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Soft Computing in Absorption Cooling Systems

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Soft Computing in Green and Renewable Energy Systems

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 269))

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Abstract

Absorption cooling systems make sense in many applications for process water cooling. Instead of mechanically compressing a refrigerant gas, as in the conventional vapor compression process, absorption cooling uses a thermo-chemical process. Two different fluids are used, a refrigerant and an absorbent. Heat directly from natural gas combustion, solar energy, waste-heat source or indirectly from a boiler, drives the process.

In recent years, soft computing (SC) methods have been widely utilized in the analysis of absorption cooling systems. Soft computing is becoming useful as an alternate approach to conventional techniques. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation.

In this chapter, the research of applying soft computing methods for absorption cooling applications is presented.

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References

  • Absorption Chillers (2010), www.absorptionchillers.com (accessed 12.12.2010)

  • Aly, A.A., Zeidan, E.-S.B., Hamed, A.M.: Performance evaluation of open-cycle solar regenerator using artificial neural network technique. Energy and Buildings 43, 454–457 (2011)

    Article  Google Scholar 

  • Chaturvedi, D.K.: Soft Computing: Techniques and Its Applications in Electrical Engineering. Springer, Heidelberg (2008) ISBN 978-3-540-77480-8

    MATH  Google Scholar 

  • Chow, T.T., Zhang, G.Q., Lin, Z., Song, C.: Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Building 34, 103–109 (2002)

    Article  Google Scholar 

  • Cobaner, M., Unal, B., Kisi, O.: Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of Hydrology 367, 52–61 (2009)

    Article  Google Scholar 

  • Colorado, D., Hernández, J.A., Rivera, W., Martínez, H., Juárez, D.: Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse. Applied Energy 88, 1281–1290 (2011)

    Article  Google Scholar 

  • Dipama, J., Teyssedou, A., Sorin, M.: Synthesis of heat exchanger networks using genetic algorithms. Applied Thermal Engineering 28, 1763–1773 (2008)

    Article  Google Scholar 

  • Efendigil, T., Onut, S., Kahraman, C.: A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications 36, 6697–6707 (2009)

    Article  Google Scholar 

  • Ertunc, H.M., Hosoz, M.: Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration 31, 1426–1436 (2008)

    Article  Google Scholar 

  • Foryuna, L., Rizzotto, G., Lavorgna, M., Nunnari, G., Xibilia, M.G., Caponetto, R.: Soft computing: new trends and applications. Springer-Verlag London Limited, Great Britain (2001) ISBN 1-85233-308-1

    Google Scholar 

  • Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Harlow (1989)

    MATH  Google Scholar 

  • Gosselin, L., Tye-Gingras, M., Mathieu-Potvin, F.: Review of utilization of genetic algorithms in heat transfer problems. International Journal of Heat and Mass Transfer 52, 2169–2188 (2009)

    Article  MATH  Google Scholar 

  • Haykin, S.: Neural networks: a comprehensive foundation. Macmillan, New York (1994)

    MATH  Google Scholar 

  • Hernandez, J.A., Bassam, A., Siqueiros, J., Juarez-Romero, D.: Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse. Renewable Energy 34, 1084–1091 (2009)

    Article  Google Scholar 

  • Herold, K.E., Radermacher, R., Klein, S.A.: Absorption chilles and heat pumps. CRC Press, New York (1996)

    Google Scholar 

  • Hoffmann, D., Apostolakis, J.: Crystal Structure Prediction by Data Mining. Journal of Molecular Structure 647, 17–39 (2003)

    Article  Google Scholar 

  • Holland, J.H.: Adaptations in Natural Artificial Systems. University of Michigan Press, Michigan (1975)

    Google Scholar 

  • Jang, J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  MathSciNet  Google Scholar 

  • Kalogirou, S.A.: Applications of artifcial neural-networks for energy systems. Applied Energy 67, 17–35 (2000)

    Article  Google Scholar 

  • Kalogirou, S.A.: Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews 5, 373–401 (2001)

    Article  Google Scholar 

  • Kalogirou, S.A.: Optimization of solar systems using artificial neural-networks and genetic algorithms. Applied Energy 77, 383–405 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lau, H.C.W., Cheng, E.N.M., Lee, C.K.M., Ho, G.T.S.: A fuzzy logic approach to forecast energy consumption change in a manufacturing system. Expert Systems with Applications 34, 1813–1824 (2008)

    Article  Google Scholar 

  • Li, S.T., Shue, L.Y.: Data Mining To Aid Policy Making in Air Pollution Management. Expert System With Applications 27, 331–340 (2004)

    Article  Google Scholar 

  • Lygouras, J.N., Botsaris, P.N., Vourvoulakis, J., Kodogiannis, V.: Fuzzy logic controller implementation for a solar air-conditioning system. Applied Energy 84, 1305–1318 (2007)

    Article  Google Scholar 

  • Lygouras, J.N., Kodogiannis, V.S., Pachidis, T., Tarchanidis, K.N., Koukourlis, C.S.: Variable structure TITO fuzzy-logic controller implementation for a solar air-conditioning system. Applied Energy 85, 190–203 (2008)

    Article  Google Scholar 

  • Manohar, H.J., Saravanan, R., Renganarayanan, S.: Modelling of steam fired double effect vapour absorption chiller using neural network. Energy Conversion and Management 47, 2202–2210 (2006)

    Article  Google Scholar 

  • MatLab (2010) MATLAB Neural Network Toolbox v. 4.0.4, http://www.mathworks.com/access/helpdesk/help/pdf_doc/nnet/nnet.pdf

  • Matlab Fuzzy logic toolbox user’s guide (2010) Natick: The Math Works Inc., http://www.mathworks.com/

  • Paulescu, M., Gravila, P., Tulcan-Paulescu, E.: Fuzzy logic algorithms for atmospheric transmittances of use in solar energy estimation. Energy Conversion and Management 49, 3691–3697 (2008)

    Article  Google Scholar 

  • Rosiek, S., Batlles, F.J.: Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network. Renewable Energy 35, 2894–2901 (2010)

    Article  Google Scholar 

  • Sen, Z., Oztopal, A., Sahin, A.D.: Application of genetic algorithm for determination of Angström equation coefficients. Energy Conversion & Management 42, 217–231 (2001)

    Article  Google Scholar 

  • Sencan, A., Kalogirou, S.A.: A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples. Energy Conversion and Management 46, 2405–2418 (2005)

    Article  Google Scholar 

  • Sencan, A., Yakut, K.A., Kalogirou, S.A.: Thermodynamic analysis of absorption systems using artificial neural network. Renewable Energy 31, 29–43 (2006)

    Article  Google Scholar 

  • Sencan, A., Kızılkan, O., Bezir, N.C., Kalogirou, S.A.: Different methods for modeling absorption heat transformer powered by solar pond. Energy Conversion and Management 48, 724–735 (2007)

    Article  Google Scholar 

  • Sencan, A.: Modeling of thermodynamic properties of refrigerant/absorbent couples using data mining process. Energy Conversion and Management 48, 470–480 (2007)

    Article  Google Scholar 

  • Sozen, A., Arcaklioglu, E., Ozalp, M.: A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks. Applied Thermal Engineering 23, 937–952 (2003)

    Article  Google Scholar 

  • Sozen, A., Akcayol, M.A.: Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle. Applied Energy 79, 309–325 (2004)

    Article  Google Scholar 

  • Sozen, A., Arcaklioglu, E., Ozalp, M.: Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural Networks. Energy Conversion and Management 45, 2233–2253 (2004a)

    Article  Google Scholar 

  • Sozen, A., Özalp, M., Arcaklioglu, E.: Investigation of thermodynamic properties of refrigerant/absorbent couples using artificial neural Networks. Chemical Engineering and Processing 43, 1253–1264 (2004b)

    Article  Google Scholar 

  • Sozen, A., Kurt, M., Akcayol, M.A., Ozalp, M.: Performance prediction of a solar driven ejector-absorption cycle using fuzzy logic. Renewable Energy 29, 53–71 (2004c)

    Article  Google Scholar 

  • Sozen, A., Arcaklioglu, E., Ozalp, M.: Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples. Applied Thermal Engineering 25, 1808–1820 (2005)

    Article  Google Scholar 

  • Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems Man and Cybernetics 15(1), 116–132 (1985)

    MATH  Google Scholar 

  • Xie, H.B., Jiang, Z.Y., Liu, X.H., Wang, G.D., Tieu, A.K.: Prediction of coiling temperature on run-out table of hot strip mill using data mining. Journal of Materials Processing Technology 177, 121–125 (2006)

    Article  Google Scholar 

  • Yuzgec, U., Becerikli, Y., Turker, M.: Nonlinear predictive control of a drying process using genetic Algorithms. ISA Transactions 45(4), 589–602 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Education, Technical Education Faculty, Süleyman Demirel University, 32260, Isparta, Turkey

    Arzu Şencan Şahin

  2. Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, P.O. Box 50329, 3603, Limassol, Cyprus

    Soteris A. Kalogirou

Authors
  1. Arzu Şencan Şahin
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  2. Soteris A. Kalogirou
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Editor information

Editors and Affiliations

  1. Civil Engineering Associate, Ames Lab, US Department of Energy Research Affiliate, Iowa Bioeconomy Institute, Iowa State University, 354 Town Engineering Building, 50011, Ames, IA, USA

    Kasthurirangan Gopalakrishnan

  2. Department of Electrical and Computer Engineering, Iowa State University, 1113 Coover Hall, 50011, Ames, IA, USA

    Siddhartha Kumar Khaitan

  3. Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, P.O. Box 50329, 3603, Limassol, Cyprus

    Soteris Kalogirou

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Şahin, A.Ş., Kalogirou, S.A. (2011). Soft Computing in Absorption Cooling Systems. In: Gopalakrishnan, K., Khaitan, S.K., Kalogirou, S. (eds) Soft Computing in Green and Renewable Energy Systems. Studies in Fuzziness and Soft Computing, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22176-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-22176-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22175-0

  • Online ISBN: 978-3-642-22176-7

  • eBook Packages: EngineeringEngineering (R0)

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