Experimental investigation and radial basis function network modeling of direct evaporative cooling systems

Abstract Radial basis function network method is used for modeling a direct evaporative cooling system. Air dry exit temperature, air pressure drop across the cooler and cooler efficiency are predicted using these models. The inputs are pad thickness, air inlet speed, air dry inlet temperature, relative humidity at the inlet and feed water temperature. The data for the models are taken from the experiments performed specifically for this purpose. Model validation is performed using twofold cross validation method. A grid search is used to determine optimal network parameters, such as, optimum number of radial basis elements and spread parameter. Validated models are tested against ordinary least squares models for the output variables. The results indicate that it is feasible to apply radial basis function networks to model direct evaporative coolers.

[1]  M. F. Koseoglu Investigation of water droplet carryover phenomena in industrial evaporative air-conditioning systems , 2013 .

[2]  Murat Hosoz,et al.  Modelling of a direct evaporative air cooler using artificial neural network , 2008 .

[3]  Mohammad Layeghi,et al.  Investigating the performance of cellulosic evaporative cooling pads , 2011 .

[4]  Dino Kosic,et al.  Fast Clustered Radial Basis Function Network as an adaptive predictive controller , 2015, Neural Networks.

[5]  K. Sumathy,et al.  Theoretical study on a cross-flow direct evaporative cooler using honeycomb paper as packing material , 2002 .

[6]  Mohammad Layeghi,et al.  Experimental evaluation of the performances of cellulosic pads made out of Kraft and NSSC corrugated papers as evaporative media , 2012 .

[7]  Gholam Ali Montazer,et al.  An improved radial basis function neural network for object image retrieval , 2015, Neurocomputing.

[8]  Boris Halasz,et al.  A general mathematical model of evaporative cooling devices , 1998 .

[9]  Hua Zhang,et al.  NUMERICAL INVESTIGATION ON THE HEAT AND MASS TRANSFER IN A DIRECT EVAPORATIVE COOLER , 2009 .

[10]  Antonio J. Rivera,et al.  Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks , 2013, Expert Syst. Appl..

[11]  Ala Hasan Indirect evaporative cooling of air to a sub-wet bulb temperature , 2010 .

[12]  Dae-Young Lee,et al.  Comparison of configurations for a compact regenerative evaporative cooler , 2013 .

[13]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[14]  Hoseyn Sayyaadi,et al.  A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches , 2017 .

[15]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[16]  Giacomo Capizzi,et al.  A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module , 2012, ArXiv.

[17]  Sergey Anisimov,et al.  Theoretical study of the basic cycles for indirect evaporative air cooling , 2015 .

[18]  José Luz Silveira,et al.  Thermoeconomic analysis of an evaporative desiccant air conditioning system , 2003 .

[19]  Ruzhu Wang,et al.  Experimental performance of evaporative cooling pad systems in greenhouses in humid subtropical climates , 2015 .

[20]  Ala Hasan Going below the wet-bulb temperature by indirect evaporative cooling: Analysis using a modified ε-NTU method , 2012 .

[21]  Mohammad Ameri,et al.  Comparison of evaporative inlet air cooling systems to enhance the gas turbine generated power , 2007 .

[22]  Saffa Riffat,et al.  Experimental and numerical investigation of a dew-point cooling system for thermal comfort in buildings , 2014 .

[23]  Dae-Young Lee,et al.  Experimental study of a counter flow regenerative evaporative cooler with finned channels , 2013 .

[24]  F. A. Khan,et al.  Numerical model for non-equilibrium heat and mass exchange in conjugate fluid/solid/porous domains with application to evaporative cooling and drying , 2015 .

[25]  Saffa Riffat,et al.  Novel design and modelling of an evaporative cooling system for buildings , 2006 .

[26]  Ke Wang,et al.  A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction , 2015 .

[27]  Antonio Sánchez Kaiser,et al.  NUMERICAL MODEL OF EVAPORATIVE COOLING PROCESSES IN A NEW TYPE OF COOLING TOWER , 2005 .

[28]  Quang Phuc Ha,et al.  Enhanced radial basis function neural networks for ozone level estimation , 2015, Neurocomputing.

[29]  Reza Hosseini,et al.  Numerical analysis of 3D cross flow between corrugated parallel plates in evaporative coolers , 2011 .

[30]  Huang Xiang,et al.  Theoretical analysis on heat and mass transfer in a direct evaporative cooler , 2009 .

[31]  J. R. Camargo,et al.  Experimental performance of a direct evaporative cooler operating during summer in a Brazilian city , 2005 .

[32]  José de Jesús Rubio,et al.  Uniform stable radial basis function neural network for the prediction in two mechatronic processes , 2017, Neurocomputing.