Abstract
In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.
Highlights
Cooling towers have important roles in air conditioning systems and power plants
The experiments were conducted by changing the air flow rate, water flow rate, 4.i1n. lIemt pwaacttserofteCmooplienrgatTuorwe,earnVdardioifufesrPeanrtapmaectkerisngons.ETfhfieciernepcyeatability of the experiments was cheTckheedd. eTpheenddaetnacwe eorfeccouorlviendg ftoorwaewr iedffeicviaerniceytyoonf oapirerflaotwingractoenidsitdioenpsicitnedcoionliFniggtuorwe e5rs. for three different inlet water temperatures
Cooling tower efficiency based on inlet water temperature and air flow rate
Summary
Cooling towers have important roles in air conditioning systems and power plants. A cooling tower is a device that facilitates evaporation by making a larger contact surface between water and air, leading to more immediate water cooling. Yoo et al [6] presented high-accuracy correlations for heat transfer based on experimental data They analyzed the influences of different parameters on cooling capacity and tower efficiency. Shahali et al [22] empirically studied the cooling tower performance affected by water flow rate, air flow rate, inlet water temperature, and type of packing. The obtained results and other numerical results were in good agreement They showed that the overall heat transfer rate of dry cooling tower includes a combination of forced and natural convective. Singla et al [25] numerically investigated the counter flow forced draft cooling tower with expanded wire meshed filled as a packing material They analyzed the parameters involved in controlling the water and air flow rate on parameters that are of high importance in cooling tower performance. A wide range of experiments were performed, and the optimum conditions were determined using an artificial neural network (ANN)-PSO
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