Investigation of Natural Draft Cooling Tower Performance Using Neural Network

2014 ◽  
pp. 315-327 ◽  
Author(s):  
Qasim S. Mahdi ◽  
Saad M. Saleh ◽  
Basima S. Khalaf
Keyword(s):  
Neural Network ◽  
Cooling Tower ◽  
Natural Draft

Numerical investigation of swirl effects on a short natural draft dry cooling tower under windless and crosswind conditions

2021 ◽  
Vol 188 ◽  
pp. 116628 ◽  
Author(s):  
Yuchen Dai ◽  
Yuanshen Lu ◽  
Alexander Y. Klimenko ◽  
Ying Wang ◽  
Kamel Hooman
Keyword(s):  
Numerical Investigation ◽  
Cooling Tower ◽  
Natural Draft ◽  
Dry Cooling Tower ◽  
Dry Cooling

Improving Natural Draft Cooling Tower Performance With Heat Injection

2002 ◽  
Author(s):  
Eugene Grindle ◽  
John Cooper ◽  
Roger Lawson
Keyword(s):  
Water Temperature ◽  
Computer Model ◽  
Cooling Tower ◽  
Ambient Air ◽  
Combined Cycle ◽  
Steam Boiler ◽  
Natural Draft ◽  
Circulating Water ◽  
Vapor Stream ◽  
Heat Injection

This paper presents an assessment of heat injection as a means of improving natural draft cooling tower performance. The concept involves injecting heat into the cooling tower exit air/vapor stream immediately above the drift eliminators in order to increase the difference between the density of the exit air/vapor stream and the ambient air. The density difference between the air/vapor in the cooling tower stack and the ambient air is the engine that drives airflow through the cooling tower. The enhancement of the airflow through the cooling tower (the natural draft) results in more evaporation and thus lowers the circulating water temperature. Because the heat is injected above the drift eliminators, it does not heat the circulating water. To evaluate the cooling tower performance improvement as a function of heat injection rate, a thermal/aerodynamic computer model of Entergy’s White Bluff 1 & 2 and Independence 1 & 2 (approximately 840 MW each) natural draft cooling towers was developed. The computer model demonstrated that very substantial reductions in cold water temperature (up to 7°F) are obtainable by the injection of heat. This paper also discusses a number of possible heat sources. Sources of heat covered include extraction steam, auxiliary steam, boiler blow-down, and waste heat from a combustion turbine. The latter source of heat would create a combined cycle unit with the combination taking place in the condensing part of the cycle (bottom of the cycle) instead of the steam portion of the cycle (top of the cycle).

scholarly journals Wind-break walls with optimized setting angles for natural draft dry cooling tower with vertical radiators

2017 ◽  
Vol 112 ◽  
pp. 326-339 ◽  
Author(s):  
Huan Ma ◽  
Fengqi Si ◽  
Yu Kong ◽  
Kangping Zhu ◽  
Wensheng Yan
Keyword(s):  
Cooling Tower ◽  
Natural Draft ◽  
Dry Cooling Tower ◽  
Dry Cooling

Ventilation enhancement for a natural draft dry cooling tower in crosswind via windbox installation

2018 ◽  
Vol 137 ◽  
pp. 93-100 ◽  
Author(s):  
Weiliang Wang ◽  
Hai Zhang ◽  
Junfu Lyu ◽  
Qing Liu ◽  
Guangxi Yue ◽  
...  
Keyword(s):  
Cooling Tower ◽  
Natural Draft ◽  
Dry Cooling Tower ◽  
Dry Cooling

scholarly journals Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 167
Author(s):  
Hasan Alimoradi ◽  
Madjid Soltani ◽  
Pooriya Shahali ◽  
Farshad Moradi Kashkooli ◽  
Razieh Larizadeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Temperature ◽  
Water Flow ◽  
Flow Rate ◽  
Cooling Tower ◽  
Air Flow ◽  
Water Flow Rate ◽  
Pso Algorithm ◽  
Outlet Temperature ◽  
Air Flow Rate

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.

Fresh breeze cuts down one-third ventilation rate of a natural draft dry cooling tower: A hot state modelling

2018 ◽  
Vol 131 ◽  
pp. 1-7 ◽  
Author(s):  
Weiliang Wang ◽  
Yuzhao Wang ◽  
Hai Zhang ◽  
Guanming Lin ◽  
Junfu Lu ◽  
...  
Keyword(s):  
Cooling Tower ◽  
Ventilation Rate ◽  
Natural Draft ◽  
Dry Cooling Tower ◽  
Dry Cooling
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