• DocumentCode
    559099
  • Title

    Energy saving of combined fresh water and cold generation system by compressor intercooler waste heat recovery

  • Author

    Janghorban, Iman ; Liu, Hongbin ; Ghorbannezhad, Payam ; Yoo, ChangKyoo

  • Author_Institution
    Dept. of Environ. Sci. & Eng., Kyung Hee Univ., Yongin, South Korea
  • fYear
    2011
  • fDate
    26-29 Oct. 2011
  • Firstpage
    317
  • Lastpage
    322
  • Abstract
    Fresh water and cold which are produced by desalination and cooling processes are simultaneously utilized in many factories and industries. Energy saving can be possible by integration of desalination and cooling systems. This paper contributes to a new integration scheme of the reverse osmosis (RO) and refrigeration systems. Compressor intercooler and condenser waste heat are recovered to increase the intake seawater temperature, which causes decrease in RO pump usage and compressor power consumption. The RO system and refrigeration cycle is modeled. Experimental design of central composite design (CCD) is applied to determine the input decision variables, which are consist of intercooler pressure and heat source temperature (TH) responses variables are coefficient of performance (COP) and power consumption of reverse osmosis (PRO). Multi objective optimization to minimize PRO and maximize COP is performed using genetic algorithm (GA) over the ANN model. The input decision variables corresponding to Pareto optimal sets are presented minimum as the optimal design parameters.
  • Keywords
    desalination; design of experiments; energy conservation; genetic algorithms; heat recovery; mechanical engineering computing; neural nets; power engineering computing; pumps; reverse osmosis; waste heat; ANN model; RO pump; central composite design; cold generation system; compressor intercooler waste heat recovery; cooling processes; desalination; energy saving; experimental design; genetic algorithm; refrigeration systems; reverse osmosis; Artificial neural networks; Equations; Mathematical model; Neurons; Optimization; Power demand; Reverse osmosis; Artificial neural network; Genetic algorithm; Multi objective optimization; Refrigeration; Reverse Osmosis (RO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems (ICCAS), 2011 11th International Conference on
  • Conference_Location
    Gyeonggi-do
  • ISSN
    2093-7121
  • Print_ISBN
    978-1-4577-0835-0
  • Type

    conf

  • Filename
    6106443