An experimental and multi-objective optimization study of a forced draft cooling tower with different fills

In the present study, a forced draft mechanical cooling tower has been experimentally investigated using trickle, film and splash fills. Various performance parameters such as range, tower characteristic ratio, effectiveness and water evaporation rate are first analyzed for each fill. Thereafter, based upon the experimental data, pertinent correlations have been developed for performance parameters by considering mass flow rates of water and air as design variables. Each of the performance parameters is considered to be an individual objective function and all objectives are then simultaneously optimized for maximizing the performance of the cooling tower using elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II). The multi-objective optimization algorithm gives a set of possible combinations of design variables, which is referred as the optimal Pareto-front, out of which a unique combination is selected based upon a decision making criterion. The proposed decision making procedure evaluates a Decision Making Score (DMS) based on assigned performance priorities for each point of the Pareto-front. Depending on DMS a unique combination of design variables is then selected for each type of fill that maximizes the tower’s performance. These optimal points and the corresponding objective function are finally compared and based upon the highest DMS value, the wire-mesh (trickle) fill is found to be the most efficient fill under the present experimental conditions. The methodology presented in this work has been made more generalized, so that it can be easily implemented in industrial forced draft cooling tower operating under a wide range of temperatures.

[1]  Mark D. Semon,et al.  POSTUSE REVIEW: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1982 .

[2]  Eusiel Rubio-Castro,et al.  Optimization of mechanical draft counter flow wet-cooling towers using a rigorous model , 2011 .

[3]  R. J. Moffat,et al.  The measurement chain and validation of experimental measurements , 2014 .

[4]  R. V. Rao,et al.  Optimization of mechanical draft counter flow wet-cooling tower using artificial bee colony algorithm , 2011 .

[5]  D. F. Young,et al.  A Brief Introduction to Fluid Mechanics , 1996 .

[6]  Ramkumar Ramkrishnan,et al.  Experimental study of cooling tower performance using ceramic tile packing , 2013 .

[7]  Rm Park,et al.  Manual on the Use of Thermocouples in Temperature Measurement, Fourth Edition, Sponsored by ASTM Committee E20 on Temperature Measurement , 1993 .

[8]  Giorgia F. Cortinovis,et al.  A systemic approach for optimal cooling tower operation , 2009 .

[9]  Jian-Guo Wang,et al.  Data-driven modeling approach for performance analysis and optimal operation of a cooling tower , 2014 .

[10]  J. F. Missenden,et al.  The investigation of cooling tower packing in various arrangements , 2000 .

[11]  Mehmet Sait Söylemez,et al.  On the optimum performance of forced draft counter flow cooling towers , 2004 .

[12]  Detlev G. Kröger,et al.  Loss coefficient correlation for wet-cooling tower fills , 2003 .

[13]  Ramkumar Ramakrishnan,et al.  Optimization of operating parameters and performance evaluation of forced draft cooling tower using response surface methodology (RSM) and artificial neural network (ANN) , 2012 .

[14]  Eusiel Rubio-Castro,et al.  Optimal design of effluent-cooling systems using a mathematical programming model , 2010 .

[15]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[16]  Detlev G. Kröger,et al.  A critical investigation into the heat and mass transfer analysis of counterflow wet-cooling towers , 2005 .

[17]  A. K. M. Mohiuddin,et al.  Knowledge base for the systematic design of wet cooling towers. Part I: Selection and tower characteristics , 1996 .

[18]  Mehmet Sait Söylemez,et al.  ON THE OPTIMUM SIZING OF COOLING TOWERS , 2001 .

[19]  M. Boumaza,et al.  Thermal performances investigation of a wet cooling tower , 2007 .

[20]  M. Boumaza,et al.  Experimental analysis of heat and mass transfer phenomena in a direct contact evaporative cooling tower , 2009 .

[21]  E. Al-Bassam,et al.  Measurable energy savings of installing variable frequency drives for cooling towers’ fans, compared to dual speed motors , 2013 .

[22]  J. C. Kloppers,et al.  A critical evaluation and refinement of the performance prediction of wet-cooling towers , 2003 .

[23]  Arash Mirabdolah Lavasani,et al.  Experimental study on the thermal performance of mechanical cooling tower with rotational splash type packing , 2014 .

[24]  Jason R. Picardo,et al.  The Merkel equation revisited: A novel method to compute the packed height of a cooling tower , 2012 .

[25]  S. J. Kline,et al.  Describing Uncertainties in Single-Sample Experiments , 1953 .

[26]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[27]  Ranjan Das,et al.  Tower characteristics correlation and parameter retrieval in wet-cooling tower with expanded wire mesh packing , 2016 .

[28]  Arturo Jiménez-Gutiérrez,et al.  MINLP optimization of mechanical draft counter flow wet-cooling towers , 2010 .

[29]  Kankanhalli N. Seetharamu,et al.  Experimental investigation of the performance of a counter-flow, packed-bed mechanical cooling tower , 1998 .

[30]  R. J. Moffat,et al.  Contributions to the Theory of Single-Sample Uncertainty Analysis , 1982 .

[31]  Ralph L. Webb,et al.  Design of Cooling Towers by the Effectiveness-NTU Method , 1989 .