A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems

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@Article{Sohani:2016:ATE,
  author =       "Ali Sohani and Hoseyn Sayyaadi and 
                 Hamidreza Hasani Balyani and Sina Hoseinpoori",
  title =        "A novel approach using predictive models for
                 performance analysis of desiccant enhanced evaporative
                 cooling systems",
  journal =      "Applied Thermal Engineering",
  volume =       "107",
  pages =        "227--252",
  year =         "2016",
  ISSN =         "1359-4311",
  DOI =          "doi:10.1016/j.applthermaleng.2016.06.121",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1359431116310353",
  abstract =     "A thorough investigation on parameters that having the
                 potential impact on performance of the desiccant
                 enhanced evaporative air conditioning, DEVAP, system
                 was conducted. Five soft computing and statistical
                 tools, SCST, including the artificial neural network,
                 ANN, group method of data handling, GMDH, genetic
                 programming, GP, multiple linear regression, MLR, and
                 stepwise regression method, SRM, were used to predict
                 the overall performance of DEVAP system. These SCST
                 models were trained and tested using numerical and
                 experimental data. The dehumidifier stage was assumed
                 to be incorporated separately into two different types
                 of counter flow indirect dew point evaporative coolers
                 as the second stage. For each stage, the best SCST
                 models have been determined through comparing with
                 experimental data via error criteria, including the
                 mean square error (MSE), and coefficient of
                 determination (R2). It was found that the GMDH and SRM
                 methods propose the foremost models for evaluating the
                 performance of the second stage. Furthermore, SRM
                 approach was found to be the best model describing the
                 performance of the dehumidifier. Then a comprehensive
                 sensitivity analysis was conducted for dehumidifier
                 part. It was concluded that an effective strategy for
                 improving dehumidifier is the implementing a part of
                 its product air as the working air.",
  keywords =     "genetic algorithms, genetic programming, Analytical
                 expression, Dehumidifier, Desiccant enhanced
                 evaporative cooling systems, DEVAP, M-cycle indirect
                 evaporative cooler, Soft computing and statistical
                 methods",
}

Genetic Programming entries for Ali Sohani Hoseyn Sayyaadi Hamidreza Hasani Balyani Sina Hoseinpoori

Citations