Optimization of heat pump using fuzzy logic and genetic algorithm

Abstract

Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized using genetic algorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated by FL. Thermodynamic properties obtained with FL were compared with actual results. Then, the optimum working conditions of heat pump system were determined by the GA.

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References

  1. 1.

    Chua KJ, Chou SK, Yang WM (2010) Advances in heat pump systems: a review. Appl Energy 87:3611–3624

    Article  Google Scholar 

  2. 2.

    http://www.energysavers.gov/your_home/space_heating_cooling/index.cfm/mytopic=12610 (Heat pumps 26.09.2010)

  3. 3.

    Comakli K, Simsek F, Comakli O, Sahin B (2009) Determination of optimum working conditions R22 and R404A refrigerant mixtures in heat-pumps using Taguchi method. Appl Energy 86:2451–2458

    Article  Google Scholar 

  4. 4.

    Zogou O, Stamatelos A (1998) Effect of clımatıc conditions on the design optimızation of heat pump systems for space heating and cooling. Energy Convers Mang 39(7):609–622

    Article  Google Scholar 

  5. 5.

    Ceylan I, Aktas M (2008) Modeling of a hazelnut dryer assisted heat pump by using artificial neural Networks. Appl Energy 85:841–854

    Article  Google Scholar 

  6. 6.

    Esen H, Inalli M, Sengur A, Esen M (2008) Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems. Int J Refrig 31:65–74

    Article  Google Scholar 

  7. 7.

    Sayyaadi H, Hadaddi Amlashi E, Amidpour M (2009) Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm. Energy Convers Manag 50:2035–2046

    Article  Google Scholar 

  8. 8.

    Chen L, Li J, Sun F, Wu C (2008) Performance optimization for a two-stage thermoelectric heat-pump with internal and external irreversibilities. Appl Energy 85:641–649

    Article  Google Scholar 

  9. 9.

    Sozen A, Arcaklioglu E, Erisen A, Akcayol MA (2004) Performance prediction of a vapour-compression heat-pump. Appl Energy 79:327–344

    Article  Google Scholar 

  10. 10.

    Sanaye S, Niroomand B (2009) Thermal-economic modeling and optimization of vertical ground-coupled heat pump. Energy Convers Manag 50:1136–1147

    Article  Google Scholar 

  11. 11.

    Sanaye S, Niroomand B (2010) Vertical ground coupled steam ejector heat pump; thermal-economic modeling and optimization. Int J Refrig. In Press

  12. 12.

    MATLAB Fuzzy Logic Toolbox™ 2 User’s Guide (2010) The MathWorks, Inc

  13. 13.

    Kucukali S, Baris K (2010) Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy 38:2438–2445

    Article  Google Scholar 

  14. 14.

    Jaber JO, Mamlook R, Awad W (2005) Evaluation of energy conservation programs in residential sector using fuzzy logic methodology. Energy Policy 33:1329–1338

    Article  Google Scholar 

  15. 15.

    Karunakaran R, Iniyan S, Goic R (2010) Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings. Appl Energy 87:1158–1175

    Article  Google Scholar 

  16. 16.

    Ustuntas T, Sahin AD (2008) Wind turbine power curve estimation based on cluster center fuzzy logic modeling. J Wind Eng Ind Aerodyn 96:611–620

    Article  Google Scholar 

  17. 17.

    MATLAB Global Optimization Toolbox 3 User’s Guide (2010) The MathWorks, Inc

  18. 18.

    Chakraborty D, Sharma CP, Das B, Abhishek K, Malakar T (2009) Distribution system load flow solution using genetic algorithm. In: ICPS’09 International Conference on power systems, 2009, pp 1–6

  19. 19.

    Özdemir A, Lim JY, Singh C (2005) Post-outage reactive power flow calculations by genetic algorithms: constrained optimization approach. IEEE Trans Power Syst 20(3):1266–1272

    Article  Google Scholar 

  20. 20.

    Sen Z, Oztopal A, Sahin AD (2001) Application of genetic algorithm for determination of AngstroÈm equation coeffcients. Energy Convers Manag 42:217–231

    Article  Google Scholar 

  21. 21.

    Gosselin L, Tye-Gingras M, Mathieu-Potvin F (2009) Review of utilization of genetic algorithms in heat transfer problems. Int J Heat Mass Transf 52:2169–2188

    MATH  Article  Google Scholar 

  22. 22.

    Siddhartha V (2010) Thermal performance optimization of a flat plate solar air heater using genetic algorithm. Appl Energy 87:1793–1799

    Article  Google Scholar 

  23. 23.

    Kalogirou SA (2004) Optimization of solar systems using artificial neural-networks and genetic algorithms. Appl Energy 77(4):383–405

    Article  Google Scholar 

  24. 24.

    Solkane Software (2010) http://www.solvay-fluor.com/docroot/fluor/static_files/attachments/download.htm [02.11.2010]

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Correspondence to Arzu Şencan Şahin.

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Şahin, A.Ş., Kılıç, B. & Kılıç, U. Optimization of heat pump using fuzzy logic and genetic algorithm. Heat Mass Transfer 47, 1553 (2011). https://doi.org/10.1007/s00231-011-0818-4

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Keywords

  • Heat Pump
  • Heat Pump System
  • Optimum Design Parameter
  • Average Absolute Relative Error
  • Optimum Working Condition