an iron and steel factory. Energy, 2012, 46: 78–84CrossRefGoogle Scholar
  3. 3.
    Chen W, Yin X, Ma D. A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissions. Appl Energy, 2014, 136: 1174–1183CrossRefGoogle Scholar
  4. 4.
    Meng F, Chen L, Sun F, et al. Thermoelectric power generation driven by blast furnace slag flushing water. Energy, 2014, 66: 965–972CrossRefGoogle Scholar
  5. 5.
    Lin B, Wang X. Promoting energy conservation in China’s iron & steel sector. Energy, 2014, 73: 465–474CrossRefGoogle Scholar
  6. 6.
    Xiong B, Chen L, Meng F, et al. Modeling and performance analysis of a two-stage thermoelectric energy harvesting system from blast furnace slag water waste heat. Energy, 2014, 77: 562–569CrossRefGoogle Scholar
  7. 7.
    Zhang S, Worrell E, Crijns-Graus W, et al. Co-benefits of energy efficiency improvement and air pollution abatement in the Chinese iron and steel industry. Energy, 2014, 78: 333–345CrossRefGoogle Scholar
  8. 8.
    Liu X, Chen L, Qin X, et al. Exergy loss minimization for a blast furnace with comparative analyses for energy flows and exergy flows. Energy, 2015, 93: 10–19CrossRefGoogle Scholar
  9. 9.
    Zhao X, Bai H, Lu X, et al. A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process. Appl Energy, 2015, 148: 142–158CrossRefGoogle Scholar
  10. 10.
    Liu C, Xie Z, Sun F, et al. System dynamics analysis on characteristics of iron-flow in sintering process. Appl Thermal Eng, 2015, 82: 206–211CrossRefGoogle Scholar
  11. 11.
    Chen L, Yang B, Shen X, et al. Thermodynamic optimization opportunities for the recovery and utilization of residual energy and heat in China’s iron and steel industry: A case study. Appl Thermal Eng, 2015, 86: 151–160CrossRefGoogle Scholar
  12. 12.
    Lazzarin R M, Noro M. Energy efficiency opportunities in the production process of cast iron foundries: An experience in Italy. Appl Thermal Eng, 2015, 90: 509–520CrossRefGoogle Scholar
  13. 13.
    Zhang Z, Chen L, Yang B, et al. Thermodynamic analysis and optimization of an air Brayton cycle for recovering waste heat of blast furnace slag. Appl Thermal Eng, 2015, 90: 742–748CrossRefGoogle Scholar
  14. 14.
    Maddaloni A, Porzio G F, Nastasi G, et al. Multi-objective optimization applied to retrofit analysis: A case study for the iron and steel industry. Appl Thermal Eng, 2015, 91: 638–646CrossRefGoogle Scholar
  15. 15.
    Sun Y, Sridhar S, Liu L, et al. Integration of coal gasification and waste heat recovery from high temperature steel slags: An emerging strategy to emission reduction. Sci Rep, 2015, 5: 16591CrossRefGoogle Scholar
  16. 16.
    Lin B, Wang X. Carbon emissions from energy intensive industry in China: Evidence from the iron steel industry. Renew Sustain Energ Rev, 2015, 47: 746–754CrossRefGoogle Scholar
  17. 17.
    Feng H, Chen L, Xie Z, et al. “Disc-point” heat and mass transfer constructal optimization for solid-gas reactors based on entropy generation minimization. Energy, 2015, 83: 431–437CrossRefGoogle Scholar
  18. 18.
    Doctor Y N, Patil D B T, Darekar A M. Review of optimization aspects for casting processes. Int J Sci Res, 2015, 4: 2364–2368Google Scholar
  19. 19.
    Wang X, Lin B. How to reduce CO2 emissions in China’s iron and steel industry. Renew Sustain Energ Rev, 2016, 57: 1496–1505CrossRefGoogle Scholar
  20. 20.
    Lu B, Chen G, Chen D, et al. An energy intensity optimization model for production system in iron and steel industry. Appl Thermal Eng, 2016, 100: 285–295CrossRefGoogle Scholar
  21. 21.
    Liu C, Xie Z, Sun F, et al. Optimization for sintering proportioning based on energy value. Appl Thermal Eng, 2016, 103: 1087–1094CrossRefGoogle Scholar
  22. 22.
    Rong A, Lahdelma R. Fuzzy chance constrained linear programming model for optimizing the scrap charge in steel production. Eur J Opera Res, 2008, 186: 953–964MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Yang L Z, Zhu R, Xiao F H, et al. Research on optimization of burden design for BOF (in Chinese). Indus Heating, 2011, 40: 30–35Google Scholar
  24. 24.
    Pak Y A, Shakhpazov E K, Filippov G A, et al. Effect of additions on the production cost of steel and the cost-effectiveness of converter steelmaking. Metallurgist, 2011, 55: 558–566CrossRefGoogle Scholar
  25. 25.
    Gawron M, Adamczyk C. Optimization algorithms in the charge planning for the BOF plant. J Achiev Mater Manuf Eng, 2012, 55: 483–487Google Scholar
  26. 26.
    Chen J, Li A L, Chen Y, et al. Optimization of steelmaking process for 200t converter. Adv Mater Res, 2012, 581-582: 1180–1183CrossRefGoogle Scholar
  27. 27.
    He Z P, Zhang H, Wang Y H. Terminal optimization control for converter steelmaking based on the auxiliary resources operating characteristics. Adv Mater Res, 2013, 634-638: 3265–3270CrossRefGoogle Scholar
  28. 28.
    Lytvynyuk Y, Schenk J, Hiebler M, et al. Application of thermodynamic and kinetic model for converter steelmaking to optimize manganese yield in crude steel. Berg Huettenmaenn Monatsh, 2013, 158: 457–458CrossRefGoogle Scholar
  29. 29.
    Zeng Z X, Wen H, Lin S Y. Impacts of the content of P in hot metal on the cost of converter steelmaking (in Chinese). South Met, 2013, 15: 16–20Google Scholar
  30. 30.
    Yang X, Sun F, Yang J, et al. Optimization of low phosphorus steel production with double slag process in BOF. J Iron Steel Res Int, 2013, 20: 41–47CrossRefGoogle Scholar
  31. 31.
    Lu B H, Li Y K, Qu B Z. Optimization research on converter steelmaking process parameters based on DOE. Key Eng Mater, 2013, 579-580: 128–132CrossRefGoogle Scholar
  32. 32.
    Zhang J. Optimal control problem of converter steelmaking production process based on operation optimization method. Discrete Dyn Nat Soc, 2015, 2015: 1–13MathSciNetGoogle Scholar
  33. 33.
    Bejan A. Constructal-theory network of conducting paths for cooling a heat generating volume. Int J Heat Mass Transfer, 1997, 40: 799–816CrossRefzbMATHGoogle Scholar
  34. 34.
    Bejan A. Shape and Structure, from Engineering to Nature. Cambridge, UK: Cambridge University Press, 2000zbMATHGoogle Scholar
  35. 35.
    Bejan A, Lorente S. Constructal theory of generation of configuration in nature and engineering. J Appl Phys, 2006, 100: 041301CrossRefGoogle Scholar
  36. 36.
    Bejan A, Lorente S. Design with Constructal Theory. New Jersey: Wiley, 2008CrossRefGoogle Scholar
  37. 37.
    Lorenzini G, Moretti S, Conti A. Fin Shape Thermal Optimization Using Bejan’s Constructal Theory. California: Morgan & Claypool Publishers, 2011Google Scholar
  38. 38.
    Chen L G. Progress in study on constructal theory and its applications. Sci China Tech Sci, 2012, 55: 802–820CrossRefGoogle Scholar
  39. 39.
    Bejan A, Lorente S. Constructal law of design and evolution: Physics, biology, technology, and society. J Appl Phys, 2013, 113: 151301CrossRefGoogle Scholar
  40. 40.
    Xie G, Zhang F, Sundén B, et al. Constructal design and thermal analysis of microchannel heat sinks with multistage bifurcations in single-phase liquid flow. Appl Thermal Eng, 2014, 62: 791–802CrossRefGoogle Scholar
  41. 41.
    Xie G, Shen H, Wang C C. Parametric study on thermal performance of microchannel heat sinks with internal vertical Y-shaped bifurcations. Int J Heat Mass Transfer, 2015, 90: 948–958CrossRefGoogle Scholar
  42. 42.
    Xie G, Song Y, Asadi M, et al. Optimization of pin-fins for a heat exchanger by entropy generation minimization and constructal law. J Heat Transfer, 2015, 137: 061901CrossRefGoogle Scholar
  43. 43.
    Lorenzini G, da Silva Diaz Estrada E, dos Santos E D, et al. Constructal design of convective cavities inserted into a cylindrical solid body for cooling. Int J Heat Mass Transfer, 2015, 83: 75–83CrossRefGoogle Scholar
  44. 44.
    Hajmohammadi M R, Lorenzini G, Joneydi Shariatzadeh O, et al. Evolution in the design of V-shaped highly conductive pathways embedded in a heat-generating piece. J Heat Transfer, 2015, 137: 061001CrossRefGoogle Scholar
  45. 45.
    Bejan A. Constructal law: Optimization as design evolution. J Heat Transfer, 2015, 137: 061003CrossRefGoogle Scholar
  46. 46.
    Bejan A. The Physics of Life: The Evolution of Everything. New York: St. Martin’s Press, 2016Google Scholar
  47. 47.
    Bejan A, Errera M R. Complexity, organization, evolution, and constructal law. J Appl Phys, 2016, 119: 074901CrossRefGoogle Scholar
  48. 48.
    Chen L G, Feng H J. Multi-objective Constructal Optimizations for Fluid Flow, Heat and Mass Transfer Processes (in Chinese). Beijing: Science Press, 2016Google Scholar
  49. 49.
    Chen L G, Feng H J, Xie Z H. Constructal theory developments in China during the past decade. In: Morega A M, Lorente S, Eds. CLC 2017, Constructal Law & Second Law Conference. Bucharest, Romania: The Publishing House of the Romanian Academy, 2017. 57–93Google Scholar
  50. 50.
    Xie G, Li Y, Zhang F, et al. Analysis of micro-channel heat sinks with rectangular-shaped flow obstructions. Numer Heat Transfer Part AAppl, 2016, 69: 335–351CrossRefGoogle Scholar
  51. 51.
    Xie G, Song Y, Sundén B. Computational optimization of the internal cooling passages of a guide vane by a gradient-based algorithm. Nu-mer Heat Transfer Part A-Appl, 2016, 69: 1311–1331CrossRefGoogle Scholar
  52. 52.
    Feng H, Chen L, Xie Z. Multi-disciplinary, multi-objective and multiscale constructal optimizations for heat and mass transfer processes performed in Naval University of Engineering, a review. Int J Heat Mass Transfer, 2017, 115: 86–98CrossRefGoogle Scholar
  53. 53.
    Kang D H, Lorente S, Bejan A. Constructal distribution of multi-layer insulation. Int J Energ Res, 2013, 37: 153–160CrossRefGoogle Scholar
  54. 54.
    Feng H J, Chen L G, Xie Z H, et al. Thermal insulation constructal optimization for steel rolling reheating furnace wall based on entransy dissipation extremum principle. Sci China Tech Sci, 2012, 55: 3322–3333CrossRefGoogle Scholar
  55. 55.
    Feng H, Chen L, Xie Z, et al. Constructal entransy dissipation rate minimization for variable cross-section insulation layer of the steel rolling reheating furnace wall. Int Commun Heat Mass Transfer, 2014, 52: 26–32CrossRefGoogle Scholar
  56. 56.
    Feng H, Chen L, Xie Z, et al. Constructal entransy optimizations for insulation layer of steel rolling reheating furnace wall with convective and radiative boundary conditions. Chin Sci Bull, 2014, 59: 2470–2477CrossRefGoogle Scholar
  57. 57.
    Feng H J, Chen L G, Xie Z H, et al. Constructal optimization of variable cross-section insulation layer of steel rolling reheating furnace wall based on entransy theory (in Chinese). Acta Phys Sin, 2015, 64: 054402Google Scholar
  58. 58.
    Feng H, Chen L, Xie Z, et al. Constructal designs for insulation layers of steel rolling reheating furnace wall with convective and radiative boundary conditions. Appl Thermal Eng, 2016, 100: 925–931CrossRefGoogle Scholar
  59. 59.
    Feng H, Chen L, Xie Z, et al. Generalized constructal optimization for solidification heat transfer process of slab continuous casting based on heat loss rate. Energy, 2014, 66: 991–998CrossRefGoogle Scholar
  60. 60.
    Feng H J, Chen L G, Xie Z H, et al. Generalized constructal optimization for the secondary cooling process of slab continuous casting based on entransy theory. Sci China Tech Sci, 2014, 57: 784–795CrossRefGoogle Scholar
  61. 61.
    Feng H J, Chen L G, Liu X, et al. Generalized constructal optimization of strip laminar cooling process based on entransy theory. Sci China Tech Sci, 2016, 59: 1687–1695CrossRefGoogle Scholar
  62. 62.
    Feng H, Chen L, Liu X, et al. Constructal optimization of a sinter cooling process based on exergy output maximization. Appl Thermal Eng, 2016, 96: 161–166CrossRefGoogle Scholar
  63. 63.
    Liu X, Chen L, Feng H, et al. Constructal design for blast furnace wall based on the entransy theory. Appl Thermal Eng, 2016, 100: 798–804CrossRefGoogle Scholar
  64. 64.
    Liu X, Feng H, Chen L, et al. Hot metal yield optimization of a blast furnace based on constructal theory. Energy, 2016, 104: 33–41CrossRefGoogle Scholar
  65. 65.
    Liu X, Chen L, Feng H, et al. Constructal design of a blast furnace iron-making process based on multi-objective optimization. Energy, 2016, 109: 137–151CrossRefGoogle Scholar
  66. 66.
    Wang X H. Ferrous Metallurgy-Steelmaking (in Chinese). Beijing: Higher Education Press, 2007Google Scholar
  67. 67.
    Chen L, Feng H, Xie Z. Generalized thermodynamic optimization for iron and steel production processes: Theoretical exploration and application cases. Entropy, 2016, 18: 353CrossRefGoogle Scholar
  68. 68.
    Liu C, Xie Z, Sun F, et al. Exergy analysis and optimization of coking process. Energy, 2017, 139: 694–705CrossRefGoogle Scholar
  69. 69.
    Chen L, Shen X, Xia S, et al. Thermodynamic analyses for recovering residual heat of high-temperature basic oxygen gas (BOG) by the methane reforming with carbon dioxide reaction. Energy, 2017, 118: 906–913CrossRefGoogle Scholar
  70. 70.
    Shen X, Chen L G, Xia S J, et al. Iron ores matching analysis and optimization for iron-making system by taking energy consumption, CO2 emission or cost minimization as the objective. Sci China Tech Sci, 2017, 60: 1625–1637CrossRefGoogle Scholar
  71. 71.
    Shen X, Chen L G, Xia S J, et al. Burdening proportion and new energy-saving technologies analysis and optimization for iron and steel production systems. J Cleaner Production (in press)Google Scholar
  72. 72.
    Feng H, Chen L, Liu X, et al. Constructal design for an iron and steel production process based on the objectives of steel yield and useful energy. Int J Heat Mass Transfer, 2017, 111: 1192–1205CrossRefGoogle Scholar
  73. 73.
    Chen L G, Feng H J, Xie Z H, et al. Interaction mechanism among material flows, energy flows and environment and generalized thermodynamic optimizations for iron and steel production processes (in Chinese). Sci Sin Tech, 2017, doi: 10.1360/N092017-00038Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • LinGen Chen
    • 1
    • 2
    • 3
  • Xiong Liu
    • 1
    • 2
    • 3
  • HuiJun Feng
    • 1
    • 2
    • 3
  • YanLin Ge
    • 1
    • 2
    • 3
  • ZhiHui Xie
    • 1
    • 2
    • 3
  1. 1.Institute of Thermal Science and Power EngineeringNaval University of EngineeringWuhanChina
  2. 2.Military Key Laboratory for Naval Ship Power EngineeringNaval University of EngineeringWuhanChina
  3. 3.College of Power EngineeringNaval University of EngineeringWuhanChina

Personalised recommendations

Cite article

Buy options