scholarly journals Operational cooling tower model (CTTOOL V1.0)

2015 ◽  
Author(s):  
S. Aleman ◽  
L. LocalDomainServers ◽  
A. Garrett
Keyword(s):  
Cooling Tower ◽  
Tower Model

Application of Artificial Neural Network for Modeling of Mechanical Cooling Tower

2011 ◽  
Vol 383-390 ◽  
pp. 7746-7749 ◽  
Author(s):  
Wei Shun Huang ◽  
Ching Wei Chen ◽  
Cheng Wen Lee ◽  
Ching Liang Chen ◽  
Tien Shuen Jan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Flow ◽  
Cooling Tower ◽  
Air Flow ◽  
Cooling Water ◽  
Computation Method ◽  
Cooling Load ◽  
Artificial Neural ◽  
Tower Model

The objective of the study is to focus on the application of the artificial neural network to configure a heat-radiating model for cooling towers within the parameters of fluctuating in air flow or cooling water flow. To achieve the objective, a cooling tower heat balancing equation have been used to instill the correlations between a cooling tower cooling load to the four predefined parameters. Based on the premise established, the parameters of a cooling tower’s air flow and cooling water flow in a modulated process are utilized in an experimental system for collecting relevant operating data. Lastly, the artificial neural network tool derived from the Matlab software is utilized to define the input parameters being – the cooling water temperature, ambient web-bulb temperature, cooling tower air flow, and cooling water flow, with an objective set to instilling a cooling tower model for defining a cooling tower cooling load. In addition, the tested figures are compared to the simulated figures for verifying the cooling tower model. By utilizing the method derived from the model, the mean error of between 0.72 and 2.13% is obtained, with R2 value rated at between 0.97 and 0.99. The experiment findings show a relatively high reliability that can be achieved for configuring a model by using the artificial neural network. With the support of an optimized computation method, the model can be applied as an optimization operating strategy for an air-conditioning system’s cooling water loop.

NON-INTRUSIVE COOLING TOWER MODEL: A VALIDATION CASE STUDY

2021 ◽  
Author(s):  
Suhrid Deshmukh ◽  
Leon R. Glicksman ◽  
Leslie Norford
Keyword(s):  
Cooling Tower ◽  
Tower Model

scholarly journals Predictive Factors of Legionella pneumophila Contamination in Cooling Tower Water

2014 ◽  
Vol 7 ◽  
pp. ASWR.S12972
Author(s):  
Pipat Luksamijarulkul ◽  
Sumawadee Kornkrerkkiat ◽  
Chayaporn Saranpuetti ◽  
Dusit Sujirarat
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Water Temperature ◽  
Predictive Factors ◽  
Legionella Pneumophila ◽  
Water Samples ◽  
Cooling Tower ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Tower Model

A cross-sectional study of 160 water samples collected from 72 cooling towers in 4 hospitals, 7 department stores, and 3 hotels in Bangkok was carried out to investigate Legionella pneumophila contamination and its predictive factors. All water samples were cultured for Legionella spp. and tested for L. pneumophila by real-time polymerase chain reaction (PCR). Some cooling tower parameters were measured and recorded. Data were analyzed using χ 2 -test, odds ratio and stepwise logistic regression analysis at the significant level of α = 0.05. Results revealed that the Legionella spp. contamination was 20.0% (32/160) and for L. pneumophila was 61.3% (98/160). The sensitivity of real-time PCR was higher than that of the culture. Factors significantly associated with L. pneumophila contamination by χ 2 -test were: the cooling tower model, size, use duration, pH, water temperature, use of ozone, and residual free chlorine (95% CI of OR > 1.0, P < 0.05). After stepwise logistic regression analysis, four predictive factors remained. These included the cooling tower model being a cross-flow type (adjusted OR = 3.1, 95% CI = 1.2-7.8, P = 0.017), use duration >5 years (adjusted OR = 3.6, 95% CI = 1.3-10.1, P = 0.016), water temperature <29.4°C (adjusted OR = 7.9, 95% CI = 2.1-29.6, P = 0.002), and residual free chlorine <0.2 ppm (adjusted OR = 8.5, 95% CI = 2.1-34.9, P = 0.003). Additionally, the risk probability for L. pneumophila contamination was estimated to be 13.9-97.1%, depending on the combination of predictive factors.

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