logo

SCIENCE CHINA Technological Sciences, Volume 60 , Issue 3 : 345-354(2017) https://doi.org/10.1007/s11431-016-0604-3

Multi-objective optimization of cooling air distribution of grate cooler with different inlet temperatures by using genetic algorithm

More info
  • ReceivedAug 12, 2016
  • AcceptedNov 28, 2016
  • PublishedJan 23, 2017

Abstract

The paper discussed a multi-objective optimization model of the cooling air distribution of a grate cooler according to the analogy of a cross-flow heat exchanger and entropy-generation minimization analysis. The modified entropy generation numbers caused by heat transfer and flowing resistance are regarded as objective functions, and are simultaneously optimized by a genetic algorithm. A test is conducted to validate the model. The final optimal cooling air distribution is decided by energy consumed through cooling fans minimization. Next, two schemes are investigated for inducing exhaust air of 70°C–100°C in a chimney to cool the middle and front parts of the grate cooler. Based on each scheme, sensitive analyses of cooling air inlet temperatures are conducted. The results show that the minimum energy consumption of cooling fans increases by 121.04%, and the thermal efficiencies of the grate cooler vary no more than 1.66% when inducing the exhaust air into the middle part of the grate cooler. The minimum energy consumption of cooling fans slightly varies and thermal efficiencies of grate coolers vary no more than 0.51% when inducing exhaust air to the front part of the grate cooler. It is more effective and economical to induce exhaust air at any temperature to the front part of the grate cooler.


Funded by

National Key Basic Research Program of China(2013CB228305)


Acknowledgment

This work was supported by the National Basic Research Program of China (Grant No. 2013CB228305).


References

[1] Wang J, Dai Y, Gao L. Exergy analyses and parametric optimizations for different cogeneration power plants in cement industry. Appl Energy, 2009, 86: 941-948 CrossRef Google Scholar

[2] Li N, Ma D, Chen W. Quantifying the impacts of decarbonisation in China’s cement sector: A perspective from an integrated assessment approach. Appl Energy, 2017, 185: 1840-1848 CrossRef Google Scholar

[3] Zhang S, Worrell E, Crijns-Graus W. Evaluating co-benefits of energy efficiency and air pollution abatement in China’s cement industry. Appl Energy, 2015, 147: 192-213 CrossRef Google Scholar

[4] Caputo A C, Pelagagge P M. Heat recovery from moving cooling beds: Transient modeling by dynamic simulation. Appl Thermal Eng, 1999, 19: 21-35 CrossRef Google Scholar

[5] Touil D, Belaadi S, Frances C. Energy efficiency of cement finish grinding in a dry batch ball mill. Cement Concrete Res, 2006, 36: 416-421 CrossRef Google Scholar

[6] Madlool N A, Saidur R, Hossain M S, et al. A critical review on energy use and savings in the cement industries. Renew Sustain Energy Rev, 2011, 15: 2042-2060 CrossRef Google Scholar

[7] Liu Z, Wang Z, Yuan M Z, et al. Thermal efficiency modelling of the cement clinker manufacturing process. J Energy Inst, 2015, 88: 76-86 CrossRef Google Scholar

[8] Ahmadi P, Dincer I. Thermodynamic and exergoenvironmental analyses, and multi-objective optimization of a gas turbine power plant. Appl Thermal Eng, 2011, 31: 2529-2540 CrossRef Google Scholar

[9] Ghaebi H, Saidi M H, Ahmadi P. Exergoeconomic optimization of a trigeneration system for heating, cooling and power production purpose based on TRR method and using evolutionary algorithm. Appl Thermal Eng, 2012, 36: 113-125 CrossRef Google Scholar

[10] Zheng J H, Chen J J, Wu Q H, et al. Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs embedded. Appl Energy, 2015, 154: 369-379 CrossRef Google Scholar

[11] Mamaghani A H, Najafi B, Shirazi A, et al. Exergetic, economic, and environmental evaluations and multi-objective optimization of a combined molten carbonate fuel cell-gas turbine system. Appl Thermal Eng, 2015, 77: 1-11 CrossRef Google Scholar

[12] Falke T, Krengel S, Meinerzhagen A K, et al. Multi-objective optimization and simulation model for the design of distributed energy systems. Appl Energy, 2016, 184: 1508-1516 CrossRef Google Scholar

[13] Seijo S, del Campo I, Echanobe J, et al. Modeling and multi-objective optimization of a complex CHP process. Appl Energy, 2016, 161: 309-319 CrossRef Google Scholar

[14] Ahmadi M H, Ahmadi M A, Bayat R, et al. Thermo-economic optimization of Stirling heat pump by using non-dominated sorting genetic algorithm. Energy Conv Manage, 2015, 91: 315-322 CrossRef Google Scholar

[15] Ahmadi M H, Ahmadi M A, Mehrpooya M, et al. Thermo-ecological analysis and optimization performance of an irreversible three-heat-source absorption heat pump. Energy Conv Manage, 2015, 90: 175-183 CrossRef Google Scholar

[16] Sahraie H, Mirani M R, Ahmadi M H, et al. Thermo-economic and thermodynamic analysis and optimization of a two-stage irreversible heat pump. Energy Conv Manage, 2015, 99: 81-91 CrossRef Google Scholar

[17] Caputo A C, Cardarelli G, Pelagagge P M. Analysis of heat recovery in gas-solid moving beds using a simulation approach. Appl Thermal Eng, 1996, 16: 89-99 CrossRef Google Scholar

[18] Caputo A C, Pelagagge P M. Economic design criteria for cooling solid beds. Appl Thermal Eng, 2001, 21: 1219-1230 CrossRef Google Scholar

[19] Bejan A. Entropy Generation Through Heat and Fluid Flow. New York: John Wiley and Sons Press, 1982. Google Scholar

[20] Bejan A. Second law analysis in heat transfer. Energy, 1980, 5: 720-732 CrossRef Google Scholar

[21] Hesselgreaves J E. Rationalisation of second law analysis of heat exchangers. Int J Heat Mass Transfer, 2000, 43: 4189-4204 CrossRef Google Scholar

[22] Yilmaz M, Sara O N, Karsli S. Performance evaluation criteria for heat exchangers based on second law analysis. Exergy An Int J, 2001, 1: 278-294 CrossRef Google Scholar

[23] Guo J F. Thermodynamic Analysis and Optimization Design of Heat Exchanger. Dissertation for Doctoral Degree. Jinan: Shandong University, 2011. Google Scholar

[24] Hu D H. Gas-Solid Process Engineering and Application in Cement Industry. Wuhan: Wuhan University of Technology Press, 2003. Google Scholar

[25] Wang L, Deng L, Ji C, et al. Multi-objective optimization of geometrical parameters of corrugated-undulated heat transfer surfaces. Appl Energy, 2016, 174: 25-36 CrossRef Google Scholar

  • Figure 1

    Structure of grate cooler and clinker cooling principle.

  • Figure 2

    Flow chart of the study.

  • Figure 3

    Clinker cooling in multiple crosscurrent contacting beds.

  • Figure 4

    Flow chart of optimization process.

  • Figure 5

    Pareto front for the test case by using genetic algorithm.

  • Figure 6

    (Color online) Scatter distribution of superficial velocities in chamber 1 (a), chamber 2 (b), chamber 3 (c) and thickness of clinker layer on grate plate 1 (d) with population in Pareto front.

  • Figure 7

    Two schemes of inducing exhaust air in a chimney to cool clinker again.

  • Figure 8

    The variation of two key parameters. (a) Minimum energy consumption of cooling fans; (b) thermal efficiency of grate cooler with cooling air inlet temperature in scheme A.

  • Figure 9

    The variation of two key parameters. (a) Minimum energy consumption of cooling fans; (b) thermal efficiency of grate cooler with cooling air inlet temperature in scheme B.

  • Figure 10

    Clinker outlet temperature of grate cooler with cooling air inlet temperature in schemes A and B.

  • Figure 11

    Variation of total air volume with cooling air inlet temperature in schemes A and B.

  • Figure 12

    Variation of heat recovery efficiency with cooling air inlet temperature in scheme B.

  • Table 1   Other related boundary conditions

    Parameters

    Values

    Length of chamber 1

    3.26

    Length of chamber 2

    3.26

    Length of chamber 3

    4.48

    Length of chamber 4

    3.54

    Length of chamber 5

    4.72

    Length of chamber 6

    3.93

    Length of chamber 7

    3.85

    Length of chamber 8

    4.62

    Length of chamber 9

    4.23

    Mass flow rate of clinker (kg/s)

    72.22

    Clinker inlet temperature (°C)

    1400

    Specific heat capacity of clinker (J/(kg K))

    920 [18]

    Pressure of cooling air outlet (Pa)

    –50

    Porosity of clinker

    0.4 [24]

    Average diameter of clinker particles (m)

    0.02

  • Table 2   Optimal air distribution of subschemes in scheme A

    Cooling air temperature (°C)

    Mass flow rate of chamber 1 (kg/s)

    Mass flow rate of chamber 2 (kg/s)

    Mass flow rate of chamber 3 (kg/s)

    Mass flow rate of chamber 4 (kg/s)

    Mass flow rate of chamber 5 (kg/s)

    Mass flow rate of chamber 6 (kg/s)

    Mass flow rate of chamber 7 (kg/s)

    Mass flow rate of chamber 8 (kg/s)

    Mass flow rate of chamber 9 (kg/s)

    30

    25.05

    22.02

    30.93

    18.45

    23.45

    18.90

    12.86

    12.33

    12.89

    40

    26.00

    23.32

    28.77

    19.31

    28.94

    19.59

    13.92

    12.53

    15.29

    50

    25.24

    23.59

    34.61

    19.02

    28.79

    21.93

    10.58

    13.70

    13.66

    60

    24.76

    22.03

    33.41

    20.76

    28.61

    22.89

    13.32

    14.08

    14.93

    70

    27.37

    21.46

    33.60

    22.42

    29.38

    23.09

    12.38

    14.87

    13.52

    80

    28.05

    24.75

    33.74

    25.17

    30.26

    22.58

    11.15

    14.89

    13.34

    90

    27.35

    27.28

    39.53

    24.91

    33.53

    25.07

    10.96

    15.36

    14.92

    100

    32.26

    26.57

    39.96

    24.95

    33.26

    24.15

    15.39

    15.21

    15.71

  • Table 3   Optimal air distribution of subschemes in scheme B

    Cooling air temperature (°C)

    Mass flow rate of chamber 1 (kg/s)

    Mass flow rate of chamber 2 (kg/s)

    Mass flow rate of chamber 3 (kg/s)

    Mass flow rate of chamber 4 (kg/s)

    Mass flow rate of chamber 5 (kg/s)

    Mass flow rate of chamber 6 (kg/s)

    Mass flow rate of chamber 7 (kg/s)

    Mass flow rate of chamber 8 (kg/s)

    Mass flow rate of chamber 9 (kg/s)

    30

    25.05

    22.02

    30.93

    18.45

    23.45

    18.90

    12.86

    12.33

    12.89

    40

    24.01

    19.99

    32.59

    20.38

    24.31

    18.64

    12.29

    15.00

    14.64

    50

    25.20

    21.43

    31.27

    18.64

    23.90

    19.81

    11.94

    14.34

    13.85

    60

    24.01

    22.80

    31.11

    18.56

    25.47

    19.12

    9.85

    17.33

    12.55

    70

    25.26

    21.28

    28.77

    20.89

    25.74

    20.67

    14.35

    16.15

    11.64

    80

    24.83

    21.95

    30.53

    19.46

    24.71

    20.81

    13.80

    14.55

    14.32

    90

    27.11

    21.16

    30.59

    20.32

    24.19

    18.37

    12.78

    14.94

    12.70

    100

    26.55

    21.57

    30.12

    20.30

    26.08

    17.21

    12.34

    12.70

    13.36

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备17057255号       京公网安备11010102003388号